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Parroco AM, Arcaio M, Mendola D. Does neighborhood matter? An analysis of HIV prevalence in Sub-Saharan African countries. BIODEMOGRAPHY AND SOCIAL BIOLOGY 2024; 69:149-162. [PMID: 39072483 DOI: 10.1080/19485565.2024.2384995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Over two-thirds of the population living with HIV were concentrated in Eastern, Southern, Western, and Central Africa in 2021. This paper employs data from the Demographic and Health Survey to assess the relationship between HIV prevalence and its socio-economic and demographic drivers at the neighborhood (macro-cluster) level. Additionally, the study examines the existence of differences in such relationships among countries. The results of the fractional logistic regression models highlight that highly educated neighborhoods are less likely to be affected by HIV. A greater average number of children, potentially due to programs that promote access to preventive antenatal care and prevention, is associated with a lower likelihood of residents living with HIV. Notably, HIV testing coverage is prevalent in neighborhoods with a high prevalence of HIV. It is also evident that there are notable differences between countries, which demonstrate national context plays a crucial role in the association between education, number of children, testing coverage, and HIV prevalences.
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Affiliation(s)
- Anna Maria Parroco
- Department of Psychology, Educational Sciences and Human Movement, University of Palermo, Palermo, Italy
| | - Micaela Arcaio
- Department of Psychology, Educational Sciences and Human Movement, University of Palermo, Palermo, Italy
| | - Daria Mendola
- Department of Psychology, Educational Sciences and Human Movement, University of Palermo, Palermo, Italy
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Boyle J, Ward MH, Cerhan JR, Rothman N, Wheeler DC. Assessing and attenuating the impact of selection bias on spatial cluster detection studies. Spat Spatiotemporal Epidemiol 2024; 49:100659. [PMID: 38876558 PMCID: PMC11180222 DOI: 10.1016/j.sste.2024.100659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 06/16/2024]
Abstract
Spatial cluster analyses are commonly used in epidemiologic studies of case-control data to detect whether certain areas in a study region have an excess of disease risk. Case-control studies are susceptible to potential biases including selection bias, which can result from non-participation of eligible subjects in the study. However, there has been no systematic evaluation of the effects of non-participation on the findings of spatial cluster analyses. In this paper, we perform a simulation study assessing the effect of non-participation on spatial cluster analysis using the local spatial scan statistic under a variety of scenarios that vary the location and rates of study non-participation and the presence and intensity of a zone of elevated risk for disease for simulated case-control studies. We find that geographic areas of lower participation among controls than cases can greatly inflate false-positive rates for identification of artificial spatial clusters. Additionally, we find that even modest non-participation outside of a true zone of elevated risk can decrease spatial power to identify the true zone. We propose a spatial algorithm to correct for potentially spatially structured non-participation that compares the spatial distributions of the observed sample and underlying population. We demonstrate its ability to markedly decrease false positive rates in the absence of elevated risk and resist decreasing spatial sensitivity to detect true zones of elevated risk. We apply our method to a case-control study of non-Hodgkin lymphoma. Our findings suggest that greater attention should be paid to the potential effects of non-participation in spatial cluster studies.
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Affiliation(s)
- Joseph Boyle
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
| | - Mary H Ward
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - James R Cerhan
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Nathaniel Rothman
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - David C Wheeler
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
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Barboza-Salerno GE, Thurston H, Freisthler B. The Spatial Scale and Spread of Child Victimization. JOURNAL OF INTERPERSONAL VIOLENCE 2024:8862605241245388. [PMID: 38769859 DOI: 10.1177/08862605241245388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Previous research shows that large, densely populated urban areas have higher rates of child victimization that have persisted over time. However, few investigations have inquired about the processes that produce and sustain hot and cold spots of child victimization. As a result, the mechanisms that produce the observed spatial clustering of child victimization, and hence "why" harms against children tend to cluster in space, remains unknown. Does the likelihood of being a victim of violence in one location depend on a similar event happening in a nearby location within a specified timeframe? Rather, are child victims of violence more likely to reside in suboptimal neighborhood conditions? This paper aims to present an analytical and theoretical framework for distinguishing between these locational (point) processes to determine whether the empirical spatial patterns undergirding child victimization are more reflective of the "spread" via contagion (i.e., dependency) or whether they are produced by neighborhood structural inequality resulting from spatial heterogeneity. To detect spatial dependence, we applied the inhomogeneous K-function to Los Angeles Medical Examiner data on child homicide victim locations while controlling for regional differences in victimization events (i.e., heterogeneity). Our analysis found strong evidence of spatial clustering in child victimization at small spatial scales but inhibition at larger scales. We further found limited support for the spatiotemporal clustering of child victimization indicative of a contagion effect. Overall, our results support the role of neighborhood structural vulnerability in the underlying mechanisms producing patterns of child victimization across Los Angeles County. We conclude by discussing the policy implications for understanding this spatial patterning in geographical context and for developing effective and targeted preventive interventions.
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Affiliation(s)
| | - Holly Thurston
- College of Social Work, The Ohio State University, Columbus, USA
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Adetokunboh OO, Are EB. Spatial distribution and determinants of HIV high burden in the Southern African sub-region. PLoS One 2024; 19:e0301850. [PMID: 38669230 PMCID: PMC11051620 DOI: 10.1371/journal.pone.0301850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 03/22/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Spatial analysis at different levels can help understand spatial variation of human immunodeficiency virus (HIV) infection, disease drivers, and targeted interventions. Combining spatial analysis and the evaluation of the determinants of the HIV burden in Southern African countries is essential for a better understanding of the disease dynamics in high-burden settings. METHODS The study countries were selected based on the availability of demographic and health surveys (DHS) and corresponding geographic coordinates. We used multivariable regression to evaluate the determinants of HIV burden and assessed the presence and nature of HIV spatial autocorrelation in six Southern African countries. RESULTS The overall prevalence of HIV for each country varied between 11.3% in Zambia and 22.4% in South Africa. The HIV prevalence rate was higher among female respondents in all six countries. There were reductions in prevalence estimates in most countries yearly from 2011 to 2020. The hotspot cluster findings show that the major cities in each country are the key sites of high HIV burden. Compared with female respondents, the odds of being HIV positive were lesser among the male respondents. The probability of HIV infection was higher among those who had sexually transmitted infections (STI) in the last 12 months, divorced and widowed individuals, and women aged 25 years and older. CONCLUSIONS Our research findings show that analysis of survey data could provide reasonable estimates of the wide-ranging spatial structure of the HIV epidemic in Southern African countries. Key determinants such as individuals who are divorced, middle-aged women, and people who recently treated STIs, should be the focus of HIV prevention and control interventions. The spatial distribution of high-burden areas for HIV in the selected countries was more pronounced in the major cities. Interventions should also be focused on locations identified as hotspot clusters.
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Affiliation(s)
- Olatunji O. Adetokunboh
- DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
- Department of Global Health, Division of Epidemiology and Biostatistics, Stellenbosch University, Cape Town, South Africa
| | - Elisha B. Are
- DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
- Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada
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Cuadros DF, Devi C, Singh U, Olivier S, Castle AC, Moosa Y, Edwards JA, Kim HY, Siedner MJ, Wong EB, Tanser F. Convergence of HIV and non-communicable disease epidemics: geospatial mapping of the unmet health needs in an HIV hyperendemic community in South Africa. BMJ Glob Health 2024; 9:e012730. [PMID: 38176743 PMCID: PMC10773360 DOI: 10.1136/bmjgh-2023-012730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 11/25/2023] [Indexed: 01/06/2024] Open
Abstract
INTRODUCTION As people living with HIV (PLHIV) are experiencing longer survival, the co-occurrence of HIV and non-communicable diseases has become a public health priority. In response to this emerging challenge, we aimed to characterise the spatial structure of convergence of chronic health conditions in an HIV hyperendemic community in KwaZulu-Natal, South Africa. METHODS In this cross-sectional study, we used data from a comprehensive population-based disease survey conducted in KwaZulu-Natal, South Africa, which collected data on HIV, diabetes and hypertension. We implemented a novel health needs scale to categorise participants as: diagnosed and well-controlled (Needs Score 1), diagnosed and suboptimally controlled (Score 2), diagnosed but not engaged in care (Score 3) or undiagnosed and uncontrolled (Score 4). Scores 2-4 were indicative of unmet health needs. We explored the geospatial structure of unmet health needs using different spatial clustering methods. RESULTS The analytical sample comprised 18 041 individuals. We observed a similar spatial structure for HIV among those with combined needs Score 2-3 (diagnosed but uncontrolled) and Score 4 (undiagnosed and uncontrolled), with most PLHIV with unmet needs clustered in the southern urban and peri-urban areas. Conversely, a high prevalence of need Scores 2 and 3 for diabetes and hypertension was mostly distributed in the more rural central and northern part of the surveillance area. A high prevalence of need Score 4 for diabetes and hypertension was mostly distributed in the rural southern part of the surveillance area. Multivariate clustering analysis revealed a significant overlap of all three diseases in individuals with undiagnosed and uncontrolled diseases (unmet needs Score 4) in the southern part of the catchment area. CONCLUSIONS In an HIV hyperendemic community in South Africa, areas with the highest needs for PLHIV with undiagnosed and uncontrolled disease are also areas with the highest burden of unmet needs for other chronic health conditions, such as diabetes and hypertension. Our study has revealed remarkable differences in the distribution of health needs across the rural to urban continuum even within this relatively small study site. The identification and prioritisation of geographically clustered vulnerable communities with unmet health needs for both HIV and non-communicable diseases provide a basis for policy and implementation strategies to target communities with the highest health needs.
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Affiliation(s)
- Diego F Cuadros
- Digital Epidemiology Laboratory, University of Cincinnati, Cincinnati, OH, USA
| | - Chayanika Devi
- Digital Epidemiology Laboratory, University of Cincinnati, Cincinnati, OH, USA
| | - Urisha Singh
- Africa Health Research Institute, Durban, South Africa
- Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | | | - Alison C Castle
- Africa Health Research Institute, Durban, South Africa
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical Shool, Boston, MA, USA
| | - Yumna Moosa
- Africa Health Research Institute, Durban, South Africa
| | - Johnathan A Edwards
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
- International Institute for Rural Health, University of Lincoln, Lincolnshire, UK
| | - Hae-Young Kim
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Mark J Siedner
- Africa Health Research Institute, Durban, South Africa
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical Shool, Boston, MA, USA
- School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Emily B Wong
- Africa Health Research Institute, Durban, South Africa
- Division of Infectious Diseases, University of Alabama Birmingham, Birmingham, AL, USA
| | - Frank Tanser
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa
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Li Z, Qiao S, Ning H, Sun X, Zhang J, Olatosi B, Li X. Place Visitation Data Reveals the Geographic and Racial Disparities of COVID-19 Impact on HIV Service Utilization in the Deep South. AIDS Behav 2023:10.1007/s10461-023-04163-4. [PMID: 37792234 DOI: 10.1007/s10461-023-04163-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/03/2023] [Indexed: 10/05/2023]
Abstract
BACKGROUND The COVID-19 pandemic has posed unprecedented pressure to health care systems, and interrupted health care delivery and access including HIV care in the United States' Deep South, which endures a double epidemic of HIV and COVID-19. Ryan White programs cover HIV care services for over half of PLWH in the Deep South. Given the important role of Ryan White programs, examining the visitation changes to Ryan White facilities during the pandemic offers insights into the impact of the pandemic on HIV healthcare utilization. OBJECTIVES Analyze the geographic distribution of HIV facility visitors at the county level before and during the pandemic in the nine US states of Deep South (Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas) to reveal the geographic and racial disparity in visitation disruption caused by the pandemic. METHODS We first extracted mobile device-based visitation data for Ryan White HIV facilities in the Deep South during 2019 and 2020. To quantify the disruption in visitations during 2020, we calculated the visitation reduction rate (VRR) for each county, using 2019 data as the baseline. Next, we conducted a spatial analysis of the VRR values to uncover geographical disparities in visitation interruptions. To investigate racial disparities, we performed spatial regression analyses with VRR as the dependent variable, and the percentages of Black, Hispanic, and Asian populations as the independent variables. In this analysis, we controlled for potential confounders. RESULTS Geographic disparities in visitation reduction were observed, with all nine Deep South states experiencing significant drops. Georgia experienced the highest visitation loss (VRR = -0.58), followed by Texas (-0.47), Alabama (0.47), and Tennessee (-0.46), while South Carolina had the smallest reductions (-0.11). All the regression models consistently revealed racial disparities in visitation interruption. That is, counties with a higher proportion of Black population tended to have higher RW facility visitation reductions. CONCLUSIONS Our analysis revealed distinct geographic disparities in visitation interruptions at Ryan White HIV facilities in the Deep South during the COVID-19 pandemic in 2020. Furthermore, we found that the Black/African American population experienced a greater disruption at the county level in the Deep South during this period.
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Affiliation(s)
- Zhenlong Li
- Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC, 29208, USA.
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA.
| | - Shan Qiao
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Huan Ning
- Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC, 29208, USA
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA
- Department of Geography, The Pennsylvania State University, University Park, PA, 16801, USA
| | - Xiaowen Sun
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Jiajia Zhang
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Bankole Olatosi
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA
- Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Xiaoming Li
- Big Data Health Science Center, University of South Carolina, Columbia, SC, 29208, USA
- Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
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Singh U, Olivier S, Cuadros D, Castle A, Moosa Y, Zulu T, Edwards JA, Kim HY, Gunda R, Koole O, Surujdeen A, Gareta D, Munatsi D, Modise TH, Dreyer J, Nxumalo S, Smit TK, Ordering-Jespersen G, Mpofana IB, Khan K, Sikhosana ZEL, Moodley S, Shen YJ, Khoza T, Mhlongo N, Bucibo S, Nyamande K, Baisley KJ, Grant AD, Herbst K, Seeley J, Pillay D, Hanekom W, Ndung'u T, Siedner MJ, Tanser F, Wong EB. The met and unmet health needs for HIV, hypertension, and diabetes in rural KwaZulu-Natal, South Africa: analysis of a cross-sectional multimorbidity survey. Lancet Glob Health 2023; 11:e1372-e1382. [PMID: 37591585 PMCID: PMC10447220 DOI: 10.1016/s2214-109x(23)00239-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 08/19/2023]
Abstract
BACKGROUND The convergence of infectious diseases and non-communicable diseases in South Africa is challenging to health systems. In this analysis, we assessed the multimorbidity health needs of individuals and communities in rural KwaZulu-Natal and established a framework to quantify met and unmet health needs for individuals living with infectious and non-communicable diseases. METHODS We analysed data collected between May 25, 2018, and March 13, 2020, from participants of a large, community-based, cross-sectional multimorbidity survey (Vukuzazi) that offered community-based HIV, hypertension, and diabetes screening to all residents aged 15 years or older in a surveillance area in the uMkhanyakude district in KwaZulu-Natal, South Africa. Data from the Vukuzazi survey were linked with data from demographic and health surveillance surveys with a unique identifier common to both studies. Questionnaires were used to assess the diagnosed health conditions, treatment history, general health, and sociodemographic characteristics of an individual. For each condition (ie, HIV, hypertension, and diabetes), individuals were defined as having no health needs (absence of condition), met health needs (condition that is well controlled), or one or more unmet health needs (including diagnosis, engagement in care, or treatment optimisation). We analysed met and unmet health needs for individual and combined conditions and investigated their geospatial distribution. FINDINGS Of 18 041 participants who completed the survey (12 229 [67·8%] were female and 5812 [32·2%] were male), 9898 (54·9%) had at least one of the three chronic diseases measured. 4942 (49·9%) of these 9898 individuals had at least one unmet health need (1802 [18·2%] of 9898 needed treatment optimisation, 1282 [13·0%] needed engagement in care, and 1858 [18·8%] needed a diagnosis). Unmet health needs varied by disease; 1617 (93·1%) of 1737 people who screened positive for diabetes, 2681 (58·2%) of 4603 people who screened positive for hypertension, and 1321 (21·7%) of 6096 people who screened positive for HIV had unmet health needs. Geospatially, met health needs for HIV were widely distributed and unmet health needs for all three conditions had specific sites of concentration; all three conditions had an overlapping geographical pattern for the need for diagnosis. INTERPRETATION Although people living with HIV predominantly have a well controlled condition, there is a high burden of unmet health needs for people living with hypertension and diabetes. In South Africa, adapting current, widely available HIV care services to integrate non-communicable disease care is of high priority. FUNDING Fogarty International Center and the National Institutes of Health, the Bill & Melinda Gates Foundation, the South African Department of Science and Innovation, the South African Medical Research Council, the South African Population Research Infrastructure Network, and the Wellcome Trust. TRANSLATION For the isiZulu translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Urisha Singh
- Africa Health Research Institute, KwaZulu-Natal, South Africa; Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Stephen Olivier
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Diego Cuadros
- Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati, Cincinnati, OH, USA
| | - Alison Castle
- Africa Health Research Institute, KwaZulu-Natal, South Africa; Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Harvard University, Boston, MA, USA
| | - Yumna Moosa
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Thando Zulu
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Jonathan Alex Edwards
- International Institute for Rural Health, University of Lincoln, Lincoln, UK; Department of Biostatistics and Bioinformatics, Rollins School of Public Health and Department of Biomedical Informatics, Emory University School of Medicine, Emory University, Atlanta, GA, USA
| | - Hae-Young Kim
- Department of Population Health, New York University Grossman School of Medicine, New York University, New York, NY, USA
| | - Resign Gunda
- Africa Health Research Institute, KwaZulu-Natal, South Africa; School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
| | - Olivier Koole
- Africa Health Research Institute, KwaZulu-Natal, South Africa; London School of Hygiene and Tropical Medicine, London, UK
| | | | - Dickman Gareta
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Day Munatsi
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | | | - Jaco Dreyer
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | | | - Theresa K Smit
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | | | | | - Khadija Khan
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | | | - Sashen Moodley
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Yen-Ju Shen
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Thandeka Khoza
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Ngcebo Mhlongo
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Sanah Bucibo
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Kennedy Nyamande
- Department of Pulmonology and Critical Care, Inkosi Albert Luthuli Hospital, Durban, South Africa
| | - Kathy J Baisley
- Africa Health Research Institute, KwaZulu-Natal, South Africa; London School of Hygiene and Tropical Medicine, London, UK
| | - Alison D Grant
- Africa Health Research Institute, KwaZulu-Natal, South Africa; London School of Hygiene and Tropical Medicine, London, UK; School of Public Health, University of Witwatersrand, Johannesburg, South Africa
| | - Kobus Herbst
- Africa Health Research Institute, KwaZulu-Natal, South Africa; Department of Science and Innovation, Medical Research Council, South African Population Research Infrastructure, Durban, South Africa
| | - Janet Seeley
- Africa Health Research Institute, KwaZulu-Natal, South Africa; School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa; London School of Hygiene and Tropical Medicine, London, UK
| | - Deenan Pillay
- Africa Health Research Institute, KwaZulu-Natal, South Africa; Division of Infection and Immunity, University College London, London, UK
| | - Willem Hanekom
- Africa Health Research Institute, KwaZulu-Natal, South Africa; Division of Infection and Immunity, University College London, London, UK
| | - Thumbi Ndung'u
- Africa Health Research Institute, KwaZulu-Natal, South Africa; Ragon Institute, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Harvard University, Boston, MA, USA; Division of Infection and Immunity, University College London, London, UK; HIV Pathogenesis Programme, Doris Duke Medical Research Institute, Durban, South Africa
| | - Mark J Siedner
- Africa Health Research Institute, KwaZulu-Natal, South Africa; Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa; School of Clinical Medicine, University of KwaZulu-Natal, Durban, South Africa; Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
| | - Frank Tanser
- Africa Health Research Institute, KwaZulu-Natal, South Africa; School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa; College of Health Sciences, and Centre for the AIDS Programme of Research in South Africa, University of KwaZulu-Natal, Durban, South Africa; International Institute for Rural Health, University of Lincoln, Lincoln, UK; School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Emily B Wong
- Africa Health Research Institute, KwaZulu-Natal, South Africa; Division of Infectious Diseases, University of Alabama at Birmingham, Birmingham, AL, USA.
