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Foo FY, Abdul Rahman N, Shaik Abdullah FZ, Abd Naeeim NS. Spatio-temporal clustering analysis of COVID-19 cases in Johor. Infect Dis Model 2024; 9:387-396. [PMID: 38385018 PMCID: PMC10879677 DOI: 10.1016/j.idm.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 11/17/2023] [Accepted: 01/28/2024] [Indexed: 02/23/2024] Open
Abstract
At the end of the year 2019, a virus named SARS-CoV-2 induced the coronavirus disease, which is very contagious and quickly spread around the world. This new infectious disease is called COVID-19. Numerous areas, such as the economy, social services, education, and healthcare system, have suffered grave consequences from the invasion of this deadly virus. Thus, a thorough understanding of the spread of COVID-19 is required in order to deal with this outbreak before it becomes an infectious disaster. In this research, the daily reported COVID-19 cases in 92 sub-districts in Johor state, Malaysia, as well as the population size associated to each sub-district, are used to study the propagation of COVID-19 disease across space and time in Johor. The time frame of this research is about 190 days, which started from August 5, 2021, until February 10, 2022. The clustering technique known as spatio-temporal clustering, which considers the spatio-temporal metric was adapted to determine the hot-spot areas of the COVID-19 disease in Johor at the sub-district level. The results indicated that COVID-19 disease does spike in the dynamic populated sub-districts such as the state's economic centre (Bandar Johor Bahru), and during the festive season. These findings empirically prove that the transmission rate of COVID-19 is directly proportional to human mobility and the presence of holidays. On the other hand, the result of this study will help the authority in charge in stopping and preventing COVID-19 from spreading and become worsen at the national level.
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Affiliation(s)
- Fong Ying Foo
- School of Mathematical Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia
| | - Nuzlinda Abdul Rahman
- School of Mathematical Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia
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Johnson DP, Owusu C. Examining associations between social vulnerability indices and COVID-19 incidence and mortality with spatial-temporal Bayesian modeling. Spat Spatiotemporal Epidemiol 2024; 48:100623. [PMID: 38355253 DOI: 10.1016/j.sste.2023.100623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/19/2023] [Accepted: 11/09/2023] [Indexed: 02/16/2024]
Abstract
This study compares two social vulnerability indices, the U.S. CDC SVI and SoVI (the Social Vulnerability Index developed at the Hazards Vulnerability & Resilience Institute at the University of South Carolina), on their ability to predict the risk of COVID-19 cases and deaths. We utilize COVID-19 cases and deaths data for the state of Indiana from the Regenstrief Institute in Indianapolis, Indiana, from March 1, 2020, to March 31, 2021. We then aggregate the COVID-19 data to the census tract level, obtain the input variables, domains (components), and composite measures of both CDC SVI and SoVI data to create a Bayesian spatial-temporal ecological regression model. We compare the resulting spatial-temporal patterns and relative risk (RR) of SARS-CoV-2 infection (COVID-19 cases) and associated death. Results show there are discernable spatial-temporal patterns for SARS-CoV-2 infections and deaths with the largest contiguous hotspot for SARS-CoV-2 infections found in the southwest of the Indianapolis metropolitan area. We also observed one large contiguous hotspot for deaths that stretches across Indiana from the Cincinnati area in the southeast to just east and north of Terre Haute (southeast to west central). The spatial-temporal Bayesian model shows that a 1-percentile increase in CDC SVI was significantly (p ≤ 0.05) associated with an increased risk of SARS-CoV-2 infection by 6 % (RR = 1.06, 95 %CI = 1.04 -1.08). Whereas a 1-percentile increase in SoVI was significantly predicted to increase the risk of COVID-19 death by 45 % (RR = 1.45, 95 %CI =1.38 - 1.53). Domain-specific variables related to socioeconomic status, age, and race/ethnicity were shown to increase the risk of SARS-CoV-2 infections and deaths. There were notable differences in the relative risk estimates for SARS-CoV-2 infections and deaths when each of the two indices were incorporated in the model. Observed differences between the two social vulnerability indices and infection and death are likely due to alternative methodologies of formation and differences in input variables. The findings add to the growing literature on the relationship between social vulnerability and COVID-19 and further the development of COVID-19-specific vulnerability indices by illustrating the utility of local spatial-temporal analysis.
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Affiliation(s)
- Daniel P Johnson
- Indiana University - Purdue University at Indianapolis, United States.
| | - Claudio Owusu
- Centers for Disease Control and Prevention, Agency for Toxic Substances and Disease Registry/ National Center for Environmental Health, Office of Innovation and Analytics, Geospatial Research, Analysis, and Services Program, United States
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Congdon P. Psychosis prevalence in London neighbourhoods; A case study in spatial confounding. Spat Spatiotemporal Epidemiol 2024; 48:100631. [PMID: 38355254 DOI: 10.1016/j.sste.2023.100631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 11/13/2023] [Accepted: 12/08/2023] [Indexed: 02/16/2024]
Abstract
Analysis of impacts of neighbourhood risk factors on mental health outcomes frequently adopts a disease mapping approach, with unknown neighbourhood influences summarised by random effects. However, such effects may show confounding with observed predictors, especially when such predictors have a clear spatial pattern. Here, the standard disease mapping model is compared to methods which account and adjust for spatial confounding in an analysis of psychosis prevalence in London neighbourhoods. Established area risk factors such as area deprivation, non-white ethnicity, greenspace access and social fragmentation are considered as influences on psychosis. The results show evidence of spatial confounding in the standard disease mapping model. Impacts expected on substantive grounds and available evidence are either nullified or reversed in direction. It is argued that the potential for spatial confounding to affect inferences about geographic disease patterns and risk factors should be routinely considered in ecological studies of health based on disease mapping.
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Hashemi H, Mahaki B, Farnoosh R. Relative risk of childhood and adolescence cancer in Iran: spatiotemporal analysis from 1999 to 2016. BMC Res Notes 2024; 17:29. [PMID: 38238811 PMCID: PMC10797934 DOI: 10.1186/s13104-023-06629-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 11/19/2023] [Indexed: 01/22/2024] Open
Abstract
OBJECTIVE Cancer is the third leading cause of death in the world with increasing trends in Iran. The study of epidemiology, trend, and geospatial distribution of pediatric cancers provides important information for screening as well as early detection of cancer and policy making. We aimed to assess the spatio-temporal disparity of childhood and adolescence cancer risk among provinces of Iran. METHODS In this retrospective study, we estimated geospatial relative risk (RR) of childhood cancer in provinces of Iran using data from 29198 cases. We used BYM and its extended spatiotemporal model in Bayesian setting. This hierarchical model takes spatial and temporal effects into account in the incidence rate estimation simultaneously. RESULTS The relative risk of cancer was > 1 for 45% of the provinces, where 27% of provinces had significantly ascending trend. North Khorasan, Yazd and Qazvin provinces had the highest risk rates while Sistan-Baluchistan province showed the lowest risk of cancer. However, the differential trends was highest in Sistan-Baluchistan, Bushehr, Hormozgan, and Kohgilouyeh-Boyerahmad. Both the point estimate and the trend of risk was high in Tehran. CONCLUSION The geographic pattern and trend of cancer in children seems to be different from that in adults that urges further studies. This could lead to increased health system capacity and facilitate the access to effective detection, research, care and treatment of childhood cancer.
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Affiliation(s)
- Hasti Hashemi
- Department of Statistics, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Behzad Mahaki
- Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran.
| | - Rahman Farnoosh
- School of Mathematics, Iran University of Science and Technology, Tehran, Iran
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Hogg J, Cameron J, Cramb S, Baade P, Mengersen K. Mapping the prevalence of cancer risk factors at the small area level in Australia. Int J Health Geogr 2023; 22:37. [PMID: 38115064 PMCID: PMC10729400 DOI: 10.1186/s12942-023-00352-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/01/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies. METHODS Using Australia as a case study, the 2017-2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated. RESULTS We illustrated that in 2017-2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work. CONCLUSIONS Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse.
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Affiliation(s)
- James Hogg
- Centre for Data Science, Queensland University of Technology (QUT), 2 George St, Brisbane City, Queensland, 4000, Australia.
| | - Jessica Cameron
- Centre for Data Science, Queensland University of Technology (QUT), 2 George St, Brisbane City, Queensland, 4000, Australia
- Viertel Cancer Research Centre, Cancer Council Queensland, 553 Gregory Terrace, Fortitude Valley, Queensland, 4006, Australia
| | - Susanna Cramb
- Centre for Data Science, Queensland University of Technology (QUT), 2 George St, Brisbane City, Queensland, 4000, Australia
- Australian Centre for Health Services Innovation, School of Public Health and Social Work, Queensland University of Technology (QUT), 2 George St, Brisbane City, Queensland, 4000, Australia
| | - Peter Baade
- Centre for Data Science, Queensland University of Technology (QUT), 2 George St, Brisbane City, Queensland, 4000, Australia
- Viertel Cancer Research Centre, Cancer Council Queensland, 553 Gregory Terrace, Fortitude Valley, Queensland, 4006, Australia
| | - Kerrie Mengersen
- Centre for Data Science, Queensland University of Technology (QUT), 2 George St, Brisbane City, Queensland, 4000, Australia
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Lee YP, Wen TH. Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions. Int J Health Geogr 2023; 22:36. [PMID: 38072931 PMCID: PMC10710714 DOI: 10.1186/s12942-023-00355-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/10/2023] [Indexed: 12/18/2023] Open
Abstract
Identifying clusters or hotspots from disease maps is critical in research and practice. Hotspots have been shown to have a higher potential for transmission risk and may be the source of infections, making them a priority for controlling epidemics. However, the role of edge areas of hotspots in disease transmission remains unclear. This study aims to investigate the role of edge areas in disease transmission by examining whether disease incidence rate growth is higher in the edges of disease hotspots during outbreaks. Our data is based on the three most severe dengue epidemic years in Kaohsiung city, Taiwan, from 1998 to 2020. We employed conditional autoregressive (CAR) models and Bayesian areal Wombling methods to identify significant edge areas of hotspots based on the extent of risk difference between adjacent areas. The difference-in-difference (DID) estimator in spatial panel models measures the growth rate of risk by comparing the incidence rate between two groups (hotspots and edge areas) over two time periods. Our results show that in years characterized by exceptionally large-scale outbreaks, the edge areas of hotspots have a more significant increase in disease risk than hotspots, leading to a higher risk of disease transmission and potential disease foci. This finding explains the geographic diffusion mechanism of epidemics, a pattern mixed with expansion and relocation, indicating that the edge areas play an essential role. The study highlights the importance of considering edge areas of hotspots in disease transmission. Furthermore, it provides valuable insights for policymakers and health authorities in designing effective interventions to control large-scale disease outbreaks.
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Affiliation(s)
- Ya-Peng Lee
- Department of Geography, National Taiwan University, Taipei, Taiwan
- National Science and Technology Center for Disaster Reduction, Taipei, Taiwan
| | - Tzai-Hung Wen
- Department of Geography, National Taiwan University, Taipei, Taiwan.
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Pavani J, Bastos LS, Moraga P. Joint spatial modeling of the risks of co-circulating mosquito-borne diseases in Ceará, Brazil. Spat Spatiotemporal Epidemiol 2023; 47:100616. [PMID: 38042535 DOI: 10.1016/j.sste.2023.100616] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 08/06/2023] [Accepted: 08/18/2023] [Indexed: 12/04/2023]
Abstract
Mosquito-borne diseases such as dengue and chikungunya have been co-circulating in the Americas, causing great damage to the population. In 2021, for instance, almost 1.5 million cases were reported on the continent, being Brazil the responsible for most of them. Even though they are transmitted by the same mosquito, it remains unclear whether there exists a relationship between both diseases. In this paper, we model the geographic distributions of dengue and chikungunya over the years 2016 to 2021 in the Brazilian state of Ceará. We use a Bayesian hierarchical spatial model for the joint analysis of two arboviruses that includes spatial covariates as well as specific and shared spatial effects that take into account the potential autocorrelation between the two diseases. Our findings allow us to identify areas with high risk of one or both diseases. Only 7% of the areas present high relative risk for both diseases, which suggests a competition between viruses. This study advances the understanding of the geographic patterns and the identification of risk factors of dengue and chikungunya being able to help health decision-making.
