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Li Y, Hu H, Zheng Y, Donahoo WT, Guo Y, Xu J, Chen WH, Liu N, Shenkman EA, Bian J, Guo J. Impact of Contextual-Level Social Determinants of Health on Newer Antidiabetic Drug Adoption in Patients with Type 2 Diabetes. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20054036. [PMID: 36901047 PMCID: PMC10001625 DOI: 10.3390/ijerph20054036] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 02/17/2023] [Accepted: 02/22/2023] [Indexed: 05/14/2023]
Abstract
BACKGROUND We aimed to investigate the association between contextual-level social determinants of health (SDoH) and the use of novel antidiabetic drugs (ADD), including sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1a) for patients with type 2 diabetes (T2D), and whether the association varies across racial and ethnic groups. METHODS Using electronic health records from the OneFlorida+ network, we assembled a cohort of T2D patients who initiated a second-line ADD in 2015-2020. A set of 81 contextual-level SDoH documenting social and built environment were spatiotemporally linked to individuals based on their residential histories. We assessed the association between the contextual-level SDoH and initiation of SGTL2i/GLP1a and determined their effects across racial groups, adjusting for clinical factors. RESULTS Of 28,874 individuals, 61% were women, and the mean age was 58 (±15) years. Two contextual-level SDoH factors identified as significantly associated with SGLT2i/GLP1a use were neighborhood deprivation index (odds ratio [OR] 0.87, 95% confidence interval [CI] 0.81-0.94) and the percent of vacant addresses in the neighborhood (OR 0.91, 95% CI 0.85-0.98). Patients living in such neighborhoods are less likely to be prescribed with newer ADD. There was no interaction between race-ethnicity and SDoH on the use of newer ADD. However, in the overall cohort, the non-Hispanic Black individuals were less likely to use newer ADD than the non-Hispanic White individuals (OR 0.82, 95% CI 0.76-0.88). CONCLUSION Using a data-driven approach, we identified the key contextual-level SDoH factors associated with not following evidence-based treatment of T2D. Further investigations are needed to examine the mechanisms underlying these associations.
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Affiliation(s)
- Yujia Li
- Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA
| | - Hui Hu
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Yi Zheng
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - William Troy Donahoo
- Division of Endocrinology, Diabetes and Metabolism, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Yi Guo
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Jie Xu
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Wei-Han Chen
- Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA
| | - Ning Liu
- Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA
| | - Elisabeth A. Shenkman
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Jingchuan Guo
- Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA
- Correspondence: ; Tel.: +1-352-273-6533
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Nassel A, Wilson-Barthes MG, Howe CJ, Napravnik S, Mugavero MJ, Agil D, Dulin AJ. Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information. PLoS One 2022; 17:e0278672. [PMID: 36580446 PMCID: PMC9799318 DOI: 10.1371/journal.pone.0278672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 11/21/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study's population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants' protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects. METHODS This protocol demonstrates how to: (1) securely geocode patients' residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. RESULTS Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients' coded census tract locations. CONCLUSIONS This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.
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Affiliation(s)
- Ariann Nassel
- Lister Hill Center for Health Policy, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Marta G. Wilson-Barthes
- Center for Epidemiologic Research, Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, United States of America
| | - Chanelle J. Howe
- Center for Epidemiologic Research, Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, United States of America
| | - Sonia Napravnik
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Michael J. Mugavero
- Division of Infectious Diseases, Department of Medicine, Center for AIDS Research, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Deana Agil
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Akilah J. Dulin
- Center for Health Promotion and Health Equity, Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, Rhode Island, United States of America
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Rhee SY. Spatiotemporal analyses of the Epidemiologic characteristics of Diabetes Mellitus. Epidemiol Health 2021; 43:e2021102. [PMID: 34922422 PMCID: PMC8920732 DOI: 10.4178/epih.e2021102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Research based on spatiotemporal analysis has been conducted to identify various factors that can affect an individual’s or community’s degree of health and disease. These spatiotemporal studies can effectively illustrate patterns in disease frequency, features, and temporal flow in different parts of a country. Furthermore, identifying these regional characteristics can aid in the development of disease prevention or intervention strategies.