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Mutai CK, McSharry PE, Ngaruye I, Musabanganji E. Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa. BMC Infect Dis 2023; 23:482. [PMID: 37468851 DOI: 10.1186/s12879-023-08467-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/17/2023] [Indexed: 07/21/2023] Open
Abstract
INTRODUCTION Significant regional variations in the HIV epidemic hurt effective common interventions in sub-Saharan Africa. It is crucial to analyze HIV positivity distributions within clusters and assess the homogeneity of countries. We aim at identifying clusters of countries based on socio-behavioural predictors of HIV for screening. METHOD We used an agglomerative hierarchical, unsupervised machine learning, approach for clustering to analyse data for 146,733 male and 155,622 female respondents from 13 sub-Saharan African countries with 20 and 26 features, respectively, using Population-based HIV Impact Assessment (PHIA) data from the survey years 2015-2019. We employed agglomerative hierarchical clustering and optimal silhouette index criterion to identify clusters of countries based on the similarity of socio-behavioural characteristics. We analyse the distribution of HIV positivity with socio-behavioural predictors of HIV within each cluster. RESULTS Two principal components were obtained, with the first describing 62.3% and 70.1% and the second explaining 18.3% and 20.6% variance of the total socio-behavioural variation in females and males, respectively. Two clusters per sex were identified, and the most predictor features in both sexes were: relationship with family head, enrolled in school, circumcision status for males, delayed pregnancy, work for payment in last 12 months, Urban area indicator, known HIV status and delayed pregnancy. The HIV positivity distribution with these variables was significant within each cluster. CONCLUSIONS /FINDINGS The findings provide a potential use of unsupervised machine learning approaches for substantially identifying clustered countries based on the underlying socio-behavioural characteristics.
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Affiliation(s)
- Charles K Mutai
- African Center of Excellence in Data Science, University of Rwanda, Kigali, BP 4285, Rwanda.
- Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya.
| | - Patrick E McSharry
- African Center of Excellence in Data Science, University of Rwanda, Kigali, BP 4285, Rwanda
- College of Engineering, Carnegie Mellon University Africa, Kigali, BP 6150, Rwanda
- Oxford-Man Institute of Quantitative Finance, Oxford University, Oxford, OX2 6ED, UK
| | - Innocent Ngaruye
- College of Science and Technology, University of Rwanda, Kigali, Rwanda
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Cuadros DF, Devi C, Singh U, Olivier S, Castle A, Moosa Y, Edwards JA, Kim HY, Siedner MJ, Wong EB, Tanser F. Convergence of HIV and non-communicable disease epidemics: Geospatial mapping of the unmet health needs in a HIV Hyperendemic South African community. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.27.23287807. [PMID: 37034610 PMCID: PMC10081404 DOI: 10.1101/2023.03.27.23287807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Background As people living with HIV (PLHIV) are experiencing longer survival, the co-occurrence of HIV and non-communicable diseases has become a public health priority. In response to this emerging challenge, we aimed to characterize the spatial structure of convergence of chronic health conditions in a HIV hyperendemic community in KwaZulu-Natal, South Africa. Methods We utilized data from a comprehensive population-based disease survey conducted in KwaZulu-Natal, South Africa, which collected data on HIV, diabetes, and hypertension. We implemented a novel health needs scale to categorize participants as: diagnosed and well-controlled (Needs Score 1), diagnosed and sub-optimally controlled (Score 2), diagnosed but not engaged in care (Score 3), or undiagnosed and uncontrolled (Score 4). Scores 2-4 were indicative of unmet health needs. We explored the geospatial structure of unmet health needs using different spatial clustering methods. Findings The analytical sample comprised of 18,041 individuals. We observed a similar spatial structure for HIV among those with a combined needs Score 2-3 (diagnosed but uncontrolled) and Score 4 (undiagnosed and uncontrolled), with most PLHIV with unmet needs clustered in the southern peri-urban area, which was relatively densely populated within the surveillance area. Multivariate clustering analysis revealed a significant overlap of all three diseases in individuals with undiagnosed and uncontrolled diseases (unmet needs Score 4) in the southern part of the catchment area. Interpretation In a HIV hyperendemic community in South Africa, areas with the highest needs for PLHIV with undiagnosed and uncontrolled disease are also areas with the highest burden of unmet needs for other chronic health conditions, such as diabetes and hypertension. The identification and prioritization of geographically clustered vulnerable communities with unmet health needs for both HIV and non-communicable diseases provide a basis for policy and implementation strategies to target communities with the highest health needs. Funding Research reported in this publication was supported by the Fogarty International Center (R21 TW011687; D43 TW010543), the National Institute of Mental Health, the National Institute of Allergy and Infectious Diseases (K24 HL166024; T32 AI007433) of the National Institutes of Health, and Heart Lung and Blood Institute (K24 HL166024, T32 AI007433). The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the funders.
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Affiliation(s)
- Diego F Cuadros
- Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati, Cincinnati, OH, USA
| | - Chayanika Devi
- Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati, Cincinnati, OH, USA
| | - Urisha Singh
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Stephen Olivier
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Alison Castle
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Johnathan A Edwards
- International Institute for Rural Health, University of Lincoln, Lincolnshire, UK
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- Department of Biomedical Informatics, Emory University School of Medicine, Emory University, Atlanta, GA, USA
| | - Hae-Young Kim
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Mark J. Siedner
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Emily B Wong
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Division of Infectious Diseases, University of Alabama Birmingham, Birmingham, AL, USA
| | - Frank Tanser
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa
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Singh U, Olivier S, Cuadros D, Castle A, Moosa Y, Edwards JA, Kim HY, Siedner MJ, Tanser F, Wong EB. Quantifying met and unmet health needs for HIV, hypertension and diabetes in rural KwaZulu-Natal, South Africa. RESEARCH SQUARE 2023:rs.3.rs-2702048. [PMID: 36993494 PMCID: PMC10055615 DOI: 10.21203/rs.3.rs-2702048/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/14/2023]
Abstract
The convergence of infectious and non-communicable diseases (NCDs) in South Africa poses a challenge to health systems. Here we establish a framework to quantify met and unmet health needs for individuals living with infectious and NCDs. In this study, we screened adult residents >15 years of age within the uMkhanyakude district in KwaZulu- Natal, South Africa for HIV, hypertension (HPTN) and diabetes mellitus (DM). For each condition, individuals were defined as having no unmet health needs (absence of condition), met health need (condition that is well controlled), or one or more unmet health needs (including diagnosis, engagement in care, or treatment optimization). We analyzed met and unmet health needs for individual and combined conditions and investigated their geospatial distribution. We found that of 18,041 participants, 9,898 (55%) had at least one chronic condition. 4,942 (50%) of these individuals had at least one unmet health need (18% needed treatment optimization, 13% needed engagement in care, and 19% needed diagnosis). Unmet health needs varied by disease: 93% of people with DM, 58% of people with HPTN and 21% of people with HIV had unmet health needs. Geospatially, met health needs for HIV were widely distributed, unmet health needs had specific sites of concentration whilst the need for diagnosis for all three conditions was co-located. Whilst people living with HIV are predominantly well-controlled, there is a high burden of unmet health needs for people living with HPTN and DM. Adaptation of HIV models of care to integrate HIV and NCD services is of high priority.
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Affiliation(s)
- Urisha Singh
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Nelson R Mandela School of Medicine; University of KwaZulu-Natal, Durban, SA
| | - Stephen Olivier
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Diego Cuadros
- Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati, USA
| | - Alison Castle
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Yumna Moosa
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Jonathan Alex Edwards
- International Institute for Rural Health, University of Lincoln, Lincolnshire, UK
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- Department of Biomedical Informatics, Emory University School of Medicine, Emory University, Atlanta, GA, USA
| | - Hae-Young Kim
- Department of Population Health, New York University Grossman School of Medicine, NY, USA
| | - Mark J Siedner
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Nelson R Mandela School of Medicine; University of KwaZulu-Natal, Durban, SA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
- School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Frank Tanser
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- International Institute for Rural Health, University of Lincoln, Lincolnshire, UK
- School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, South Africa
| | - Emily B Wong
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Division of Infectious Diseases, University of Alabama Birmingham, Birmingham, AL, USA
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11
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Timár G, Dorogovtsev SN, Mendes JFF. Localization of nonbacktracking centrality on dense subgraphs of sparse networks. Phys Rev E 2023; 107:014301. [PMID: 36797879 DOI: 10.1103/physreve.107.014301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 12/15/2022] [Indexed: 02/18/2023]
Abstract
The nonbacktracking matrix and the related nonbacktracking centrality (NBC) play a crucial role in models of percolation-type processes on networks, such as nonrecurrent epidemics. Here we study the localization of NBC in infinite sparse networks that contain an arbitrary finite subgraph. Assuming the local tree likeness of the enclosing network, and that branches emanating from the finite subgraph do not intersect at finite distances, we show that the largest eigenvalue of the nonbacktracking matrix of the composite network is equal to the highest of the two largest eigenvalues: that of the finite subgraph and of the enclosing network. In the localized state, when the largest eigenvalue of the subgraph is the highest of the two, we derive explicit expressions for the NBCs of nodes in the subgraph and other nodes in the network. In this state, nonbacktracking centrality is concentrated on the subgraph and its immediate neighborhood in the enclosing network. We obtain simple, exact formulas in the case where the enclosing network is uncorrelated. We find that the mean NBC decays exponentially around the finite subgraph, at a rate which is independent of the structure of the enclosing network, contrary to what was found for the localization of the principal eigenvector of the adjacency matrix. Numerical simulations confirm that our results provide good approximations even in moderately sized, loopy, real-world networks.
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Affiliation(s)
- G Timár
- Departamento de Física da Universidade de Aveiro and I3N, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
| | - S N Dorogovtsev
- Departamento de Física da Universidade de Aveiro and I3N, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
| | - J F F Mendes
- Departamento de Física da Universidade de Aveiro and I3N, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
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12
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Haeuser E, Serfes AL, Cork MA, Yang M, Abbastabar H, Abhilash ES, Adabi M, Adebayo OM, Adekanmbi V, Adeyinka DA, Afzal S, Ahinkorah BO, Ahmadi K, Ahmed MB, Akalu Y, Akinyemi RO, Akunna CJ, Alahdab F, Alanezi FM, Alanzi TM, Alene KA, Alhassan RK, Alipour V, Almasi-Hashiani A, Alvis-Guzman N, Ameyaw EK, Amini S, Amugsi DA, Ancuceanu R, Anvari D, Appiah SCY, Arabloo J, Aremu O, Asemahagn MA, Jafarabadi MA, Awedew AF, Quintanilla BPA, Ayanore MA, Aynalem YA, Azari S, Azene ZN, Darshan BB, Babalola TK, Baig AA, Banach M, Bärnighausen TW, Bell AW, Bhagavathula AS, Bhardwaj N, Bhardwaj P, Bhattacharyya K, Bijani A, Bitew ZW, Bohlouli S, Bolarinwa OA, Boloor A, Bozicevic I, Butt ZA, Cárdenas R, Carvalho F, Charan J, Chattu VK, Chowdhury MAK, Chu DT, Cowden RG, Dahlawi SMA, Damiani G, Darteh EKM, Darwesh AM, das Neves J, Weaver ND, De Leo D, De Neve JW, Deribe K, Deuba K, Dharmaratne S, Dianatinasab M, Diaz D, Didarloo A, Djalalinia S, Dorostkar F, Dubljanin E, Duko B, El Tantawi M, El-Jaafary SI, Eshrati B, Eskandarieh S, Eyawo O, Ezeonwumelu IJ, Ezzikouri S, Farzadfar F, Fattahi N, Fauk NK, Fernandes E, Filip I, Fischer F, Foigt NA, Foroutan M, Fukumoto T, Gad MM, Gaidhane AM, Gebregiorgis BG, Gebremedhin KB, Getacher L, Ghadiri K, Ghashghaee A, Golechha M, Gubari MIM, Gugnani HC, Guimarães RA, Haider MR, Haj-Mirzaian A, Hamidi S, Hashi A, Hassanipour S, Hassankhani H, Hayat K, Herteliu C, Ho HC, Holla R, Hosseini M, Hosseinzadeh M, Hwang BF, Ibitoye SE, Ilesanmi OS, Ilic IM, Ilic MD, Islam RM, Iwu CCD, Jakovljevic M, Jha RP, Ji JS, Johnson KB, Joseph N, Joshua V, Joukar F, Jozwiak JJ, Kalankesh LR, Kalhor R, Kamyari N, Kanchan T, Matin BK, Karimi SE, Kayode GA, Karyani AK, Keramati M, Khan EA, Khan G, Khan MN, Khatab K, Khubchandani J, Kim YJ, Kisa A, Kisa S, Kopec JA, Kosen S, Laxminarayana SLK, Koyanagi A, Krishan K, Defo BK, Kugbey N, Kulkarni V, Kumar M, Kumar N, Kusuma D, La Vecchia C, Lal DK, Landires I, Larson HJ, Lasrado S, Lee PH, Li S, Liu X, Maleki A, Malik P, Mansournia MA, Martins-Melo FR, Mendoza W, Menezes RG, Mengesha EW, Meretoja TJ, Mestrovic T, Mirica A, Moazen B, Mohamad O, Mohammad Y, Mohammadian-Hafshejani A, Mohammadpourhodki R, Mohammed S, Mohammed S, Mokdad AH, Moradi M, Moraga P, Mubarik S, Mulu GBB, Mwanri L, Nagarajan AJ, Naimzada MD, Naveed M, Nazari J, Ndejjo R, Negoi I, Ngalesoni FN, Nguefack-Tsague G, Ngunjiri JW, Nguyen CT, Nguyen HLT, Nnaji CA, Noubiap JJ, Nuñez-Samudio V, Nwatah VE, Oancea B, Odukoya OO, Olagunju AT, Olakunde BO, Olusanya BO, Olusanya JO, Bali AO, Onwujekwe OE, Orisakwe OE, Otstavnov N, Otstavnov SS, Owolabi MO, Mahesh PA, Padubidri JR, Pana A, Pandey A, Pandi-Perumal SR, Kan FP, Patton GC, Pawar S, Peprah EK, Postma MJ, Preotescu L, Syed ZQ, Rabiee N, Radfar A, Rafiei A, Rahim F, Rahimi-Movaghar V, Rahmani AM, Ramezanzadeh K, Rana J, Ranabhat CL, Rao SJ, Rawaf DL, Rawaf S, Rawassizadeh R, Regassa LD, Rezaei N, Rezapour A, Riaz MA, Ribeiro AI, Ross JM, Rubagotti E, Rumisha SF, Rwegerera GM, Moghaddam SS, Sagar R, Sahiledengle B, Sahu M, Salem MR, Kafil HS, Samy AM, Sartorius B, Sathian B, Seidu AA, Shaheen AA, Shaikh MA, Shamsizadeh M, Shiferaw WS, Shin JI, Shrestha R, Singh JA, Skryabin VY, Skryabina AA, Soltani S, Sufiyan MB, Tabuchi T, Tadesse EG, Taveira N, Tesfay FH, Thapar R, Tovani-Palone MR, Tsegaye GW, Umeokonkwo CD, Unnikrishnan B, Villafañe JH, Violante FS, Vo B, Vu GT, Wado YD, Waheed Y, Wamai RG, Wang Y, Ward P, Wickramasinghe ND, Wilson K, Yaya S, Yip P, Yonemoto N, Yu C, Zastrozhin MS, Zhang Y, Zhang ZJ, Hay SI, Dwyer-Lindgren L. Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000-2018. BMC Med 2022; 20:488. [PMID: 36529768 PMCID: PMC9760541 DOI: 10.1186/s12916-022-02639-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 10/26/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is still among the leading causes of disease burden and mortality in sub-Saharan Africa (SSA), and the world is not on track to meet targets set for ending the epidemic by the Joint United Nations Programme on HIV/AIDS (UNAIDS) and the United Nations Sustainable Development Goals (SDGs). Precise HIV burden information is critical for effective geographic and epidemiological targeting of prevention and treatment interventions. Age- and sex-specific HIV prevalence estimates are widely available at the national level, and region-wide local estimates were recently published for adults overall. We add further dimensionality to previous analyses by estimating HIV prevalence at local scales, stratified into sex-specific 5-year age groups for adults ages 15-59 years across SSA. METHODS We analyzed data from 91 seroprevalence surveys and sentinel surveillance among antenatal care clinic (ANC) attendees using model-based geostatistical methods to produce estimates of HIV prevalence across 43 countries in SSA, from years 2000 to 2018, at a 5 × 5-km resolution and presented among second administrative level (typically districts or counties) units. RESULTS We found substantial variation in HIV prevalence across localities, ages, and sexes that have been masked in earlier analyses. Within-country variation in prevalence in 2018 was a median 3.5 times greater across ages and sexes, compared to for all adults combined. We note large within-district prevalence differences between age groups: for men, 50% of districts displayed at least a 14-fold difference between age groups with the highest and lowest prevalence, and at least a 9-fold difference for women. Prevalence trends also varied over time; between 2000 and 2018, 70% of all districts saw a reduction in prevalence greater than five percentage points in at least one sex and age group. Meanwhile, over 30% of all districts saw at least a five percentage point prevalence increase in one or more sex and age group. CONCLUSIONS As the HIV epidemic persists and evolves in SSA, geographic and demographic shifts in prevention and treatment efforts are necessary. These estimates offer epidemiologically informative detail to better guide more targeted interventions, vital for combating HIV in SSA.
