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Guerra-Tort C, López-Vizcaíno E, Santiago-Pérez MI, Rey-Brandariz J, Candal-Pedreira C, Varela-Lema L, Schiaffino A, Ruano-Ravina A, Perez- Rios M. Validation of a small-area model for estimation of smoking prevalence at a subnational level. Tob Induc Dis 2023; 21:112. [PMID: 37664442 PMCID: PMC10472341 DOI: 10.18332/tid/169683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/05/2023] [Accepted: 07/16/2023] [Indexed: 09/05/2023] Open
Abstract
INTRODUCTION Small-area estimation methods are an alternative to direct survey-based estimates in cases where a survey's sample size does not suffice to ensure representativeness. Nevertheless, the information yielded by small-area estimation methods must be validated. The objective of this study was thus to validate a small-area model. METHODS The prevalence of smokers, ex-smokers, and never smokers by sex and age group (15-34, 35-54, 55-64, 65-74, ≥75 years) was calculated in two Spanish Autonomous Regions (ARs) by applying a weighted ratio estimator (direct estimator) to data from representative surveys. These estimates were compared against those obtained with a small-area model applied to another survey, specifically the Spanish National Health Survey, which did not guarantee representativeness for these two ARs by sex and age. To evaluate the concordance of the estimates, we calculated the intraclass correlation coefficient (ICC) and the 95% confidence intervals of the differences between estimates. To assess the precision of the estimates, the coefficients of variation were obtained. RESULTS In all cases, the ICC was ≥0.87, indicating good concordance between the direct and small-area model estimates. Slightly more than eight in ten 95% confidence intervals for the differences between estimates included zero. In all cases, the coefficient of variation of the small-area model was <30%, indicating a good degree of precision in the estimates. CONCLUSIONS The small-area model applied to national survey data yields valid estimates of smoking prevalence by sex and age group at the AR level. These models could thus be applied to a single year's data from a national survey, which does not guarantee regional representativeness, to characterize various risk factors in a population at a subnational level.
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Affiliation(s)
- Carla Guerra-Tort
- Área de Medicina Preventiva e Saúde Pública, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Esther López-Vizcaíno
- Servizo de Difusión e Información, Instituto Galego de Estatística, Xunta de Galicia, Santiago de Compostela, Spain
| | - María I. Santiago-Pérez
- Servizo de Epidemioloxía, Dirección Xeral de Saúde Pública, Xunta de Galicia, Santiago de Compostela, Spain
| | - Julia Rey-Brandariz
- Área de Medicina Preventiva e Saúde Pública, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Cristina Candal-Pedreira
- Área de Medicina Preventiva e Saúde Pública, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Leonor Varela-Lema
- Área de Medicina Preventiva e Saúde Pública, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Epidemiología y Salud Pública, Centro de Investigación Biomédica en Red (CIBERESP), Madrid, Spain
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Anna Schiaffino
- Departament de Salut, Direcció General de Planificació en Salut, Generalitat de Catalunya, Barcelona, Spain
| | - Alberto Ruano-Ravina
- Área de Medicina Preventiva e Saúde Pública, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Epidemiología y Salud Pública, Centro de Investigación Biomédica en Red (CIBERESP), Madrid, Spain
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Monica Perez- Rios
- Área de Medicina Preventiva e Saúde Pública, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Epidemiología y Salud Pública, Centro de Investigación Biomédica en Red (CIBERESP), Madrid, Spain
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
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Santiago-Pérez MI, López-Vizcaíno E, Pérez-Ríos M, Guerra-Tort C, Rey-Brandariz J, Varela-Lema L, Martín-Gisbert L, Ruano-Ravina A, Schiaffino A, Galán I, Candal-Pedreira C, Montes A, Ahluwalia J. Small-area models to assess the geographical distribution of tobacco consumption by sex and age in Spain. Tob Induc Dis 2023; 21:63. [PMID: 37215189 PMCID: PMC10194049 DOI: 10.18332/tid/162379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/31/2023] [Accepted: 03/19/2023] [Indexed: 05/24/2023] Open
Abstract