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Affiliation(s)
- Sang Youl Rhee
- Department of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Korea
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4
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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: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [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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Zalan A, Sheikh-Muhammad A, Khatib M, Sharkia R. The Current and Forecasted Status of Type 2 Diabetes in the Arab Society of Israel. Curr Diabetes Rev 2021; 17:e050421192659. [PMID: 33820521 DOI: 10.2174/1573399817666210405100108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/20/2021] [Accepted: 02/13/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Diabetes mellitus (DM) is considered one of the main causes of mortality, morbidity, and health care expenditures. Effectively treating this disease is of crucial importance and imposes a global challenge. The incidence of Type 2 DM (T2DM) is rapidly rising in both developing and developed countries. The Arab community in Israel is a distinct ethnic group with unique characteristics. Recently, this community has undergone major changes in its lifestyle, adopting the Westernized one, which could have caused an increase in the T2DM incidence rate. OBJECTIVE This review aims to shed light on various studies undertaken to explore the prevalence of diabetes and determine its current status in the Arab society of Israel, resting on previous and current data. It is presented to highlight the status of diabetes globally and to focus on its current situation in the Arab society of Israel, attempting to forecast its direction in the upcoming decade. METHODS Data were obtained from our previous comprehensive socio-economic and health crosssectional surveys for successive periods from 2004 to 2017. These surveys were conducted on the Arab society of Israel by the Galilee Society. RESULTS Our results showed a progressive increase in the prevalence of T2DM from 3.4% to 7.6% in the Arab society of Israel. This trend is expected to continue rising in the coming decade, and based on our predictions, may exceed 12% in 2030. CONCLUSION Substantial and practical health-related actions must be initiated to prevent an increasing number of adults from developing diabetes and its complications.
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Affiliation(s)
- Abdelnaser Zalan
- Unit of Human Biology and Genetics, The Triangle Regional Research and Development Center, Kfar-Qari, Israel
| | - Ahmad Sheikh-Muhammad
- The Galilee Society - The Arab National Society for Research and Health Services, Shefa-Amr, Israel
| | - Mohammad Khatib
- The Galilee Society - The Arab National Society for Research and Health Services, Shefa-Amr, Israel
| | - Rajech Sharkia
- Unit of Human Biology and Genetics, The Triangle Regional Research and Development Center, Kfar-Qari, Israel
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7
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Kolak M, Abraham G, Talen MR. Mapping Census Tract Clusters of Type 2 Diabetes in a Primary Care Population. Prev Chronic Dis 2019; 16:E59. [PMID: 31095922 PMCID: PMC6549437 DOI: 10.5888/pcd16.180502] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
| | - Geethi Abraham
- McGaw Medical Center of Northwestern University, 710 N Lake Shore Dr, Abbott Hall, 4th Floor, Chicago, IL 60611-2909. .,Erie Family Health Center, Chicago, Illinois
| | - Mary R Talen
- McGaw Medical Center of Northwestern University, Chicago, Illinois.,Erie Family Health Center, Chicago, Illinois
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8
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Abstract
Background: Type 2 Diabetes Mellitus (T2DM) is becoming increasingly prevalent and is considered to be a major public health threat worldwide. Behavioral and sociodemographic factors associated with T2DM vary within different societies. Objective: The aim of this study is to determine the various behavioral and sociodemographic factors associated with T2DM in the Arab society in Israel. Methods: A cross-sectional study was conducted based on data from 1,894 residents over the age of 21 belonging to the Arab population in Israel. The data collected from the subjects were subjected to statistical analyses using the SPSS program. Findings: Of the total sample population, 13.7% were found to be affected with T2DM. The prevalence of T2DM increased sharply in the successive age groups of both men and women. The prevalence of T2DM was found to increase progressively particularly in women with an increase in BMI (~20%, 37% and 44% respectively), while, in men it increased sharply (from 25% to ~50%) until a BMI of 29.9; it then decreased drastically (to ~24%) for a BMI of ≥30. About 85% of the men affected with T2DM were physically inactive, while 97% of the affected women were physically inactive. Almost half of the participants with diabetes have a family history of the disease in both genders. In the multivariate analysis, it was found that age, obesity, physical inactivity and family history of the disease were the significant factors associated with the prevalence of diabetes. Conclusions: It could be concluded that age, obesity, family history and physical inactivity were the significant factors associated with the prevalence of T2DM within the Arab society in Israel.
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Villalobos Dintrans P. Do long-term care services match population needs? A spatial analysis of nursing homes in Chile. PLoS One 2018; 13:e0199522. [PMID: 29944690 PMCID: PMC6019744 DOI: 10.1371/journal.pone.0199522] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Accepted: 06/09/2018] [Indexed: 11/18/2022] Open
Abstract
Chile is experiencing a period of rapid aging, which increases the need of long-term care services in the country. Nursing homes have been the traditional alternative to deal with the increase of elderly population in the country, with services supplied by a mix of for-profit and nonprofit providers. Additionally, population exhibits a high degree of geographical concentration. The study aims to identify the determinants of the geographical location of nursing homes in Chile at municipality level. The analysis takes into account the different location criteria for different types of nursing homes as well as potential spatial effects. The paper uses spatial analysis tools to identify clusters of nursing homes and population characteristics and to estimate the determinants of nursing homes availability and coverage in the country. The analysis–based on spatial global and local tests, and spatial autoregressive models- show the existence of clusters of nursing homes as well as clusters of municipalities according to elderly population, income, poverty, population density, and public health insurance coverage. Residuals from ordinary least squares regressions were spatially autocorrelated, showing the need of using spatial models. Estimations show that availability and coverage of nursing homes are positively related with municipality income, and that for-profit and nonprofit facilities respond differently to different factors. A negative coefficient was found for poverty in nonprofit nursing homes, raising doubts about the effectiveness of giving public subsidies to incentive the installation of facilities in areas with high needs and low income.