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Affiliation(s)
- Jessica Pavani
- Department of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Leonardo S Bastos
- Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Paula Moraga
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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Gao L, Banerjee S, Ritz B. Spatial Difference Boundary Detection for Multiple Outcomes Using Bayesian Disease Mapping. Biostatistics 2023; 24:922-944. [PMID: 35657087 PMCID: PMC11004976 DOI: 10.1093/biostatistics/kxac013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 03/24/2022] [Accepted: 04/03/2022] [Indexed: 10/19/2023] Open
Abstract
Regional aggregates of health outcomes over delineated administrative units (e.g., states, counties, and zip codes), or areal units, are widely used by epidemiologists to map mortality or incidence rates and capture geographic variation. To capture health disparities over regions, we seek "difference boundaries" that separate neighboring regions with significantly different spatial effects. Matters are more challenging with multiple outcomes over each unit, where we capture dependence among diseases as well as across the areal units. Here, we address multivariate difference boundary detection for correlated diseases. We formulate the problem in terms of Bayesian pairwise multiple comparisons and seek the posterior probabilities of neighboring spatial effects being different. To achieve this, we endow the spatial random effects with a discrete probability law using a class of multivariate areally referenced Dirichlet process models that accommodate spatial and interdisease dependence. We evaluate our method through simulation studies and detect difference boundaries for multiple cancers using data from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute.
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Affiliation(s)
- Leiwen Gao
- Department of Biostatistics, University of California, 650 Charles E. Young Drive South, Los Angeles, CA 90095-1772, USA
| | - Sudipto Banerjee
- Department of Biostatistics, University of California, 650 Charles E. Young Drive South, Los Angeles, CA 90095-1772, USA
| | - Beate Ritz
- Department of Epidemiology, University of California, 650 Charles E. Young Drive South, Los Angeles, CA 90095-1772, USA
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Kitawa YS, Asfaw ZG. Space-time modelling of monthly malaria incidence for seasonal associated drivers and early epidemic detection in Southern Ethiopia. Malar J 2023; 22:301. [PMID: 37814300 PMCID: PMC10563281 DOI: 10.1186/s12936-023-04742-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 10/04/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Although Ethiopia has made great strides in recent years to reduce the threat of malaria, the disease remains a significant issue in most districts of the country. It constantly disappears in parts of the areas before reappearing in others with erratic transmission rates. Thus, developing a malaria epidemic early warning system is important to support the prevention and control of the incidence. METHODS Space-time malaria risk mapping is essential to monitor and evaluate priority zones, refocus intervention, and enable planning for future health targets. From August 2013 to May 2019, the researcher considered an aggregated count of genus Plasmodium falciparum from 149 districts in Southern Ethiopia. Afterwards, a malaria epidemic early warning system was developed using model-based geostatistics, which helped to chart the disease's spread and future management. RESULTS Risk factors like precipitation, temperature, humidity, and nighttime light are significantly associated with malaria with different rates across the districts. Districts in the southwest, including Selamago, Bero, and Hamer, had higher rates of malaria risk, whereas in the south and centre like Arbaminch and Hawassa had moderate rates. The distribution is inconsistent and varies across time and space with the seasons. CONCLUSION Despite the importance of spatial correlation in disease risk mapping, it may occasionally be a good idea to generate epidemic early warning independently in each district to get a quick picture of disease risk. A system like this is essential for spotting numerous inconsistencies in lower administrative levels early enough to take corrective action before outbreaks arise.
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Affiliation(s)
- Yonas Shuke Kitawa
- Department of Statistics, College of Natural and Computational Sciences, Hawassa University, Hawassa, Ethiopia.
| | - Zeytu Gashaw Asfaw
- Department of Bio-statistics and Epidemiology, School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia
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Sun N, Bursac Z, Dryden I, Lucchini R, Dabo-Niang S, Ibrahimou B. Bayesian spatiotemporal modelling for disease mapping: an application to preeclampsia and gestational diabetes in Florida, United States. Environ Sci Pollut Res Int 2023; 30:109283-109298. [PMID: 37770738 PMCID: PMC10726673 DOI: 10.1007/s11356-023-29953-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 09/14/2023] [Indexed: 09/30/2023]
Abstract
Morbidities generally show patterns of concentration that vary by space and time. Disease mapping models are useful in estimating the spatiotemporal patterns of disease risks and are therefore pivotal for effective disease surveillance, resource allocation, and the development of prevention strategies. This study considers six spatiotemporal Bayesian hierarchical models based on two spatial conditional autoregressive priors. It could serve as a guideline on the development and application of Bayesian hierarchical models to assess the emerging risk trends, risk clustering, and spatial inequality trends, with estimation of covariables' effects on the interested disease risk. The method is applied to the Florida Birth Record data between 2006 and 2015 to study two cardiovascular risk factors: preeclampsia and gestational diabetes. High-risk clusters were detected in North Central Florida for preeclampsia and in Central Florida for gestational diabetes. While the adjusted disease trend was stable, spatial inequality peaked in 2011-2012 for both diseases. Exposure to PM2.5 at first or/and second trimester increased the risk of preeclampsia and gestational diabetes, but the magnitude is less severe compared to previous studies. In conclusion, this study underscores the significance of selecting appropriate disease mapping models in estimating the intricate spatiotemporal patterns of disease risk and suggests the importance of localized interventions to reduce health disparities. The result also identified an opportunity to study potential risk factors of preeclampsia, as the spike of risk in North Central Florida cannot be explained by current covariables.
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Affiliation(s)
- Ning Sun
- Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL, USA
| | - Zoran Bursac
- Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL, USA
| | - Ian Dryden
- Department of Mathematics and Statistics, College of Arts, Science and Education, Florida International University, Miami, FL, USA
| | - Roberto Lucchini
- Environmental Health Science Department, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL, USA
| | - Sophie Dabo-Niang
- Laboratory PAINLEVE UMR 8524, Inria-MODAL, University of Lille, BP 60149, 59653, Villeneuve d'ascq cedex, France
| | - Boubakari Ibrahimou
- Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL, USA.
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Roy C, Ahmed R, Ghosh MK, Rahman MM. Spatio-temporal evaluation of respiratory disease based on the information provided by patients admitted to a medical college hospital in Bangladesh using geographic information system. Heliyon 2023; 9:e19596. [PMID: 37809954 PMCID: PMC10558838 DOI: 10.1016/j.heliyon.2023.e19596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 08/13/2023] [Accepted: 08/28/2023] [Indexed: 10/10/2023] Open
Abstract
In Bangladesh respiratory illnesses are one of the leading risk factors for death and disability. Limited access to healthcare services, indoor and outdoor air pollution, large-scale use of smoking materials, allergens, and lack of awareness are among the known leading factors contributing to respiratory illness in Bangladesh. Key initiatives taken by the government to handle respiratory illnesses include, changing of respiratory health policy, building awareness, enhancing healthcare facility, and promoting prevention measures. Despite all these efforts, the number of individuals suffering from respiratory diseases has increased steadily during the recent years. This study aims at examining the distribution pattern of respiratory diseases over space and time using Geographic Information System, which is expected to contribute to the better understand of the factors contributing to respiratory illness development. To achieve the aims of the study two interviews were conducted among patients with respiratory sickness in the medicine and respiratory medicine units of Rajshahi Medical College Hospital between January and April of 2019 and 2020 following the guidelines provided by the Ethics Committee, Department of Geography and Environmental Studies, University of Rajshahi, Bangladesh (ethical approval reference number: 2018/08). Principal component extraction and spatial statistical analyses were performed to identify the key respiratory illnesses and their geographical distribution pattern respectively. The results indicate, during January-February the number of patients was a lot higher compared to March-April. The patients were hospitalized mainly due to four respiratory diseases (chronic obstructive pulmonary disease, asthma, pneumonia, and pulmonary hypertension). Geographical distribution pattern of respiratory disease cases also varied considerably between the years as well as months of the years. This information seems reasonable to elucidate the spatio-temporal distribution of respiratory disease and thus improve the existing prevention, control, and cure practices of respiratory illness of the study area. Approach used in this study to elicit spatio-temporal distribution of repertory disease can easily be implemented in other areas with similar geographical settings and patients' illness information from hospital.
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Affiliation(s)
- Chandan Roy
- Department of Geography and Environmental Studies, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Raquib Ahmed
- Department of Geography and Environmental Studies, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Manoj Kumer Ghosh
- Department of Geography and Environmental Studies, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Matinur Rahman
- Institute of Bangladesh Studies, University of Rajshahi, Rajshahi, 6205, Bangladesh
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Mohamad MS, Abdul Maulud KN, Faes C. A practical illustration of spatial smoothing methods for disconnected regions with INLA: spatial survey on overweight and obesity in Malaysia. Int J Health Geogr 2023; 22:14. [PMID: 37344913 DOI: 10.1186/s12942-023-00336-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/01/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND National prevalence could mask subnational heterogeneity in disease occurrence, and disease mapping is an important tool to illustrate the spatial pattern of disease. However, there is limited information on techniques for the specification of conditional autoregressive models in disease mapping involving disconnected regions. This study explores available techniques for producing district-level prevalence estimates for disconnected regions, using as an example childhood overweight in Malaysia, which consists of the Peninsular and Borneo regions separated by the South China Sea. We used data from Malaysia National Health and Morbidity Survey conducted in 2015. We adopted Bayesian hierarchical modelling using the integrated nested Laplace approximation (INLA) program in R-software to model the spatial distribution of overweight among 6301 children aged 5-17 years across 144 districts located in two disconnected regions. We illustrate different types of spatial models for prevalence mapping across disconnected regions, taking into account the survey design and adjusting for district-level demographic and socioeconomic covariates. RESULTS The spatial model with split random effects and a common intercept has the lowest Deviance and Watanabe Information Criteria. There was evidence of a spatial pattern in the prevalence of childhood overweight across districts. An increasing trend in smoothed prevalence of overweight was observed when moving from the east to the west of the Peninsular and Borneo regions. The proportion of Bumiputera ethnicity in the district had a significant negative association with childhood overweight: the higher the proportion of Bumiputera ethnicity in the district, the lower the prevalence of childhood overweight. CONCLUSION This study illustrates different available techniques for mapping prevalence across districts in disconnected regions using survey data. These techniques can be utilized to produce reliable subnational estimates for any areas that comprise of disconnected regions. Through the example, we learned that the best-fit model was the one that considered the separate variations of the individual regions. We discovered that the occurrence of childhood overweight in Malaysia followed a spatial pattern with an east-west gradient trend, and we identified districts with high prevalence of overweight. This information could help policy makers in making informed decisions for targeted public health interventions in high-risk areas.
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Affiliation(s)
- Maria Safura Mohamad
- Faculty of Social Sciences, Unit of Health Sciences, Tampere University, Arvo Ylpön Katu 34, 33520, Tampere, Finland.
| | - Khairul Nizam Abdul Maulud
- Department of Civil Engineering, Faculty of Engineering & Built Environment, National University of Malaysia, 43600, Bangi, Selangor, Malaysia
- Earth Observation Centre, Institute of Climate Change, National University of Malaysia, 43600, Bangi, Selangor, Malaysia
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
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González-Iglesias V, Martínez-Pérez I, Rodríguez Suárez V, Fernández-Somoano A. Spatial distribution of hospital admissions for asthma in the central area of Asturias, Northern Spain. BMC Public Health 2023; 23:787. [PMID: 37118792 PMCID: PMC10141842 DOI: 10.1186/s12889-023-15731-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 04/22/2023] [Indexed: 04/30/2023] Open
Abstract
BACKGROUND Asturias is one of the communities with the highest rates of hospital admission for asthma in Spain. The environmental pollution or people lifestyle are some of the factors that contribute to the appearance or aggravation of this illness. The aim of this study was to show the spatial distribution of asthma admissions risks in the central municipalities of Asturias and to analyze the observed spatial patterns. METHODS Urgent hospital admissions for asthma and status asthmaticus occurred between 2016 to 2018 on the public hospitals of the central area of Asturias were used. Population data were assigned in 5 age groups. Standardised admission ratio (SAR), smoothed relative risk (SRR) and posterior risk probability (PP) were calculated for each census tract (CT). A spatial trend analysis was run, a spatial autocorrelation index (Morans I) was calculated and a cluster and outlier analysis (Anselin Local Morans I) was finally performed in order to analyze spatial clusters. RESULTS The total number of hospital urgent asthma admissions during the study period was 2324, 1475 (63.46%) men and 849 (36.56%) women. The municipalities with the highest values of SRR and PP were located on the northwest area: Avilés, Gozón, Carreño, Corvera de Asturias, Castrillón and Illas. A high risk cluster was found for the municipalities of Avilés, Gozón y Corvera de Asturias. CONCLUSIONS The spatial analysis showed high risk of hospitalization for asthma on the municipalities of the northwest area of the study, which highlight the existence of spatial inequalities on the distribution of urgent hospital admissions.