INTRODUCTION Complete and accurate data on smoking prevalence at a local level would enable health authorities to plan context-dependent smoking interventions. However, national health surveys do not generally provide direct estimates of smoking prevalence by sex and age groups at the subnational level. This study uses a small-area model-based methodology to obtain precise estimations of smoking prevalence by sex, age group and region, from a population-based survey. METHODS The areas targeted for analysis consisted of 180 groups based on a combination of sex, age group (15-34, 35-54, 55-64, 65-74, and ≥75 years), and Autonomous Region. Data on tobacco use came from the 2017 Spanish National Health Survey (2017 SNHS). In each of the 180 groups, we estimated the prevalence of smokers (S), ex-smokers (ExS) and never smokers (NS), as well as their coefficients of variation (CV), using a weighted ratio estimator (direct estimator) and a multinomial logistic model with random area effects. RESULTS When smoking prevalence was estimated using the small-area model, the precision of direct estimates improved; the CV of S and ExS decreased on average by 26%, and those of NS by 25%. The range of S prevalence was 11-46% in men and 4-37% in women, excluding the group aged ≥75 years. CONCLUSIONS This study proposes a methodology for obtaining reliable estimates of smoking prevalence in groups or areas not covered in the survey design. The model applied is a good alternative for enhancing the precision of estimates at a detailed level, at a much lower cost than that involved in conducting large-scale surveys. This method could be easily integrated into routine data processing of population health surveys. Having such estimates directly after completing a health survey would help characterize the tobacco epidemic and/or any other risk factor more precisely.
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Affiliation(s)
- María I. Santiago-Pérez
- Epidemiology Department, Directorate-General of Public Health, Galician Regional Health Authority, Santiago de Compostela, Spain
| | - Esther López-Vizcaíno
- Diffusion and Information Service, Galician Institute of Statistics, Santiago de Compostela, Spain
| | - Mónica Pérez-Ríos
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública/CIBERESP), Santiago de Compostela, Spain
- Health Research Institute of Santiago de Compostela (Instituto de Investigación Sanitaria de Santiago de Compostela - IDIS), Santiago de Compostela, Spain
| | - Carla Guerra-Tort
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Julia Rey-Brandariz
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Leonor Varela-Lema
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública/CIBERESP), Santiago de Compostela, Spain
- Health Research Institute of Santiago de Compostela (Instituto de Investigación Sanitaria de Santiago de Compostela - IDIS), Santiago de Compostela, Spain
| | - Lucía Martín-Gisbert
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Alberto Ruano-Ravina
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública/CIBERESP), Santiago de Compostela, Spain
- Health Research Institute of Santiago de Compostela (Instituto de Investigación Sanitaria de Santiago de Compostela - IDIS), Santiago de Compostela, Spain
| | - Anna Schiaffino
- Directorate-General of Health Planning, Health Department, Catalonian Regional Authority, Barcelona, Spain
| | - Iñaki Galán
- National Centre for Epidemiology, Carlos III Institute of Health, Madrid, Spain
- Department of Preventive Medicine and Public Health, Autonomous University of Madrid/IdiPAZ, Madrid, Spain
| | - Cristina Candal-Pedreira
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Agustín Montes
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública/CIBERESP), Santiago de Compostela, Spain
- Health Research Institute of Santiago de Compostela (Instituto de Investigación Sanitaria de Santiago de Compostela - IDIS), Santiago de Compostela, Spain
| | - Jasjit Ahluwalia
- Department of Medicine, Alpert School of Medicine, Brown University, Providence, United States
- Department of Behavioral and Social Science, School of Public Health, Brown University, Providence, United States
- Legoretta Cancer Center, Division of Biology and Medicine, Brown University, Providence, United States
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Wang Y, Holt JB, Zhang X, Lu H, Shah SN, Dooley DP, Matthews KA, Croft JB. Comparison of Methods for Estimating Prevalence of Chronic Diseases and Health Behaviors for Small Geographic Areas: Boston Validation Study, 2013. Prev Chronic Dis 2017; 14:E99. [PMID: 29049020 PMCID: PMC5652237 DOI: 10.5888/pcd14.170281] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Introduction Local health authorities need small-area estimates for prevalence of chronic diseases and health behaviors for multiple purposes. We generated city-level and census-tract–level prevalence estimates of 27 measures for the 500 largest US cities. Methods To validate the methodology, we constructed multilevel logistic regressions to predict 10 selected health indicators among adults aged 18 years or older by using 2013 Behavioral Risk Factor Surveillance System (BRFSS) data; we applied their predicted probabilities to census population data to generate city-level, neighborhood-level, and zip-code–level estimates for the city of Boston, Massachusetts. Results By comparing the predicted estimates with their corresponding direct estimates from a locally administered survey (Boston BRFSS 2010 and 2013), we found that our model-based estimates for most of the selected health indicators at the city level were close to the direct estimates from the local survey. We also found strong correlation between the model-based estimates and direct survey estimates at neighborhood and zip code levels for most indicators. Conclusion Findings suggest that our model-based estimates are reliable and valid at the city level for certain health outcomes. Local health authorities can use the neighborhood-level estimates if high quality local health survey data are not otherwise available.