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Affiliation(s)
- Emily Haeuser
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
| | - Audrey L Serfes
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Michael A Cork
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Mingyou Yang
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Hedayat Abbastabar
- Advanced Diagnostic and Interventional Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - E S Abhilash
- Department of Botany, Sree Narayana Guru College Chelannur, Kozhikode, India
| | - Maryam Adabi
- Hamadan University of Medical Sciences, Hamadan, Iran
| | | | - Victor Adekanmbi
- Department of Population Medicine, Cardiff University, Cardiff, UK
| | - Daniel Adedayo Adeyinka
- Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Public Health, Federal Ministry of Health, Abuja, Nigeria
| | - Saira Afzal
- Department of Community Medicine, King Edward Memorial Hospital, Lahore, Pakistan
- Department of Public Health, Public Health Institute, Lahore, Pakistan
| | - Bright Opoku Ahinkorah
- The Australian Centre for Public and Population Health Research (ACPPHR), University of Technology Sydney, Sydney, NSW, Australia
| | - Keivan Ahmadi
- School of Public Health, Imperial College London, London, UK
| | - Muktar Beshir Ahmed
- Department of Epidemiology, Jimma University, Jimma, Ethiopia
- Australian Center for Precision Health, University of South Australia, Adelaide, SA, Australia
| | - Yonas Akalu
- Department of Medical Physiology, University of Gondar, Gondar, Ethiopia
| | - Rufus Olusola Akinyemi
- Institute for Advanced Medical Research and Training, University of Ibadan, Ibadan, Nigeria
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Chisom Joyqueenet Akunna
- Department of Public Health, The Intercountry Centre for Oral Health (ICOH) for Africa, Jos, Nigeria
- Department of Public Health, Federal Ministry of Health, Garki, Nigeria
| | - Fares Alahdab
- Mayo Evidence-based Practice Center, Mayo Clinic Foundation for Medical Education and Research, Rochester, MN, USA
| | | | - Turki M Alanzi
- Health Information Management and Technology Department, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Kefyalew Addis Alene
- Faculty of Health Sciences, Curtin University, Perth, WA, Australia
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, WA, Australia
| | - Robert Kaba Alhassan
- Institute of Health Research, University of Health and Allied Sciences, Ho, Ghana
| | - Vahid Alipour
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
- Department of Health Economics, Iran University of Medical Sciences, Tehran, Iran
| | | | - Nelson Alvis-Guzman
- Research Group in Hospital Management and Health Policies, Universidad de la Costa (University of the Coast), Barranquilla, Colombia
- Research Group in Health Economics, University of Cartagena, Cartagena, Colombia
| | - Edward Kwabena Ameyaw
- The Australian Centre for Public and Population Health Research (ACPPHR), University of Technology Sydney, Sydney, NSW, Australia
| | - Saeed Amini
- Department of Health Services Management, Khomein University of Medical Sciences, Khomein, Iran
| | - Dickson A Amugsi
- Department of Maternal and Child Wellbeing, African Population and Health Research Center, Nairobi, Kenya
| | - Robert Ancuceanu
- Pharmacy Department, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Davood Anvari
- Department of Parasitology, Mazandaran University of Medical Sciences, Sari, Iran
- Department of Parasitology, Iranshahr University of Medical Sciences, Iranshahr, Iran
| | - Seth Christopher Yaw Appiah
- Department of Sociology and Social Work, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
- Center for International Health, Ludwig Maximilians University, Munich, Germany
| | - Jalal Arabloo
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Olatunde Aremu
- Department of Public Health, Birmingham City University, Birmingham, UK
| | | | - Mohammad Asghari Jafarabadi
- Department of Biostatistics and Epidemiology, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Biostatistics and Epidemiology, Zanjan University of Medical Sciences, Zanjan, Iran
| | | | | | - Martin Amogre Ayanore
- Department of Health Policy Planning and Management, University of Health and Allied Sciences, Ho, Ghana
- Department of Health Economics, Centre for Health Policy Advocacy Innovation & Research in Africa (CHPAIR-Africa), Accra, Ghana
| | | | - Samad Azari
- Hospital Management Research Center, Iran University of Medical Sciences, Tehran, Iran
| | | | - B B Darshan
- Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, India
| | - Tesleem Kayode Babalola
- Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa
- Department of Community Health and Primary Care, University of Lagos, Lagos, Nigeria
| | - Atif Amin Baig
- Unit of Biochemistry, Universiti Sultan Zainal Abidin (Sultan Zainal Abidin University), Kuala Terengganu, Malaysia
| | - Maciej Banach
- Department of Hypertension, Medical University of Lodz, Lodz, Poland
- Polish Mothers' Memorial Hospital Research Institute, Lodz, Poland
| | - Till Winfried Bärnighausen
- Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
- T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Arielle Wilder Bell
- Department of Global Health and Social Medicine, Harvard University, Boston, MA, USA
- Department of Social Services, Tufts Medical Center, Boston, MA, USA
| | - Akshaya Srikanth Bhagavathula
- Department of Social and Clinical Pharmacy, Charles University, Hradec Kralova, Czech Republic
- Institute of Public Health, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Nikha Bhardwaj
- Department of Anatomy, All India Institute of Medical Sciences, Jodhpur, India
| | - Pankaj Bhardwaj
- Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Jodhpur, India
- School of Public Health, All India Institute of Medical Sciences, Jodhpur, India
| | - Krittika Bhattacharyya
- Department of Statistical and Computational Genomics, National Institute of Biomedical Genomics, Kalyani, India
- Department of Statistics, University of Calcutta, Kolkata, India
| | - Ali Bijani
- Social Determinants of Health Research Center, Babol University of Medical Sciences, Babol, Iran
| | - Zebenay Workneh Bitew
- Nutrition Department, St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia
- St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia
| | - Somayeh Bohlouli
- Department of Veterinary Medicine, Islamic Azad University, Kermanshah, Iran
| | | | - Archith Boloor
- Department of Internal Medicine, Manipal Academy of Higher Education, Mangalore, India
| | - Ivana Bozicevic
- WHO Collaborating Centre for HIV Strategic Information, University of Zagreb, Zagreb, Croatia
- Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK
| | - Zahid A Butt
- School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada
- Al Shifa School of Public Health, Al Shifa Trust Eye Hospital, Rawalpindi, Pakistan
| | - Rosario Cárdenas
- Department of Health Care, Metropolitan Autonomous University, Mexico City, Mexico
| | - Felix Carvalho
- Research Unit on Applied Molecular Biosciences (UCIBIO), University of Porto, Porto, Portugal
| | - Jaykaran Charan
- Department of Pharmacology, All India Institute of Medical Sciences, Jodhpur, India
| | - Vijay Kumar Chattu
- Department of Community Medicine, Datta Meghe Institute of Medical Sciences, Sawangi, India
- Saveetha Medical College, Saveetha University, Chennai, India
| | - Mohiuddin Ahsanul Kabir Chowdhury
- James P Grant School of Public Health, BRAC University, Dhaka, Bangladesh
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
| | - Dinh-Toi Chu
- Center for Biomedicine and Community Health, VNU-International School, Hanoi, Vietnam
| | - Richard G Cowden
- Department of Psychology, University of the Free State, Park West, South Africa
| | - Saad M A Dahlawi
- Environmental Health Department, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Giovanni Damiani
- IRCCS Istituto Ortopedico Galeazzi (Galeazzi Orthopedic Institute IRCCS), University of Milan, Milan, Italy
- Department of Dermatology, Case Western Reserve University, Cleveland, OH, USA
| | | | - Aso Mohammad Darwesh
- Department of Information Technology, University of Human Development, Sulaymaniyah, Iraq
| | - José das Neves
- Institute for Research and Innovation in Health, University of Porto, Porto, Portugal
- Institute of Biomedical Engineering (INEB), University of Porto, Porto, Portugal
| | - Nicole Davis Weaver
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Diego De Leo
- Australian Institute for Suicide Research and Prevention, Griffith University, Mount Gravatt, QLD, Australia
| | - Jan-Walter De Neve
- Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
| | - Kebede Deribe
- Wellcome Trust Brighton and Sussex Centre for Global Health Research, Brighton and Sussex Medical School, Brighton, UK
- School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia
| | - Keshab Deuba
- National Centre for AIDS and STD Control, Save the Children, Kathmandu, Nepal
- Department of Global Public Health, Karolinska Institute, Stockholm, Sweden
| | - Samath Dharmaratne
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Community Medicine, University of Peradeniya, Peradeniya, Sri Lanka
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Mostafa Dianatinasab
- Department of Epidemiology, Maastricht University, Maastricht, Netherlands
- Department of Epidemiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Daniel Diaz
- Center of Complexity Sciences, National Autonomous University of Mexico, Mexico City, Mexico
- Faculty of Veterinary Medicine and Zootechnics, Autonomous University of Sinaloa, Rosales, Culiacán, Mexico
| | - Alireza Didarloo
- Department of Community Medicine and Public Health, Urmia University of Medical Science, Urmia, Iran
| | - Shirin Djalalinia
- Development of Research and Technology Center, Ministry of Health and Medical Education, Tehran, Iran
| | - Fariba Dorostkar
- Department of Medical Laboratory Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Eleonora Dubljanin
- Institute of Microbiology and Immunology, University of Belgrade, Belgrade, Serbia
| | - Bereket Duko
- School of Public Health, Hawassa University, Hawassa, Ethiopia
- School of Public Health, Curtin University, Perth, WA, Australia
| | - Maha El Tantawi
- Pediatric Dentistry and Dental Public Health Department, Alexandria University, Alexandria, Egypt
| | | | - Babak Eshrati
- Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Sharareh Eskandarieh
- Multiple Sclerosis Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Ifeanyi Jude Ezeonwumelu
- Institute for Health Science Research Germans Trias i Pujol, Autonomous University of Barcelona, Badalona, Spain
- IrsiCaixa AIDS Research Institute, Badalona, Spain
| | - Sayeh Ezzikouri
- Department of Virology, Pasteur Institute of Morocco, Casablanca, Morocco
| | - Farshad Farzadfar
- Non-communicable Diseases Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Nazir Fattahi
- Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Nelsensius Klau Fauk
- Torrens University Australia, Adelaide, SA, Australia
- Institute of Resource Governance and Social Change, Kupang, Indonesia
| | - Eduarda Fernandes
- Associated Laboratory for Green Chemistry (LAQV), University of Porto, Porto, Portugal
| | - Irina Filip
- Psychiatry Department, Kaiser Permanente, Fontana, CA, USA
- School of Health Sciences, A.T. Still University, Mesa, AZ, USA
| | - Florian Fischer
- Institute of Public Health, Charité Universitätsmedizin Berlin (Charité Medical University Berlin), Berlin, Germany
| | - Nataliya A Foigt
- Institute of Gerontology, National Academy of Medical Sciences of Ukraine, Kyiv, Ukraine
| | - Masoud Foroutan
- Department of Medical Parasitology, Abadan University of Medical Sciences, Abadan, Iran
- Faculty of Medicine, Abadan University of Medical Sciences, Abadan, Iran
| | | | - Mohamed M Gad
- Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH, USA
- Gillings School of Global Public Health, University of North Carolina Chapel Hill, Chapel Hill, NC, USA
| | | | | | | | - Lemma Getacher
- Department of Public Health, Debre Berhan University, Debre Berhan, Ethiopia
| | - Keyghobad Ghadiri
- Infectious Disease Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
- Pediatric Department, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ahmad Ghashghaee
- School of Public Health, Qazvin University of Medical Sciences, Qazvin, Iran
| | - Mahaveer Golechha
- Health Systems and Policy Research, Indian Institute of Public Health, Gandhinagar, India
| | | | - Harish Chander Gugnani
- Department of Microbiology, Saint James School of Medicine, The Valley, Anguilla
- Department of Epidemiology, Saint James School of Medicine, The Valley, Anguilla
| | | | | | - Arvin Haj-Mirzaian
- Department of Pharmacology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Obesity Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Samer Hamidi
- School of Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates
| | - Abdiwahab Hashi
- Department of Public Health, Jigjiga University, Jijiga, Ethiopia
| | - Soheil Hassanipour
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
- Caspian Digestive Disease Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Hadi Hassankhani
- School of Nursing and Midwifery, Tabriz University of Medical Sciences, Tabriz, Iran
- Independent Consultant, Tabriz, Iran
| | - Khezar Hayat
- Institute of Pharmaceutical Sciences, University of Veterinary and Animal Sciences, Lahore, Pakistan
- Department of Pharmacy Administration and Clinical Pharmacy, Xian Jiaotong University, Xian, China
| | - Claudiu Herteliu
- Department of Statistics and Econometrics, Bucharest University of Economic Studies, Bucharest, Romania
- School of Business, London South Bank University, London, UK
| | - Hung Chak Ho
- Department of Urban Planning and Design, University of Hong Kong, Hong Kong, China
| | - Ramesh Holla
- Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, India
| | - Mostafa Hosseini
- Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran
- Pediatric Chronic Kidney Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Hosseinzadeh
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- Department of Computer Science, University of Human Development, Sulaymaniyah, Iraq
| | - Bing-Fang Hwang
- Department of Occupational Safety and Health, China Medical University, Taichung, Taiwan
| | | | - Olayinka Stephen Ilesanmi
- Department of Community Medicine, University of Ibadan, Ibadan, Nigeria
- Department of Community Medicine, University College Hospital, Ibadan, Ibadan, Nigeria
| | - Irena M Ilic
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Milena D Ilic
- Department of Epidemiology, University of Kragujevac, Kragujevac, Serbia
| | - Rakibul M Islam
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Chidozie C D Iwu
- School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa
| | - Mihajlo Jakovljevic
- Institute of Advanced Manufacturing Technologies, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
- Institute of Comparative Economic Studies, Hosei University, Tokyo, Japan
| | - Ravi Prakash Jha
- Department of Community Medicine, Dr. Baba Saheb Ambedkar Medical College & Hospital, Delhi, India
- Department of Community Medicine, Banaras Hindu University, Varanasi, India
| | - John S Ji
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Kimberly B Johnson
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nitin Joseph
- Department of Community Medicine, Manipal Academy of Higher Education, Mangalore, India
| | - Vasna Joshua
- National Institute of Epidemiology, Indian Council of Medical Research, Chennai, India
| | - Farahnaz Joukar
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
- Caspian Digestive Disease Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Jacek Jerzy Jozwiak
- Department of Family Medicine and Public Health, University of Opole, Opole, Poland
| | - Leila R Kalankesh
- School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Rohollah Kalhor
- Institute for Prevention of Non-communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran
- Health Services Management Department, Qazvin University of Medical Sciences, Qazvin, Iran
| | - Naser Kamyari
- Department of Biostatistics, Abadan University of Medical Sciences, Abadan, Iran
| | - Tanuj Kanchan
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India
| | - Behzad Karami Matin
- Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Salah Eddin Karimi
- Social Determinants of Health Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Gbenga A Kayode
- International Research Center of Excellence, Institute of Human Virology Nigeria, Abuja, Nigeria
- Julius Centre for Health Sciences and Primary Care, Utrecht University, Utrecht, Netherlands
| | - Ali Kazemi Karyani
- Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | | | - Ejaz Ahmad Khan
- Department of Epidemiology and Biostatistics, Health Services Academy, Islamabad, Pakistan
| | - Gulfaraz Khan
- Department of Medical Microbiology & Immunology, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Md Nuruzzaman Khan
- Department of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh, Bangladesh
| | - Khaled Khatab
- Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield, UK
- College of Arts and Sciences, Ohio University, Zanesville, OH, USA
| | | | - Yun Jin Kim
- School of Traditional Chinese Medicine, Xiamen University Malaysia, Sepang, Malaysia
| | - Adnan Kisa
- School of Health Sciences, Kristiania University College, Oslo, Norway
- Department of Global Community Health and Behavioral Sciences, Tulane University, New Orleans, LA, USA
| | - Sezer Kisa
- Department of Nursing and Health Promotion, Oslo Metropolitan University, Oslo, Norway
| | - Jacek A Kopec
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- Arthritis Research Canada, Richmond, Canada
| | | | | | - Ai Koyanagi
- Biomedical Research Networking Center for Mental Health Network (CIBERSAM), San Juan de Dios Sanitary Park, Sant Boi de Llobregat, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Kewal Krishan
- Department of Anthropology, Panjab University, Chandigarh, India
| | - Barthelemy Kuate Defo
- Department of Demography, University of Montreal, Montreal, QC, Canada
- Department of Social and Preventive Medicine, University of Montreal, Montreal, Canada
| | - Nuworza Kugbey
- University of Environment and Sustainable Development, Somanya, Ghana
| | - Vaman Kulkarni
- Department of Community Medicine, Manipal Academy of Higher Education, Mangalore, India
| | - Manasi Kumar
- Department of Psychiatry, University of Nairobi, Nairobi, Kenya
- Division of Psychology and Language Sciences, University College London, London, UK
| | - Nithin Kumar
- Department of Community Medicine, Manipal Academy of Higher Education, Mangalore, India
| | - Dian Kusuma
- Imperial College Business School, Imperial College London, London, UK
- Faculty of Public Health, University of Indonesia, Depok, Indonesia
| | - Carlo La Vecchia
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | | | - Iván Landires
- Unit of Genetics and Public Health, Institute of Medical Sciences, Las Tablas, Panama
- Ministry of Health, Herrera, Panama
| | - Heidi Jane Larson
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Savita Lasrado
- Department of Otorhinolaryngology, Father Muller Medical College, Mangalore, India
| | - Paul H Lee
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Shanshan Li
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Xuefeng Liu
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Quantitative Health Science, Case Western Reserve University, Cleveland, OH, USA
| | - Afshin Maleki
- Department of Environmental Health Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Preeti Malik
- Department of Pediatrics, Montefiore Medical Center, New York, NY, USA
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Walter Mendoza
- Peru Country Office, United Nations Population Fund (UNFPA), Lima, Peru
| | - Ritesh G Menezes
- Forensic Medicine Division, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | | | - Tuomo J Meretoja
- Breast Surgery Unit, Helsinki University Hospital, Helsinki, Finland
- University of Helsinki, Helsinki, Finland
| | - Tomislav Mestrovic
- Clinical Microbiology and Parasitology Unit, Dr. Zora Profozic Polyclinic, Zagreb, Croatia
- University Centre Varazdin, University North, Varazdin, Croatia
| | - Andreea Mirica
- Department of Statistics and Econometrics, Bucharest University of Economic Studies, Bucharest, Romania
| | - Babak Moazen
- Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
- Institute of Addiction Research (ISFF), Frankfurt University of Applied Sciences, Frankfurt, Germany
| | - Osama Mohamad
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Yousef Mohammad
- Internal Medicine Department, King Saud University, Riyadh, Saudi Arabia
| | | | | | - Salahuddin Mohammed
- Department of Biomolecular Sciences, University of Mississippi, Oxford, MS, USA
- Department of Pharmacy, Mizan-Tepi University, Mizan, Ethiopia
| | - Shafiu Mohammed
- Health Systems and Policy Research Unit, Ahmadu Bello University, Zaria, Nigeria
- Department of Health Care Management, Technical University of Berlin, Berlin, Germany
| | - Ali H Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Masoud Moradi
- Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Paula Moraga
- Computer, Electrical, and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Sumaira Mubarik
- Department of Epidemiology and Biostatistics, Wuhan University, Wuhan, China
| | - Getaneh Baye B Mulu
- Department of Pediatrics and Child Health, Debre Berhan University, Debre Berhan, Ethiopia
| | - Lillian Mwanri
- College of Medicine and Public Health, Flinders University, Adeaide, SA, Australia
| | - Ahamarshan Jayaraman Nagarajan
- Research and Analytics Department, Initiative for Financing Health and Human Development, Chennai, India
- Department of Research and Analytics, Bioinsilico Technologies, Chennai, India
| | - Mukhammad David Naimzada
- Laboratory of Public Health Indicators Analysis and Health Digitalization, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- Experimental Surgery and Oncology Laboratory, Kursk State Medical University, Kursk, Russia
| | - Muhammad Naveed
- Department of Biotechnology, University of Central Punjab, Lahore, Pakistan
| | - Javad Nazari
- Department of Pediatrics, Arak University of Medical Sciences, Arak, Iran
| | - Rawlance Ndejjo
- Department of Disease Control and Environmental Health, Makerere University, Kampala, Uganda
| | - Ionut Negoi
- Department of General Surgery, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Department of General Surgery, Emergency Hospital of Bucharest, Bucharest, Romania
| | - Frida N Ngalesoni
- Ministry of Health, Community Development, Gender, Elderly and Children, Dar es Salaam, Tanzania
| | | | | | - Cuong Tat Nguyen
- Institute for Global Health Innovations, Duy Tan University, Hanoi, Vietnam
| | | | - Chukwudi A Nnaji
- South African Medical Research Council, Cape Town, South Africa
- School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa
| | - Jean Jacques Noubiap
- Centre for Heart Rhythm Disorders, University of Adelaide, Adelaide, SA, Australia
| | - Virginia Nuñez-Samudio
- Unit of Microbiology and Public Health, Institute of Medical Sciences, Las Tablas, Panama
- Department of Public Health, Ministry of Health, Herrera, Panama
| | - Vincent Ebuka Nwatah
- Department of Pediatrics, National Hospital, Abuja, Nigeria
- Department of International Public Health, University of Liverpool, Liverpool, UK
| | - Bogdan Oancea
- Administrative and Economic Sciences Department, University of Bucharest, Bucharest, Romania
| | - Oluwakemi Ololade Odukoya
- Department of Community Health and Primary Care, University of Lagos, Idi Araba, Nigeria
- Department of Family and Preventive Medicine, University of Utah, Salt Lake City, UT, USA
| | - Andrew T Olagunju
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
- Department of Psychiatry, University of Lagos, Lagos, Nigeria
| | | | | | | | - Ahmed Omar Bali
- Diplomacy and Public Relations Department, University of Human Development, Sulaymaniyah, Iraq
| | - Obinna E Onwujekwe
- Department of Pharmacology and Therapeutics, University of Nigeria Nsukka, Enugu, Nigeria
| | | | - Nikita Otstavnov
- Laboratory of Public Health Indicators Analysis and Health Digitalization, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Stanislav S Otstavnov
- Laboratory of Public Health Indicators Analysis and Health Digitalization, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- Department of Project Management, National Research University Higher School of Economics, Moscow, Russia
| | - Mayowa O Owolabi
- Department of Medicine, University of Ibadan, Ibadan, Nigeria
- Department of Medicine, University College Hospital, Ibadan, Ibadan, Nigeria
| | - P A Mahesh
- Department of Respiratory Medicine, Jagadguru Sri Shivarathreeswara Academy of Health Education and Research, Mysore, India
| | - Jagadish Rao Padubidri
- Department of Forensic Medicine and Toxicology, Manipal Academy of Higher Education, Manipal, India
| | - Adrian Pana