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Affiliation(s)
- Pablo Villalobos Dintrans
- Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- * E-mail:
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10
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Who is where at risk for Chronic Obstructive Pulmonary Disease? A spatial epidemiological analysis of health insurance claims for COPD in Northeastern Germany. PLoS One 2018; 13:e0190865. [PMID: 29414997 PMCID: PMC5802453 DOI: 10.1371/journal.pone.0190865] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 12/21/2017] [Indexed: 11/19/2022] Open
Abstract
Background Chronic obstructive pulmonary disease (COPD) has a high prevalence rate in Germany and a further increase is expected within the next years. Although risk factors on an individual level are widely understood, only little is known about the spatial heterogeneity and population-based risk factors of COPD. Background knowledge about broader, population-based processes could help to plan the future provision of healthcare and prevention strategies more aligned to the expected demand. The aim of this study is to analyze how the prevalence of COPD varies across northeastern Germany on the smallest spatial-scale possible and to identify the location-specific population-based risk factors using health insurance claims of the AOK Nordost. Methods To visualize the spatial distribution of COPD prevalence at the level of municipalities and urban districts, we used the conditional autoregressive Besag–York–Mollié (BYM) model. Geographically weighted regression modelling (GWR) was applied to analyze the location-specific ecological risk factors for COPD. Results The sex- and age-adjusted prevalence of COPD was 6.5% in 2012 and varied widely across northeastern Germany. Population-based risk factors consist of the proportions of insurants aged 65 and older, insurants with migration background, household size and area deprivation. The results of the GWR model revealed that the population at risk for COPD varies considerably across northeastern Germany. Conclusion Area deprivation has a direct and an indirect influence on the prevalence of COPD. Persons ageing in socially disadvantaged areas have a higher chance of developing COPD, even when they are not necessarily directly affected by deprivation on an individual level. This underlines the importance of considering the impact of area deprivation on health for planning of healthcare. Additionally, our results reveal that in some parts of the study area, insurants with migration background and persons living in multi-persons households are at elevated risk of COPD.
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Kauhl B, Maier W, Schweikart J, Keste A, Moskwyn M. Exploring the small-scale spatial distribution of hypertension and its association to area deprivation based on health insurance claims in Northeastern Germany. BMC Public Health 2018; 18:121. [PMID: 29321032 PMCID: PMC5761146 DOI: 10.1186/s12889-017-5017-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Accepted: 12/21/2017] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Hypertension is one of the most frequently diagnosed chronic conditions in Germany. Targeted prevention strategies and allocation of general practitioners where they are needed most are necessary to prevent severe complications arising from high blood pressure. However, data on chronic diseases in Germany are mostly available through survey data, which do not only underestimate the actual prevalence but are also only available on coarse spatial scales. The discussion of including area deprivation for planning of healthcare is still relatively young in Germany, although previous studies have shown that area deprivation is associated with adverse health outcomes, irrespective of individual characteristics. The aim of this study is therefore to analyze the spatial distribution of hypertension at very fine geographic scales and to assess location-specific associations between hypertension, socio-demographic population characteristics and area deprivation based on health insurance claims of the AOK Nordost. METHODS To visualize the spatial distribution of hypertension prevalence at very fine geographic scales, we used the conditional autoregressive Besag-York-Mollié (BYM) model. Geographically weighted regression modelling (GWR) was applied to analyze the location-specific association of hypertension to area deprivation and further socio-demographic population characteristics. RESULTS The sex- and age-adjusted prevalence of hypertension was 33.1% in 2012 and varied widely across northeastern Germany. The main risk factors for hypertension were proportions of insurants aged 45-64, 65 and older, area deprivation and proportion of persons commuting to work outside their residential municipality. The GWR model revealed important regional variations in the strength of the examined associations. CONCLUSION Area deprivation has only a significant and therefore direct influence in large parts of Mecklenburg-West Pomerania. However, the spatially varying strength of the association between demographic variables and hypertension indicates that there also exists an indirect effect of area deprivation on the prevalence of hypertension. It can therefore be expected that persons ageing in deprived areas will be at greater risk of hypertension, irrespective of their individual characteristics. The future planning and allocation of primary healthcare in northeastern Germany would therefore greatly benefit from considering the effect of area deprivation.