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Affiliation(s)
- Yan Wang
- Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford Hwy, Atlanta, GA 30341.
| | - James B Holt
- Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Xingyou Zhang
- Economic Research Service, US Department of Agriculture, Washington, District of Columbia
| | - Hua Lu
- Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Snehal N Shah
- Boston Public Health Commission, Boston, Massachusetts.,Boston University, School of Medicine, Boston, Massachusetts
| | | | - Kevin A Matthews
- Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Janet B Croft
- Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
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Wang Y, Ponce NA, Wang P, Opsomer JD, Yu H. Generating Health Estimates by Zip Code: A Semiparametric Small Area Estimation Approach Using the California Health Interview Survey. Am J Public Health 2016; 105:2534-40. [PMID: 26544642 DOI: 10.2105/ajph.2015.302810] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
OBJECTIVES We propose a method to meet challenges in generating health estimates for granular geographic areas in which the survey sample size is extremely small. METHODS Our generalized linear mixed model predicts health outcomes using both individual-level and neighborhood-level predictors. The model's feature of nonparametric smoothing function on neighborhood-level variables better captures the association between neighborhood environment and the outcome. Using 2011 to 2012 data from the California Health Interview Survey, we demonstrate an empirical application of this method to estimate the fraction of residents without health insurance for Zip Code Tabulation Areas (ZCTAs). RESULTS Our method generated stable estimates of uninsurance for 1519 of 1765 ZCTAs (86%) in California. For some areas with great socioeconomic diversity across adjacent neighborhoods, such as Los Angeles County, the modeled uninsured estimates revealed much heterogeneity among geographically adjacent ZCTAs. CONCLUSIONS The proposed method can increase the value of health surveys by providing modeled estimates for health data at a granular geographic level. It can account for variations in health outcomes at the neighborhood level as a result of both socioeconomic characteristics and geographic locations.
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Affiliation(s)
- Yueyan Wang
- Yueyan Wang, Ninez A. Ponce, Pan Wang, and Hongjian Yu are with the Center for Health Policy Research, University of California, Los Angeles (UCLA). Jean D. Opsomer is with Department of Statistics, Colorado State University, Fort Collins. Ninez A. Ponce is also with the Department of Health Policy and Management, Fielding School of Public Health, UCLA
| | - Ninez A Ponce
- Yueyan Wang, Ninez A. Ponce, Pan Wang, and Hongjian Yu are with the Center for Health Policy Research, University of California, Los Angeles (UCLA). Jean D. Opsomer is with Department of Statistics, Colorado State University, Fort Collins. Ninez A. Ponce is also with the Department of Health Policy and Management, Fielding School of Public Health, UCLA
| | - Pan Wang
- Yueyan Wang, Ninez A. Ponce, Pan Wang, and Hongjian Yu are with the Center for Health Policy Research, University of California, Los Angeles (UCLA). Jean D. Opsomer is with Department of Statistics, Colorado State University, Fort Collins. Ninez A. Ponce is also with the Department of Health Policy and Management, Fielding School of Public Health, UCLA
| | - Jean D Opsomer
- Yueyan Wang, Ninez A. Ponce, Pan Wang, and Hongjian Yu are with the Center for Health Policy Research, University of California, Los Angeles (UCLA). Jean D. Opsomer is with Department of Statistics, Colorado State University, Fort Collins. Ninez A. Ponce is also with the Department of Health Policy and Management, Fielding School of Public Health, UCLA
| | - Hongjian Yu