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Affiliation(s)
- Verónica González-Iglesias
- Departamento de Medicina, IUOPA-Área de Medicina Preventiva Y Salud Pública, Universidad de Oviedo. C/Julián Clavería S/N, 33006, Oviedo (Asturias), Spain
| | - Isabel Martínez-Pérez
- Departamento de Medicina, IUOPA-Área de Medicina Preventiva Y Salud Pública, Universidad de Oviedo. C/Julián Clavería S/N, 33006, Oviedo (Asturias), Spain.
| | - Valentín Rodríguez Suárez
- Dirección General de Salud Pública, Consejería de Salud, Principado de Asturias, C/ Ciriaco Miguel Vigil, 9, 33006, Oviedo, Spain
| | - Ana Fernández-Somoano
- Departamento de Medicina, IUOPA-Área de Medicina Preventiva Y Salud Pública, Universidad de Oviedo. C/Julián Clavería S/N, 33006, Oviedo (Asturias), Spain
- CIBER Epidemiología Y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Avenida Monforte de Lemos, 3-5, 28029, Madrid, Spain
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Avenida Roma, S/N, 33001, Oviedo, Spain
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14
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Cuadros DF, Branscum AJ, Moreno CM, MacKinnon NJ. Narrative minireview of the spatial epidemiology of substance use disorder in the United States: Who is at risk and where? World J Clin Cases 2023; 11:2374-2385. [PMID: 37123313 PMCID: PMC10131000 DOI: 10.12998/wjcc.v11.i11.2374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/31/2023] [Accepted: 03/20/2023] [Indexed: 04/06/2023] Open
Abstract
Drug overdose is the leading cause of death by injury in the United States. The incidence of substance use disorder (SUD) in the United States has increased steadily over the past two decades, becoming a major public health problem for the country. The drivers of the SUD epidemic in the United States have changed over time, characterized by an initial heroin outbreak between 1970 and 1999, followed by a painkiller outbreak, and finally by an ongoing synthetic opioid outbreak. The nature and sources of these abused substances reveal striking differences in the socioeconomic and behavioral factors that shape the drug epidemic. Moreover, the geospatial distribution of the SUD epidemic is not homogeneous. The United States has specific locations where vulnerable communities at high risk of SUD are concentrated, reaffirming the multifactorial socioeconomic nature of this epidemic. A better understanding of the SUD epidemic under a spatial epidemiology framework is necessary to determine the factors that have shaped its spread and how these patterns can be used to predict new outbreaks and create effective mitigation policies. This narrative minireview summarizes the current records of the spatial distribution of the SUD epidemic in the United States across different periods, revealing some spatiotemporal patterns that have preceded the occurrence of outbreaks. By analyzing the epidemic of SUD-related deaths, we also describe the epidemic behavior in areas with high incidence of cases. Finally, we describe public health interventions that can be effective for demographic groups, and we discuss future challenges in the study and control of the SUD epidemic in the country.
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Affiliation(s)
- Diego F Cuadros
- Digital Futures, University of Cincinnati, Cincinnati, OH 45206, United States
| | - Adam J Branscum
- Department of Biostatistics, Oregon State University, Corvallis, OR 97331, United States
| | - Claudia M Moreno
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, United States
| | - Neil J MacKinnon
- Department of Population Health Sciences, Augusta University, Augusta, GA 30912, United States
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15
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Martínez-Pérez I, González-Iglesias V, Suárez VR, Fernández-Somoano A. Spatial distribution of unscheduled hospital admissions for chronic obstructive pulmonary disease in the central area of Asturias, Spain. BMC Pulm Med 2023; 23:101. [PMID: 36978049 PMCID: PMC10053433 DOI: 10.1186/s12890-023-02395-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/21/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is one of the major causes of mortality worldwide and also reports high morbidity rates and the global burden COPD has continued to rise over the last several decades. The best-known COPD risk factors are tobacco smoke and air pollution, but genetics, age, sex, and socioeconomic status are additional factors. This study aimed to assess the spatial distribution of unscheduled COPD hospital admissions of men and women in the central area of Asturias during 2016-2018 and identify trends, spatial patterns, or clusters in the area. METHODS Unscheduled COPD hospital admissions in the central area of Asturias were registered, geocoded, and grouped by census tracts (CTs), age, and sex. Standardized admission ratio, smoothed relative risk, posterior risk probability, and spatial clusters between relative risks throughout the study area were calculated and mapped. RESULTS The spatial distribution of COPD hospital admissions differed between men and women. For men, high-risk values were located primarily in the northwestern area of the study, whereas for women the cluster pattern was not as clear and high-risk CTs also reached central and southern areas. In both men and women, the north-northwest area included the majority of CTs with high-risk values. CONCLUSIONS The present study showed the existence of a spatial distribution pattern of unscheduled COPD hospital admissions in the central area of Asturias that was more pronounced for men than for women. This study could provide a starting point for generating knowledge about COPD epidemiology in Asturias.
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Affiliation(s)
- Isabel Martínez-Pérez
- Departamento de Medicina, IUOPA-Área de Medicina Preventiva y Salud Pública, Universidad de Oviedo, C/Julián Clavería s/n, Oviedo (Asturias), 33006, Spain
| | - Verónica González-Iglesias
- Departamento de Medicina, IUOPA-Área de Medicina Preventiva y Salud Pública, Universidad de Oviedo, C/Julián Clavería s/n, Oviedo (Asturias), 33006, Spain
| | - Valentín Rodríguez Suárez
- Dirección General de Salud Pública. Consejería de Salud, Principado de Asturias. C/Ciriaco Miguel Vigil, 9, Oviedo, 33006, Spain
| | - Ana Fernández-Somoano
- Departamento de Medicina, IUOPA-Área de Medicina Preventiva y Salud Pública, Universidad de Oviedo, C/Julián Clavería s/n, Oviedo (Asturias), 33006, Spain.
- CIBER Epidemiología y Salud Pública (CIBERESP) - Instituto de Salud Carlos III, Monforte de Lemos Avenue, 3-5, Madrid, 28029, Spain.
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Roma Avenue s/n, Oviedo, Asturias, 33001, Spain.
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16
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Alsaedi SB, Mineta K, Gao X, Gojobori T. Computational network analysis of host genetic risk variants of severe COVID-19. Hum Genomics 2023; 17:17. [PMID: 36859360 PMCID: PMC9977643 DOI: 10.1186/s40246-023-00454-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 01/28/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks. RESULTS We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction. CONCLUSIONS This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.
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Affiliation(s)
- Sakhaa B. Alsaedi
- grid.45672.320000 0001 1926 5090Division of Computer, Electrical and Mathematical Sciences and Engineering, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900 Saudi Arabia ,grid.412892.40000 0004 1754 9358College of Computer Science and Engineering (CCSE), Taibah University, Medina, Saudi Arabia
| | - Katsuhiko Mineta
- grid.45672.320000 0001 1926 5090Division of Computer, Electrical and Mathematical Sciences and Engineering, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900 Saudi Arabia ,grid.5290.e0000 0004 1936 9975AND Research Organization for Nano and Life Innovation, Waseda University, Tokyo, 162-0041 Japan
| | - Xin Gao
- grid.45672.320000 0001 1926 5090Division of Computer, Electrical and Mathematical Sciences and Engineering, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900 Saudi Arabia
| | - Takashi Gojobori
- Division of Computer, Electrical and Mathematical Sciences and Engineering, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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17
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Hepler SA, Kline DM, Bonny A, McKnight E, Waller LA. An integrated abundance model for estimating county-level prevalence of opioid misuse in Ohio. J R Stat Soc Ser A Stat Soc 2023; 186:43-60. [PMID: 37261313 PMCID: PMC10227692 DOI: 10.1093/jrsssa/qnac013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Opioid misuse is a national epidemic and a significant drug related threat to the United States. While the scale of the problem is undeniable, estimates of the local prevalence of opioid misuse are lacking, despite their importance to policy-making and resource allocation. This is due, in part, to the challenge of directly measuring opioid misuse at a local level. In this paper, we develop a Bayesian hierarchical spatio-temporal abundance model that integrates indirect county-level data on opioid-related outcomes with state-level survey estimates on prevalence of opioid misuse to estimate the latent county-level prevalence and counts of people who misuse opioids. A simulation study shows that our integrated model accurately recovers the latent counts and prevalence. We apply our model to county-level surveillance data on opioid overdose deaths and treatment admissions from the state of Ohio. Our proposed framework can be applied to other applications of small area estimation for hard to reach populations, which is a common occurrence with many health conditions such as those related to illicit behaviors.
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Affiliation(s)
- Staci A Hepler
- Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, USA
| | - David M Kline
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, USA
| | - Andrea Bonny
- Division of Adolescent Medicine, Nationwide Children's Hospital, Department of Pediatrics, The Ohio State University, Columbus, USA
| | - Erin McKnight
- Division of Adolescent Medicine, Nationwide Children's Hospital, Department of Pediatrics, The Ohio State University, Columbus, USA
| | - Lance A Waller
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, USA
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18
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Macharia PM, Ray N, Gitonga CW, Snow RW, Giorgi E. Combining school-catchment area models with geostatistical models for analysing school survey data from low-resource settings: Inferential benefits and limitations. Spat Stat 2022; 51:100679. [PMID: 35880005 PMCID: PMC7613137 DOI: 10.1016/j.spasta.2022.100679] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
School-based sampling has been used to inform targeted responses for malaria and neglected tropical diseases. Standard geostatistical methods for mapping disease prevalence use the school location to model spatial correlation, which is questionable since exposure to the disease is more likely to occur in the residential location. In this paper, we propose to overcome the limitations of standard geostatistical methods by introducing a modelling framework that accounts for the uncertainty in the location of the residence of the students. By using cost distance and cost allocation models to define spatial accessibility and in absence of any information on the travel mode of students to school, we consider three school catchment area models that assume walking only, walking and bicycling and, walking and motorized transport. We illustrate the use of this approach using two case studies of malaria in Kenya and compare it with the standard approach that uses the school locations to build geostatistical models. We argue that the proposed modelling framework presents several inferential benefits, such as the ability to combine data from multiple surveys some of which may also record the residence location, and to deal with ecological bias when estimating the effects of malaria risk factors. However, our results show that invalid assumptions on the modes of travel to school can worsen the predictive performance of geostatistical models. Future research in this area should focus on collecting information on the modes of transportation to school which can then be used to better parametrize the catchment area models.
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Affiliation(s)
- Peter M. Macharia
- Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, LA1 4YW, UK
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, PO, Box 43640, Nairobi, Kenya
| | - Nicolas Ray
- GeoHealth group, Institute of Global Health, University of Geneva, Geneva, Switzerland
- Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland
| | - Caroline W. Gitonga
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, PO, Box 43640, Nairobi, Kenya
| | - Robert W. Snow
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, PO, Box 43640, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7LG, UK
| | - Emanuele Giorgi
- Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, LA1 4YW, UK
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19
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Martinez-Beneito MA, Mateu J, Botella-Rocamora P. Spatio-temporal small area surveillance of the COVID-19 pandemic. Spat Stat 2022; 49:100551. [PMID: 34782854 PMCID: PMC8574159 DOI: 10.1016/j.spasta.2021.100551] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 10/21/2021] [Accepted: 10/21/2021] [Indexed: 06/01/2023]
Abstract
The emergence of COVID-19 requires new effective tools for epidemiological surveillance. Spatio-temporal disease mapping models, which allow dealing with small units of analysis, are a priority in this context. These models provide geographically detailed and temporally updated overviews of the current state of the pandemic, making public health interventions more effective. These models also allow estimating epidemiological indicators highly demanded for COVID-19 surveillance, such as the instantaneous reproduction number R t , even for small areas. In this paper, we propose a new spatio-temporal spline model particularly suited for COVID-19 surveillance, which allows estimating and monitoring R t for small areas. We illustrate our proposal on the study of the disease pandemic in two Spanish regions. As a result, we show how tourism flows have shaped the spatial distribution of the disease in these regions. In these case studies, we also develop new epidemiological tools to be used by regional public health services for small area surveillance.