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Affiliation(s)
- B. Kauhl
- AOK Nordost – Die Gesundheitskasse, Department of Medical Care, Berlin, Germany
- Beuth University of Applied Sciences, Department III, Civil Engineering and Geoinformatics, Berlin, Germany
| | - W. Maier
- Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Health Economics and Health Care Management, Neuherberg, Germany
| | - J. Schweikart
- Beuth University of Applied Sciences, Department III, Civil Engineering and Geoinformatics, Berlin, Germany
| | - A. Keste
- AOK Nordost – Die Gesundheitskasse, Department of Medical Care, Berlin, Germany
| | - M. Moskwyn
- AOK Nordost – Die Gesundheitskasse, Department of Medical Care, Berlin, Germany
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12
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Lamy S, Ducros D, Diméglio C, Colineaux H, Fantin R, Berger E, Grosclaude P, Delpierre C, Bouhanick B. Disentangling the influence of living place and socioeconomic position on health services use among diabetes patients: A population-based study. PLoS One 2017; 12:e0188295. [PMID: 29186165 PMCID: PMC5706715 DOI: 10.1371/journal.pone.0188295] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 11/04/2017] [Indexed: 12/17/2022] Open
Abstract
This research investigates the influence of place of residence and diabetic patient’s socioeconomic position on their use of health services in a universal health care system. This retrospective cross-sectional population-based study is based on the joint use of the Health Insurance information systems, an ecological indicator of social deprivation and an indicator of potential spatial accessibility of healthcare provision in the Midi-Pyrénées region. Using French healthcare insurance population-based data on reimbursement of out-of-hospital care during the year 2012, we study the use of health services among patients aged 50 and over (n = 90,136).We built logistic regression models linking health services use to socioeconomic position by geographic area, adjusted for age, gender, healthcare provision, information regarding patients precariousness, and long-term condition, used as proxy for the state of health. After adjustment for healthcare provision, the lower population density in the geographical area of concern, the lower the access to specialised care, independent of the patients’ SEP. General practitioner attendance was higher among the patients with the lowest SEP without being clearly influenced by their living place. We found no clear influence of either patients’ SEP or their living place on their access to biological follow-up. This study is an attempt to account for the geographical context and to go further in studying the social determinants of health among diabetes patients.
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Affiliation(s)
- Sébastien Lamy
- University of Toulouse III Paul Sabatier, Toulouse, France
- Department of Clinical Pharmacology, Toulouse University Hospital, Toulouse, France
- LEASP UMR1027 INSERM (The French National Institute of Health and Medical Research), Toulouse, France
- * E-mail: ,
| | - Denis Ducros
- Agence Regionale de Santé (Regional Healthcare Agency), Occitanie, Toulouse, France
| | - Chloé Diméglio
- University of Toulouse III Paul Sabatier, Toulouse, France
- LEASP UMR1027 INSERM (The French National Institute of Health and Medical Research), Toulouse, France
- Department of Epidemiology, Toulouse University Hospital, Toulouse, France
| | - Hélène Colineaux
- University of Toulouse III Paul Sabatier, Toulouse, France
- LEASP UMR1027 INSERM (The French National Institute of Health and Medical Research), Toulouse, France
- Department of Epidemiology, Toulouse University Hospital, Toulouse, France
| | - Romain Fantin
- LEASP UMR1027 INSERM (The French National Institute of Health and Medical Research), Toulouse, France
| | - Eloïse Berger
- LEASP UMR1027 INSERM (The French National Institute of Health and Medical Research), Toulouse, France
| | - Pascale Grosclaude
- LEASP UMR1027 INSERM (The French National Institute of Health and Medical Research), Toulouse, France
- Tarn Cancers Registry, Albi, France
- Institut Universtaire du Cancer de Toulouse–Oncopole, Toulouse, France
| | - Cyrille Delpierre
- LEASP UMR1027 INSERM (The French National Institute of Health and Medical Research), Toulouse, France
| | - Béatrice Bouhanick
- University of Toulouse III Paul Sabatier, Toulouse, France
- LEASP UMR1027 INSERM (The French National Institute of Health and Medical Research), Toulouse, France
- Department of Hypertension and Therapeutics, Toulouse University Hospital, Toulouse, France
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Chen X, Wang Y, Schoenfeld E, Saltz M, Saltz J, Wang F. Spatio-temporal Analysis for New York State SPARCS Data. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2017; 2017:483-492. [PMID: 28815148 PMCID: PMC5543354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Increased accessibility of health data provides unique opportunities to discover spatio-temporal patterns of diseases. For example, New York State SPARCS (Statewide Planning and Research Cooperative System) data collects patient level detail on patient demographics, diagnoses, services, and charges for each hospital inpatient stay and outpatient visit. Such data also provides home addresses for each patient. This paper presents our preliminary work on spatial, temporal, and spatial-temporal analysis of disease patterns for New York State using SPARCS data. We analyzed spatial distribution patterns of typical diseases at ZIP code level. We performed temporal analysis of common diseases based on 12 years' historical data. We then compared the spatial variations for diseases with different levels of clustering tendency, and studied the evolution history of such spatial patterns. Case studies based on asthma demonstrated that the discovered spatial clusters are consistent with prior studies. We visualized our spatial-temporal patterns as animations through videos.
