{"title":"印度缓解和预防糖尿病增长的脆弱性指数:分类分析","authors":"Sujata Sujata PhD , Gayathri B. PhD , Ramna Thakur PhD","doi":"10.1016/j.vhri.2023.09.009","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p>This study aimed to provide a vulnerability index (VI) for identifying vulnerable regions in different states of India, which may serve as a tool for state- and district-level planning for mitigation and prevention of diabetes growth in the country.</p></div><div><h3>Methods</h3><p>Using data on 13 indicators under 4 domains, we generated domain-specific and overall VIs at state (36 states/union territories) and district levels (640 districts) using the percentile ranking method. The association of diabetes with individuals’ socioeconomic status at different levels of regional vulnerability has also been observed through multivariable logistic regression models.</p></div><div><h3>Results</h3><p>On a scale of 0 to 1, there are 13 states with an overall VI of >0.70, of which 5 states are from southern regions of India. A low VI has been achieved by socioeconomically backward states. We observed that prevalence rates and vulnerability levels for most of the top and bottom 11 states are in the same line. District-level analysis showed that the 20 most vulnerable and least vulnerable districts are mostly from coastal and socioeconomically backward states of the country, respectively. Furthermore, logistic regression revealed that rural adults and females are less likely to be diabetic in all vulnerability quartiles. The oldest, Muslims, wealthiest, widowed/deserted/separated, and those with schooling ≤12 years are significantly more likely to be diabetic than their counterparts.</p></div><div><h3>Conclusion</h3><p>The constructed VI is vital for identifying vulnerable areas and planners and policy-makers may use this comprehensive index and domain-specific VIs to prioritize resource allocation.</p></div>","PeriodicalId":23497,"journal":{"name":"Value in health regional issues","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Vulnerability Index for Mitigation and Prevention of Diabetes Growth in India: A Disaggregated Analysis\",\"authors\":\"Sujata Sujata PhD , Gayathri B. PhD , Ramna Thakur PhD\",\"doi\":\"10.1016/j.vhri.2023.09.009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><p>This study aimed to provide a vulnerability index (VI) for identifying vulnerable regions in different states of India, which may serve as a tool for state- and district-level planning for mitigation and prevention of diabetes growth in the country.</p></div><div><h3>Methods</h3><p>Using data on 13 indicators under 4 domains, we generated domain-specific and overall VIs at state (36 states/union territories) and district levels (640 districts) using the percentile ranking method. The association of diabetes with individuals’ socioeconomic status at different levels of regional vulnerability has also been observed through multivariable logistic regression models.</p></div><div><h3>Results</h3><p>On a scale of 0 to 1, there are 13 states with an overall VI of >0.70, of which 5 states are from southern regions of India. A low VI has been achieved by socioeconomically backward states. We observed that prevalence rates and vulnerability levels for most of the top and bottom 11 states are in the same line. District-level analysis showed that the 20 most vulnerable and least vulnerable districts are mostly from coastal and socioeconomically backward states of the country, respectively. Furthermore, logistic regression revealed that rural adults and females are less likely to be diabetic in all vulnerability quartiles. The oldest, Muslims, wealthiest, widowed/deserted/separated, and those with schooling ≤12 years are significantly more likely to be diabetic than their counterparts.</p></div><div><h3>Conclusion</h3><p>The constructed VI is vital for identifying vulnerable areas and planners and policy-makers may use this comprehensive index and domain-specific VIs to prioritize resource allocation.</p></div>\",\"PeriodicalId\":23497,\"journal\":{\"name\":\"Value in health regional issues\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2023-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Value in health regional issues\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S221210992300095X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Value in health regional issues","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221210992300095X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
A Vulnerability Index for Mitigation and Prevention of Diabetes Growth in India: A Disaggregated Analysis
Objectives
This study aimed to provide a vulnerability index (VI) for identifying vulnerable regions in different states of India, which may serve as a tool for state- and district-level planning for mitigation and prevention of diabetes growth in the country.
Methods
Using data on 13 indicators under 4 domains, we generated domain-specific and overall VIs at state (36 states/union territories) and district levels (640 districts) using the percentile ranking method. The association of diabetes with individuals’ socioeconomic status at different levels of regional vulnerability has also been observed through multivariable logistic regression models.
Results
On a scale of 0 to 1, there are 13 states with an overall VI of >0.70, of which 5 states are from southern regions of India. A low VI has been achieved by socioeconomically backward states. We observed that prevalence rates and vulnerability levels for most of the top and bottom 11 states are in the same line. District-level analysis showed that the 20 most vulnerable and least vulnerable districts are mostly from coastal and socioeconomically backward states of the country, respectively. Furthermore, logistic regression revealed that rural adults and females are less likely to be diabetic in all vulnerability quartiles. The oldest, Muslims, wealthiest, widowed/deserted/separated, and those with schooling ≤12 years are significantly more likely to be diabetic than their counterparts.
Conclusion
The constructed VI is vital for identifying vulnerable areas and planners and policy-makers may use this comprehensive index and domain-specific VIs to prioritize resource allocation.