- Department of Statistics and Econometrics, Bucharest University of Economic Studies, Bucharest, Romania
- Department of Health Metrics, Center for Health Outcomes & Evaluation, Bucharest, Romania
| | - Ashok Pandey
- Research Department, Nepal Health Research Council, Kathmandu, Nepal
- Research Department, Public Health Research Society Nepal, Kathmandu, Nepal
| | | | | | - George C Patton
- Department of Pediatrics, University of Melbourne, Melbourne, VIC, Australia
- Population Health Theme, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Shrikant Pawar
- Department of Genetics, Yale University, New Haven, CT, USA
| | - Emmanuel K Peprah
- School of Global Public Health, New York University, New York, NY, USA
| | - Maarten J Postma
- University Medical Center Groningen, University of Groningen, Groningen, Netherlands
- School of Economics and Business, University of Groningen, Groningen, Netherlands
| | - Liliana Preotescu
- National Institute of Infectious Diseases, Bucuresti, Romania
- Department of Infectious Diseases, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Zahiruddin Quazi Syed
- Department of Community Medicine, Datta Meghe Institute of Medical Sciences, Wardha, India
| | - Navid Rabiee
- School of Engineering, Macquarie University, Sydney, NSW, Australia
- Pohang University of Science and Technology, Pohang, South Korea
| | - Amir Radfar
- College of Medicine, University of Central Florida, Orlando, FL, USA
| | - Alireza Rafiei
- Department of Immunology, Mazandaran University of Medical Sciences, Sari, Iran
- Molecular and Cell Biology Research Center, Mazandaran University of Medical Sciences, Sari, Iran
| | - Fakher Rahim
- Metabolomics and Genomics Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Vafa Rahimi-Movaghar
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Masoud Rahmani
- Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Taiwan
| | - Kiana Ramezanzadeh
- Department of Pharmacology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Juwel Rana
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
- Research and Innovation Division, South Asian Institute for Social Transformation (SAIST), Dhaka, Bangladesh
| | - Chhabi Lal Ranabhat
- Research Department, Policy Research Institute, Kathmandu, Nepal
- Health and Public Policy Department, Global Center for Research and Development, Kathmandu, Nepal
| | - Sowmya J Rao
- Department of Oral Pathology, Sharavathi Dental College and Hospital, Shimogga, India
| | - David Laith Rawaf
- WHO Collaborating Centre for Public Health Education and Training, Imperial College London, London, UK
- University College London Hospitals, London, UK
| | - Salman Rawaf
- Department of Primary Care and Public Health, Imperial College London, London, UK
- Academic Public Health England, Public Health England, London, UK
| | | | | | - Nima Rezaei
- Research Center for Immunodeficiencies, Tehran University of Medical Sciences, Tehran, Iran
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Aziz Rezapour
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mavra A Riaz
- Faculty of Business and Management, Universiti Sultan Zainal Abidin (Sultan Zainal Abidin University), Kuala Terengganu, Malaysia
| | - Ana Isabel Ribeiro
- Epidemiology Research Unit (EPIUnit), University of Porto, Porto, Portugal
| | - Jennifer M Ross
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Global Health, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Enrico Rubagotti
- African Genome Center, Mohammed VI Polytechnic University (UM6P), Ben Guerir, Morocco
- Centro de Investigaciones en Anomalías Congénitas y Enfermedades Raras (Center for Research in Congenital Anomalies and Rare Diseases), Universidad ICESI (ICESI University), Cali, Colombia
| | - Susan Fred Rumisha
- Malaria Atlas Project, Telethon Kids Institute, Perth, Australia
- Department of Health Statistics, National Institute for Medical Research, Dar es Salaam, Tanzania
| | | | - Sahar Saeedi Moghaddam
- Non-communicable Diseases Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Rajesh Sagar
- Department of Psychiatry, All India Institute of Medical Sciences, New Delhi, India
| | | | - Maitreyi Sahu
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Marwa Rashad Salem
- Public Health and Community Medicine Department, Cairo University, Giza, Egypt
| | - Hossein Samadi Kafil
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Abdallah M Samy
- Department of Entomology, Ain Shams University, Cairo, Egypt
| | - Benn Sartorius
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Brijesh Sathian
- Geriatric and Long Term Care Department, Hamad Medical Corporation, Doha, Qatar
- Faculty of Health & Social Sciences, Bournemouth University, Bournemouth, UK
| | - Abdul-Aziz Seidu
- Department of Population and Health, University of Cape Coast, Cape Coast, Ghana
- College of Public Health, Medical and Veterinary Sciences, James Cook University, QLD, Townsville, Australia
| | - Amira A Shaheen
- Public Health Division, An-Najah National University, Nablus, Palestine
| | | | - Morteza Shamsizadeh
- Faculty of Caring Science, Work Life, and Social Welfare, University of Borås, Borås, Sweden
| | | | - Jae Il Shin
- College of Medicine, Yonsei University, Seoul, South Korea
| | - Roman Shrestha
- Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Jasvinder A Singh
- School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
- Medicine Service, US Department of Veterans Affairs (VA), Birmingham, AL, USA
| | | | - Anna Aleksandrovna Skryabina
- Department of Infectious Diseases and Epidemiology, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Shahin Soltani
- Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | | | - Takahiro Tabuchi
- Cancer Control Center, Osaka International Cancer Institute, Osaka, Japan
| | | | - Nuno Taveira
- University Institute "Egas Moniz", Monte da Caparica, Portugal
- Research Institute for Medicines, University of Lisbon, Lisbon, Portugal
| | - Fisaha Haile Tesfay
- School of Public Health, Mekelle University, Mekelle, Ethiopia
- Southgate Institute for Health and Society, Flinders University, Adelaide, SA, Australia
| | - Rekha Thapar
- Department of Community Medicine, Manipal Academy of Higher Education, Mangalore, India
| | - Marcos Roberto Tovani-Palone
- Department of Pathology and Legal Medicine, University of São Paulo, Ribeirão Preto, Brazil
- Modestum LTD, London, UK
| | | | - Chukwuma David Umeokonkwo
- Department of Community Medicine, Alex Ekwueme Federal University Teaching Hospital Abakaliki, Abakaliki, Nigeria
| | | | | | - Francesco S Violante
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
- Occupational Health Unit, Sant'Orsola Malpighi Hospital, Bologna, Italy
| | - Bay Vo
- Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam
| | - Giang Thu Vu
- Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
| | - Yohannes Dibaba Wado
- Population Dynamics and Sexual and Reproductive Health, African Population and Health Research Center, Nairobi, Kenya
| | - Yasir Waheed
- Foundation University Medical College, Foundation University Islamabad, Islamabad, Pakistan
| | - Richard G Wamai
- Department of Cultures, Societies and Global Studies, Northeastern University, Boston, MA, USA
- School of Public Health, University of Nairobi, Nairobi, Kenya
| | - Yanzhong Wang
- School of Population Health and Environmental Sciences, King's College London, London, UK
| | - Paul Ward
- Centre for Health Policy Research, Torrens University Australia, Adelaide, SA, Australia
| | | | - Katherine Wilson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Sanni Yaya
- School of International Development and Global Studies, University of Ottawa, Ottawa, ON, Canada
- The George Institute for Global Health, University of Oxford, Oxford, UK
| | - Paul Yip
- Centre for Suicide Research and Prevention, University of Hong Kong, Hong Kong, China
- Department of Social Work and Social Administration, University of Hong Kong, Hong Kong, China
| | - Naohiro Yonemoto
- Department of Neuropsychopharmacology, National Center of Neurology and Psychiatry, Kodaira, Japan
- Department of Public Health, Juntendo University, Tokyo, Japan
| | - Chuanhua Yu
- Department of Epidemiology and Biostatistics, Wuhan University, Wuhan, China
| | - Mikhail Sergeevich Zastrozhin
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
- Addictology Department, Russian Medical Academy of Continuous Professional Education, Moscow, Russia
| | - Yunquan Zhang
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan, China
| | | | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Laura Dwyer-Lindgren
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
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Spatial Co-Clustering of Tuberculosis and HIV in Ethiopia. Diseases 2022; 10:diseases10040106. [PMID: 36412600 PMCID: PMC9680430 DOI: 10.3390/diseases10040106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 11/05/2022] [Accepted: 11/08/2022] [Indexed: 11/19/2022] Open
Abstract
Background: Tuberculosis (TB) and HIV are epidemiologically associated, and their co-dynamics suggest that the two diseases are directly related at the population level and within the host. However, there is no or little information on the joint spatial patterns of the two diseases in Ethiopia. The main objective of the current study was to assess the spatial co-clustering of TB and HIV cases simultaneously in Ethiopia at the district level. Methods: District-level aggregated data collected from the national Health Management Information System (HMIS) for the years 2015 to 2018 on the number of TB cases enrolled in directly observed therapy, short course (DOTS) who were tested for HIV and the number of HIV patients enrolled in HIV care who were screened for TB during their last visit to health care facilities were used in this study. The univariate and bivariate global and local Moran’s I indices were applied to assess the spatial clustering of TB and HIV separately and jointly. Results: The results of this study show that the two diseases were significantly (p-value <0.001) spatially autocorrelated at the district level with minimum and maximum global Moran’s I values of 0.407 and 0.432 for TB, 0.102 and 0.247 for HIV, and 0.152 and 0.251 for joint TB/HIV. The district-level TB/HIV spatial co-clustering patterns in Ethiopia in most cases overlapped with the hot spots of TB and HIV. The TB/HIV hot-spot clusters may appear due to the observed high TB and HIV prevalence rates in the hot-spot districts. Our results also show that there were low-low TB/HIV co-clusters or cold spots in most of the Afar and Somali regions, which consistently appeared for the period 2015−2018. This may be due to very low notifications of both diseases in the regions. Conclusions: This study expanded knowledge about TB and HIV co-clustering in Ethiopia at the district level. The findings provide information to health policymakers in the country to plan geographically targeted and integrated interventions to jointly control TB and HIV.
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Ugwu CLJ, Ncayiyana JR. Spatial disparities of HIV prevalence in South Africa. Do sociodemographic, behavioral, and biological factors explain this spatial variability? Front Public Health 2022; 10:994277. [PMID: 36438270 PMCID: PMC9692089 DOI: 10.3389/fpubh.2022.994277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 10/25/2022] [Indexed: 11/13/2022] Open
Abstract
Background In 2021, an estimated 38 million people were living with human immunodeficiency virus (HIV) globally, with over two-thirds living in African regions. In South Africa, ~20% of South African adults are living with HIV. Accurate estimation of the risk factors and spatial patterns of HIV risk using individual-level data from a nationally representative sample is invaluable for designing geographically targeted intervention and control programs. Methods Data were obtained from the 2016 South Africa Demographic and Health Survey (SDHS16). The study involved all men and women aged 15 years and older, who responded to questions and tested for HIV in the SDHS. Generalized additive models (GAMs) were fitted to our data with a nonparametric bivariate smooth term of spatial location parameters (X and Y coordinates). The GAMs were used to assess the spatial disparities and the potential contribution of sociodemographic, biological, and behavioral factors to the spatial patterns of HIV prevalence in South Africa. Results A significantly highest risk of HIV was observed in east coast, central and north-eastern regions. South African men and women who are widowed and divorced had higher odds of HIV as compared to their counterparts. Additionally, men and women who are unemployed had higher odds of HIV as compared to the employed. Surprisingly, the odds of HIV infection among men residing in rural areas were 1.60 times higher (AOR 1.60, 95% CI 1.12, 2.29) as compared to those in urban areas. But men who were circumcised had lower odds of HIV (AOR 0.73, 95% CI 0.52, 0.98), while those who had STI in the last 12 months prior to the survey had higher odds of HIV (AOR 1.76, 95% CI 1.44, 3.68). Conclusion Spatial heterogeneity in HIV risk persisted even after covariate adjustment but differed by sex, suggesting that there are plausible unobserved influencing factors contributing to HIV uneven variation. This study's findings could guide geographically targeted public health policy and effective HIV intervention in South Africa.
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Affiliation(s)
| | - Jabulani R. Ncayiyana
- Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
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15
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Syndemics of intimate partner violence among women in HIV endemic South Africa: geospatial analysis of nationally representative data. Sci Rep 2022; 12:18083. [PMID: 36302814 PMCID: PMC9613977 DOI: 10.1038/s41598-022-20230-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/09/2022] [Indexed: 12/30/2022] Open
Abstract
Despite some improvement in lowering HIV incidence, HIV-related challenges, such as intimate partner violence (IPV), remain unacceptably high among women in South Africa. For decades, researchers and activists have pointed to the complex and intertwined reality of the substance abuse, violence and AIDS (SAVA) syndemic that endangers women. However, more recent systematic review/meta-analysis evidence points to inconclusive association between IPV and alcohol use. Furthermore, much of the evidence is often non-population-based that focuses on the co-occurrence rather than synergistic SAVA interaction. In this study, using the latest data from the South Africa Demographic and Health Survey (SA-DHS), we identified geographic synergistic clustering of IPV associated with HIV and substance abuse in South Africa as a measure of population-level interactions among these factors. The SA-DHS is a nationally representative sample that includes wide-ranging data on health, social challenges and household geo-locations of 5,874 women who participated in the domestic violence module. First, geographical IPV, harmful alcohol use (as the substance abuse measure available in SA-DHS) and HIV clusters were identified using the Kulldorff spatial scan statistic in SaTScan. Second, synergistic interactions related to recent IPV (i.e. recent physical, sexual, emotional violence during the last 12 months) with harmful alcohol use and HIV challenge were measured using RERI [Relative excess risk due to interaction], AP [attributable proportion] and S [Synergy index]. In our results, we spatially identified geographical physical IPV syndemic interactions in parts of the Eastern Cape/Free State Provinces (RERI = 4.42 [95% CI: 2.34-6.51], AP = 0.56 [95% CI: 0.44-0.68], S = 2.77 [95% CI: 2.01-3.84], but not in other forms of IPV. Although IPV, based on decade old concept of SAVA syndemic, was less common/widespread than expected from the national scale population-based data, we identified population-level physical violence syndemic occurring in South Africa. Our study highlights the need to prioritize public health response targeting vulnerable populations residing in these high-risk areas of syndemic mechanisms linking these synergistic epidemics that women face in South Africa.
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Soogun AO, Kharsany ABM, Zewotir T, North D, Ogunsakin E, Rakgoale P. Spatiotemporal Variation and Predictors of Unsuppressed Viral Load among HIV-Positive Men and Women in Rural and Peri-Urban KwaZulu-Natal, South Africa. Trop Med Infect Dis 2022; 7:232. [PMID: 36136643 PMCID: PMC9502339 DOI: 10.3390/tropicalmed7090232] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 02/05/2023] Open
Abstract
Unsuppressed HIV viral load is an important marker of sustained HIV transmission. We investigated the prevalence, predictors, and high-risk areas of unsuppressed HIV viral load among HIV-positive men and women. Unsuppressed HIV viral load was defined as viral load of ≥400 copies/mL. Data from the HIV Incidence District Surveillance System (HIPSS), a longitudinal study undertaken between June 2014 to June 2016 among men and women aged 15−49 years in rural and peri-urban KwaZulu-Natal, South Africa, were analysed. A Bayesian geoadditive regression model which includes a spatial effect for a small enumeration area was applied using an integrated nested Laplace approximation (INLA) function while accounting for unobserved factors, non-linear effects of selected continuous variables, and spatial autocorrelation. The prevalence of unsuppressed HIV viral load was 46.1% [95% CI: 44.3−47.8]. Predictors of unsuppressed HIV viral load were incomplete high school education, being away from home for more than a month, alcohol consumption, no prior knowledge of HIV status, not ever tested for HIV, not on antiretroviral therapy (ART), on tuberculosis (TB) medication, having two or more sexual partners in the last 12 months, and having a CD4 cell count of <350 cells/μL. A positive non-linear effect of age, household size, and the number of lifetime HIV tests was identified. The higher-risk pattern of unsuppressed HIV viral load occurred in the northwest and northeast of the study area. Identifying predictors of unsuppressed viral load in a localized geographic area and information from spatial risk maps are important for targeted prevention and treatment programs to reduce the transmission of HIV.
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Affiliation(s)
- Adenike O. Soogun
- Department of Statistics, School of Mathematics, Statistics and Computer Science, College of Agriculture Engineering and Science, University of KwaZulu-Natal, Durban 4001, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), Doris Duke Medical Research Institute, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban 4001, South Africa
| | - Ayesha B. M. Kharsany
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), Doris Duke Medical Research Institute, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban 4001, South Africa
- School of Laboratory Medicine & Medical Science, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban 4001, South Africa
| | - Temesgen Zewotir
- Department of Statistics, School of Mathematics, Statistics and Computer Science, College of Agriculture Engineering and Science, University of KwaZulu-Natal, Durban 4001, South Africa
| | - Delia North
- Department of Statistics, School of Mathematics, Statistics and Computer Science, College of Agriculture Engineering and Science, University of KwaZulu-Natal, Durban 4001, South Africa
| | - Ebenezer Ogunsakin
- Discipline of Public Health Medicine, University of KwaZulu-Natal, Durban 4001, South Africa
| | - Perry Rakgoale
- Department of Geography, School of Agriculture, Earth, and Environmental Science, University of KwaZulu-Natal, Durban 4001, South Africa
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Kauhl B, Vietzke M, König J, Schönfelder M. Exploring regional and sociodemographic disparities associated with unenrollment for the disease management program for type 2 Diabetes Mellitus using Bayesian spatial modelling. RESEARCH IN HEALTH SERVICES & REGIONS 2022; 1:7. [PMID: 39177711 PMCID: PMC11281746 DOI: 10.1007/s43999-022-00007-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 07/21/2022] [Indexed: 08/24/2024]
Abstract
BACKGROUND The disease management program (DMP) for type 2 Diabetes Mellitus (T2DM) is the largest DMP in Germany. Our goal was to analyze regional differences in unenrollment rates, suggest areas for intervention and provide background information, which population groups in which locations are currently not enrolled in the DMP for T2DM. METHODS In this study, we used data of the 1.7 mil. insurants of the AOK Nordost health insurance. For the visualization of enrollment potential, we used the Besag-York-Mollie model (BYM). The spatial scan statistic (SaTScan) was used to detect areas of unusually high rates of unenrolled diabetics to prioritize areas for intervention. To explore sociodemographic associations, we used Bayesian spatial global regression models. A Spatially varying coefficient model (SVC) revealed in how far the detected associations vary over space. RESULTS The proportion of diabetics currently not enrolled in the DMP T2DM was 36.8% in 2019 and varied within northeastern Germany. Local clusters were detected mainly in Mecklenburg-West-Pomerania and Berlin. The main sociodemographic variables associated with unenrollment were female sex, younger age, being unemployed, foreign citizenship, small household size and the proportion of persons commuting to work outside their residential municipality. The SVC model revealed important spatially varying effects for some but not all associations. CONCLUSION Lower socioeconomic status and foreign citizenship had an ubiquitous effect on not being enrolled. The DMP T2DM therefore does currently not reach those population groups, which have a higher risk for secondary diseases and possible avoidable hospitalizations. Logically, future interventions should focus on these groups. Our methodology clearly suggests areas for intervention and points out, which population group in which locations should be specifically approached.
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Affiliation(s)
- B Kauhl
- AOK Nordost - Die Gesundheitskasse, Potsdam, Germany.
| | - M Vietzke
- AOK Nordost - Die Gesundheitskasse, Potsdam, Germany
| | - J König
- AOK Nordost - Die Gesundheitskasse, Potsdam, Germany
| | - M Schönfelder
- AOK Nordost - Die Gesundheitskasse, Potsdam, Germany
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Mapping HIV prevalence in Nigeria using small area estimates to develop a targeted HIV intervention strategy. PLoS One 2022; 17:e0268892. [PMID: 35675346 PMCID: PMC9176772 DOI: 10.1371/journal.pone.0268892] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 05/10/2022] [Indexed: 11/25/2022] Open
Abstract
Objective Although geographically specific data can help target HIV prevention and treatment strategies, Nigeria relies on national- and state-level estimates for policymaking and intervention planning. We calculated sub-state estimates along the HIV continuum of care in Nigeria. Design Using data from the Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS) (July–December 2018), we conducted a geospatial analysis estimating three key programmatic indicators: prevalence of HIV infection among adults (aged 15–64 years); antiretroviral therapy (ART) coverage among adults living with HIV; and viral load suppression (VLS) rate among adults living with HIV. Methods We used an ensemble modeling method called stacked generalization to analyze available covariates and a geostatistical model to incorporate the output from stacking as well as spatial autocorrelation in the modeled outcomes. Separate models were fitted for each indicator. Finally, we produced raster estimates of each indicator on an approximately 5×5-km grid and estimates at the sub-state/local government area (LGA) and state level. Results Estimates for all three indicators varied both within and between states. While state-level HIV prevalence ranged from 0.3% (95% uncertainty interval [UI]: 0.3%–0.5%]) to 4.3% (95% UI: 3.7%–4.9%), LGA prevalence ranged from 0.2% (95% UI: 0.1%–0.5%) to 8.5% (95% UI: 5.8%–12.2%). Although the range in ART coverage did not substantially differ at state level (25.6%–76.9%) and LGA level (21.9%–81.9%), the mean absolute difference in ART coverage between LGAs within states was 16.7 percentage points (range, 3.5–38.5 percentage points). States with large differences in ART coverage between LGAs also showed large differences in VLS—regardless of level of effective treatment coverage—indicating that state-level geographic targeting may be insufficient to address coverage gaps. Conclusion Geospatial analysis across the HIV continuum of care can effectively highlight sub-state variation and identify areas that require further attention in order to achieve epidemic control. By generating local estimates, governments, donors, and other implementing partners will be better positioned to conduct targeted interventions and prioritize resource distribution.