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Yeh HL, Hsu SW, Chang YC, Chan TC, Tsou HC, Chang YC, Chiang PH. Spatial Analysis of Ambient PM 2.5 Exposure and Bladder Cancer Mortality in Taiwan. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14050508. [PMID: 28489042 PMCID: PMC5451959 DOI: 10.3390/ijerph14050508] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Revised: 05/05/2017] [Accepted: 05/05/2017] [Indexed: 12/12/2022]
Abstract
Fine particulate matter (PM2.5) is an air pollutant that is receiving intense regulatory attention in Taiwan. In previous studies, the effect of air pollution on bladder cancer has been explored. This study was conducted to elucidate the effect of atmospheric PM2.5 and other local risk factors on bladder cancer mortality based on available 13-year mortality data. Geographically weighted regression (GWR) was applied to estimate and interpret the spatial variability of the relationships between bladder cancer mortality and ambient PM2.5 concentrations, and other variables were covariates used to adjust for the effect of PM2.5. After applying a GWR model, the concentration of ambient PM2.5 showed a positive correlation with bladder cancer mortality in males in northern Taiwan and females in most of the townships in Taiwan. This is the first time PM2.5 has been identified as a risk factor for bladder cancer based on the statistical evidence provided by GWR analysis.
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Affiliation(s)
- Hsin-Ling Yeh
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan 350, Taiwan.
| | - Shang-Wei Hsu
- Department of Healthcare Administration, Asia University, Taichung 413, Taiwan.
| | - Yu-Chia Chang
- Department of Healthcare Administration, Asia University, Taichung 413, Taiwan.
| | - Ta-Chien Chan
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei 115, Taiwan.
| | - Hui-Chen Tsou
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan 350, Taiwan.
| | - Yen-Chen Chang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan 350, Taiwan.
| | - Po-Huang Chiang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan 350, Taiwan.
- Department of Public Health, China Medical University, Taichung 400, Taiwan.
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Kauhl B, Heil J, Hoebe CJPA, Schweikart J, Krafft T, Dukers-Muijrers NHTM. Is the current pertussis incidence only the results of testing? A spatial and space-time analysis of pertussis surveillance data using cluster detection methods and geographically weighted regression modelling. PLoS One 2017; 12:e0172383. [PMID: 28278180 PMCID: PMC5344341 DOI: 10.1371/journal.pone.0172383] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 02/03/2017] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Despite high vaccination coverage, pertussis incidence in the Netherlands is amongst the highest in Europe with a shifting tendency towards adults and elderly. Early detection of outbreaks and preventive actions are necessary to prevent severe complications in infants. Efficient pertussis control requires additional background knowledge about the determinants of testing and possible determinants of the current pertussis incidence. Therefore, the aim of our study is to examine the possibility of locating possible pertussis outbreaks using space-time cluster detection and to examine the determinants of pertussis testing and incidence using geographically weighted regression models. METHODS We analysed laboratory registry data including all geocoded pertussis tests in the southern area of the Netherlands between 2007 and 2013. Socio-demographic and infrastructure-related population data were matched to the geo-coded laboratory data. The spatial scan statistic was applied to detect spatial and space-time clusters of testing, incidence and test-positivity. Geographically weighted Poisson regression (GWPR) models were then constructed to model the associations between the age-specific rates of testing and incidence and possible population-based determinants. RESULTS Space-time clusters for pertussis incidence overlapped with space-time clusters for testing, reflecting a strong relationship between testing and incidence, irrespective of the examined age group. Testing for pertussis itself was overall associated with lower socio-economic status, multi-person-households, proximity to primary school and availability of healthcare. The current incidence in contradiction is mainly determined by testing and is not associated with a lower socioeconomic status. DISCUSSION Testing for pertussis follows to an extent the general healthcare seeking behaviour for common respiratory infections, whereas the current pertussis incidence is largely the result of testing. More testing would thus not necessarily improve pertussis control. Detecting outbreaks using space-time cluster detection is feasible but needs to adjust for the strong impact of testing on the detection of pertussis cases.