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Affiliation(s)
- Miguel A Martinez-Beneito
- Department of Statistics and Operations Research, University of Valencia, Burjassot (Valencia), Spain
- Unitat Mixta de recerca en mètodes estadístics per a dades biomédiques i sanitàries, UV-FISABIO, Spain
| | - Jorge Mateu
- Department of Mathematics, University Jaume I of Castellon, Castelló de la Plana, Spain
| | - Paloma Botella-Rocamora
- Subdirección General de Epidemiologia, Vigilancia de la Salud i Sanidad Ambiental, Conselleria de Sanitat Universal i Salut Pública, Valencia, Spain
- Unitat Mixta de recerca en mètodes estadístics per a dades biomédiques i sanitàries, UV-FISABIO, Spain
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20
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Urdangarin A, Goicoa T, Dolores Ugarte M. Space-time interactions in Bayesian disease mapping with recent tools: Making things easier for practitioners. Stat Methods Med Res 2022; 31:1085-1103. [PMID: 35179396 DOI: 10.1177/09622802221079351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Spatio-temporal disease mapping studies the distribution of mortality or incidence risks in space and its evolution in time, and it usually relies on fitting hierarchical Poisson mixed models. These models are complex for practitioners as they generally require adding constraints to correctly identify and interpret the different model terms. However, including constraints may not be straightforward in some recent software packages. This paper focuses on NIMBLE, a library of algorithms that contains among others a configurable system for Markov chain Monte Carlo (MCMC) algorithms. In particular, we show how to fit different spatio-temporal disease mapping models with NIMBLE making emphasis on how to include sum-to-zero constraints to solve identifiability issues when including spatio-temporal interactions. Breast cancer mortality data in Spain during the period 1990-2010 is used for illustration purposes. A simulation study is also conducted to compare NIMBLE with R-INLA in terms of parameter estimates and relative risk estimation. The results are very similar but differences are observed in terms of computing time.
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Affiliation(s)
- Arantxa Urdangarin
- Department of Statistics, Computer Science, and Mathematics, Public University of Navarre, Spain
- INAMAT2 (Institute for Advanced Materials and Mathematics), Public University of Navarre, Spain
| | - Tomás Goicoa
- Department of Statistics, Computer Science, and Mathematics, Public University of Navarre, Spain
- INAMAT (Institute for Advanced Materials and Mathematics), Public University of Navarre, Spain
- Institute of Health Research, IdisNA, Spain
| | - María Dolores Ugarte
- Department of Statistics, Computer Science, and Mathematics, Public University of Navarre, Spain
- INAMAT (Institute for Advanced Materials and Mathematics), Public University of Navarre, Spain
- Institute of Health Research, IdisNA, Spain
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21
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De Nicola G, Schneble M, Kauermann G, Berger U. Regional now- and forecasting for data reported with delay: toward surveillance of COVID-19 infections. Adv Stat Anal 2022; 106:407-426. [PMID: 35069920 PMCID: PMC8764329 DOI: 10.1007/s10182-021-00433-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 12/17/2021] [Indexed: 12/20/2022]
Abstract
Governments around the world continue to act to contain and mitigate the spread of COVID-19. The rapidly evolving situation compels officials and executives to continuously adapt policies and social distancing measures depending on the current state of the spread of the disease. In this context, it is crucial for policymakers to have a firm grasp on what the current state of the pandemic is, and to envision how the number of infections is going to evolve over the next days. However, as in many other situations involving compulsory registration of sensitive data, cases are reported with delay to a central register, with this delay deferring an up-to-date view of the state of things. We provide a stable tool for monitoring current infection levels as well as predicting infection numbers in the immediate future at the regional level. We accomplish this through nowcasting of cases that have not yet been reported as well as through predictions of future infections. We apply our model to German data, for which our focus lies in predicting and explain infectious behavior by district.
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Affiliation(s)
- Giacomo De Nicola
- Department of Statistics, Ludwig-Maximillians-Universität München, Munich, Germany
| | - Marc Schneble
- Department of Statistics, Ludwig-Maximillians-Universität München, Munich, Germany
| | - Göran Kauermann
- Department of Statistics, Ludwig-Maximillians-Universität München, Munich, Germany
| | - Ursula Berger
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximillians-Universität München, Munich, Germany
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22
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Adin A, Congdon P, Santafé G, Ugarte MD. Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach. Stoch Environ Res Risk Assess 2022; 36:2995-3010. [PMID: 35075346 PMCID: PMC8771626 DOI: 10.1007/s00477-022-02175-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/07/2022] [Indexed: 06/15/2023]
Abstract
The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.
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Affiliation(s)
- A. Adin
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain
| | - P. Congdon
- School of Geography, Queen Mary University of London, London, UK
| | - G. Santafé
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain
| | - M. D. Ugarte
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain
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23
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Adin A, Congdon P, Santafé G, Ugarte MD. Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach. Stoch Environ Res Risk Assess 2022; 36:2995-3010. [PMID: 35075346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.
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Affiliation(s)
- A Adin
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain
| | - P Congdon
- School of Geography, Queen Mary University of London, London, UK
| | - G Santafé
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain
| | - M D Ugarte
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain
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24
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Hassanzad M, Farnia P, Farnia P, Arian M, Valinejadi A, Ghaffaripour H, Baghaie N, Hassanzad N, Mohammadpour L, Velayati AA. Assessment of Cystic Fibrosis Distribution Based on Air Pollution by Geographical Information System (GIS). Tanaffos 2022; 21:31-44. [PMID: 36258909 PMCID: PMC9571236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 12/12/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND It is widely accepted that concerns have been recently raised regarding the impact of air pollution on the health of children with cystic fibrosis (CF). Air pollution probably affects the exacerbation of CF and its laboratory findings. On the other hand, the World Health Organization (WHO) has asked all countries to update their data and reports on the distribution and prevalence of CF in different areas. The purpose of the present study was to investigate the distribution and prevalence of CF based on the levels of atmospheric pollutants, such as PM10, PM2.5, SO2, NO2, CO, and O3 in 22 zones of Tehran, and to report the abnormal laboratory findings that might indicate the exacerbation of CF. MATERIALS AND METHODS The studied statistical population included children with CF referred to Masih Daneshvari Hospital from 2003 to 2020. Demographic data, location of living area, and laboratory findings were extracted from patient records. The geographic information system (GIS) was applied to indicate the distribution and dispersion of the disease. The information related to air pollutants was collected from all stations in Tehran during the studied period by the Department of Environment of Tehran Province, and the average levels were used for final reporting. RESULTS The analysis results on 287 CF patients demonstrated that the risk of disease exacerbation significantly increased by the presence of air pollutants. In areas with multiple air pollutants, more laboratory findings were observed to be abnormal, and the lower survival rate for patients with CF was recorded. Investigating the CF distribution pattern based on climatic layers and above mean sea level (AMSL) indicated that distribution of the disease was higher in dry areas with lower AMSL and the higher volume of the atmospheric pollutants, which were primarily centralized in southern and central Tehran. CONCLUSION Environmental factors, such as air pollution, can be considered vital parameters, along with high-risk factors, such as pure and integrated race, migration, and mutation, influencing the prevalence and exacerbation of CF symptoms. Considering the higher prevalence of CF in deprived areas of Tehran, households' cultural and economic level appears to be a factor in the lack of diagnostic screening and prevention of CF in these areas. On the other hand, continuous monitoring of the air pollution caused by traffic and giving warnings to CF patients and their parents is particularly important.
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Affiliation(s)
- Maryam Hassanzad
- Pediatric Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Parissa Farnia
- Mycobacteriology Research Center, NRITLD, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Poopak Farnia
- Department of Biotechnology, School of Advanced Technology in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahdieh Arian
- Nursing and Midwifery Care Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ali Valinejadi
- Department of Health Information Technology, School of Allied Medical Sciences, Semnan University of Medical Sciences, Semnan, Iran,,Correspondence to: Valinejadi A Address: Department of Health Information Technology, School of Allied Medical Sciences, Semnan University of Medical Sciences, Semnan, Iran Email address:
| | - Hosseinali Ghaffaripour
- Pediatric Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Noushin Baghaie
- Pediatric Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nima Hassanzad
- Shahid Beheshti University of Medical Sciences, Tehran, Iran,
| | - Leila Mohammadpour
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Ali Akbar Velayati
- Mycobacteriology Research Center, NRITLD, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Neupane N, Goldbloom-Helzner A, Arab A. Spatio-temporal modeling for confirmed cases of lyme disease in Virginia. Ticks Tick Borne Dis 2021; 12:101822. [PMID: 34555712 DOI: 10.1016/j.ttbdis.2021.101822] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 03/18/2021] [Accepted: 08/09/2021] [Indexed: 11/24/2022]
Abstract
Epidemiological data often include characteristics such as spatial and/or temporal dependencies and excess zero counts, which pose modeling challenges. Excess zeros in such data may arise from imperfect detection and/or relative rareness of the disease in a given location. Here, we studied the spatio-temporal variation in annual Lyme disease cases in Virginia from 2001-2016 and modeled the disease with a spatio-temporal hierarchical Bayesian model. Using observed ecological and environmental covariates, we constructed a predictive model for the disease spread over space and time, including spatial and temporal random effects. We considered several different models and found that the negative binomial hurdle model performs the best for such epidemiological data. Among the various ecological predictors, the North-South (V component) of winds and relative humidity significantly contributed to predicting the Lyme cases. Our model results provide important insights on the spread of the disease in Virginia and the proposed modeling framework offers epidemiologists and health policymakers a useful tool for improving disease preparedness and control plans for the future.
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Affiliation(s)
- Naresh Neupane
- Georgetown University, Department of Biology, Washington, DC 20057, USA.
| | | | - Ali Arab
- Georgetown University, Department of Mathematics and Statistics, Washington, DC 20057, USA
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Correa-Agudelo E, Kim HY, Musuka GN, Mukandavire Z, Akullian A, Cuadros DF. Associated health and social determinants of mobile populations across HIV epidemic gradients in Southern Africa. J Migr Health 2021; 3:100038. [PMID: 34405186 PMCID: PMC8352162 DOI: 10.1016/j.jmh.2021.100038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 03/11/2021] [Accepted: 03/22/2021] [Indexed: 11/05/2022] Open
Abstract
Background Growing travel connectivity and economic development have dramatically increased the magnitude of human mobility in Africa. In public health, vulnerable population groups such as mobile individuals are at an elevated risk of sexually transmitted diseases, including HIV. Methods The population-based Demographic Health Survey data of five Southern African countries with different HIV epidemic intensities (Angola, Malawi, South Africa, Zambia, and Zimbabwe) were used to investigate the association between HIV serostatus and population mobility adjusting for socio-demographic, sexual behavior and spatial covariates. Results Mobility was associated with HIV seropositive status only in Zimbabwe (adjusted odds ratio [AOR] = 1.37 [95% confidence interval [CI]: 1.01–1.67]). These associations were not significant in Angola, Malawi, South Africa, and Zambia. Females had higher odds of mobility than males in Zimbabwe (AOR = 1.37, CI: 1.10–1.69). The odds of mobility decreased with age in all five countries. Conclusions Our findings highlight the heterogeneity of the social and health determinants of mobile populations in several countries with different HIV epidemic intensities. Effective interventions using precise geographic focus combined with detailed attribute characterization of mobile populations can enhance their impact especially in areas with high density of mobile individuals and high HIV prevalence.