- Yueyan Wang, Ninez A. Ponce, Pan Wang, and Hongjian Yu are with the Center for Health Policy Research, University of California, Los Angeles (UCLA). Jean D. Opsomer is with Department of Statistics, Colorado State University, Fort Collins. Ninez A. Ponce is also with the Department of Health Policy and Management, Fielding School of Public Health, UCLA
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Zhang X, Holt JB, Yun S, Lu H, Greenlund KJ, Croft JB. Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system. Am J Epidemiol 2015; 182:127-37. [PMID: 25957312 DOI: 10.1093/aje/kwv002] [Citation(s) in RCA: 104] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Accepted: 01/06/2015] [Indexed: 12/14/2022] Open
Abstract
Small area estimation is a statistical technique used to produce reliable estimates for smaller geographic areas than those for which the original surveys were designed. Such small area estimates (SAEs) often lack rigorous external validation. In this study, we validated our multilevel regression and poststratification SAEs from 2011 Behavioral Risk Factor Surveillance System data using direct estimates from 2011 Missouri County-Level Study and American Community Survey data at both the state and county levels. Coefficients for correlation between model-based SAEs and Missouri County-Level Study direct estimates for 115 counties in Missouri were all significantly positive (0.28 for obesity and no health-care coverage, 0.40 for current smoking, 0.51 for diabetes, and 0.69 for chronic obstructive pulmonary disease). Coefficients for correlation between model-based SAEs and American Community Survey direct estimates of no health-care coverage were 0.85 at the county level (811 counties) and 0.95 at the state level. Unweighted and weighted model-based SAEs were compared with direct estimates; unweighted models performed better. External validation results suggest that multilevel regression and poststratification model-based SAEs using single-year Behavioral Risk Factor Surveillance System data are valid and could be used to characterize geographic variations in health indictors at local levels (such as counties) when high-quality local survey data are not available.
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Holmes LM, Marcelli EA. Neighborhood social cohesion and smoking among legal and unauthorized Brazilian migrants in metropolitan Boston. J Urban Health 2014; 91:1175-88. [PMID: 25331821 PMCID: PMC4242854 DOI: 10.1007/s11524-014-9912-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Tobacco smoking is estimated to be the largest preventable cause of mortality in the USA, but little is known about the relationship between neighborhood social environment and current smoking behavior or how this may differ by population and geography. We investigate how neighborhood social cohesion and disorder are associated with smoking behavior among legal and unauthorized Brazilian migrant adults using data from the 2007 Harvard-UMASS Boston Metropolitan Immigrant Health and Legal Status Survey (BM-IHLSS), a probabilistic household survey of adult Brazilian migrants. We employ logistic regression to estimate associations between neighborhood social cohesion, neighborhood disorder, and current smoking. We find that neighborhood-level social cohesion is associated with lower likelihood of being a current smoker (O.R. = .836; p < .05), and neighborhood disorder, measured as crime experienced in the neighborhood, is not associated with current smoking. Neighborhood population density, age, being male, and residing with someone who smokes are each positively associated with current smoking (p < .10). The health of participants' parents at the age of 35, being married, and individual earnings are associated with a reduction in the probability of being a current smoker (p < .05). Migrant legal status and length of residence in the USA are not associated with current smoking. Our findings suggest that neighborhood social cohesion may be protective against smoking. Alternatively, neighborhood disorder does not appear to be related to current smoking among Brazilian migrants.