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Affiliation(s)
- Boris Kauhl
- Department of Health, Ethics and Society, School of Public Health and Primary Care (CAPHRI), Faculty of Health, Medicine and Life Sciences. Maastricht University, Maastricht, the Netherlands
| | - Jeanne Heil
- Department of Sexual Health, Infectious Diseases and Environmental Health, South Limburg Public Health Service (GGD Zuid Limburg), Geleen, The Netherlands
- Department of Medical Microbiology, School of Public Health and Primary Care (CAPHRI), Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
| | - Christian J. P. A. Hoebe
- Department of Sexual Health, Infectious Diseases and Environmental Health, South Limburg Public Health Service (GGD Zuid Limburg), Geleen, The Netherlands
- Department of Medical Microbiology, School of Public Health and Primary Care (CAPHRI), Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
| | - Jürgen Schweikart
- Beuth University of Applied Sciences, Department III, Civil Engineering and Geoinformatics, Berlin, Germany
| | - Thomas Krafft
- Department of Health, Ethics and Society, School of Public Health and Primary Care (CAPHRI), Faculty of Health, Medicine and Life Sciences. Maastricht University, Maastricht, the Netherlands
| | - Nicole H. T. M. Dukers-Muijrers
- Department of Sexual Health, Infectious Diseases and Environmental Health, South Limburg Public Health Service (GGD Zuid Limburg), Geleen, The Netherlands
- Department of Medical Microbiology, School of Public Health and Primary Care (CAPHRI), Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
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Chen X, Wang F. Integrative Spatial Data Analytics for Public Health Studies of New York State. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2017; 2016:391-400. [PMID: 28269834 PMCID: PMC5333201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Increased accessibility of health data made available by the government provides unique opportunity for spatial analytics with much higher resolution to discover patterns of diseases, and their correlation with spatial impact indicators. This paper demonstrated our vision of integrative spatial analytics for public health by linking the New York Cancer Mapping Dataset with datasets containing potential spatial impact indicators. We performed spatial based discovery of disease patterns and variations across New York State, and identify potential correlations between diseases and demographic, socio-economic and environmental indicators. Our methods were validated by three correlation studies: the correlation between stomach cancer and Asian race, the correlation between breast cancer and high education population, and the correlation between lung cancer and air toxics. Our work will allow public health researchers, government officials or other practitioners to adequately identify, analyze, and monitor health problems at the community or neighborhood level for New York State.
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Affiliation(s)
- Xin Chen
- Stony Brook University, Stony Brook, NY
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Kauhl B, Schweikart J, Krafft T, Keste A, Moskwyn M. Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression. Int J Health Geogr 2016; 15:38. [PMID: 27809861 PMCID: PMC5094025 DOI: 10.1186/s12942-016-0068-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 10/21/2016] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND The provision of general practitioners (GPs) in Germany still relies mainly on the ratio of inhabitants to GPs at relatively large scales and barely accounts for an increased prevalence of chronic diseases among the elderly and socially underprivileged populations. Type 2 Diabetes Mellitus (T2DM) is one of the major cost-intensive diseases with high rates of potentially preventable complications. Provision of healthcare and access to preventive measures is necessary to reduce the burden of T2DM. However, current studies on the spatial variation of T2DM in Germany are mostly based on survey data, which do not only underestimate the true prevalence of T2DM, but are also only available on large spatial scales. The aim of this study is therefore to analyse the spatial distribution of T2DM at fine geographic scales and to assess location-specific risk factors based on data of the AOK health insurance. METHODS To display the spatial heterogeneity of T2DM, a bivariate, adaptive kernel density estimation (KDE) was applied. The spatial scan statistic (SaTScan) was used to detect areas of high risk. Global and local spatial regression models were then constructed to analyze socio-demographic risk factors of T2DM. RESULTS T2DM is especially concentrated in rural areas surrounding Berlin. The risk factors for T2DM consist of proportions of 65-79 year olds, 80 + year olds, unemployment rate among the 55-65 year olds, proportion of employees covered by mandatory social security insurance, mean income tax, and proportion of non-married couples. However, the strength of the association between T2DM and the examined socio-demographic variables displayed strong regional variations. CONCLUSION The prevalence of T2DM varies at the very local level. Analyzing point data on T2DM of northeastern Germany's largest health insurance provider thus allows very detailed, location-specific knowledge about increased medical needs. Risk factors associated with T2DM depend largely on the place of residence of the respective person. Future allocation of GPs and current prevention strategies should therefore reflect the location-specific higher healthcare demand among the elderly and socially underprivileged populations.
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Affiliation(s)
- Boris Kauhl
- Department of Medical Care, AOK Nordost - Die Gesundheitskasse, Berlin, Germany.
- Department III, Civil Engineering and Geoinformatics, Beuth University of Applied Sciences, Berlin, Germany.