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Affiliation(s)
- Esteban Correa-Agudelo
- Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH, 45221, USA.,Health Geography and Disease Modeling Laboratory, University of Cincinnati, Cincinnati, USA
| | - Hae-Young Kim
- Africa Health Research Institute, KwaZulu-Natal, South Africa.,KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP) KwaZulu-Natal, South Africa.,Department of Population Health, New York University Grossman School of Medicine, USA
| | | | - Zindoga Mukandavire
- Centre for Data Science, Coventry University, UK.,School of Computing, Electronics and Mathematics, Coventry University, UK
| | - Adam Akullian
- Institute for Disease Modeling, Global Good Fund, Bellevue, Washington, USA.,Department of Global Health, University of Washington, Seattle, Washington, USA
| | - Diego F Cuadros
- Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH, 45221, USA.,Health Geography and Disease Modeling Laboratory, University of Cincinnati, Cincinnati, USA
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Cuadros DF, Li J, Musuka G, Awad SF. Spatial epidemiology of diabetes: Methods and insights. World J Diabetes 2021; 12:1042-1056. [PMID: 34326953 PMCID: PMC8311478 DOI: 10.4239/wjd.v12.i7.1042] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/07/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Diabetes mellitus (DM) is a growing epidemic with global proportions. It is estimated that in 2019, 463 million adults aged 20-79 years were living with DM. The latest evidence shows that DM continues to be a significant global health challenge and is likely to continue to grow substantially in the next decades, which would have major implications for healthcare expenditures, particularly in developing countries. Hence, new conceptual and methodological approaches to tackle the epidemic are long overdue. Spatial epidemiology has been a successful approach to control infectious disease epidemics like malaria and human immunodeficiency virus. The implementation of this approach has been expanded to include the study of non-communicable diseases like cancer and cardiovascular diseases. In this review, we discussed the implementation and use of spatial epidemiology and Geographic Information Systems to the study of DM. We reviewed several spatial methods used to understand the spatial structure of the disease and identify the potential geographical drivers of the spatial distribution of DM. Finally, we discussed the use of spatial epidemiology on the design and implementation of geographically targeted prevention and treatment interventions against DM.
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Affiliation(s)
- Diego F Cuadros
- Geography and Geographic Information Systems, University of Cincinnati, Cincinnati, OH 45221, United States
| | - Jingjing Li
- Urban Health Collaborative, Drexel University, Philadelphia, PA 19104, United States
| | | | - Susanne F Awad
- Infectious Disease Epidemiology Group, Weill Cornell Medicine – Qatar, Cornell University, Doha 24144, Qatar
- World Health Organization Collaborating Centre for Disease Epidemiology Analytics on HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis, Weill Cornell Medicine – Qatar, Cornell University, Doha 24144, Qatar
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10044, United States
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MohammadEbrahimi S, Mohammadi A, Bergquist R, Dolatkhah F, Olia M, Tavakolian A, Pishgar E, Kiani B. Epidemiological characteristics and initial spatiotemporal visualisation of COVID-19 in a major city in the Middle East. BMC Public Health 2021; 21:1373. [PMID: 34247616 PMCID: PMC8272989 DOI: 10.1186/s12889-021-11326-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 06/18/2021] [Indexed: 12/24/2022] Open
Abstract
Background The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) emerged initially in China in December 2019 causing the COVID-19 disease, which quickly spread worldwide. Iran was one of the first countries outside China to be affected in a major way and is now under the spell of a fourth wave. This study aims to investigate the epidemiological characteristics of COVID-19 cases in north-eastern Iran through mapping the spatiotemporal trend of the disease. Methods The study comprises data of 4000 patients diagnosed by laboratory assays or clinical investigation from the beginning of the disease on Feb 14, 2020, until May 11, 2020. Epidemiological features and spatiotemporal trends of the disease in the study area were explored by classical statistical approaches and Geographic Information Systems. Results Most common symptoms were dyspnoea (69.4%), cough (59.4%), fever (54.4%) and weakness (19.5%). Approximately 82% of those who did not survive suffered from dyspnoea. The highest Case Fatality Rate (CFR) was related to those with cardiovascular disease (27.9%) and/or diabetes (18.1%). Old age (≥60 years) was associated with an almost five-fold increased CFR. Odds Ratio (OR) showed malignancy (3.8), nervous diseases (2.2), and respiratory diseases (2.2) to be significantly associated with increased CFR with developments, such as hospitalization at the ICU (2.9) and LOS (1.1) also having high correlations. Furthermore, spatial analyses revealed a geographical pattern in terms of both incidence and mortality rates, with COVID-19 first being observed in suburban areas from where the disease swiftly spread into downtown reaching a peak between 25 February to 06 March (4 incidences per km2). Mortality peaked 3 weeks later after which the infection gradually decreased. Out of patients investigated by the spatiotemporal approach (n = 727), 205 (28.2%) did not survive and 66.8% of them were men. Conclusions Older adults and people with severe co-morbidities were at higher risk for developing serious complications due to COVID-19. Applying spatiotemporal methods to identify the transmission trends and high-risk areas can rapidly be documented, thereby assisting policymakers in designing and implementing tailored interventions to control and prevent not only COVID-19 but also other rapidly spreading epidemics/pandemics.
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Affiliation(s)
- Shahab MohammadEbrahimi
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.,Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.
| | - Robert Bergquist
- Ingerod, Brastad, Sweden.,(Formerly with the UNICEF/UNDP/World Bank/WHO Special Program for Research and Training in Tropical Diseases, World Health Organization), Geneva, Switzerland
| | - Fatemeh Dolatkhah
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Microbiology and Virology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahsa Olia
- Department of Anaesthesiology, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Ayoub Tavakolian
- Department of Emergency Medicine, Faculty of Medicine, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Elahe Pishgar
- Department of Human Geography and Logistics, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran
| | - Behzad Kiani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Aheto JMK, Utuama OA, Dagne GA. Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012-2016 SEER data. BMC Cancer 2021; 21:508. [PMID: 33957887 PMCID: PMC8101113 DOI: 10.1186/s12885-021-08254-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 04/26/2021] [Indexed: 03/17/2023] Open
Abstract
Background Prostate cancer (CaP) cases are high in the United States. According to the American Cancer Society, there are an estimated number of 174,650 CaP new cases in 2019. The estimated number of deaths from CaP in 2019 is 31,620, making CaP the second leading cause of cancer deaths among American men with lung cancer been the first. Our goal is to estimate and map prostate cancer relative risk, with the ultimate goal of identifying counties at higher risk where interventions and further research can be targeted. Methods The 2012–2016 Surveillance, Epidemiology, and End Results (SEER) Program data was used in this study. Analyses were conducted on 159 Georgia counties. The outcome variable is incident prostate cancer. We employed a Bayesian geospatial model to investigate both measured and unmeasured spatial risk factors for prostate cancer. We visualised the risk of prostate cancer by mapping the predicted relative risk and exceedance probabilities. We finally developed interactive web-based maps to guide optimal policy formulation and intervention strategies. Results Number of persons above age 65 years and below poverty, higher median family income, number of foreign born and unemployed were risk factors independently associated with prostate cancer risk in the non-spatial model. Except for the number of foreign born, all these risk factors were also significant in the spatial model with the same direction of effects. Substantial geographical variations in prostate cancer incidence were found in the study. The predicted mean relative risk was 1.20 with a range of 0.53 to 2.92. Individuals residing in Towns, Clay, Union, Putnam, Quitman, and Greene counties were at increased risk of prostate cancer incidence while those residing in Chattahoochee were at the lowest risk of prostate cancer incidence. Conclusion Our results can be used as an effective tool in the identification of counties that require targeted interventions and further research by program managers and policy makers as part of an overall strategy in reducing the prostate cancer burden in Georgia State and the United States as a whole. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08254-0.
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Affiliation(s)
- Justice Moses K Aheto
- Department of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, P. O. Box LG13, Legon, Accra, Ghana. .,College of Public Health, University of South Florida, Tampa, USA.
| | - Ovie A Utuama
- College of Public Health, University of South Florida, Tampa, USA
| | - Getachew A Dagne
- College of Public Health, University of South Florida, Tampa, USA
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Cuadros DF, Branscum AJ, Mukandavire Z, Miller FD, MacKinnon N. Dynamics of the COVID-19 epidemic in urban and rural areas in the United States. Ann Epidemiol 2021; 59:16-20. [PMID: 33894385 PMCID: PMC8061094 DOI: 10.1016/j.annepidem.2021.04.007] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 03/29/2021] [Accepted: 04/14/2021] [Indexed: 12/21/2022]
Abstract
Purpose There is a growing concern about the COVID-19 epidemic intensifying in rural areas in the United States (U.S.). In this study, we described the dynamics of COVID-19 cases and deaths in rural and urban counties in the U.S. Methods Using data from April 1 to November 12, 2020, from Johns Hopkins University, we estimated COVID-19 incidence and mortality rates and conducted comparisons between urban and rural areas in three time periods at the national level, and in states with higher and lower COVID-19 incidence rates. Results Results at the national level showed greater COVID-19 incidence rates in urban compared to rural counties in the Northeast and Mid-Atlantic regions of the U.S. at the beginning of the epidemic. However, the intensity of the epidemic has shifted to a rapid surge in rural areas. In particular, high incidence states located in the Mid-west of the country had more than 3,400 COVID-19 cases per 100,000 people compared to 1,284 cases per 100,000 people in urban counties nationwide during the third period (August 30 to November 12). Conclusions Overall, the current epicenter of the epidemic is located in states with higher infection rates and mortality in rural areas. Infection prevention and control efforts including healthcare capacity should be scaled up in these vulnerable rural areas.
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Affiliation(s)
- Diego F Cuadros
- Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH; Health Geography and Disease Modeling Laboratory, University of Cincinnati, Cincinnati, OH; Geospatial Health Advising Group, University of Cincinnati, Cincinnati, OH.
| | - Adam J Branscum
- Department of Biostatistics, College of Public Health and Human Sciences, Oregon State University, Corvallis, OH
| | - Zindoga Mukandavire
- Centre for Data Science, Coventry University, UK; Centre for Data Science and Artificial Intelligence, Emirates Aviation University, Dubai, UAE
| | - F DeWolfe Miller
- Department of Tropical Medicine and Medical Microbiology and Pharmacology, University of Hawaii, Honolulu, HI
| | - Neil MacKinnon
- Geospatial Health Advising Group, University of Cincinnati, Cincinnati, OH; James L. Winkle College of Pharmacy, University of Cincinnati, Cincinnati, OH; Office of the Provost, Augusta University, Augusta, GA
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Piel FB, Parkes B, Hambly P, Roca-Barceló A, McCallion M, Leonardi G, Strosnider H, Yip F, Elliott P, Hansell AL. Software application profile: the Rapid Inquiry Facility 4.0: an open access tool for environmental public health tracking. Int J Epidemiol 2021; 49 Suppl 1:i38-i48. [PMID: 32293011 PMCID: PMC7158065 DOI: 10.1093/ije/dyz094] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2019] [Indexed: 12/02/2022] Open
Abstract
The Rapid Inquiry Facility 4.0 (RIF) is a new user-friendly and open-access tool, developed by the UK Small Area Health Statistics Unit (SAHSU), to facilitate environment public health tracking (EPHT) or surveillance (EPHS). The RIF is designed to help public health professionals and academics to rapidly perform exploratory investigations of health and environmental data at the small-area level (e.g. postcode or detailed census areas) in order to identify unusual signals, such as disease clusters and potential environmental hazards, whether localized (e.g. industrial site) or widespread (e.g. air and noise pollution). The RIF allows the use of advanced disease mapping methods, including Bayesian small-area smoothing and complex risk analysis functionalities, while accounting for confounders. The RIF could be particularly useful to monitor spatio-temporal trends in mortality and morbidity associated with cardiovascular diseases, cancers, diabetes and chronic lung diseases, or to conduct local or national studies on air pollution, flooding, low-magnetic fields or nuclear power plants.
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Affiliation(s)
- Frédéric B Piel
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK.,MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Brandon Parkes
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Peter Hambly
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Aina Roca-Barceló
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Martin McCallion
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Giovanni Leonardi
- Environmental Epidemiology Group, Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Chilton, UK
| | - Heather Strosnider
- Environmental Public Health Tracking Program, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, US
| | - Fuyuen Yip
- Environmental Public Health Tracking Program, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, US
| | - Paul Elliott
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK.,MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Anna L Hansell
- UK Small Area Health Statistics Unit (SAHSU), Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK.,Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK
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Jalilian A, Mateu J. A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany. Stoch Environ Res Risk Assess 2021; 35:797-812. [PMID: 33776559 PMCID: PMC7985594 DOI: 10.1007/s00477-021-02003-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/08/2021] [Indexed: 05/07/2023]
Abstract
The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 can result in informed and timely public health policies. In this paper, we use a spatio-temporal stochastic model to explain the temporal and spatial variations in the daily number of new confirmed cases in Spain, Italy and Germany from late February 2020 to mid January 2021. Using a hierarchical Bayesian framework, we found that the temporal trends of the epidemic in the three countries rapidly reached their peaks and slowly started to decline at the beginning of April and then increased and reached their second maximum in the middle of November. However decline and increase of the temporal trend seems to show different patterns in Spain, Italy and Germany.
