Localized spatial clustering of HIV infections in a widely disseminated rural South African epidemic

Does neighborhood matter? An analysis of HIV prevalence in Sub-Saharan African countries

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.

Assessing and attenuating the impact of selection bias on spatial cluster detection studies

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.

The Spatial Scale and Spread of Child Victimization

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.

Spatial distribution and determinants of HIV high burden in the Southern African sub-region

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.

Convergence of HIV and non-communicable disease epidemics: geospatial mapping of the unmet health needs in an HIV hyperendemic community in South Africa

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.

Place Visitation Data Reveals the Geographic and Racial Disparities of COVID-19 Impact on HIV Service Utilization in the Deep South

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.

The met and unmet health needs for HIV, hypertension, and diabetes in rural KwaZulu-Natal, South Africa: analysis of a cross-sectional multimorbidity survey

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.

Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa

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.

Convergence of HIV and non-communicable disease epidemics: Geospatial mapping of the unmet health needs in a HIV Hyperendemic South African community

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.

Quantifying met and unmet health needs for HIV, hypertension and diabetes in rural KwaZulu-Natal, South Africa

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.

Localization of nonbacktracking centrality on dense subgraphs of sparse networks

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.

Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000-2018

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.

Spatial Co-Clustering of Tuberculosis and HIV in Ethiopia

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.

Spatial disparities of HIV prevalence in South Africa. Do sociodemographic, behavioral, and biological factors explain this spatial variability?

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.

Syndemics of intimate partner violence among women in HIV endemic South Africa: geospatial analysis of nationally representative data

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.

Spatiotemporal Variation and Predictors of Unsuppressed Viral Load among HIV-Positive Men and Women in Rural and Peri-Urban KwaZulu-Natal, South Africa

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.

Exploring regional and sociodemographic disparities associated with unenrollment for the disease management program for type 2 Diabetes Mellitus using Bayesian spatial modelling

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.

Mapping HIV prevalence in Nigeria using small area estimates to develop a targeted HIV intervention strategy

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.

Do spatiotemporal units matter for exploring the microgeographies of epidemics?

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.

Getting to zero HIV/AIDS in sub-Saharan Africa: Understanding perceptions of locals using the social determinants of health framework

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.

The role of high-risk geographies in the perpetuation of the HIV epidemic in rural South Africa: A spatial molecular epidemiology study

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.

Predicting the Risk of Hiv-1 Acquisition in Rural South Africa Using Geospatial Data

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.

A spatiotemporal geographic information system-based assessment of human immunodeficiency virus/acquired immune deficiency syndrome distribution in Manipur, India

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.

The geography and inter-community configuration of new sexual partnership formation in a rural South African population over fourteen years (2003-2016)

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.

Epidemiology incidence and geographical distribution of leptospirosis using GIS and its incidence prediction in Iran in 2021

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.

Epidemiology incidence and geographical distribution of Pertussis using GIS and its incidence prediction in Iran in 2021

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.

A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa

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.

Mapping geographic clusters of new HIV diagnoses to inform granular-level interventions for HIV epidemic control in western Kenya

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.

Characteristics of women who use hormonal contraceptives and their population-level impacts on HIV infection in a cohort of South African women (2002-2016)

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.

Large age shifts in HIV-1 incidence patterns in KwaZulu-Natal, South Africa

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.

Convergence of infectious and non-communicable disease epidemics in rural South Africa: a cross-sectional, population-based multimorbidity study

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.

Temporal and spatial monitoring of HIV prevalence and incidence rates using geospatial models: Results from South African women

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.

Geographical Disparities in HIV Seroprevalence Among Men Who Have Sex with Men and People Who Inject Drugs in Nigeria: Exploratory Spatial Data Analysis

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.

"The devil is in the detail": geographical inequalities of femicides in Ecuador

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.

Building a social media-based HIV risk behavior index to inform the prediction of HIV new diagnosis: a feasibility study

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.

County-level predictors of retention in care status among people living with HIV in South Carolina from 2010 to 2016: a data-driven approach

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.

Spatial Analysis of HIV Infection and Associated Risk Factors in Botswana

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.

Geographical distribution of Typhoid using Geographic Information System (GIS) during 2009-2014 in Iran

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.

Spatiotemporal Analysis of AIDS Incidence and Its Influencing Factors on the Chinese Mainland, 2005-2017

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.

Prevalence and Risk Factors for Mycobacterium tuberculosis Infection Among Adolescents in Rural South Africa

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.

Effects of social distancing and isolation on epidemic spreading modeled via dynamical density functional theory

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.

Spatial patterns and associated factors of HIV Seropositivity among adults in Ethiopia from EDHS 2016: a spatial and multilevel analysis

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.

HIV incidence declines in a rural South African population: a G-imputation approach for inference

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.

Spatial analysis of factors associated with HIV infection in Malawi: indicators for effective prevention

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.

Temporal trends in sexual behaviours and their impacts on HIV incidence among South African women: 2002-2016

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.

From the micro to the macro to improve health: microorganism ecology and society in teaching infectious disease epidemiology

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.

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

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.

HIV-Prevalence in South Africa by settlement type: A repeat population-based cross-sectional analysis of men and women

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.

Localized outbreaks in an S-I-R model with diffusion

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.

The Arts and Tools for Using Routine Health Data to Establish HIV High Burden Areas: The Pilot Case of KwaZulu-Natal South Africa

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.