- Department of Health, Ethics and Society, School of Public Health and Primary Care (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
| | - Jürgen Schweikart
- Department III, Civil Engineering and Geoinformatics, Beuth University of Applied Sciences, Berlin, Germany
| | - Thomas Krafft
- Department of Health, Ethics and Society, School of Public Health and Primary Care (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Andrea Keste
- Department of Medical Care, AOK Nordost - Die Gesundheitskasse, Berlin, Germany
| | - Marita Moskwyn
- Department of Medical Care, AOK Nordost - Die Gesundheitskasse, Berlin, Germany
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Jo EK, Seo EW, Lee KS. Spatial Distribution of Diabetes Prevalence Rates and Its Relationship with the Regional Characteristics. HEALTH POLICY AND MANAGEMENT 2016. [DOI: 10.4332/kjhpa.2016.26.1.30] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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The Spatial Distribution of Hepatitis C Virus Infections and Associated Determinants--An Application of a Geographically Weighted Poisson Regression for Evidence-Based Screening Interventions in Hotspots. PLoS One 2015; 10:e0135656. [PMID: 26352611 PMCID: PMC4564162 DOI: 10.1371/journal.pone.0135656] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2015] [Accepted: 07/23/2015] [Indexed: 12/18/2022] Open
Abstract
Background Hepatitis C Virus (HCV) infections are a major cause for liver diseases. A large proportion of these infections remain hidden to care due to its mostly asymptomatic nature. Population-based screening and screening targeted on behavioural risk groups had not proven to be effective in revealing these hidden infections. Therefore, more practically applicable approaches to target screenings are necessary. Geographic Information Systems (GIS) and spatial epidemiological methods may provide a more feasible basis for screening interventions through the identification of hotspots as well as demographic and socio-economic determinants. Methods Analysed data included all HCV tests (n = 23,800) performed in the southern area of the Netherlands between 2002–2008. HCV positivity was defined as a positive immunoblot or polymerase chain reaction test. Population data were matched to the geocoded HCV test data. The spatial scan statistic was applied to detect areas with elevated HCV risk. We applied global regression models to determine associations between population-based determinants and HCV risk. Geographically weighted Poisson regression models were then constructed to determine local differences of the association between HCV risk and population-based determinants. Results HCV prevalence varied geographically and clustered in urban areas. The main population at risk were middle-aged males, non-western immigrants and divorced persons. Socio-economic determinants consisted of one-person households, persons with low income and mean property value. However, the association between HCV risk and demographic as well as socio-economic determinants displayed strong regional and intra-urban differences. Discussion The detection of local hotspots in our study may serve as a basis for prioritization of areas for future targeted interventions. Demographic and socio-economic determinants associated with HCV risk show regional differences underlining that a one-size-fits-all approach even within small geographic areas may not be appropriate. Future screening interventions need to consider the spatially varying association between HCV risk and associated demographic and socio-economic determinants.
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Chan TC, Chiang PH, Su MD, Wang HW, Liu MSY. Geographic disparity in chronic obstructive pulmonary disease (COPD) mortality rates among the Taiwan population. PLoS One 2014; 9:e98170. [PMID: 24845852 PMCID: PMC4028296 DOI: 10.1371/journal.pone.0098170] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Accepted: 04/29/2014] [Indexed: 11/18/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) causes a high disease burden among the elderly worldwide. In Taiwan, the long-term temporal trend of COPD mortality is declining, but the geographical disparity of the disease is not yet known. Nationwide COPD age-adjusted mortality at the township level during 1999-2007 is used for elucidating the geographical distribution of the disease. With an ordinary least squares (OLS) model and geographically weighted regression (GWR), the ecologic risk factors such as smoking rate, area deprivation index, tuberculosis exposure, percentage of aborigines, density of health care facilities, air pollution and altitude are all considered in both models to evaluate their effects on mortality. Global and local Moran's I are used for examining their spatial autocorrelation and identifying clusters. During the study period, the COPD age-adjusted mortality rates in males declined from 26.83 to 19.67 per 100,000 population, and those in females declined from 8.98 to 5.70 per 100,000 population. Overall, males' COPD mortality rate was around three times higher than females'. In the results of GWR, the median coefficients of smoking rate, the percentage of aborigines, PM10 and the altitude are positively correlated with COPD mortality in males and females. The median value of density of health care facilities is negatively correlated with COPD mortality. The overall adjusted R-squares are about 20% higher in the GWR model than in the OLS model. The local Moran's I of the GWR's residuals reflected the consistent high-high cluster in southern Taiwan. The findings indicate that geographical disparities in COPD mortality exist. Future epidemiological investigation is required to understand the specific risk factors within the clustering areas.
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Affiliation(s)
- Ta-Chien Chan
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan, Republic of China (R.O.C.)
| | - Po-Huang Chiang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan, Republic of China (R.O.C.)
| | - Ming-Daw Su
- Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan, Republic of China (R.O.C.)
| | - Hsuan-Wen Wang
- Master of Public Health Program, School of Public Health, National Taiwan University, Taipei, Taiwan, Republic of China (R.O.C.)
- Division of Family Medicine, Fangliao General Hospital, Pingtung, Taiwan, Republic of China (R.O.C.)
| | - Michael Shi-yung Liu
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan, Republic of China (R.O.C.)
- Institute of Taiwan History, Academia Sinica, Taipei, Taiwan, Republic of China (R.O.C.)