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Affiliation(s)
- Abdollah Jalilian
- Department of Statistics, Razi University, Kermanshah, 67149-67346 Iran
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Perez-Panades J, Botella-Rocamora P, Martinez-Beneito MA. Beyond standardized mortality ratios; some uses of smoothed age-specific mortality rates on small areas studies. Int J Health Geogr 2020; 19:54. [PMID: 33276785 PMCID: PMC7716592 DOI: 10.1186/s12942-020-00251-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 11/19/2020] [Indexed: 11/30/2022] Open
Abstract
Background Most epidemiological risk indicators strongly depend on the age composition of populations, which makes the direct comparison of raw (unstandardized) indicators misleading because of the different age structures of the spatial units of study. Age-standardized rates (ASR) are a common solution for overcoming this confusing effect. The main drawback of ASRs is that they depend on age-specific rates which, when working with small areas, are often based on very few, or no, observed cases for most age groups. A similar effect occurs with life expectancy at birth and many more epidemiological indicators, which makes standardized mortality ratios (SMR) the omnipresent risk indicator for small areas epidemiologic studies. Methods To deal with this issue, a multivariate smoothing model, the M-model, is proposed in order to fit the age-specific probabilities of death (PoDs) for each spatial unit, which assumes dependence between closer age groups and spatial units. This age–space dependence structure enables information to be transferred between neighboring consecutive age groups and neighboring areas, at the same time, providing more reliable age-specific PoDs estimates. Results Three case studies are presented to illustrate the wide range of applications that smoothed age specific PoDs have in practice . The first case study shows the application of the model to a geographical study of lung cancer mortality in women. This study illustrates the convenience of considering age–space interactions in geographical studies and to explore the different spatial risk patterns shown by the different age groups. Second, the model is also applied to the study of ischaemic heart disease mortality in women in two cities at the census tract level. Smoothed age-standardized rates are derived and compared for the census tracts of both cities, illustrating some advantages of this mortality indicator over traditional SMRs. In the latest case study, the model is applied to estimate smoothed life expectancy (LE), which is the most widely used synthetic indicator for characterizing overall mortality differences when (not so small) spatial units are considered. Conclusion Our age–space model is an appropriate and flexible proposal that provides more reliable estimates of the probabilities of death, which allow the calculation of enhanced epidemiological indicators (smoothed ASR, smoothed LE), thus providing alternatives to traditional SMR-based studies of small areas.
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Affiliation(s)
- Jordi Perez-Panades
- Direcció General de Salut Pública i Addiccions, Conselleria de Sanitat Universal i Salut Pública, Avda/Cataluña, 21, 46020, Valencia, Spain.
| | - Paloma Botella-Rocamora
- Direcció General de Salut Pública i Addiccions, Conselleria de Sanitat Universal i Salut Pública, Avda/Cataluña, 21, 46020, Valencia, Spain
| | - Miguel Angel Martinez-Beneito
- Departament d'Estadística i Investigació Operativa, Universitat de València, C/Dr. Moliner, 50, 46100, Burjassot, Valencia, Spain
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Neyens T, Faes C, Vranckx M, Pepermans K, Hens N, Van Damme P, Molenberghs G, Aerts J, Beutels P. Can COVID-19 symptoms as reported in a large-scale online survey be used to optimise spatial predictions of COVID-19 incidence risk in Belgium? Spat Spatiotemporal Epidemiol 2020; 35:100379. [PMID: 33138946 PMCID: PMC7518805 DOI: 10.1016/j.sste.2020.100379] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/01/2020] [Accepted: 09/24/2020] [Indexed: 01/31/2023]
Abstract
Although COVID-19 has been spreading throughout Belgium since February, 2020, its spatial dynamics in Belgium remain poorly understood, partly due to the limited testing of suspected cases during the epidemic's early phase. We analyse data of COVID-19 symptoms, as self-reported in a weekly online survey, which is open to all Belgian citizens. We predict symptoms' incidence using binomial models for spatially discrete data, and we introduce these as a covariate in the spatial analysis of COVID-19 incidence, as reported by the Belgian government during the days following a survey round. The symptoms' incidence is moderately predictive of the variation in the relative risks based on the confirmed cases; exceedance probability maps of the symptoms' incidence and confirmed cases' relative risks overlap partly. We conclude that this framework can be used to detect COVID-19 clusters of substantial sizes, but it necessitates spatial information on finer scales to locate small clusters.
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Affiliation(s)
- Thomas Neyens
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt B-3500, Belgium; I-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Kapucijnenvoer 35, Leuven B-3000, Belgium.
| | - Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt B-3500, Belgium
| | - Maren Vranckx
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt B-3500, Belgium
| | - Koen Pepermans
- Faculty of Social Sciences, University of Antwerp, Sint-Jacobstraat 2, Antwerp 2000, Belgium
| | - Niel Hens
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt B-3500, Belgium; Center for Health Economics Research and Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Antwerp 2610, Belgium
| | - Pierre Van Damme
- Center for Health Economics Research and Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Antwerp 2610, Belgium
| | - Geert Molenberghs
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt B-3500, Belgium; I-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Kapucijnenvoer 35, Leuven B-3000, Belgium
| | - Jan Aerts
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt B-3500, Belgium
| | - Philippe Beutels
- Center for Health Economics Research and Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Antwerp 2610, Belgium
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Tuson M, Yap M, Kok MR, Boruff B, Murray K, Vickery A, Turlach BA, Whyatt D. Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single-aggregation disease maps. Int J Health Geogr 2020; 19:40. [PMID: 33010800 PMCID: PMC7532343 DOI: 10.1186/s12942-020-00236-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 09/21/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND In disease mapping, fine-resolution spatial health data are routinely aggregated for various reasons, for example to protect privacy. Usually, such aggregation occurs only once, resulting in 'single-aggregation disease maps' whose representation of the underlying data depends on the chosen set of aggregation units. This dependence is described by the modifiable areal unit problem (MAUP). Despite an extensive literature, in practice, the MAUP is rarely acknowledged, including in disease mapping. Further, despite single-aggregation disease maps being widely relied upon to guide distribution of healthcare resources, potential inefficiencies arising due to the impact of the MAUP on such maps have not previously been investigated. RESULTS We introduce the overlay aggregation method (OAM) for disease mapping. This method avoids dependence on any single set of aggregate-level mapping units through incorporating information from many different sets. We characterise OAM as a novel smoothing technique and show how its use results in potentially dramatic improvements in resource allocation efficiency over single-aggregation maps. We demonstrate these findings in a simulation context and through applying OAM to a real-world dataset: ischaemic stroke hospital admissions in Perth, Western Australia, in 2016. CONCLUSIONS The ongoing, widespread lack of acknowledgement of the MAUP in disease mapping suggests that unawareness of its impact is extensive or that impact is underestimated. Routine implementation of OAM can help avoid resource allocation inefficiencies associated with this phenomenon. Our findings have immediate worldwide implications wherever single-aggregation disease maps are used to guide health policy planning and service delivery.
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Affiliation(s)
- Matthew Tuson
- Medical School, University of Western Australia, Perth, Australia.,Department of Mathematics and Statistics, University of Western Australia, Perth, Australia
| | - Matthew Yap
- Medical School, University of Western Australia, Perth, Australia
| | - Mei Ruu Kok
- Medical School, University of Western Australia, Perth, Australia
| | - Bryan Boruff
- UWA School of Agriculture and Environment, University of Western Australia, Perth, Australia.,Department of Geography, University of Western Australia, Perth, Australia
| | - Kevin Murray
- School of Population and Global Health, University of Western Australia, Perth, Australia
| | - Alistair Vickery
- Medical School, University of Western Australia, Perth, Australia
| | - Berwin A Turlach
- Department of Mathematics and Statistics, University of Western Australia, Perth, Australia
| | - David Whyatt
- Medical School, University of Western Australia, Perth, Australia.
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Trandafir PC, Adin A, Ugarte MD. Space-time analysis of ovarian cancer mortality rates by age groups in spanish provinces (1989-2015). BMC Public Health 2020; 20:1244. [PMID: 32807139 PMCID: PMC7430125 DOI: 10.1186/s12889-020-09267-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 07/15/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Ovarian cancer is a silent and largely asymptomatic cancer, leading to late diagnosis and worse prognosis. The late-stage detection and low survival rates, makes the study of the space-time evolution of ovarian cancer particularly relevant. In addition, research of this cancer in small areas (like provinces or counties) is still scarce. METHODS The study presented here covers all ovarian cancer deaths for women over 50 years of age in the provinces of Spain during the period 1989-2015. Spatio-temporal models have been fitted to smooth ovarian cancer mortality rates in age groups [50,60), [60,70), [70,80), and [80,+), borrowing information from spatial and temporal neighbours. Model fitting and inference has been carried out using the Integrated Nested Laplace Approximation (INLA) technique. RESULTS Large differences in ovarian cancer mortality among the age groups have been found, with higher mortality rates in the older age groups. Striking differences are observed between northern and southern Spain. The global temporal trends (by age group) reveal that the evolution of ovarian cancer over the whole of Spain has remained nearly constant since the early 2000s. CONCLUSION Differences in ovarian cancer mortality exist among the Spanish provinces, years, and age groups. As the exact causes of ovarian cancer remain unknown, spatio-temporal analyses by age groups are essential to discover inequalities in ovarian cancer mortality. Women over 60 years of age should be the focus of follow-up studies as the mortality rates remain constant since 2002. High-mortality provinces should also be monitored to look for specific risk factors.
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Affiliation(s)
- Paula Camelia Trandafir
- Department of Statistics, Computer Science, and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona, 31006 Spain
- INAMAT, Public University of Navarre, Campus de Arrosadia, Pamplona, 31006 Spain
| | - Aritz Adin
- Department of Statistics, Computer Science, and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona, 31006 Spain
- INAMAT, Public University of Navarre, Campus de Arrosadia, Pamplona, 31006 Spain
| | - María Dolores Ugarte
- Department of Statistics, Computer Science, and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona, 31006 Spain
- INAMAT, Public University of Navarre, Campus de Arrosadia, Pamplona, 31006 Spain
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Abstract
The rapid spread of the SARS-CoV-2 epidemic has simultaneous time and space dynamics. This behaviour results from a complex combination of factors, including social ones, which lead to significant differences in the evolution of the spatiotemporal pattern between and within countries. Usually, spatial smoothing techniques are used to map health outcomes, and rarely uncertainty of the spatial predictions are assessed. As an alternative, we propose to apply direct block sequential simulation to model the spatial distribution of the COVID-19 infection risk in mainland Portugal. Given the daily number of infection data provided by the Portuguese Directorate-General for Health, the daily updates of infection rates are calculated by municipality and used as experimental data in the geostatistical simulation. The model considers the uncertainty/error associated with the size of each municipality's population. The calculation of daily updates of the infection risk maps results from the median model of one ensemble of 100 geostatistical realizations of daily updates of the infection risk. The ensemble of geostatistical realizations is also used to calculate the associated spatial uncertainty of the spatial prediction using the interquartile distance. The risk maps are updated daily and show the regions with greater risks of infection and the critical dynamics related to its development over time.