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19
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Zhang S, Wang M, Yang Z, Zhang B. Do spatiotemporal units matter for exploring the microgeographies of epidemics? APPLIED GEOGRAPHY (SEVENOAKS, ENGLAND) 2022; 142:102692. [PMID: 35399592 PMCID: PMC8982866 DOI: 10.1016/j.apgeog.2022.102692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 02/04/2022] [Accepted: 03/25/2022] [Indexed: 05/17/2023]
Abstract
From the onset of the COVID-19 pandemic in 2020, studies on the microgeographies of epidemics have surged. However, studies have neglected the significant impact of multiple spatiotemporal units, such as report timestamps and spatial scales. This study examines three cities with localized COVID-19 resurgence after the first wave of the pandemic in mainland China to estimate the differential impact of spatiotemporal unit on exploring the influencing factors of epidemic spread at the microscale. The quantitative analysis results suggest that future spatial epidemiology research should give greater attention to the "symptom onset" timestamp instead of only the "confirmed" data and that "spatial transmission" should not be confused with "spatial sprawling" of epidemics, which can greatly reduce comparability between epidemiology studies. This research also highlights the importance of considering the modifiable areal unit problem (MAUP) and the uncertain geographic context problem (UGCoP) in future studies.
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Affiliation(s)
- Sui Zhang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Minghao Wang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Zhao Yang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Baolei Zhang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
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20
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McMahan LD, Lombe M, Evans CBR, Enelamah NV, Chu Y, Simms S, Verkamp-Ruthven J, Martinez JR, Mweemba O, LaForest L, Weiss DJ, Wideman L. Getting to zero HIV/AIDS in sub-Saharan Africa: Understanding perceptions of locals using the social determinants of health framework. HEALTH & SOCIAL CARE IN THE COMMUNITY 2022; 30:e739-e748. [PMID: 34028915 DOI: 10.1111/hsc.13444] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 04/20/2021] [Accepted: 05/04/2021] [Indexed: 06/12/2023]
Abstract
This study explored a community perception of the facilitators and inhibitors of Getting to Zero (GTZ) in rural Zambia, sub-Saharan Africa. Data were collected in 2017. We use the Social Determinants of Health framework to guide organisation of key themes emerging from semistructured, focus group interviews with community members (N = 52). Data were analysed through an iterative descriptive/thematic approach which allowed for the highlighting of key themes. Emerging themes point to the significance of (a) individual, (b) sociocultural, (c) environmental and (d) economic factors, for example, treatment adherence, gender norms, food security and access to health care as important in GTZ. Implications for policy, practise and scholarship are suggested.
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Affiliation(s)
- Lyndsey D McMahan
- School of Social Work, Boston College, Chestnut Hill, MA, USA
- Department of Conflict Resolution, Human Security & Global Governance, McCormack Graduate School of Policy and Global Studies, University of Massachusetts Boston, Boston, MA, USA
| | - Margaret Lombe
- School of Social Work, Boston College, Chestnut Hill, MA, USA
| | | | | | - Yoosun Chu
- Department of Social Welfare, Keimyung University, Daegu, South Korea
| | | | | | | | | | | | | | - Laura Wideman
- Department of Psychology, North Carolina State University, Raleigh, NC, USA
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21
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Cuadros DF, de Oliveira T, Gräf T, Junqueira DM, Wilkinson E, Lemey P, Bärnighausen T, Kim HY, Tanser F. The role of high-risk geographies in the perpetuation of the HIV epidemic in rural South Africa: A spatial molecular epidemiology study. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000105. [PMID: 36962341 PMCID: PMC10021703 DOI: 10.1371/journal.pgph.0000105] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 11/15/2021] [Indexed: 11/18/2022]
Abstract
In this study, we hypothesize that HIV geographical clusters (geospatial areas with significantly higher numbers of HIV positive individuals) can behave as the highly connected nodes in the transmission network. Using data come from one of the most comprehensive demographic surveillance systems in Africa, we found that more than 70% of the HIV transmission links identified were directly connected to an HIV geographical cluster located in a peri-urban area. Moreover, we identified a single central large community of highly connected nodes located within the HIV cluster. This module was composed by nodes highly connected among them, forming a central structure of the network that was also connected with the small sparser modules located outside of the HIV geographical cluster. Our study supports the evidence of the high level of connectivity between HIV geographical high-risk populations and the entire community.
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Affiliation(s)
- Diego F. Cuadros
- Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH, United States of America
- Health Geography and Disease Modeling Laboratory, University of Cincinnati, Cincinnati, OH, United States of America
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- School of Laboratory Medicine and Medical Science, Department of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Tiago Gräf
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Fundação Oswaldo Cruz (FIOCRUZ), Instituto Gonçalo Moniz, Salvador, Brazil
| | - Dennis M. Junqueira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- School of Laboratory Medicine and Medical Science, Department of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Eduan Wilkinson
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- School of Laboratory Medicine and Medical Science, Department of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute for Medical Research, University of Leuven, Leuven, Belgium
| | - Till Bärnighausen
- Africa Health Research Institute, University of KwaZulu-Natal, Durban, South Africa
- Heidelberg Institute for Public Health, University of Heidelberg, Heidelberg, Germany
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Hae-Young Kim
- Africa Health Research Institute, University of KwaZulu-Natal, Durban, South Africa
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States of America
| | - Frank Tanser
- Africa Health Research Institute, University of KwaZulu-Natal, Durban, South Africa
- School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
- Lincoln International Institute for Rural Health, University of Lincoln, Lincoln, United Kingdom
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa
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22
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Roberts DA, Cuadros D, Vandormael A, Gareta D, Barnabas RV, Herbst K, Tanser F, Akullian A. Predicting the Risk of Hiv-1 Acquisition in Rural South Africa Using Geospatial Data. Clin Infect Dis 2022; 75:1224-1231. [PMID: 35100612 PMCID: PMC9525068 DOI: 10.1093/cid/ciac069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Indexed: 12/04/2022] Open
Abstract
Background Accurate human immunodeficiency virus (HIV) risk assessment can guide optimal HIV prevention. We evaluated the performance of risk prediction models incorporating geospatial measures. Methods We developed and validated HIV risk prediction models in a population-based cohort in South Africa. Individual-level covariates included demographic and sexual behavior measures, and geospatial covariates included community HIV prevalence and viral load estimates. We trained models on 2012–2015 data using LASSO Cox models and validated predictions in 2016–2019 data. We compared full models to simpler models restricted to only individual-level covariates or only age and geospatial covariates. We compared the spatial distribution of predicted risk to that of high incidence areas (≥ 3/100 person-years). Results Our analysis included 19 556 individuals contributing 44 871 person-years and 1308 seroconversions. Incidence among the highest predicted risk quintile using the full model was 6.6/100 person-years (women) and 2.8/100 person-years (men). Models using only age group and geospatial covariates had similar performance (women: AUROC = 0.65, men: AUROC = 0.71) to the full models (women: AUROC = 0.68, men: AUROC = 0.72). Geospatial models more accurately identified high incidence regions than individual-level models; 20% of the study area with the highest predicted risk accounted for 60% of the high incidence areas when using geospatial models but only 13% using models with only individual-level covariates. Conclusions Geospatial models with no individual measures other than age group predicted HIV risk nearly as well as models that included detailed behavioral data. Geospatial models may help guide HIV prevention efforts to individuals and geographic areas at highest risk.
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Affiliation(s)
- D Allen Roberts
- Department of Epidemiology, University of Washington, Seattle, USA
| | - Diego Cuadros
- Department of Geography, University of Cincinnati, Cincinnati, USA
| | - Alain Vandormael
- Heidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany
| | - Dickman Gareta
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Ruanne V Barnabas
- Department of Epidemiology, University of Washington, Seattle, USA.,Department of Global Health, University of Washington, Seattle, USA.,Department of Medicine, University of Washington, Seattle, USA
| | - Kobus Herbst
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,DSI-MRC South African Population Research Infrastructure Network (SAPRIN), Durban, South Africa
| | - Frank Tanser
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,Lincoln International Institute for Rural Health, University of Lincoln, Lincoln, United Kingdom.,School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa.,Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, South Africa
| | - Adam Akullian
- Department of Global Health, University of Washington, Seattle, USA.,Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA
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23
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Oinam B, Anand V, Kajal RK. A spatiotemporal geographic information system-based assessment of human immunodeficiency virus/acquired immune deficiency syndrome distribution in Manipur, India. Indian J Public Health 2022; 65:362-368. [PMID: 34975079 DOI: 10.4103/ijph.ijph_1308_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Background Geographic information system (GIS) is a versatile tool that assists in health education, planning, research, monitoring, and evaluation of programs related to health. One of the epidemics which threaten the overall human welfare is human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS). In Manipur, the cases of HIV/AIDS have been reported at significant level. Objective The study aimed to detect the hotspot regions of HIV/AIDS prevalence in Manipur and to identify the significant factors which influence the HIV prevalence. Methods This study evaluates the spatial variations of HIV/AIDS prevalence in the state of Manipur, India, from 2011 to 2018. In this study, Getis-Ord Gi* statistic was used to detect the HIV/AIDS prevalent regions. The ordinary least square (OLS) spatial statistics embedded in the ArcGIS were employed for exploring the spatial relation between HIV/AIDS occurrence and the predictors. Results It was observed from the hotspot results that Churachandpur, Ukhrul, and Thoubal are the blocks where HIV/AIDS is more prevalent. Six factors associated with the prevalence of HIV/AIDS were found to be significant. The most obvious factor influencing HIV in the region is illiteracy. The constructed OLS model has the highest value of adjusted R2 statistic equals to 0.67 and the lowest value of the Akaike Information Criterion statistic equals to 474.55. Conclusion The use of hotspot analysis, regression analysis, spatial autocorrelation, and GIS can aid health planners in properly assessing and identifying spatial prevalence of diseases among the masses to better guide evidence-based health planning decisions.
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Affiliation(s)
- Bakimchandra Oinam
- Associate Professor, National Institute of Technology, Imphal, Manipur, India
| | - Vicky Anand
- Research Scholar, National Institute of Technology, Imphal, Manipur, India
| | - R K Kajal
- Project Assistant, National Institute of Technology, Imphal, Manipur, India
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Kim HY, Cuadros D, Wilkinson E, Junqueira DM, Oliveira TD, Tanser F. The geography and inter-community configuration of new sexual partnership formation in a rural South African population over fourteen years (2003-2016). PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000055. [PMID: 36962292 PMCID: PMC10022024 DOI: 10.1371/journal.pgph.0000055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/22/2021] [Indexed: 11/18/2022]
Abstract
Understanding spatial configuration of sexual network structure is critical for effective use of HIV preventative interventions in a community. However, this has never been described at the population level for any setting in sub-Saharan Africa. We constructed the comprehensive geospatial sexual network among new heterosexual partnerships in rural KwaZulu-Natal, South Africa. In the Africa Health Research Institute (AHRI)'s population-based surveillance, we identified stable sexual partnerships among individuals (≥15 years) from 2003 to 2016. Sexual partnerships and residency were recorded via household surveys (every 4-6 months). We geolocated residents and migration events and mapped the geospatial linkages of sexual partners at the start of sexual partnerships. In a grid composed by 108 cells (nodes; 3kmx3km per cell) covering the surveillance area (438km2), we calculated the degree of connectivity and centrality of the nodes and examined their association with HIV prevalence and incidence per cell. Of 2401 new sexual partnerships, 21% (n = 495) had both partners living within the surveillance area at the start of sexual partnerships, and 76% (376/495) were linked to the geographic HIV cluster with high HIV prevalence identified in a peri-urban community. Overall, 57 nodes had at least one connection to another node. The nodes in the peri-urban cluster had higher connectivity (mean = 19, range: 9-32), compared to outside the cluster (6, range: 1-16). The node's degree of connectivity was positively associated with HIV prevalence of the cell (Pearson correlation coefficient = 0.67; p <0.005). The peri-urban cluster contained nine of the 10 nodes that composed of a single large central module in the community. About 17% of sexual partnerships (n = 421) were formed between a resident and a non-resident partner who out-migrated. Most of these non-resident partners lived in KwaZulu-Natal (86.7%), followed by Gauteng (9.7%), and the median distance between a resident and a non-resident partner was 50.1km (IQR: 23.2-177.2). We found that the peri-urban HIV cluster served as the highly connected central node of the network for sexual partnership formation. The network was also connected beyond the surveillance area across South Africa. Understanding spatial sexual network can improve the provision of spatially targeted and effective interventions.
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Affiliation(s)
- Hae-Young Kim
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States of America
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), KwaZulu-Natal, Durban, South Africa
| | - Diego Cuadros
- Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH, United States of America
| | - Eduan Wilkinson
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), KwaZulu-Natal, Durban, South Africa
| | - Dennis M Junqueira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), KwaZulu-Natal, Durban, South Africa
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), KwaZulu-Natal, Durban, South Africa
| | - Frank Tanser
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
- Lincoln International Institute for Rural Health, University of Lincoln, Lincoln, United Kingdom
- School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa
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Moradi G, Masoumi Asl H, Bahmani N, Vahabi A, Shirzadi S, Zare Z, Goodarzi E, Naemi H, Khazaei Z, Karimi A. Epidemiology incidence and geographical distribution of leptospirosis using GIS and its incidence prediction in Iran in 2021. Med J Islam Repub Iran 2021; 35:109. [PMID: 34956955 PMCID: PMC8683794 DOI: 10.47176/mjiri.35.109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Indexed: 11/30/2022] Open
Abstract
Background: Leptospirosis is known as a public health problem in developing and developed countries. The aim of this study was to investigate the incidence and geographical distribution of leptospirosis using the Geographic Information System (GIS) and to predict its incidence in Iran in 2021.
Methods: This was a descriptive analytical study. Information on leptospirosis was obtained from the Center for Communicable Diseases Control during 2009-2015. In the next step, The ArcGIS 9.3 was used to prepare geographic maps of the disease incidence and frequency. Therefore, using the Raster Calculator tool, the disease prediction map was drawn.
Results: The results showed that the highest incidence of leptospirosis during 2009-2015 was observed in Gilan, Mazandaran, and Golestan provinces, respectively. The incidence of the disease had an increasing trend from 2013 to 2015. Based on the results of the modeling in Iran, the provinces of Gilan, Mazandaran, and Golestan, with 72.18%, 8.54%, and 4.95% of their area, respectively, have the largest areas at a high-risk for leptospirosis in the coming years.
Conclusion: The prevalence of leptospirosis is affected by geographical and climatic conditions of every region; thus, the incidence of the disease is higher in the provinces located at the Caspian coastal side and in some regions in Semnan province. Hence, if health authorities pay more attention to developing health plans to prevent the disease, the risk of disease in these areas will be reduced in the future.
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Affiliation(s)
- Ghobad Moradi
- Social Determinants of Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Hossein Masoumi Asl
- Center for Communicable Diseases Control, Ministry of Health and Medical Education, Tehran, Iran
| | - Nasrin Bahmani
- Zoonoses Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Ahmad Vahabi
- Zoonoses Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | | | - Zahra Zare
- Zoonosis Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Elham Goodarzi
- Social Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran
| | - Hasan Naemi
- Cellular and Molecular Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Zaher Khazaei
- Department of Public Health, School of Medicine, Dezful University of Medical Sciences, Dezful, Iran
| | - Asrin Karimi
- Social Determinants of Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
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Zahraei SM, Mohamadi P, Moradi G, Shirzadi S, Azimian F, Khazaei Z, Naemi H, Goodarzi E. Epidemiology incidence and geographical distribution of Pertussis using GIS and its incidence prediction in Iran in 2021. Med J Islam Repub Iran 2021; 35:108. [PMID: 34956954 PMCID: PMC8683795 DOI: 10.47176/mjiri.35.108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Indexed: 11/25/2022] Open
Abstract
Background: Pertussis is a respiratory tract infection caused by Bordetella pertussis, which causes inflammation of the lungs and respiratory tract. The purpose of this study was to investigate the incidence and geographical distribution of pertussis using the geographic information system (GIS) and to predict its incidence in Iran in 2021.
Methods: This was a descriptive analytical study. Information on pertussis was obtained from the Center for Communicable Diseases Control during 2009-2015. In the next step, the ArcGIS 9.3 was used to prepare geographic maps of the disease incidence and frequency. Therefore, the disease prediction map was drawn. using the Raster Calculator tool.
Results: The results showed that the highest incidence of pertussis during 2009-2015 was in Zanjan, Qom, Mazandaran, and Qazvin provinces. The incidence of pertussis in Iran increased from 0.74 in 2009 to 1.53 in 2015. Based on the modeling results for Iran, Qom, Mazandaran, Tehran, Qazvin, and Zanjan provinces, with 76.76%, 73.69%, 66.32%, 30.94% and 24.18% of their areas (Km2), are at high risk for pertussis in the coming years, respectively.
Conclusion: The incidence of the disease has been increasing in recent years, indicating the emergence of the disease in Iran. The modeling maps show that the Iranian provinces of Qom, Tehran, Zanjan, and Qazvin are at risk of the disease incidence in the coming years, indicating the need for planning, appropriate interventions and more precise implementation of the vaccination program against the disease.
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Affiliation(s)
- Seyed Mohsen Zahraei
- Center for Communicable Diseases Control, Ministry of Health and Medical Education, Tehran, Iran
| | - Parvin Mohamadi
- Department of Medical Sciences, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
| | - Ghobad Moradi
- Social Determinants of Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | | | - Fatemeh Azimian
- Iranian Center for Communicable Disease Control, Ministry of Health & Medical Education, Tehran, Iran
| | - Zaher Khazaei
- Department of Public Health, School of Medicine, Dezful University of Medical Sciences, Dezful, Iran
| | - Hasan Naemi
- Cellular and Molecular Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Elham Goodarzi
- Social Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran
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Ayalew KA, Manda S, Cai B. A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111215. [PMID: 34769735 PMCID: PMC8582764 DOI: 10.3390/ijerph182111215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/08/2021] [Accepted: 10/09/2021] [Indexed: 11/16/2022]
Abstract
Despite making significant progress in tackling its HIV epidemic, South Africa, with 7.7 million people living with HIV, still has the biggest HIV epidemic in the world. The Government, in collaboration with developmental partners and agencies, has been strengthening its responses to the HIV epidemic to better target the delivery of HIV care, treatment strategies and prevention services. Population-based household HIV surveys have, over time, contributed to the country’s efforts in monitoring and understanding the magnitude and heterogeneity of the HIV epidemic. Local-level monitoring of progress made against HIV and AIDS is increasingly needed for decision making. Previous studies have provided evidence of substantial subnational variation in the HIV epidemic. Using HIV prevalence data from the 2016 South African Demographic and Health Survey, we compare three spatial smoothing models, namely, the intrinsically conditionally autoregressive normal, Laplace and skew-t (ICAR-normal, ICAR-Laplace and ICAR-skew-t) in the estimation of the HIV prevalence across 52 districts in South Africa. The parameters of the resulting models are estimated using Bayesian approaches. The skewness parameter for the ICAR-skew-t model was not statistically significant, suggesting the absence of skewness in the HIV prevalence data. Based on the deviance information criterion (DIC) model selection, the ICAR-normal and ICAR-Laplace had DIC values of 291.3 and 315, respectively, which were lower than that of the ICAR-skewed t (348.1). However, based on the model adequacy criterion using the conditional predictive ordinates (CPO), the ICAR-skew-t distribution had the lowest CPO value. Thus, the ICAR-skew-t was the best spatial smoothing model for the estimation of HIV prevalence in our study.