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Affiliation(s)
- Louisa M Holmes
- Center for Tobacco Control Research and Education, University of California San Francisco, 530 Parnassus Avenue, Suite 366, San Francisco, CA, 94143, USA,
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Shah SN, Russo ET, Earl TR, Kuo T. Measuring and monitoring progress toward health equity: local challenges for public health. Prev Chronic Dis 2014; 11:E159. [PMID: 25232746 PMCID: PMC4170727 DOI: 10.5888/pcd11.130440] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
To address health disparities, local health departments need high-resolution data on subpopulations and geographic regions, but the quality and availability of these data are often suboptimal. The Boston Public Health Commission and the Los Angeles County Department of Public Health faced challenges in acquiring and using community-level data essential for the design and implementation of programs that can improve the health of those who have social or economic disadvantages. To overcome these challenges, both agencies used practical and innovative strategies for data management and analysis, including augmentation of existing population surveys, the use of combined data sets, and the generation of small-area estimates. These and other strategies show how community-level health data can be analyzed, expanded, and integrated into existing public health surveillance and program infrastructure to inform jurisdictional planning and tailoring of interventions aimed at achieving optimal health for all members of a community.
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Affiliation(s)
- Snehal N Shah
- Research and Evaluation Office, Boston Public Health Commission, 1010 Massachusetts Ave, 6th Floor, Boston, MA 02118. E-mail: . Dr Shah is also affiliated with Boston University School of Medicine, Boston, Massachusetts
| | | | | | - Tony Kuo
- Los Angeles County Department of Public Health and UCLA David Geffen School of Medicine, Los Angeles, California
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Hirve S. 'In general, how do you feel today?'--self-rated health in the context of aging in India. Glob Health Action 2014; 7:23421. [PMID: 24762983 PMCID: PMC3999953 DOI: 10.3402/gha.v7.23421] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2013] [Revised: 02/25/2014] [Accepted: 03/22/2014] [Indexed: 11/14/2022] Open
Abstract
This thesis is centered on self-rated health (SRH) as an outcome measure, as a predictor, and as a marker. The thesis uses primary data from the WHO Study on global AGEing and adult health (SAGE) implemented in India in 2007. The structural equation modeling approach is employed to understand the pathways through which the social environment, disability, disease, and sociodemographic characteristics influence SRH among older adults aged 50 years and above. Cox proportional hazard model is used to explore the role of SRH as a predictor for mortality and the role of disability in modifying this effect. The hierarchical ordered probit modeling approach, which combines information from anchoring vignettes with SRH, was used to address the long overlooked methodological concern of interpersonal incomparability. Finally, multilevel model-based small area estimation techniques were used to demonstrate the use of large national surveys and census information to derive precise SRH prevalence estimates at the district and sub-district level. The thesis advocates the use of such a simple measure to identify vulnerable communities for targeted health interventions, to plan and prioritize resource allocation, and to evaluate health interventions in resource-scarce settings. The thesis provides the basis and impetus to generate and integrate similar and harmonized adult health and aging data platforms within demographic surveillance systems in different regions of India and elsewhere.
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Hirve S, Vounatsou P, Juvekar S, Blomstedt Y, Wall S, Chatterji S, Ng N. Self-rated health: small area large area comparisons amongst older adults at the state, district and sub-district level in India. Health Place 2014; 26:31-8. [PMID: 24361576 PMCID: PMC3944101 DOI: 10.1016/j.healthplace.2013.12.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2013] [Revised: 11/05/2013] [Accepted: 12/01/2013] [Indexed: 11/22/2022]
Abstract
We compared prevalence estimates of self-rated health (SRH) derived indirectly using four different small area estimation methods for the Vadu (small) area from the national Study on Global AGEing (SAGE) survey with estimates derived directly from the Vadu SAGE survey. The indirect synthetic estimate for Vadu was 24% whereas the model based estimates were 45.6% and 45.7% with smaller prediction errors and comparable to the direct survey estimate of 50%. The model based techniques were better suited to estimate the prevalence of SRH than the indirect synthetic method. We conclude that a simplified mixed effects regression model can produce valid small area estimates of SRH.
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Affiliation(s)
- Siddhivinayak Hirve
- Vadu Rural Health Program, KEM Hospital Research Center, Pune, India; Umeå Centre for Global Health Research, Division of Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
| | | | - Sanjay Juvekar
- Vadu Rural Health Program, KEM Hospital Research Center, Pune, India.
| | - Yulia Blomstedt
- Umeå Centre for Global Health Research, Division of Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
| | - Stig Wall
- Umeå Centre for Global Health Research, Division of Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
| | | | - Nawi Ng
- Umeå Centre for Global Health Research, Division of Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
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