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Affiliation(s)
- Leonardo Azevedo
- CERENA, DECivil, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| | - Maria João Pereira
- CERENA, DECivil, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| | - Manuel C. Ribeiro
- CERENA, DECivil, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| | - Amílcar Soares
- CERENA, DECivil, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
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Sheehan A, Freni Sterrantino A, Fecht D, Elliott P, Hodgson S. Childhood type 1 diabetes: an environment-wide association study across England. Diabetologia 2020; 63:964-976. [PMID: 31980846 PMCID: PMC7145790 DOI: 10.1007/s00125-020-05087-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 12/06/2019] [Indexed: 12/14/2022]
Abstract
AIMS/HYPOTHESIS Type 1 diabetes is an autoimmune disease affecting ~400,000 people across the UK. It is likely that environmental factors trigger the disease process in genetically susceptible individuals. We assessed the associations between a wide range of environmental factors and childhood type 1 diabetes incidence in England, using an agnostic, ecological environment-wide association study (EnWAS) approach, to generate hypotheses about environmental triggers. METHODS We undertook analyses at the local authority district (LAD) level using a national hospital episode statistics-based incident type 1 diabetes dataset comprising 13,948 individuals with diabetes aged 0-9 years over the period April 2000 to March 2011. We compiled LAD level estimates for a range of potential demographic and environmental risk factors including meteorological, land use and environmental pollution variables. The associations between type 1 diabetes incidence and risk factors were assessed via Poisson regression, disease mapping and ecological regression. RESULTS Case counts by LAD varied from 1 to 236 (median 33, interquartile range 24-46). Overall type 1 diabetes incidence was 21.2 (95% CI 20.9, 21.6) per 100,000 individuals. The EnWAS and disease mapping indicated that 15 out of 53 demographic and environmental risk factors were significantly associated with diabetes incidence, after adjusting for multiple testing. These included air pollutants (particulate matter, nitrogen dioxide, nitrogen oxides, carbon monoxide; all inversely associated), as well as lead in soil, radon, outdoor light at night, overcrowding, population density and ethnicity. Disease mapping revealed spatial heterogeneity in type 1 diabetes risk. The ecological regression found an association between type 1 diabetes and the living environment domain of the Index of Multiple Deprivation (RR 0.995; 95% credible interval [CrI] 0.991, 0.998) and radon potential class (RR 1.044; 95% CrI 1.015, 1.074). CONCLUSIONS/INTERPRETATION Our analysis identifies a range of demographic and environmental factors associated with type 1 diabetes in children in England.
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Affiliation(s)
- Annalisa Sheehan
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- School of Population Health and Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Anna Freni Sterrantino
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- UK Small Area Health Statistics Unit, School of Public Health, Imperial College London, London, UK
| | - Daniela Fecht
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- UK Small Area Health Statistics Unit, School of Public Health, Imperial College London, London, UK
| | - Paul Elliott
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- UK Small Area Health Statistics Unit, School of Public Health, Imperial College London, London, UK
- Imperial College NIHR Biomedical Research Centre, Imperial College London, London, UK
| | - Susan Hodgson
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK.
- UK Small Area Health Statistics Unit, School of Public Health, Imperial College London, London, UK.
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Kim H, Miller FD, Hernandez A, Tanser F, Mogeni P, Cuadros DF. Spatiotemporal analysis of insecticide-treated net use for children under 5 in relation to socioeconomic gradients in Central and East Africa. Malar J 2020; 19:163. [PMID: 32321547 PMCID: PMC7178571 DOI: 10.1186/s12936-020-03236-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 04/15/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Insecticide-treated net (ITN) use is the core intervention among the strategies against malaria in sub-Saharan Africa (SSA) and the percentage of ITN ownership has increased from 47% in 2010 to 72% in 2017 across countries in SSA. Regardless of this massive expansion of ITN distribution, considerable gap between ownership and use of ITNs has been reported. Using data from more than 100,000 households in Central and East Africa (CEA) countries, the main aim of this study was to identify barriers associated with low ITN use and conduct geospatial analyses to estimate numbers and locations of vulnerable children living in areas with high malaria and low ITN use. METHODS Main sources of data for this study were the Demographic and Health Surveys and Malaria Indicator Surveys conducted in 11 countries in CEA. Logistic regression models for each country were built to assess the association between ITN ownership or ITN use and several socioeconomic and demographic variables. A density map of children under 5 living in areas at high-risk of malaria and low ITN use was generated to estimate the number of children who are living in these high malaria burden areas. RESULTS Results obtained suggest that factors such as the number of members in the household, total number of children in the household, education and place of residence can be key factors linked to the use of ITN for protecting children against malaria in CEA. Results from the spatiotemporal analyses found that although total rates of ownership and use of ITNs across CEA have increased up to 70% and 48%, respectively, a large proportion of children under 5 (19,780,678; 23% of total number of children) still lives in high-risk malaria areas with low use of ITNs. CONCLUSION The results indicate that despite substantial progress in the distribution of ITNs in CEA, with about 70% of the households having an ITN, several socioeconomic factors have compromised the effectiveness of this control intervention against malaria, and only about 48% of the households protect their children under 5 with ITNs. Increasing the effective ITN use by targeting these factors and the areas where vulnerable children reside can be a core strategy meant to reducing malaria transmission.
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Affiliation(s)
- Hana Kim
- Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH, 45221, USA.,Health Geography and Disease Modeling Laboratory, University of Cincinnati, Cincinnati, USA
| | - F DeWolfe Miller
- Department of Tropical Medicine and Medical Microbiology and Pharmacology, University of Hawaii, Honolulu, HI, USA
| | - Andres Hernandez
- Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH, 45221, USA.,Health Geography and Disease Modeling Laboratory, University of Cincinnati, Cincinnati, USA
| | - Frank Tanser
- Research Department of Infection & Population Health, University College London, London, UK.,Africa Health Research Institute, Durban, Kwazulu-Natal, South Africa.,School of Nursing and Public Health, University of KwaZulu-Natal, Durban, Kwazulu-Natal, South Africa
| | - Polycarp Mogeni
- Africa Health Research Institute, Durban, Kwazulu-Natal, South Africa.,School of Nursing and Public Health, University of KwaZulu-Natal, Durban, Kwazulu-Natal, South Africa
| | - Diego F Cuadros
- Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH, 45221, USA. .,Health Geography and Disease Modeling Laboratory, University of Cincinnati, Cincinnati, USA.
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Bergamaschi R, Monti MC, Trivelli L, Introcaso VP, Mallucci G, Borrelli P, Gerosa L, Montomoli C. Increased prevalence of multiple sclerosis and clusters of different disease risk in Northern Italy. Neurol Sci 2019; 41:1089-1095. [PMID: 31872352 DOI: 10.1007/s10072-019-04205-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 12/17/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND The increasing multiple sclerosis (MS) prevalence is varying across the macroscopic regional areas. Only few studies have explored the microscopic geographic variation of MS prevalence, which could highlight MS spatial clusters. OBJECTIVE In this ecological study, we aimed to estimate 2016 MS prevalence in the province of Pavia (Northern Italy) and to describe MS risk geographical variation across small area units, compared to the year 2000. METHODS Bayesian models were fit to estimate area-specific MS relative risks. The mean of the posterior marginal distribution of relative risks differences for each area were used to describe the risk variation. RESULTS The 2016 overall prevalence was 169.4 per 100,000 inhabitants (95% CI 158.8-180.6). The Bayesian mapping of MS showed some clusters of higher and lower disease prevalence. Furthermore, several municipalities located in the north part of the province were more at risk with respect to the year 2000. CONCLUSIONS The current MS prevalence sets the province of Pavia among high-risk areas and, compared with the previous prevalence estimate (86 per 100,000 in year 2000), indicates an increased MS risk. The Bayesian mapping highlighted area with a significantly higher/lower MS risk where to investigate etiologic hypotheses based on environmental and genetic exposures.
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Affiliation(s)
- Roberto Bergamaschi
- Multiple Sclerosis Center, IRCCS Mondino Foundation, via Mondino 2, 27100, Pavia, Italy
| | - Maria Cristina Monti
- Department of Public Health, Experimental and Forensic Medicine, Unit of Biostatistics and Clinical Epidemiology, University of Pavia, Pavia, Italy
| | - Leonardo Trivelli
- Department of Public Health, Experimental and Forensic Medicine, Unit of Biostatistics and Clinical Epidemiology, University of Pavia, Pavia, Italy
| | | | - Giulia Mallucci
- Multiple Sclerosis Center, IRCCS Mondino Foundation, via Mondino 2, 27100, Pavia, Italy.
| | - Paola Borrelli
- Department of Public Health, Experimental and Forensic Medicine, Unit of Biostatistics and Clinical Epidemiology, University of Pavia, Pavia, Italy
| | - Leonardo Gerosa
- Multiple Sclerosis Center, IRCCS Mondino Foundation, via Mondino 2, 27100, Pavia, Italy
| | - Cristina Montomoli
- Department of Public Health, Experimental and Forensic Medicine, Unit of Biostatistics and Clinical Epidemiology, University of Pavia, Pavia, Italy
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Datta A, Banerjee S, Hodges JS, Gao L. Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models. Bayesian Anal 2019; 14:1221-1244. [PMID: 33859772 PMCID: PMC8046356 DOI: 10.1214/19-ba1177] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modeled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the precision matrix include the widely used ICAR model, which is singular, and its nonsingular extension which lacks interpretability. We propose a new parametric model for the precision matrix based on a directed acyclic graph (DAG) representation of the spatial dependence. Our model guarantees positive definiteness and, hence, in addition to being a valid prior for regional spatially correlated random effects, can also directly model the outcome from dependent data like images and networks. Theoretical results establish a link between the parameters in our model and the variance and covariances of the random effects. Substantive simulation studies demonstrate that the improved interpretability of our model reaps benefits in terms of accurately recovering the latent spatial random effects as well as for inference on the spatial covariance parameters. Under modest spatial correlation, our model far outperforms the CAR models, while the performances are similar when the spatial correlation is strong. We also assess sensitivity to the choice of the ordering in the DAG construction using theoretical and empirical results which testify to the robustness of our model. We also present a large-scale public health application demonstrating the competitive performance of the model.
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Vranckx M, Neyens T, Faes C. Comparison of different software implementations for spatial disease mapping. Spat Spatiotemporal Epidemiol 2019; 31:100302. [PMID: 31677763 DOI: 10.1016/j.sste.2019.100302] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 06/30/2019] [Accepted: 08/06/2019] [Indexed: 11/28/2022]
Abstract
Disease mapping is a scientific field that aims to understand and predict disease risk based on counts of observed cases within small regions of a study area of interest. Hierarchical model-based approaches that borrow information from neighbouring areas via conditional autoregressive (CAR) random effects on the local disease rates have gained a lot of popularity, thanks to the readily implemented Markov chain Monte Carlo methods. Nowadays, many software implementations to model risk distributions exist. Many of these applications differ, to varying degrees, in the underlying methodology. This paper provides an in-depth comparison between analysis results, coming from R-packages CARBayes, R2OpenBUGS, NIMBLE, R2BayesX, R-INLA, and RStan. We investigate CAR models typically used in disease mapping for spatially discrete count data. Data about diabetics in children and young adults in Belgium are used in a case study, while simulation studies are undertaken to assess software performance in different settings.
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Affiliation(s)
- M Vranckx
- I-BioStat, Hasselt University, Diepenbeek, Belgium.
| | - T Neyens
- I-BioStat, Hasselt University, Diepenbeek, Belgium
| | - C Faes
- I-BioStat, Hasselt University, Diepenbeek, Belgium
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Jack E, Lee D, Dean N. Estimating the changing nature of Scotland's health inequalities by using a multivariate spatiotemporal model. J R Stat Soc Ser A Stat Soc 2019; 182:1061-1080. [PMID: 31217673 PMCID: PMC6563432 DOI: 10.1111/rssa.12447] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Health inequalities are the unfair and avoidable differences in people's health between different social groups. These inequalities have a huge influence on people's lives, particularly those who live at the poorer end of the socio-economic spectrum, as they result in prolonged ill health and shorter lives. Most studies estimate health inequalities for a single disease, but this will give an incomplete picture of the overall inequality in population health. Here we propose a novel multivariate spatiotemporal model for quantifying health inequalities in Scotland across multiple diseases, which will enable us to understand better how these inequalities vary across disease and have changed over time. In developing this model we are interested in estimating health inequalities between Scotland's 14 regional health boards, who are responsible for the protection and improvement of their population's health. The methodology is applied to hospital admissions data for cerebrovascular disease, coronary heart disease and respiratory disease, which are three of the leading causes of death, from 2003 to 2012 across Scotland.