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Affiliation(s)
- Kassahun Abere Ayalew
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa;
- Correspondence:
| | - Samuel Manda
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa;
- Biostatistics Unit, South African Medical Research Council, Pretoria 0001, South Africa
- Department of Statistics, University of Pretoria, Pretoria 0028, South Africa
| | - Bo Cai
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA;
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Muttai H, Guyah B, Achia T, Musingila P, Nakhumwa J, Oyoo R, Olweny W, Odeny R, Ohaga S, Agot K, Oruenjo K, Awino B, Joseph RH, Miruka F, Zielinski-Gutierrez E. Mapping geographic clusters of new HIV diagnoses to inform granular-level interventions for HIV epidemic control in western Kenya. BMC Public Health 2021; 21:1926. [PMID: 34688267 PMCID: PMC8542332 DOI: 10.1186/s12889-021-11890-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 09/30/2021] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND As countries make progress towards HIV epidemic control, there is increasing need to identify finer geographic areas to target HIV interventions. We mapped geographic clusters of new HIV diagnoses, and described factors associated with HIV-positive diagnosis, in order to inform targeting of HIV interventions to finer geographic areas and sub-populations. METHODS We analyzed data for clients aged > 15 years who received home-based HIV testing as part of a routine public health program between May 2016 and July 2017 in Siaya County, western Kenya. Geospatial analysis using Kulldorff's spatial scan statistic was used to detect geographic clusters (radius < 5 kilometers) of new HIV diagnoses. Factors associated with new HIV diagnosis were assessed in a spatially-integrated Bayesian hierarchical model. RESULTS Of 268,153 clients with HIV test results, 2906 (1.1%) were diagnosed HIV-positive. We found spatial variation in the distribution of new HIV diagnoses, and identified nine clusters in which the number of new HIV diagnoses was significantly (1.56 to 2.64 times) higher than expected. Sub-populations with significantly higher HIV-positive yield identified in the multivariable spatially-integrated Bayesian model included: clients aged 20-24 years [adjusted relative risk (aRR) 3.45, 95% Bayesian Credible Intervals (CI) 2.85-4.20], 25-35 years (aRR 4.76, 95% CI 3.92-5.81) and > 35 years (aRR 2.44, 95% CI 1.99-3.00); those in polygamous marriage (aRR 1.84, 95% CI 1.55-2.16), or separated/divorced (aRR 3.36, 95% CI 2.72-4.08); and clients who reported having never been tested for HIV (aRR 2.35, 95% CI 2.02-2.72), or having been tested > 12 months ago (aRR 1.53, 95% CI 1.41-1.66). CONCLUSION Our study used routine public health program data to identify granular geographic clusters of higher new HIV diagnoses, and sub-populations with higher HIV-positive yield in the setting of a generalized HIV epidemic. In order to target HIV testing and prevention interventions to finer granular geographic areas for maximal epidemiologic impact, integrating geospatial analysis into routine public health programs can be useful.
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Affiliation(s)
- Hellen Muttai
- Division of Global HIV & TB, United States Centers for Disease Control and Prevention, 00621, Nairobi, Kenya.
| | - Bernard Guyah
- School of Public Health, Maseno University, Kisumu, Kenya
| | - Thomas Achia
- Division of Global HIV & TB, United States Centers for Disease Control and Prevention, 00621, Nairobi, Kenya
| | - Paul Musingila
- Division of Global HIV & TB, United States Centers for Disease Control and Prevention, 00621, Nairobi, Kenya
| | - Jesse Nakhumwa
- Impact Research and Development Organization, Kisumu, Kenya
| | - Rose Oyoo
- Impact Research and Development Organization, Kisumu, Kenya
| | | | | | - Spala Ohaga
- Impact Research and Development Organization, Kisumu, Kenya
| | - Kawango Agot
- Impact Research and Development Organization, Kisumu, Kenya
| | | | - Bob Awino
- Siaya County Department of Health, Siaya, Kenya
| | - Rachael H Joseph
- Division of Global HIV & TB, United States Centers for Disease Control and Prevention, 00621, Nairobi, Kenya
| | - Fredrick Miruka
- Division of Global HIV & TB, United States Centers for Disease Control and Prevention, 00621, Nairobi, Kenya
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Wand H, Morris N, Reddy T. Characteristics of women who use hormonal contraceptives and their population-level impacts on HIV infection in a cohort of South African women (2002-2016). EUR J CONTRACEP REPR 2021; 25:449-455. [PMID: 33140988 DOI: 10.1080/13625187.2020.1831469] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
PURPOSE Women's choice of contraceptive can have broader implications and may play a significant role in shaping their sexual practices. We aim to identify the characteristics of women who use hormonal injectables and those at high-risk of HIV infection. We also quantify the population-level impact of their shared characteristics on HIV incidence rates. MATERIALS AND METHODS Data from 9948 women who enrolled in six-HIV prevention trials conducted in South Africa (2002-2016) were included. We used logistic and Cox regression models and estimated the population-level impact of the use of injectables on HIV incidence in the multifactorial-model setting. RESULTS Using hormonal injectables were associated with increased risk of HIV infection (adjusted Hazard ratio (aHR):1.51, 95% CI:1.22, 1.86). At the population level less than 20% of the infections were associated with injectable contraceptives among younger women (i.e., less than 35 years of age). Factors including being single/not-cohabiting, using condoms at last sex, partner-related factors, and STI diagnosis were all identified as shared characteristics of women who preferred using hormonal injectables and those at high-risk of HIV infection. At the population level, these factors were associated with more than 50% of the infections among women younger than 35 years of age. CONCLUSIONS Our analysis presented evidence for the overlapping characteristics of the women who used hormonal injectables and those at high-risk of HIV infection. These findings reinforce the importance of comprehensive contraceptive counselling to women about the importance of dual protection, such as male condoms and hormonal contraceptives use.
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Affiliation(s)
- Handan Wand
- Kirby Institute, University of New South Wales, Kensington, Australia
| | - Natashia Morris
- Biostatistics Unit: GIS, South African Medical Research Council, Durban, South Africa
| | - Tarylee Reddy
- Biostatistics Unit, South African Medical Research Council, Durban, South Africa
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Abstract
HIV incidence has recently been in decline across some of the most intense epidemics in sub-Saharan Africa due to the scale-up of prevention and transmission-blocking treatments. Understanding whether declines in incidence are being felt equally across age and gender can help prioritize demographic groups where more effort is needed to lower transmission. We found that HIV incidence has declined disproportionately in the youngest men and women in a population with the highest HIV prevalence in the world. Shifts in the age distribution of risk may be the consequence of aging prevalence, prioritized prevention to younger individuals, and delays in age at infection from reduced overall force of infection. Our results highlight the need to expand age targets for HIV prevention. Recent declines in adult HIV-1 incidence have followed the large-scale expansion of antiretroviral therapy and primary HIV prevention across high-burden communities of sub-Saharan Africa. Mathematical modeling suggests that HIV risk will decline disproportionately in younger adult age-groups as interventions scale, concentrating new HIV infections in those >age 25 over time. Yet, no empirical data exist to support these projections. We conducted a population-based cohort study over a 16-y period (2004 to 2019), spanning the early scale-up of antiretroviral therapy and voluntary medical male circumcision, to estimate changes in the age distribution of HIV incidence in a hyperepidemic region of KwaZulu-Natal, South Africa, where adult HIV incidence has recently declined. Median age of HIV seroconversion increased by 5.5 y in men and 3.0 y in women, and the age of peak HIV incidence increased by 5.0 y in men and 2.0 y in women. Incidence declined disproportionately among young men (64% in men 15 to 19, 68% in men 20 to 24, and 46% in men 25 to 29) and young women (44% in women 15 to 19, 24% in women 20 to 24) comparing periods pre- versus post-universal test and treat. Incidence was stable (<20% change) in women aged 30 to 39 and men aged 30 to 34. Age shifts in incidence occurred after 2012 and were observed earlier in men than in women. These results provide direct epidemiological evidence of the changing demographics of HIV risk in sub-Saharan Africa in the era of large-scale treatment and prevention. More attention is needed to address lagging incidence decline among older individuals.
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Wong EB, Olivier S, Gunda R, Koole O, Surujdeen A, Gareta D, Munatsi D, Modise TH, Dreyer J, Nxumalo S, Smit TK, Ording-Jespersen G, Mpofana IB, Khan K, Sikhosana ZEL, Moodley S, Shen YJ, Khoza T, Mhlongo N, Bucibo S, Nyamande K, Baisley KJ, Cuadros D, Tanser F, Grant AD, Herbst K, Seeley J, Hanekom WA, Ndung'u T, Siedner MJ, Pillay D. Convergence of infectious and non-communicable disease epidemics in rural South Africa: a cross-sectional, population-based multimorbidity study. Lancet Glob Health 2021; 9:e967-e976. [PMID: 34143995 PMCID: PMC8220132 DOI: 10.1016/s2214-109x(21)00176-5] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/22/2021] [Accepted: 03/23/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND There has been remarkable progress in the treatment of HIV throughout sub-Saharan Africa, but there are few data on the prevalence and overlap of other significant causes of disease in HIV endemic populations. Our aim was to identify the prevalence and overlap of infectious and non-communicable diseases in such a population in rural South Africa. METHODS We did a cross-sectional study of eligible adolescents and adults from the Africa Health Research Institute demographic surveillance area in the uMkhanyakude district of KwaZulu-Natal, South Africa. The participants, who were 15 years or older, were invited to participate at a mobile health camp. Medical history for HIV, tuberculosis, hypertension, and diabetes was established through a questionnaire. Blood pressure measurements, chest x-rays, and tests of blood and sputum were taken to estimate the population prevalence and geospatial distribution of HIV, active and lifetime tuberculosis, elevated blood glucose, elevated blood pressure, and combinations of these. FINDINGS 17 118 adolescents and adults were recruited from May 25, 2018, to Nov 28, 2019, and assessed. Overall, 52·1% (95% CI 51·3-52·9) had at least one active disease. 34·2% (33·5-34·9) had HIV, 1·4% (1·2-1·6) had active tuberculosis, 21·8% (21·2-22·4) had lifetime tuberculosis, 8·5% (8·1-8·9) had elevated blood glucose, and 23·0% (22·4-23·6) had elevated blood pressure. Appropriate treatment and optimal disease control was highest for HIV (78·1%), and lower for elevated blood pressure (42·5%), active tuberculosis (29·6%), and elevated blood glucose (7·1%). Disease prevalence differed notably by sex, across age groups, and geospatially: men had a higher prevalence of active and lifetime tuberculosis, whereas women had a substantially high prevalence of HIV at 30-49 years and an increasing prevalence of multiple and poorly controlled non-communicable diseases when older than 50 years. INTERPRETATION We found a convergence of infectious and non-communicable disease epidemics in a rural South African population, with HIV well treated relative to all other diseases, but tuberculosis, elevated blood glucose, and elevated blood pressure poorly diagnosed and treated. A public health response that expands the successes of the HIV testing and treatment programme to provide multidisease care targeted to specific populations is required to optimise health in such settings in sub-Saharan Africa. FUNDING Wellcome Trust, Bill & Melinda Gates Foundation, the South African Department of Science and Innovation, South African Medical Research Council, and South African Population Research Infrastructure Network. TRANSLATION For the isiZulu translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Emily B Wong
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa; Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA; Division of Infectious Diseases, University of Alabama Birmingham, Birmingham, AL, USA; Division of Infection and Immunity, University College London, London, UK.
| | - Stephen Olivier
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Resign Gunda
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa; School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Olivier Koole
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa; London School of Hygiene & Tropical Medicine, London, UK
| | - Ashmika Surujdeen
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Dickman Gareta
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Day Munatsi
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | | | - Jaco Dreyer
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Siyabonga Nxumalo
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Theresa K Smit
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | | | | | - Khadija Khan
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | | | - Sashen Moodley
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Yen-Ju Shen
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Thandeka Khoza
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Ngcebo Mhlongo
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Sanah Bucibo
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Kennedy Nyamande
- Department of Pulmonology and Critical Care, Inkosi Albert Luthuli Hospital, Durban, South Africa; Department of Respiratory Medicine, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Kathy J Baisley
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa; London School of Hygiene & Tropical Medicine, London, UK
| | - Diego Cuadros
- Department of Geography, University of Cincinnati, USA
| | - Frank Tanser
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa; School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa; Lincoln International Institute for Rural Health, University of Lincoln, UK; Centre for the AIDS Programme of Research in South Africa, University of KwaZulu-Natal, Durban, South Africa
| | - Alison D Grant
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa; School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa; London School of Hygiene & Tropical Medicine, London, UK; School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Kobus Herbst
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa; Department of Science and Innovation, Medical Research Council, South African Population Research Infrastructure, Durban, South Africa
| | - Janet Seeley
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa; London School of Hygiene & Tropical Medicine, London, UK
| | - Willem A Hanekom
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa; Division of Infection and Immunity, University College London, London, UK
| | - Thumbi Ndung'u
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa; Division of Infection and Immunity, University College London, London, UK; HIV Pathogenesis Programme, Doris Duke Medical Research Institute, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa; The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard Medical School, Cambridge, MA, USA; Max Planck Institute for Infection Biology, Berlin, Germany
| | - Mark J Siedner
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa; Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA; School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Deenan Pillay
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa; Division of Infection and Immunity, University College London, London, UK
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Wand H, Morris N, Reddy T. Temporal and spatial monitoring of HIV prevalence and incidence rates using geospatial models: Results from South African women. Spat Spatiotemporal Epidemiol 2021; 37:100413. [PMID: 33980413 DOI: 10.1016/j.sste.2021.100413] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 12/15/2020] [Accepted: 02/01/2021] [Indexed: 11/18/2022]
Abstract
Generalized additive models (GAMs) were used to predict non-linear distributions of HIV prevalence and incidence based on semiparametric methods. The GAMs also provide smooth intensity maps by projecting the predicted HIV prevalence (or incidence) into the contour maps. Two sets of geo-coded data sources were used: (1) population-based cross-sectional data from 10,928 women who participated in four HIV behavioral surveys (2002-2017), (2) clinic-based longitudinal data from 7,557 women who resided in KwaZulu-Natal (2002-2016). Model estimated degrees of freedoms were 15.84,12.17,7.64 and 15.08 (2002-2012), indicating substantial spatial variations in HIV prevalence overtime. At localized-level these HIV incidence ranged from 15 to 18 per 100 person-year and scattered across the relatively homogeneous area within less than 100 km radius. These significant quantitative evidence were further supported by continuous-scale intensity maps. Our findings provided empirical and visual evidence for the changing face of the epidemic in South Africa using geospatial methods.
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Affiliation(s)
- Handan Wand
- Kirby Institute, University of New South Wales, Kensington, 2052, New South Wales, Australia.
| | - Natashia Morris
- Biostatistics Unit: GIS, South African Medical Research Council, Durban, 4091, KwaZulu-Natal, South Africa.
| | - Tarylee Reddy
- Biostatistics Unit, South African Medical Research Council, Durban, Kwazulu-Natal, South Africa.
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Onovo A, Kalaiwo A, Katbi M, Ogorry O, Jaquet A, Keiser O. Geographical Disparities in HIV Seroprevalence Among Men Who Have Sex with Men and People Who Inject Drugs in Nigeria: Exploratory Spatial Data Analysis. JMIR Public Health Surveill 2021; 7:e19587. [PMID: 34028360 PMCID: PMC8185612 DOI: 10.2196/19587] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 07/25/2020] [Accepted: 02/26/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The assessment of geographical heterogeneity of HIV among men who have sex with men (MSM) and people who inject drugs (PWID) can usefully inform targeted HIV prevention and care strategies. OBJECTIVE We aimed to measure HIV seroprevalence and identify hotspots of HIV infection among MSM and PWID in Nigeria. METHODS We included all MSM and PWID accessing HIV testing services across 7 prioritized states (Lagos, Nasarawa, Akwa Ibom, Cross Rivers, Rivers, Benue, and the Federal Capital Territory) in 3 geographic regions (North Central, South South, and South West) between October 1, 2016, and September 30, 2017. We extracted data from national testing registers, georeferenced all HIV test results aggregated at the local government area level, and calculated HIV seroprevalence. We calculated and compared HIV seroprevalence from our study to the 2014 integrated biological and behavioural surveillance survey and used global spatial autocorrelation and hotspot analysis to highlight patterns of HIV infection and identify areas of significant clustering of HIV cases. RESULTS MSM and PWID had HIV seroprevalence rates of 12.14% (3209/26,423) and 11.88% (1126/9474), respectively. Global spatial autocorrelation Moran I statistics revealed a clustered distribution of HIV infection among MSM and PWID with a <5% and <1% likelihood that this clustered pattern could be due to chance, respectively. Significant clusters of HIV infection (Getis-Ord-Gi* statistics) confined to the North Central and South South regions were identified among MSM and PWID. Compared to the 2014 integrated biological and behavioural surveillance survey, our results suggest an increased HIV seroprevalence among PWID and a substantial decrease among MSM. CONCLUSIONS This study identified geographical areas to prioritize for control of HIV infection among MSM and PWID, thus demonstrating that geographical information system technology is a useful tool to inform public health planning for interventions targeting epidemic control of HIV infection.
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Affiliation(s)
- Amobi Onovo
- Institute of Global Health, University of Geneva, Geneva, Switzerland
- U S Agency for International Development, Abuja, Nigeria
| | - Abiye Kalaiwo
- U S Agency for International Development, Abuja, Nigeria
| | - Moses Katbi
- U S Agency for International Development, Abuja, Nigeria
| | - Otse Ogorry
- The President's Emergency Plan For AIDS Relief Coordination Office, Abuja, Nigeria
| | - Antoine Jaquet
- Institut de Santé Publique d'épidémiologie et de développement, University of Bordeaux, Bordeaux, France
| | - Olivia Keiser
- Institute of Global Health, University of Geneva, Geneva, Switzerland
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Fonseca-Rodríguez O, San Sebastián M. "The devil is in the detail": geographical inequalities of femicides in Ecuador. Int J Equity Health 2021; 20:115. [PMID: 33947404 PMCID: PMC8097816 DOI: 10.1186/s12939-021-01454-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 04/20/2021] [Indexed: 11/10/2022] Open
Abstract
Background Femicide is a very important public health problem in Ecuador. Since regional and country-level femicide rates can obscure significant variations at the sub-national level, it is important to provide information at the lowest relevant level of disaggregation to be able to develop targeted preventive policies. The aim of this study was to assess the spatial distribution of the femicide rate and to examine its spatial clustering at the canton level in Ecuador in the period 2018–2019. Methods Data on cases were collected by a national network of non-governmental organizations. Two age-disaggregated analyses were done, one for the 15 to 24 years-olds and the other for the female population of 15 and older. Age-specific population data were obtained from the National Institute of Statistics for the study period. Standardized mortality ratios for mapping the mortality were calculated using hierarchical Bayesian models and spatial scan statistics were applied to identify local clusters. Thematic maps of age-specific femicide rates were also constructed. Results During the two-year period, 61 and 183 women were killed in the age ranges 15–24 and 15 years and older, respectively. The annual rate of femicides in Ecuador was 1.0 and 0.8 per 100,000 in the female population aged 15–24 and 15+, respectively, with substantial variations among cantons. The spatial analysis contributed to visualize high risk cantons, which were mainly located in a small area in the central part of the country (for those 15+) but especially in the Amazon region, for both of the studied age groups. Conclusions This study has shown the usefulness of applying spatial analysis to the problem of femicides in Ecuador. The study has revealed important variations among cantons but also a spatial clustering, mainly in the Amazon region of the country. The results should help policymakers to focus on current prevention programmes for violence against women into these high-risk areas. Continuous monitoring of femicides at low-level geographical areas is highly recommended. Supplementary Information The online version contains supplementary material available at 10.1186/s12939-021-01454-x.