- * E-mail:
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Geographical distribution patterns of iodine in drinking-water and its associations with geological factors in Shandong Province, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2014; 11:5431-44. [PMID: 24852390 PMCID: PMC4053898 DOI: 10.3390/ijerph110505431] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Revised: 04/29/2014] [Accepted: 05/04/2014] [Indexed: 11/17/2022]
Abstract
County-based spatial distribution characteristics and the related geological factors for iodine in drinking-water were studied in Shandong Province (China). Spatial autocorrelation analysis and spatial scan statistic were applied to analyze the spatial characteristics. Generalized linear models (GLMs) and geographically weighted regression (GWR) studies were conducted to explore the relationship between water iodine level and its related geological factors. The spatial distribution of iodine in drinking-water was significantly heterogeneous in Shandong Province (Moran's I = 0.52, Z = 7.4, p < 0.001). Two clusters for high iodine in drinking-water were identified in the south-western and north-western parts of Shandong Province by the purely spatial scan statistic approach. Both GLMs and GWR indicated a significantly global association between iodine in drinking-water and geological factors. Furthermore, GWR showed obviously spatial variability across the study region. Soil type and distance to Yellow River were statistically significant at most areas of Shandong Province, confirming the hypothesis that the Yellow River causes iodine deposits in Shandong Province. Our results suggested that the more effective regional monitoring plan and water improvement strategies should be strengthened targeting at the cluster areas based on the characteristics of geological factors and the spatial variability of local relationships between iodine in drinking-water and geological factors.
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Wu JY, Zhou YB, Li LH, Zheng SB, Liang S, Coatsworth A, Ren GH, Song XX, He Z, Cai B, You JB, Jiang QW. Identification of optimum scopes of environmental factors for snails using spatial analysis techniques in Dongting Lake Region, China. Parasit Vectors 2014; 7:216. [PMID: 24886456 PMCID: PMC4025561 DOI: 10.1186/1756-3305-7-216] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2014] [Accepted: 05/01/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Owing to the harmfulness and seriousness of Schistosomiasis japonica in China, the control and prevention of S. japonica transmission are imperative. As the unique intermediate host of this disease, Oncomelania hupensis plays an important role in the transmission. It has been reported that the snail population in Qiangliang Lake district, Dongting Lake Region has been naturally declining and is slowly becoming extinct. Considering the changes of environmental factors that may cause this phenomenon, we try to explore the relationship between circumstance elements and snails, and then search for the possible optimum scopes of environmental factors for snails. METHODS Moisture content of soil, pH, temperature of soil and elevation were collected by corresponding apparatus in the study sites. The LISA statistic and GWR model were used to analyze the association between factors and mean snail density, and the values in high-high clustered areas and low-low clustered areas were extracted to find out the possible optimum ranges of these elements for snails. RESULTS A total of 8,589 snail specimens were collected from 397 sampling sites in the study field. Besides the mean snail density, three environmental factors including water content, pH and temperature had high spatial autocorrelation. The spatial clustering suggested that the possible optimum scopes of moisture content, pH, temperature of the soil and elevation were 58.70 to 68.93%, 6.80 to 7.80, 22.73 to 24.23°C and 23.50 to 25.97 m, respectively. Moreover, the GWR model showed that the possible optimum ranges of these four factors were 36.58 to 61.08%, 6.541 to 6.89, 24.30 to 25.70°C and 23.50 to 29.44 m, respectively. CONCLUSION The results indicated the association between snails and environmental factors was not linear but U-shaped. Considering the results of two analysis methods, the possible optimum scopes of moisture content, pH, temperature of the soil and elevation were 58.70% to 68.93%, 6.6 to 7.0, 22.73°C to 24.23°C, and 23.5 m to 26.0 m, respectively. The findings in this research will help in making an effective strategy to control snails and provide a method to analyze other factors.
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Affiliation(s)
- Jin-Yi Wu
- Department of Epidemiology, School of Public Health, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
- Center for Tropical Disease Research, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
| | - Yi-Biao Zhou
- Department of Epidemiology, School of Public Health, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
- Center for Tropical Disease Research, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
| | - Lin-Han Li
- Department of Epidemiology, School of Public Health, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
- Center for Tropical Disease Research, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
| | - Sheng-Bang Zheng
- Department of Epidemiology, School of Public Health, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
- Center for Tropical Disease Research, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
| | - Song Liang
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Ashley Coatsworth
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Guang-Hui Ren
- Hunan station for Schistosomiasis Control, Changsha, Hunan Province 410000, China
| | - Xiu-Xia Song
- Department of Epidemiology, School of Public Health, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
- Center for Tropical Disease Research, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
| | - Zhong He
- Junshan office of Leading Group for Schistosomiasis Control, Yueyang, Hunan province 414000, China
| | - Bin Cai
- Junshan station for Schistosomiasis Control, Yueyang, Hunan Province 414000, China
| | - Jia-Bian You
- Qianlianghu station for Schistosomiasis Control, Yueyang, Hunan Province 414000, China
| | - Qing-Wu Jiang
- Department of Epidemiology, School of Public Health, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
- Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
- Center for Tropical Disease Research, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China
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