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Pires Espíndola J, Balbinott N, Trevisan Gressler L, Machado G, Silene Klein C, Rebelatto R, Gutiérrez Martín CB, Kreutz LC, Schryvers AB, Frandoloso R. Molecular serotyping of clinical strains of Haemophilus (Glaesserella) parasuis brings new insights regarding Glässer's disease outbreaks in Brazil. PeerJ 2019; 7:e6817. [PMID: 31198621 PMCID: PMC6535215 DOI: 10.7717/peerj.6817] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 03/17/2019] [Indexed: 11/23/2022] Open
Abstract
Glässer’s disease (GD) is an important infectious disease of swine caused by Haemophilus (Glaesserella) parasuis. Vaccination with inactivated whole cell vaccines is the major approach for prevention of H. parasuis infection worldwide, but the immunity induced is predominantly against the specific polysaccharide capsule. As a consequence, the available vaccines may not induce adequate protection against the field strains, when the capsules present in the vaccine strains are different from those in strains isolated from the farms. Therefore, it is crucial to map H. parasuis serovars associated with regional outbreaks so that appropriate bacterin vaccines can be developed and distributed for prevention of infection. In this study, 459 H. parasuis field strains isolated from different Glässer’s disease outbreaks that occurred in 10 different Brazilian States were analyzed for serotype using PCR-based approaches. Surprisingly, non-typeable (NT) strains were the second most prevalent group of field strains and along with serovars 4, 5 and 1 comprised more than 70% of the isolates. A PCR-based approach designed to amplify the entire polysaccharide capsule locus revealed 9 different band patterns in the NT strains, and 75% of the NT strains belonged to three clusters, suggesting that a number of new serovars are responsible for a substantial proportion of disease. These results indicate that commercially available vaccines in Brazil do not cover the most prevalent H. parasuis serovars associated with GD.
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Affiliation(s)
- Julia Pires Espíndola
- Laboratory of Microbiology and Advanced Immunology, Faculty of Agronomy and Veterinary Medicine, University of Passo Fundo, Passo Fundo, Rio Grande do Sul, Brazil
| | - Natalia Balbinott
- Laboratory of Microbiology and Advanced Immunology, Faculty of Agronomy and Veterinary Medicine, University of Passo Fundo, Passo Fundo, Rio Grande do Sul, Brazil
| | - Letícia Trevisan Gressler
- Laboratory of Microbiology and Advanced Immunology, Faculty of Agronomy and Veterinary Medicine, University of Passo Fundo, Passo Fundo, Rio Grande do Sul, Brazil
| | - Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, United States of America
| | | | | | - César Bernardo Gutiérrez Martín
- Section of Microbiology and Immunology, Department of Animal Health, Faculty of Veterinary, University of León, León, Castilla y León, Spain
| | - Luiz Carlos Kreutz
- Laboratory of Microbiology and Advanced Immunology, Faculty of Agronomy and Veterinary Medicine, University of Passo Fundo, Passo Fundo, Rio Grande do Sul, Brazil
| | - Anthony Bernard Schryvers
- Department of Microbiology & Infectious Diseases, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Rafael Frandoloso
- Laboratory of Microbiology and Advanced Immunology, Faculty of Agronomy and Veterinary Medicine, University of Passo Fundo, Passo Fundo, Rio Grande do Sul, Brazil
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Kline D, Hepler S, Bonny A, McKnight E. A joint spatial model of opioid-associated deaths and treatment admissions in Ohio. Ann Epidemiol 2019; 33:19-23. [PMID: 30948153 PMCID: PMC6502680 DOI: 10.1016/j.annepidem.2019.02.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 01/10/2019] [Accepted: 02/20/2019] [Indexed: 10/27/2022]
Abstract
PURPOSE Opioid misuse is a national epidemic, and Ohio is one of the states most impacted by this crisis. Ohio collects county-level counts of opioid-associated deaths and treatment admissions. We jointly model these two outcomes and assess the association of each rate with social and structural factors. METHODS We use a joint spatial rates model of death and treatment counts using a generalized common spatial factor model. In addition to covariate effects, we estimate a spatial factor for each county that characterizes structural factors not accounted for by other covariates in the model that are associated with both outcomes. RESULTS We observed an association of health professional shortage area with death rates and the rate of people 18-64 on disability with treatment rates. The proportion of single female households was associated with both outcomes. We estimated the presence of unmeasured risk factors in the southwestern part of the state and unmeasured protective factors in the eastern region. CONCLUSIONS We described associations of social and structural covariates with the death and treatment rates. We also characterized counties with latent risk that can provide a launching point for future investigations to determine potential sources of that risk.
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Affiliation(s)
- David Kline
- Center for Biostatistics, Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH.
| | - Staci Hepler
- Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC
| | - Andrea Bonny
- Division of Adolescent Medicine, Nationwide Children's Hospital, Columbus, OH; Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH
| | - Erin McKnight
- Division of Adolescent Medicine, Nationwide Children's Hospital, Columbus, OH; Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH
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Roy PK, Khan MHR, Akter T, Rahman MS. Exploring socio-demographic-and geographical-variations in prevalence of diabetes and hypertension in Bangladesh: Bayesian spatial analysis of national health survey data. Spat Spatiotemporal Epidemiol. 2019;29:71-83. [PMID: 31128633 DOI: 10.1016/j.sste.2019.03.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 11/24/2018] [Accepted: 03/12/2019] [Indexed: 12/22/2022]
Abstract
Bangladesh has been experiencing an epidemiological transition from communicable diseases to non-communicable disease (NCDs), with a rapid increase in the NCD related morbidity and mortality in the last decade. Hypertension and diabetes are two important risk factors of NCDs that significantly increase the burden of cardiovascular diseases and risk of death. While the prevalence of people with both hypertension and diabetes has been increasing dramatically over time, it is essential to identify relatively more prevalent socio-demographic groups and geographical regions (local administrative districts) to reduce the NCDs related deaths in an urgent basis. This study focused on examining the association of socio-demographic factors with both hypertension and diabetes and exploring the regional variations in their prevalence using nationally representative survey data on adult population of age over 35 years. Bayesian spatial analysis was performed for both hypertension and diabetes data separately by fitting a model, that accounts for spatial variations, using integrated nested laplace approximation. The area-specific prevalence was then estimated as weighted average of the corresponding individual level predicted probabilities of being diseased derived from the fitted model, with weight from the individual level sampling weight. Finally, the estimated area-specific prevalence estimates were sketched in country-map to explore regional variations and identify regions with relatively higher prevalence. The results revealed that people of older age, higher education, better socio-economic condition, higher BMI are at greater risk of having hypertension and diabetes. Significant regional variations were observed with prevalence for hypertension ranges between 10% and 35% and for diabetes between 6% and 19% while their national prevalence were reported as 24% and 11%, respectively. The western regions of the country including middle capital city were found to be relatively more prevalent for hypertension while the middle-east and south-east regions were observed to be more prevalent for diabetes. The capital Dhaka region was observed as the most prevalent for both diabetes and hypertension. Details explanations of the findings and evidence based policy implications were discussed.
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Adin A, Goicoa T, Ugarte MD. Online relative risks/rates estimation in spatial and spatio-temporal disease mapping. Comput Methods Programs Biomed 2019; 172:103-116. [PMID: 30846296 DOI: 10.1016/j.cmpb.2019.02.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 02/13/2019] [Accepted: 02/25/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Spatial and spatio-temporal analyses of count data are crucial in epidemiology and other fields to unveil spatial and spatio-temporal patterns of incidence and/or mortality risks. However, fitting spatial and spatio-temporal models is not easy for non-expert users. The objective of this paper is to present an interactive and user-friendly web application (named SSTCDapp) for the analysis of spatial and spatio-temporal mortality or incidence data. Although SSTCDapp is simple to use, the underlying statistical theory is well founded and all key issues such as model identifiability, model selection, and several spatial priors and hyperpriors for sensitivity analyses are properly addressed. METHODS The web application is designed to fit an extensive range of fairly complex spatio-temporal models to smooth the very often extremely variable standardized incidence/mortality risks or crude rates. The application is built with the R package shiny and relies on the well founded integrated nested Laplace approximation technique for model fitting and inference. RESULTS The use of the web application is shown through the analysis of Spanish spatio-temporal breast cancer data. Different possibilities for the analysis regarding the type of model, model selection criteria, and a range of graphical as well as numerical outputs are provided. CONCLUSIONS Unlike other software used in disease mapping, SSTCDapp facilitates the fit of complex statistical models to non-experts users without the need of installing any software in their own computers, since all the analyses and computations are made in a powerful remote server. In addition, a desktop version is also available to run the application locally in those cases in which data confidentiality is a serious issue.
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Affiliation(s)
- Aritz Adin
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain; InaMAT, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain.
| | - Tomás Goicoa
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain; InaMAT, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain.
| | - María Dolores Ugarte
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain; InaMAT, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain.
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Lew D, Rigdon SE. Mapping rates of inpatient hospitalizations related to mental disorders in the state of Missouri: A conditional autoregressive model with zip code-level data. Spat Spatiotemporal Epidemiol 2019; 28:24-32. [PMID: 30739652 DOI: 10.1016/j.sste.2018.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 10/18/2018] [Accepted: 11/03/2018] [Indexed: 10/27/2022]
Abstract
Nearly one in five American adults suffers from mental illness in a given year. Mental health conditions are known to be spatially clustered, but no prior work has examined the clustering of mental health related hospitalizations. This analysis uses Bayesian hierarchical models to predict rates of inpatient hospitalizations attributed to mental disorders within zip codes in Missouri, USA. Eight separate models were run, and all models yielded similar estimates for the average rate of mental health related hospitalizations (around 13 per 1000 population). The percent of families receiving food stamps and percent of vacant housing were found to be significantly associated with hospitalization rates, after controlling for age, gender, and race. These rates were also significantly spatially clustered (Moran's I > 0.3 and p < 0.05 for all models). Health professionals can use these results to prioritize regions throughout the state that have the greatest need for mental health service providers and interventions.
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Affiliation(s)
- Daphne Lew
- Department of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, 3545 Lafayette Avenue, Saint Louis, MO, USA.
| | - Steven E Rigdon
- Department of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, 3545 Lafayette Avenue, Saint Louis, MO, USA
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Machado G, Alvarez J, Bakka HC, Perez A, Donato LE, de Ferreira Lima Júnior FE, Alves RV, Del Rio Vilas VJ. Revisiting area risk classification of visceral leishmaniasis in Brazil. BMC Infect Dis 2019; 19:2. [PMID: 30606104 PMCID: PMC6318941 DOI: 10.1186/s12879-018-3564-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 11/28/2018] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Visceral leishmaniasis (VL) is a neglected tropical disease of public health relevance in Brazil. To prioritize disease control measures, the Secretaria de Vigilância em Saúde of Brazil's Ministry of Health (SVS/MH) uses retrospective human case counts from VL surveillance data to inform a municipality-based risk classification. In this study, we compared the underlying VL risk, using a spatiotemporal explicit Bayesian hierarchical model (BHM), with the risk classification currently in use by the Brazil's Ministry of Health. We aim to assess how well the current risk classes capture the underlying VL risk as modelled by the BHM. METHODS Annual counts of human VL cases and the population at risk for all Brazil's 5564 municipalities between 2004 and 2014 were used to fit a relative risk BHM. We then computed the predicted counts and exceedence risk for each municipality and classified them into four categories to allow comparison with the four risk categories by the SVS/MH. RESULTS Municipalities identified as high-risk by the model partially agreed with the current risk classification by the SVS/MH. Our results suggest that counts of VL cases may suffice as general indicators of the underlying risk, but can underestimate risks, especially in areas with intense transmission. CONCLUSION According to our BHM the SVS/MH risk classification underestimated the risk in several municipalities with moderate to intense VL transmission. Newly identified high-risk areas should be further evaluated to identify potential risk factors and assess the needs for additional surveillance and mitigation efforts.
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Affiliation(s)
- Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, 1060 William Moore Drive, Raleigh, NC 27607 USA
| | - Julio Alvarez
- VISAVET Health Surveillance Center, Universidad Complutense, Avda Puerta de Hierro S/N, 28040 Madrid, Spain
- Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense, Avda Puerta de Hierro S/N, 28040 Madrid, Spain
| | | | - Andres Perez
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St Paul, MN 55108 USA
| | - Lucas Edel Donato
- Secretaria de Vigilância em Saúde, Ministério da Saúde (SVS-MH), Brasília, Brazil
| | | | - Renato Vieira Alves
- Secretaria de Vigilância em Saúde, Ministério da Saúde (SVS-MH), Brasília, Brazil
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Abstract
Spatial epidemiology is a rapidly advancing field, pushing our abilities to measure, monitor and map pathogens at increasingly finer spatiotemporal scales. However, these scales often do not align with the abilities of control programmes to act at them, building a disconnect between academia and implementation. Efforts are being made to feed innovations into government, build spatial data skills, and strengthen links between disease control programmes and universities, yet work remains to be done if goals for disease control, elimination and 'leaving no one behind' are to be met.
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Affiliation(s)
- Andrew J Tatem
- WorldPop, Department of Geography and Environment, University of Southampton, Highfield, Southampton, SO17 1BJ, UK.
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