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Affiliation(s)
| | - Miguel San Sebastián
- Department of Epidemiology and Global Health, Umeå University, Umeå, 901 87, Sweden
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LI Z, QIAO S, JIANG Y, LI X. Building a social media-based HIV risk behavior index to inform the prediction of HIV new diagnosis: a feasibility study. AIDS 2021; 35:S91-S99. [PMID: 33867492 PMCID: PMC8172091 DOI: 10.1097/qad.0000000000002787] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Analysis of geolocation-based social media Big Data provides unprecedented opportunities for a broad range of domains including health as health is intrinsically linked to the geographic characteristics of places. HIV infection is largely driven by HIV risk behaviors, such as unsafe sexual behavior and drug abuse/addiction. This study explores the feasibility of building a Social media-based HIV Risk Behavior (SRB) index at the United States county level for informing HIV surveillance and prevention, considering social determinants of health and geographic locations. METHODS The SRB index, defined as the proportion of risk behavior related Twitter users among all Twitter users, was calculated at the county level for each year. To evaluate the performance of the new SRB index, the relationships between the county-level SRB and rate of new HIV diagnoses from AIDSVu were analyzed using multivariate regression while simultaneously considering five socioeconomic status (SES) factors (percentage uninsured, median household income, Gini coefficient, percentage living in poverty, percentage high school graduates) in the model. Moran's I and geographically weighted regression analyses (GWR) were leveraged to examine spatial autocorrelations and reveal the potential spatial heterogeneity (geographical variability) of the associations. RESULTS County-level multivariate regression results revealed that SRB has the strongest association with new HIV diagnosis rate (r > 0.36; P < 0.0001) in both years compared with the five SES factors. Hierarchical regression analysis suggested that the SRB index explains significant additional variance in addition to the five SES factors. The results from GWR analysis not only greatly improved the model explanation power (bringing the adjusted r-square from 0.25 to 0.47 in 2016 and 0.26 to 0.55 in 2017) but also revealed SRB index is the most spatially consistent measurement compared with the five SES factors in terms of impact direction (negative or positive correlation). CONCLUSION It is feasible to build a social media-based HIV risk behavior index (SRB) as a new indicator for HIV surveillance at county level. The SRB index improves the regression model explanation power of new HIV diagnosis by providing additional information beyond the traditional social determinant measures, such as SES indicators. SRB index will allow researchers to utilize data captured within existing social media platforms to better understand the geospatial patterns of HIV risk behavior and to inform population-based HIV surveillance and other efforts of HIV prevention and control.
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Affiliation(s)
- Zhenlong LI
- Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208
- Big Data Health Science Center, Arnold School of Public Health, University of South Carolina Columbia, SC 29208
| | - Shan QIAO
- Big Data Health Science Center, Arnold School of Public Health, University of South Carolina Columbia, SC 29208
| | - Yuqin JIANG
- Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208
| | - Xiaoming LI
- Big Data Health Science Center, Arnold School of Public Health, University of South Carolina Columbia, SC 29208
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Zeng C, Zhang J, Sun X, Li Z, Weissman S, Olatosi B, Li X. County-level predictors of retention in care status among people living with HIV in South Carolina from 2010 to 2016: a data-driven approach. AIDS 2021; 35:S53-S64. [PMID: 33867489 PMCID: PMC8098716 DOI: 10.1097/qad.0000000000002832] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The aim of this study was to examine the geospatial variation of retention in care (RIC) across the counties in South Carolina (SC) from 2010 to 2016 and identify the relevant county-level predictors. DESIGN Aggregated data on county-level RIC among HIV patients from 2010 to 2016 were retrieved from an electronic HIV/AIDS reporting system in SC Department of Health and Environmental Control. Sociological framework of health was used to select potential county-level predictors from multiple public datasets. METHODS Geospatial mapping was used to display the spatial heterogeneity of county-level RIC rate in SC. Generalized linear mixed effect regression with least absolute shrinkage and selection operator (LASSO) was employed to identify county-level predictors related to the change of RIC status over time. Confusion matrix and area under the curve statistics were used to evaluate model performance. RESULTS More than half of the counties had their RIC rates lower than the national average. The change of county-level RIC rate from 2010 to 2016 was not significant, and spatial heterogeneity in RIC rate was identified. A total of 22 of the 31 county-level predictors were selected by LASSO for predicting county-level RIC status. Counties with lower collective efficacy, larger proportions of men and/or persons with high education were more likely to have their RIC rates lower than the national average. In contrast, numbers of accessible mental health centres were positively related to county-level RIC status. CONCLUSION Spatial variation in RIC could be identified, and county-level factors associated with accessible healthcare facilities and social capital significantly contributed to these variations. Structural and individual interventions targeting these factors are needed to improve the county-level RIC and reduce the spatial variation in HIV care.
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Affiliation(s)
- Chengbo Zeng
- South Carolina SmartState Center for Healthcare Quality, University of South Carolina
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina
- University of South Carolina Big Data Health Science Center
| | - Jiajia Zhang
- South Carolina SmartState Center for Healthcare Quality, University of South Carolina
- University of South Carolina Big Data Health Science Center
- Department of Epidemiology and Biostatistics, Arnold School of Public Health
| | - Xiaowen Sun
- South Carolina SmartState Center for Healthcare Quality, University of South Carolina
- University of South Carolina Big Data Health Science Center
- Department of Epidemiology and Biostatistics, Arnold School of Public Health
| | - Zhenlong Li
- University of South Carolina Big Data Health Science Center
- Geoinformation and Big Data Research Lab, Department of Geography, College of Arts and Sciences
| | - Sharon Weissman
- University of South Carolina Big Data Health Science Center
- School of Medicine, University of South Carolina, Columbia, South Carolina, USA
| | - Bankole Olatosi
- South Carolina SmartState Center for Healthcare Quality, University of South Carolina
- University of South Carolina Big Data Health Science Center
- Department of Health Services, Policy, and Management, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
| | - Xiaoming Li
- South Carolina SmartState Center for Healthcare Quality, University of South Carolina
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina
- University of South Carolina Big Data Health Science Center
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Spatial Analysis of HIV Infection and Associated Risk Factors in Botswana. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18073424. [PMID: 33806151 PMCID: PMC8037802 DOI: 10.3390/ijerph18073424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/19/2021] [Accepted: 03/20/2021] [Indexed: 11/17/2022]
Abstract
Botswana has the third highest human immunodeficiency virus (HIV) prevalence globally, and the severity of the epidemic within the country varies considerably between the districts. This study aimed to identify clusters of HIV and associated factors among adults in Botswana. Data from the Botswana Acquired Immunodeficiency Syndrome (AIDS) Impact Survey IV (BIAS IV), a nationally representative household-based survey, were used for this study. Multivariable logistic regression and Kulldorf’s scan statistics were used to identify the risk factors and HIV clusters. Socio-demographic characteristics were compared within and outside the clusters. HIV prevalence among the study participants was 25.1% (95% CI 23.3–26.4). HIV infection was significantly higher among the female gender, those older than 24 years and those reporting the use of condoms, while tertiary education had a protective effect. Two significant HIV clusters were identified, one located between Selibe-Phikwe and Francistown and another in the Central Mahalapye district. Clusters had higher levels of unemployment, less people with tertiary education and more people residing in rural areas compared to regions outside the clusters. Our study identified high-risk populations and regions with a high burden of HIV infection in Botswana. This calls for focused innovative and cost-effective HIV interventions on these vulnerable populations and regions to curb the HIV epidemic in Botswana.
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Ghaderi E, Zahraei SM, Moradi G, Goodarzi E, Norouzinejad A, Mohsenpour B, Naemi H, Khazaei Z. Geographical distribution of Typhoid using Geographic Information System (GIS) during 2009-2014 in Iran. Med J Islam Repub Iran 2021; 35:35. [PMID: 34211937 PMCID: PMC8236083 DOI: 10.47176/mjiri.35.35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Indexed: 11/09/2022] Open
Abstract
Background: Salmonella induced infections remain one of the most important health problems worldwide. The purpose of this study is to investigate the incidence and geographical distribution of typhoid using GIS and to predict its incidence in Iran in 2021.
Methods: This study is a descriptive analytical study. Information on pertussis was obtained from the Center for Communicable Diseases Control during 2009-2015. In the next step, ArcGIS 9.3 was used to prepare geographic maps of the disease incidence and frequency Therefore, using the Raster Calculator tool, the disease prediction map was drawn.
Results: The results showed that the highest incidence of typhoid during 2009-2014 was in Kermanshah, Lorestan, Hamadan, Kurdistan, and Ilam provinces. The incidence of typhoid in Iran increased during 2009-2010. The annual incidence of typhoid decreased from 0.85 per 100,000 in 2010 to 0.5 in 2014. Based on the modeling results for Iran, Kermanshah, Lorestan, Kurdistan, Ilam and Hamadan provinces with 92.17%, 46.56%, 31.74%, 25.62% and 22.96% of their areas (Km2) are at high risk for typhoid in the coming years, respectively.
Conclusion: Considering that the provinces of Kermanshah, Lorestan, Kurdistan, Ilam, and Hamadan are at risk of typhoid incidence in the coming years in Iran, and given that salmonella infections have a direct relationship with the individual’s health status and individual’s environmental health and socioeconomic status, improving the health status and disease control in carriers as well as improving the socio-economic status of the population living in these areas can prevent the disease in the years to come.
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Affiliation(s)
- Ebrahim Ghaderi
- Zoonoses Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Seyed Mohsen Zahraei
- Center for Communicable Diseases Control, Ministry of Health and Medical Education, Tehran, Iran
| | - Ghobad Moradi
- Social Determinants of Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Elham Goodarzi
- Social Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran
| | - Abbas Norouzinejad
- Deputy for Administrative Affairs The Center for Communicable Disease Control, Ministry of Health and Medical Education, Tehran, Iran
| | - Behzad Mohsenpour
- Department of Infectious Disease, Faculty of Medicine, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Hasan Naemi
- Cellular and Molecular Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Zaher Khazaei
- Department of Public Health,School of Medicine, Dezful University of Medical Sciences, Dezful, Iran
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Wang Y, Zhao C, Liu Z, Gao D. Spatiotemporal Analysis of AIDS Incidence and Its Influencing Factors on the Chinese Mainland, 2005-2017. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:1043. [PMID: 33503938 PMCID: PMC7908178 DOI: 10.3390/ijerph18031043] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/13/2021] [Accepted: 01/19/2021] [Indexed: 11/17/2022]
Abstract
Acquired Immune Deficiency Syndrome (AIDS) has become one of the most severe public health issues and nowadays around 38 million people are living with the human immunodeficiency virus (HIV). Ensuring healthy lives and promoting well-being is one of 17 United Nations Sustainable Development Goals. Here, we used the Markov chain matrix and geospatial clustering to comprehensively quantify the trends of the AIDS epidemic at the provincial administrate level in the mainland of China from 2005 to 2017. The Geographically Weighted Regression (GWR) model was further adopted to explore four groups of potential influencing factors (i.e., economy, traffic and transportation, medical care, and education) of the AIDS incidence rate in 2017 and their spatially distributed patterns. Results showed that the AIDS prevalence in southeastern China had been dominant and become prevalent in the past decade. The AIDS intensity level had been increasing between 2008 and 2011 but been gradually decreasing afterward. The analysis of the Markov chain matrix indicated that the AIDS epidemic has been generally in control on the Chinese mainland. The economic development was closely related to the rate of AIDS incidence on the Chinese mainland. The GWR result further suggested that medical care and the education effects on AIDS incidence rate can vary with different regions, but significant conclusions cannot be directly demonstrated. Our findings contribute an analytical framework of understanding AIDS epidemic trends and spatial variability of potential underlying factors throughout a complex extent to customize scientific prevention.
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Affiliation(s)
| | | | | | - Decai Gao
- Key Laboratory of Geographical Processes and Ecological Security of Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun 130000, China; (Y.W.); (C.Z.); (Z.L.)
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Mzembe T, Lessells R, Karat AS, Randera-Rees S, Edwards A, Khan P, Tomita A, Tanser F, Baisley K, Grant AD. Prevalence and Risk Factors for Mycobacterium tuberculosis Infection Among Adolescents in Rural South Africa. Open Forum Infect Dis 2021; 8:ofaa520. [PMID: 33511219 PMCID: PMC7814392 DOI: 10.1093/ofid/ofaa520] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 10/21/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND We aimed to estimate the prevalence of and explore risk factors for Mycobacterium tuberculosis infection among adolescents in a high tuberculosis (TB) and human immunodeficiency virus (HIV) prevalence setting. METHODS A cross-sectional study of adolescents (10-19 years) randomly selected from a demographic surveillance area (DSA) in rural KwaZulu-Natal, South Africa. We determined M tuberculosis infection status using the QuantiFERON-TB Gold-plus assay. We used HIV data from the DSA to estimate community-level adult HIV prevalence and random-effects logistic regression to identify risk factors for TB infection. RESULTS We enrolled 1094 adolescents (548 [50.1%] female); M tuberculosis infection prevalence (weighted for nonresponse by age, sex, and urban/rural residence) was 23.0% (95% confidence interval [CI], 20.6-25.6%). Mycobacterium tuberculosis infection was associated with older age (adjusted odds ratio [aOR], 1.37; 95% CI, 1.10-1.71, for increasing age-group [12-14, 15-17, and 18-19 vs 10-11 years]), ever (vs never) having a household TB contact (aOR, 2.13; 95% CI, 1.25-3.64), and increasing community-level HIV prevalence (aOR, 1.43 and 95% CI, 1.07-1.92, for increasing HIV prevalence category [25%-34.9%, 35%-44.9%, ≥45% vs <25%]). CONCLUSIONS Our data support prioritizing TB prevention and care activities in TB-affected households and high HIV prevalence communities.
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Affiliation(s)
- Themba Mzembe
- TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Richard Lessells
- TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom
- KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), UKZN, Durban, South Africa
| | - Aaron S Karat
- TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | | | - Anita Edwards
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Palwasha Khan
- TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Interactive Research and Development, Karachi, Pakistan
| | - Andrew Tomita
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa
- Centre for Rural Health, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
| | - Frank Tanser
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), UKZN, Durban, South Africa
- School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
- Lincoln Institute for Health, University of Lincoln, Lincoln, United Kingdom
- School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Kathy Baisley
- TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Alison D Grant
- TB Centre, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
- School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
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Te Vrugt M, Bickmann J, Wittkowski R. Effects of social distancing and isolation on epidemic spreading modeled via dynamical density functional theory. Nat Commun 2020; 11:5576. [PMID: 33149128 PMCID: PMC7643184 DOI: 10.1038/s41467-020-19024-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 09/25/2020] [Indexed: 12/13/2022] Open
Abstract
For preventing the spread of epidemics such as the coronavirus disease COVID-19, social distancing and the isolation of infected persons are crucial. However, existing reaction-diffusion equations for epidemic spreading are incapable of describing these effects. In this work, we present an extended model for disease spread based on combining a susceptible-infected-recovered model with a dynamical density functional theory where social distancing and isolation of infected persons are explicitly taken into account. We show that the model exhibits interesting transient phase separation associated with a reduction of the number of infections, and allows for new insights into the control of pandemics.
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Affiliation(s)
- Michael Te Vrugt
- Institut für Theoretische Physik, Center for Soft Nanoscience, Westfälische Wilhelms-Universität Münster, D-48149, Münster, Germany
| | - Jens Bickmann
- Institut für Theoretische Physik, Center for Soft Nanoscience, Westfälische Wilhelms-Universität Münster, D-48149, Münster, Germany
| | - Raphael Wittkowski
- Institut für Theoretische Physik, Center for Soft Nanoscience, Westfälische Wilhelms-Universität Münster, D-48149, Münster, Germany.
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Hailu BA, Tadese F, Bogale GG, Molla A, Miheretu BA, Beyene J. Spatial patterns and associated factors of HIV Seropositivity among adults in Ethiopia from EDHS 2016: a spatial and multilevel analysis. BMC Infect Dis 2020; 20:751. [PMID: 33054788 PMCID: PMC7557038 DOI: 10.1186/s12879-020-05456-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 09/27/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND HIV is a major public health issue, especially in developing countries. It is important to track and design successful intervention programs to explore the spatial pattern, distribution, and associated factors of HIV Seropositivity. This study therefore showed the spatial variation of HIV Seropositivity and related factors in Ethiopia. METHODS A total sample of 25,774 individual data collected from the 2016 EDHS data were primarily HIV biomarkers, IR, MR, and GPS. Spatial heterogeneity analysis was used with methods such as Morans I, Interpolation, and Kulldorff 's scan statistic. Spatial analysis was conducted using open source tools (QGIS, GeoDa, SaTScan). Multilevel logistic regression analysis was performed using Stata14 to identify HIV-associated factors. Finally, the AOR with a 95% confidence interval was used to report the mixed-effect logistic regression result in the full model. RESULT The prevalence of HIV / AIDS at national level was 0.93%. The highest prevalence regions were Gambela, Addis Abeba, Harari and Diredawa, accounting for 4.79, 3.36, 2.65 and 2.6%, respectively. Higher HIV seropositive spatial clusters have been established in the Gambela and Addis Ababa regions. Multilevel analysis at the individual level being married [AOR = 2.19 95% CI: (1.11-4.31)] and previously married [AOR = 6.45, 95% CI: (3.06-13.59)], female [AOR = 1.8, 95% CI: (1.19-2.72)], first-sex at age ≤15 [AOR = 4.39, 95% CI: (1.70-11.34)], 18-19 [AOR = 2.67 95% CI: (1.05-6.8)], middle age group (25-34) [AOR = 6.53, 95% CI: (3.67-11.75)], older age group (>34) [AOR = 2.67 95% CI: (1.05-6.8)], primary school [AOR = 3.03, 95% CI: (1.92-4.79)], secondary school [AOR = 3.37, 95% CI: (1.92-5.92) were significantly associated with serropositivity. Regarding household level, place of residence [urban: AOR = 6.13 CI: (3.12, 12.06)], female-headed households (AOR = 2.24 95% CI: (1.57-3.73), media exposure [low exposure (AOR = 0.53 95% CI: (0.33-0.86), no exposure AOR = 0.39 95% CI: (0.23-0.65)] and increased household size [AOR = 0.72 95% CI: (0.65-0.8)] were associated with HIV Seropositivity. CONCLUSION High cluster HIV cases were found in Gambela, Addis Abeba, Harari, and Diredawa. Having a history of married, start sex at a younger age, female-headed household, urban residence, and lower household size is more affected by HIV/AIDS. So any concerned body work around this risk group and area can be effective in the reduction of transmission.
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Affiliation(s)
- Bayuh Asmamaw Hailu
- Department of Epidemiology and Biostatistics, School of Public Health, College of Medicine and Health Sciences Wollo University, Dessie, Ethiopia.
| | - Fentaw Tadese
- Department of Epidemiology and Biostatistics, School of Public Health, College of Medicine and Health Sciences Wollo University, Dessie, Ethiopia
| | - Getahun Gebre Bogale
- Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia
| | - Asressie Molla
- Department of Epidemiology and Biostatistics, School of Public Health, College of Medicine and Health Sciences Wollo University, Dessie, Ethiopia
| | | | - Joseph Beyene
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
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Vandormael A, Cuadros D, Dobra A, Bärnighausen T, Tanser F. HIV incidence declines in a rural South African population: a G-imputation approach for inference. BMC Public Health 2020; 20:1205. [PMID: 32762668 PMCID: PMC7409400 DOI: 10.1186/s12889-020-09193-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 07/01/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Ad hoc assumptions about the unobserved infection event, which is known only to occur between the latest-negative and earliest-positive test dates, can lead to biased HIV incidence rate estimates. Using a G-imputation approach, we infer the infection dates from covariate data to estimate the HIV incidence rate in a hyper-endemic South African setting. METHODS A large demographic surveillance system has annually tested a cohort of HIV-uninfected participants living in the KwaZulu-Natal province. Using this data, we estimated a cumulative baseline hazard function and the effects of time-dependent covariates on the interval censored infection dates. For each HIV-positive participant in the cohort, we derived a cumulative distribution function and sampled multiple infection dates conditional on the unique covariate values. We right censored the data at the imputed dates, calculated the annual HIV incidence rate per 100 person-years, and used Rubin's rules to obtain the 95% confidence intervals. RESULTS A total of 20,011 uninfected individuals with a repeat HIV test participated in the incidence cohort between 2005 and 2018. We observed 2,603 infections per 58,769 person-years of follow-up among women and 845 infections per 41,178 person-years of follow-up among men. Conditional on age and circumcision status (men only), the female HIV incidence rate declined by 25%, from 5.0 to 3.7 infections per 100 person-years between 2014 and 2018. During this period, the HIV incidence rate among men declined from 2.1 to 1.1 infections per 100 person-years-a reduction of 49%. We observed similar reductions in male and female HIV incidence conditional on condom-use, marital status, urban residential status, migration history, and the HIV prevalence in the surrounding community. CONCLUSION We have followed participants in one of the world's largest and longest running HIV cohorts to estimate long-term trends in the population-wide incidence of infection. Using a G-imputation approach, we present further evidence for HIV incidence rate declines in this hyper-endemic South African setting.
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Affiliation(s)
- Alain Vandormael
- Africa Health Research Institute (AHRI), Durban, South Africa. .,Heidelberg Institute of Global Health, University of Heidelberg, 130.3 Im Neuenheimer Feld, Heidelberg, 69115, Germany. .,KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), University of KwaZulu-Natal (UKZN), Durban, South Africa.
| | - Diego Cuadros
- Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, USA
| | - Adrian Dobra
- Department of Statistics, Center for Statistics and the Social Sciences, and Center for Studies in Demography and Ecology, University of Washington, Seattle, USA
| | - Till Bärnighausen
- Africa Health Research Institute (AHRI), Durban, South Africa.,Heidelberg Institute of Global Health, University of Heidelberg, 130.3 Im Neuenheimer Feld, Heidelberg, 69115, Germany.,Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, USA
| | - Frank Tanser
- Africa Health Research Institute (AHRI), Durban, South Africa.,Lincoln International Institute for Rural Health, University of Lincoln, Lincoln, UK.,School of Nursing and Public Health, UKZN, Durban, South Africa.,Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
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44
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Nutor JJ, Duah HO, Agbadi P, Duodu PA, Gondwe KW. Spatial analysis of factors associated with HIV infection in Malawi: indicators for effective prevention. BMC Public Health 2020; 20:1167. [PMID: 32711500 PMCID: PMC7382788 DOI: 10.1186/s12889-020-09278-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 07/19/2020] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND The objective of this study was to model the predictors of HIV prevalence in Malawi through a complex sample logistic regression and spatial mapping approach using the national Demographic and Health Survey datasets. METHODS We conducted a secondary data analysis using the 2015-2016 Malawi Demographic and Health Survey and AIDS Indicator Survey. The analysis was performed in three stages while incorporating population survey sampling weights to: i) interpolate HIV data, ii) identify the spatial clusters with the high prevalence of HIV infection, and iii) perform a multivariate complex sample logistic regression. RESULTS In all, 14,779 participants were included in the analysis with an overall HIV prevalence of 9% (7.0% in males and 10.8% in females). The highest prevalence was found in the southern region of Malawi (13.2%), and the spatial interpolation revealed that the HIV epidemic is worse at the south-eastern part of Malawi. The districts in the high HIV prevalent zone of Malawi are Thyolo, Zomba, Mulanje, Phalombe and Blantyre. In central and northern region, the district HIV prevalence map identified Lilongwe in the central region and Karonga in the northern region as districts that equally deserve attention. People residing in urban areas had a 2.2 times greater risk of being HIV-positive compared to their counterparts in the rural areas (AOR = 2.16; 95%CI = 1.57-2.97). Other independent predictors of HIV prevalence were gender, age, marital status, number of lifetime sexual partners, extramarital partners, the region of residence, condom use, history of STI in the last 12 months, and household wealth index. Disaggregated analysis showed in-depth sociodemographic regional variations in HIV prevalence. CONCLUSION These findings identify high-risk populations and regions to be targeted for Pre-Exposure Prophylaxis (PrEP) campaigns, HIV testing, treatment and education to decrease incidence, morbidity, and mortality related to HIV infection in Malawi.
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Affiliation(s)
- Jerry John Nutor
- Department of Family Health Care Nursing, School of Nursing, University of California, San Francisco, USA.
| | | | - Pascal Agbadi
- Department of Nursing, Faculty of Allied Health Sciences, College of Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | | | - Kaboni W Gondwe
- College of Nursing, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.,Center for Advancing Population Science, Medical College of Wisconsin, Milwaukee, WI, USA
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Wand H, Reddy T, Ramjee G. Temporal trends in sexual behaviours and their impacts on HIV incidence among South African women: 2002-2016. AIDS Care 2020; 33:1002-1008. [PMID: 32698610 DOI: 10.1080/09540121.2020.1789054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
South Africa has the highest number of HIV infected individuals in the world. The primary objective of the current study was to describe temporal changes in HIV incidence rates using the data from 9,948 women who enrolled in one of the six HIV prevention trials conducted in KwaZulu-Natal, South Africa. Characteristics of the study population were presented and compared across the four study periods: 2002-2004, 2005-2008, 2009-2011 and 2012-2016. HIV infection rates increased from 6.2 to 9.3 per 100 person-year over the 15 years. These rates were as high as 14 per 100 person-year among women younger than 20 years age. Being single/not cohabiting, using injectable contraceptives, having less than two children, and diagnosed with STI(s) were associated with increased risk of HIV infection. These four factors were associated with 71%, 75%, 80% and 88% of the HIV seroconversions in four study periods. As the research continues to find ways of controlling the spread of the infections, quantifying the temporal trends in risk factors and their population-level impacts on HIV infection may have significant implications. This information may assist in developing effective counselling and education programs by targeting the sexually active single women and delivering more realistic messages.
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Affiliation(s)
- Handan Wand
- Kirby Institute, University of New South Wales, Kensington, Australia
| | - Tarylee Reddy
- Biostatistics Unit, South African Medical Research Council, HIV Prevention Research Unit, Durban, South Africa
| | - Gita Ramjee
- South African Medical Research Council, HIV Prevention Research Unit, Durban, South Africa
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Shahmanesh M, Harling G, Coltart CEM, Bailey H, King C, Gibbs J, Seeley J, Phillips A, Sabin CA, Aldridge RW, Sonnenberg P, Hart G, Rowson M, Pillay D, Johnson AM, Abubakar I, Field N. From the micro to the macro to improve health: microorganism ecology and society in teaching infectious disease epidemiology. THE LANCET. INFECTIOUS DISEASES 2020; 20:e142-e147. [PMID: 32386611 PMCID: PMC7252039 DOI: 10.1016/s1473-3099(20)30136-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 02/14/2020] [Accepted: 02/18/2020] [Indexed: 12/21/2022]
Abstract
Chronic and emerging infectious diseases and antimicrobial resistance remain a substantial global health threat. Microbiota are increasingly recognised to play an important role in health. Infections also have a profound effect beyond health, especially on global and local economies. To maximise health improvements, the field of infectious disease epidemiology needs to derive learning from ecology and traditional epidemiology. New methodologies and tools are transforming understanding of these systems, from a better understanding of socioeconomic, environmental, and cultural drivers of infection, to improved methods to detect microorganisms, describe the immunome, and understand the role of human microbiota. However, exploiting the potential of novel methods to improve global health remains elusive. We argue that to exploit these advances a shift is required in the teaching of infectious disease epidemiology to ensure that students are well versed in a breadth of disciplines, while maintaining core epidemiological skills. We discuss the following key points using a series of teaching vignettes: (1) integrated training in classic and novel techniques is needed to develop future scientists and professionals who can work from the micro (interactions between pathogens, their cohabiting microbiota, and the host at a molecular and cellular level), with the meso (the affected communities), and to the macro (wider contextual drivers of disease); (2) teach students to use a team-science multidisciplinary approach to effectively integrate biological, clinical, epidemiological, and social tools into public health; and (3) develop the intellectual skills to critically engage with emerging technologies and resolve evolving ethical dilemmas. Finally, students should appreciate that the voices of communities affected by infection need to be kept at the heart of their work.
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Affiliation(s)
- Maryam Shahmanesh
- Institute for Global Health, University College London, London, UK; Africa Health Research Institute, Durban, South Africa.
| | - Guy Harling
- Institute for Global Health, University College London, London, UK; Africa Health Research Institute, Durban, South Africa; MRC/Wits-Agincourt Unit, University of the Witwatersrand, Johannesburg, South Africa; Harvard Centre for Population and Development Studies, Harvard T H Chan School of Public Health, Boston, MA, USA
| | | | - Heather Bailey
- Institute for Global Health, University College London, London, UK
| | - Carina King
- Institute for Global Health, University College London, London, UK; Department of Public Health Sciences, Karolinska Institutet, Solna, Sweden
| | - Jo Gibbs
- Institute for Global Health, University College London, London, UK
| | - Janet Seeley
- Africa Health Research Institute, Durban, South Africa; London School of Hygiene and Tropical Medicine, London, UK
| | - Andrew Phillips
- Institute for Global Health, University College London, London, UK
| | - Caroline A Sabin
- Institute for Global Health, University College London, London, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, UK
| | - Pam Sonnenberg
- Institute for Global Health, University College London, London, UK
| | - Graham Hart
- Institute for Global Health, University College London, London, UK
| | - Mike Rowson
- Institute for Global Health, University College London, London, UK
| | - Deenan Pillay
- Division of infection and immunity, University College London, London, UK; Africa Health Research Institute, Durban, South Africa
| | - Anne M Johnson
- Institute for Global Health, University College London, London, UK
| | - Ibrahim Abubakar
- Institute for Global Health, University College London, London, UK
| | - Nigel Field
- Institute for Global Health, University College London, London, UK
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Vandormael A, Cuadros D, Kim HY, Bärnighausen T, Tanser F. The state of the HIV epidemic in rural KwaZulu-Natal, South Africa: a novel application of disease metrics to assess trajectories and highlight areas for intervention. Int J Epidemiol 2020; 49:666-675. [PMID: 31930292 PMCID: PMC7266544 DOI: 10.1093/ije/dyz269] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 12/06/2019] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND South Africa is at the epicentre of the HIV pandemic, with the world's highest number of new infections and the largest treatment programme. Using metrics proposed by the Joint United Nations Programme on AIDS (UNAIDS), we evaluate progress toward epidemic control and highlight areas for intervention in a hyperendemic South African setting. METHODS The Africa Health Research Institute (AHRI) maintains a comprehensive population-based surveillance system in the Hlabisa sub-district of KwaZulu-Natal. Between 2005 and 2017, we tested 39 735 participants (aged 15-49 years) for HIV and followed 22 758 HIV-negative and 13 460 HIV-positive participants to identify new infections and all-cause AIDS-related deaths, respectively. Using these data, we estimated the percentage reduction in incidence, the absolute incidence rate, the incidence-mortality ratio and the incidence-prevalence ratio over place and time. RESULTS We observed a 62% reduction in the number of new infections among men between 2012 and 2017 and a 34% reduction among women between 2014 and 2017. Among men, the incidence-mortality ratio peaked at 4.1 in 2013 and declined to 3.1 in 2017, and among women it fell from a high of 6.4 in 2014 to 4.3 in 2017. Between 2012 and 2017, the female-incidence/male-prevalence ratio declined from 0.24 to 0.13 and the male-incidence/female-prevalence ratio from 0.05 to 0.02. CONCLUSIONS Using data from a population-based cohort study, we report impressive progress toward HIV epidemic control in a severely affected South African setting. However, overall progress is off track for 2020 targets set by the UNAIDS. Spatial estimates of the metrics, which demonstrate remarkable heterogeneity over place and time, indicate areas that could benefit from additional or optimized HIV prevention services.
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Affiliation(s)
- Alain Vandormael
- Africa Health Research Institute (AHRI), KwaZulu-Natal, Durban, South Africa
- School of Nursing and Public Health, University of KwaZulu-Natal (UKZN), Durban, South Africa
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), UKZN, Durban, South Africa
- Heidelberg Institute of Global Health, Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
| | - Diego Cuadros
- Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH, USA
| | - Hae-Young Kim
- Africa Health Research Institute (AHRI), KwaZulu-Natal, Durban, South Africa
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), UKZN, Durban, South Africa
- Department of Population Health, New York University School of Medicine, New York, USA
| | - Till Bärnighausen
- Africa Health Research Institute (AHRI), KwaZulu-Natal, Durban, South Africa
- Heidelberg Institute of Global Health, Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Frank Tanser
- Africa Health Research Institute (AHRI), KwaZulu-Natal, Durban, South Africa
- School of Nursing and Public Health, University of KwaZulu-Natal (UKZN), Durban, South Africa
- Lincoln Institute for Health, University of Lincoln, Lincoln, UK
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), UKZN, Durban, South Africa
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Gibbs A, Reddy T, Dunkle K, Jewkes R. HIV-Prevalence in South Africa by settlement type: A repeat population-based cross-sectional analysis of men and women. PLoS One 2020; 15:e0230105. [PMID: 32182255 PMCID: PMC7077822 DOI: 10.1371/journal.pone.0230105] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 02/21/2020] [Indexed: 01/08/2023] Open
Abstract
To assess i) whether there is an independent association between HIV-prevalence and settlement types (urban formal, urban informal, rural formal, rural informal), and, ii) whether this changes over time, in South Africa. We draw on four (2002; 2005; 2008; 2012) cross-sectional South African household surveys. Data is analysed by sex (male/female), and for women by age categories (15-49; and 15-24; 25-49) at all-time points, for men in 2012 data is analysed by age categories (15-24; 25-49). By settlement type and sex/age combinations, we descriptively assess the association between socio-demographic and HIV-risk factors; HIV-prevalence; and trends in HIV-prevalence by time. Relative risk ratios assess unadjusted and adjusted risk for HIV-prevalence by settlement type. All estimates are weighted, and account for survey design. In all survey years, and combinations of sex/age categorisations, HIV-prevalence is highest in urban informal settlements. For men (15-49) an increasing HIV-prevalence over time in rural informal settlements was seen (p = 0.001). For women (15-49) HIV-prevalence increases over time for urban informal, rural informal, rural formal, and women (15-24) decreases in urban formal and urban informal, and women (25-49) increases urban informal and rural informal settlements. In analyses adjusting for potential socio-demographic and risk factors, compared to urban formal settlements, urban informal settlements had consistently higher relative risk of HIV for women, in all age categorisations, for instance in 2012 this was RR1.89 (1.50, 2.40) for all women (15-49), for 15-24 (RR1.79, 1.17-2.73), and women 25-49 (RR1.91, 1.47-2.48). For men, in the overall age categorization, urban informal settlements had a higher relative risk for HIV in all years. In 2012, when this was disaggregated by age, for men 15-24 rural informal (IRR2.69, 1.28-5.67), and rural formal (RR3.59, 1.49-8.64), and for men 25-49 it was urban informal settlements with the highest (RR1.68, 1.11-2.54). In 2012, rural informal settlements also had higher adjusted relative risk for HIV-prevalence for men (15-49) and women (15-49; 15-24; 25-49). In South Africa, HIV-prevalence is patterned geographically, with urban informal settlements having a particularly high burden. Geographical targeting of responses is critical for the HIV-response.
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Affiliation(s)
- Andrew Gibbs
- Gender and Health Research Unit, South African Medical Research Council, Pretoria, South Africa
- Centre for Rural Health, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
| | - Tarylee Reddy
- Biostatistics Unit, South African Medical Research Council, Durban, South Africa
| | - Kristin Dunkle
- Gender and Health Research Unit, South African Medical Research Council, Pretoria, South Africa
| | - Rachel Jewkes
- Gender and Health Research Unit, South African Medical Research Council, Pretoria, South Africa
- Office of the Executive Scientist, South African Medical Research Council, Pretoria, South Africa
- School of Public Health, University of Witwatersrand, Johannesburg, South Africa
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49
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Gai C, Iron D, Kolokolnikov T. Localized outbreaks in an S-I-R model with diffusion. J Math Biol 2020; 80:1389-1411. [PMID: 31950259 DOI: 10.1007/s00285-020-01466-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 12/31/2019] [Indexed: 11/25/2022]
Abstract
We investigate an SIRS epidemic model with spatial diffusion and nonlinear incidence rates. We show that for small diffusion rate of the infected class [Formula: see text], the infected population tends to be highly localized at certain points inside the domain, forming K spikes. We then study three distinct destabilization mechanisms, as well as a transition from localized spikes to plateau solutions. Two of the instabilities are due to coarsening (spike death) and self-replication (spike birth), and have well-known analogues in other reaction-diffusion systems such as the Schnakenberg model. The third transition is when a single spike becomes unstable and moves to the boundary. This happens when the diffusion of the recovered class, [Formula: see text] becomes sufficiently small. In all cases, the stability thresholds are computed asymptotically and are verified by numerical experiments. We also show that the spike solution can transit into an plateau-type solution when the diffusion rates of recovered and susceptible class are sufficiently small. Implications for disease spread and control through quarantine are discussed.
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Affiliation(s)
- Chunyi Gai
- Department of Mathematics and Statistics, Dalhousie University, Halifax, Canada
| | - David Iron
- Department of Mathematics and Statistics, Dalhousie University, Halifax, Canada
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50
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Wabiri N, Naidoo I, Mungai E, Samuel C, Ngwenya T. The Arts and Tools for Using Routine Health Data to Establish HIV High Burden Areas: The Pilot Case of KwaZulu-Natal South Africa. Front Public Health 2019; 7:335. [PMID: 31781533 PMCID: PMC6861206 DOI: 10.3389/fpubh.2019.00335] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 10/25/2019] [Indexed: 11/13/2022] Open
Abstract
Background: To optimally allocate limited health resources in responding to the HIV epidemic, South Africa has undertaken to generate local epidemiological profiles identifying high disease burden areas. Central to achieving this, is the need for readily available quality health data linked to both large and small geographic areas. South Africa has relied on national population-based surveys: the Household HIV Survey and the National Antenatal Sentinel HIV and Syphilis Prevalence Survey (ANC) amongst others for such data for informing policy decisions. However, these surveys are conducted approximately every 2 and 3 years creating a gap in data and evidence required for policy. At subnational levels, timely decisions are required with frequent course corrections in the interim. Routinely collected HIV testing data at public health facilities have the potential to provide this much needed information, as a proxy measure of HIV prevalence in the population, when survey data is not available. The South African District health information system (DHIS) contains aggregated routine health data from public health facilities which is used in this article. Methods: Using spatial interpolation methods we combine three "types" of data: (1) 2015 gridded high-resolution population data, (2) age-structure data as defined in South Africa mid-year population estimates, 2015; and (3) georeferenced health facilities HIV-testing data from DHIS for individuals (15-49 years old) who tested in health care facilities in the district in 2015 to delineate high HIV disease burden areas using density surface of either HIV positivity and/or number of people living with HIV (PLHIV). For validation, we extracted interpolated values at the facility locations and compared with the real observed values calculating the residuals. Lower residuals means the Inverse Weighted Distance (IDW) interpolator provided reliable prediction at unknown locations. Results were adjusted to provincial published HIV estimates and aggregated to municipalities. Uncertainty measures map at municipalities is provided. Data on major cities and roads networks was only included for orientation and better visualization of the high burden areas. Results: Results shows the HIV burden at local municipality level, with high disease burden in municipalities in eThekwini, iLembe and uMngundgudlovu; and around major cities and national routes. Conclusion: The methods provide accurate estimates of the local HIV burden at the municipality level. Areas with high population density have high numbers of PLHIV. The analysis puts into the hand of decision makers a tool that they can use to generate evidence for HIV programming. The method allows decision makers to routinely update and use facility level data in understanding the local epidemic.
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Affiliation(s)
- Njeri Wabiri
- Social Aspects of Public Health Research, Human Sciences Research Council, Pretoria, South Africa
| | - Inbarani Naidoo
- Social Aspects of Public Health Research, Human Sciences Research Council, Pretoria, South Africa
| | - Esther Mungai
- Kwa-Zulu Natal (KZN) Provincial Treasury Global Fund Supported Programme, Pietermaritzburg, South Africa
| | - Candice Samuel
- KZN Provincial Department of Health-GIS Directorate, Pietermaritzburg, South Africa
| | - Tryphinah Ngwenya
- Kwa-Zulu Natal (KZN) Provincial Treasury Global Fund Supported Programme, Pietermaritzburg, South Africa
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