Chenghui Li PhD, Cheng Peng PhD, Peter DelNero PhD, Mahima Saini B.Pharm, Mario Schootman PhD
{"title":"阿肯色州所有付费者索赔数据库的抽样覆盖范围，按县的持续贫困状况分类。","authors":"Chenghui Li PhD, Cheng Peng PhD, Peter DelNero PhD, Mahima Saini B.Pharm, Mario Schootman PhD","doi":"10.1111/1475-6773.14342","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objectives</h3>\n \n <p>To evaluate the quality of Arkansas All-Payer Claims Database (APCD) for disparity research in persistent poverty areas by determining (1) its representativeness of Arkansas population, (2) variation by county, and (3) differences in coverage between persistent poverty and other counties.</p>\n </section>\n \n <section>\n \n <h3> Data Sources</h3>\n \n <p>Cross-sectional study using 2019 Arkansas APCD member enrollment data and county-level data from various agencies.</p>\n </section>\n \n <section>\n \n <h3> Data Collection/Extraction Methods</h3>\n \n <p>An alias identifier linked persons across insurance plans. County FIPS codes were used to extract county-level variables.</p>\n </section>\n \n <section>\n \n <h3> Study Design</h3>\n \n <p>Cohort 1 included individuals with ≥1 day of medical coverage in 2019. Cohort 2 included individuals with medical coverage in June, 2019. Cohort 3 included individuals with continuous medical coverage in 2019. Sampling proportions of a county's population in the three cohorts were compared between persistent poverty and other counties. Inverse-variance weighted linear regression was used to identify county-level socioeconomic and demographic characteristics associated with inclusion in each cohort.</p>\n </section>\n \n <section>\n \n <h3> Principal Findings</h3>\n \n <p>In 2019, 73.6% of Arkansans had medical coverage for ≥1 day (Cohort 1), 66.3% had coverage in June (Cohort 2), and 58.8% had continuous coverage (Cohort 3) in APCD. Sampling proportions varied by county (median[range]: Cohort 1, 78% [58%–95%]; Cohort 2, 71% [51%–88%]; and Cohort 3, 64% [44%–80%]), and were higher among persistent poverty counties than others for all three cohorts (mean [SD], persistent poverty vs. other: Cohort 1: 80.9% [6.4%] vs. 77.1% [6.3%], <i>p</i> = 0.04; Cohort 2: 74.0% [6.4%] vs. 70.1% [6.2%], <i>p</i> = 0.03; Cohort 3: 66.4% [6.1%] vs. 62.7% [6.0%], <i>p</i> = 0.03). In the 2019 APCD, larger counties and those with higher proportions of females or persons 65+ years had higher coverage, whereas counties with higher per capita household income, median home value, or disproportionately more persons of other races (non-White and non-Black) had lower coverage (<i>p</i> < 0.05 for all three cohorts).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>The Arkansas APCD had good coverage of Arkansas population. Coverage was higher in persistent poverty counties than others.</p>\n </section>\n </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 4","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sampling coverage of the Arkansas all-payer claims database by County's persistent poverty designation\",\"authors\":\"Chenghui Li PhD, Cheng Peng PhD, Peter DelNero PhD, Mahima Saini B.Pharm, Mario Schootman PhD\",\"doi\":\"10.1111/1475-6773.14342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objectives</h3>\\n \\n <p>To evaluate the quality of Arkansas All-Payer Claims Database (APCD) for disparity research in persistent poverty areas by determining (1) its representativeness of Arkansas population, (2) variation by county, and (3) differences in coverage between persistent poverty and other counties.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Data Sources</h3>\\n \\n <p>Cross-sectional study using 2019 Arkansas APCD member enrollment data and county-level data from various agencies.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Data Collection/Extraction Methods</h3>\\n \\n <p>An alias identifier linked persons across insurance plans. County FIPS codes were used to extract county-level variables.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Study Design</h3>\\n \\n <p>Cohort 1 included individuals with ≥1 day of medical coverage in 2019. Cohort 2 included individuals with medical coverage in June, 2019. Cohort 3 included individuals with continuous medical coverage in 2019. Sampling proportions of a county's population in the three cohorts were compared between persistent poverty and other counties. Inverse-variance weighted linear regression was used to identify county-level socioeconomic and demographic characteristics associated with inclusion in each cohort.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Principal Findings</h3>\\n \\n <p>In 2019, 73.6% of Arkansans had medical coverage for ≥1 day (Cohort 1), 66.3% had coverage in June (Cohort 2), and 58.8% had continuous coverage (Cohort 3) in APCD. Sampling proportions varied by county (median[range]: Cohort 1, 78% [58%–95%]; Cohort 2, 71% [51%–88%]; and Cohort 3, 64% [44%–80%]), and were higher among persistent poverty counties than others for all three cohorts (mean [SD], persistent poverty vs. other: Cohort 1: 80.9% [6.4%] vs. 77.1% [6.3%], <i>p</i> = 0.04; Cohort 2: 74.0% [6.4%] vs. 70.1% [6.2%], <i>p</i> = 0.03; Cohort 3: 66.4% [6.1%] vs. 62.7% [6.0%], <i>p</i> = 0.03). In the 2019 APCD, larger counties and those with higher proportions of females or persons 65+ years had higher coverage, whereas counties with higher per capita household income, median home value, or disproportionately more persons of other races (non-White and non-Black) had lower coverage (<i>p</i> < 0.05 for all three cohorts).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>The Arkansas APCD had good coverage of Arkansas population. Coverage was higher in persistent poverty counties than others.</p>\\n </section>\\n </div>\",\"PeriodicalId\":55065,\"journal\":{\"name\":\"Health Services Research\",\"volume\":\"59 4\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Services Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/1475-6773.14342\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Services Research","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/1475-6773.14342","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Sampling coverage of the Arkansas all-payer claims database by County's persistent poverty designation
Objectives
To evaluate the quality of Arkansas All-Payer Claims Database (APCD) for disparity research in persistent poverty areas by determining (1) its representativeness of Arkansas population, (2) variation by county, and (3) differences in coverage between persistent poverty and other counties.
Data Sources
Cross-sectional study using 2019 Arkansas APCD member enrollment data and county-level data from various agencies.
Data Collection/Extraction Methods
An alias identifier linked persons across insurance plans. County FIPS codes were used to extract county-level variables.
Study Design
Cohort 1 included individuals with ≥1 day of medical coverage in 2019. Cohort 2 included individuals with medical coverage in June, 2019. Cohort 3 included individuals with continuous medical coverage in 2019. Sampling proportions of a county's population in the three cohorts were compared between persistent poverty and other counties. Inverse-variance weighted linear regression was used to identify county-level socioeconomic and demographic characteristics associated with inclusion in each cohort.
Principal Findings
In 2019, 73.6% of Arkansans had medical coverage for ≥1 day (Cohort 1), 66.3% had coverage in June (Cohort 2), and 58.8% had continuous coverage (Cohort 3) in APCD. Sampling proportions varied by county (median[range]: Cohort 1, 78% [58%–95%]; Cohort 2, 71% [51%–88%]; and Cohort 3, 64% [44%–80%]), and were higher among persistent poverty counties than others for all three cohorts (mean [SD], persistent poverty vs. other: Cohort 1: 80.9% [6.4%] vs. 77.1% [6.3%], p = 0.04; Cohort 2: 74.0% [6.4%] vs. 70.1% [6.2%], p = 0.03; Cohort 3: 66.4% [6.1%] vs. 62.7% [6.0%], p = 0.03). In the 2019 APCD, larger counties and those with higher proportions of females or persons 65+ years had higher coverage, whereas counties with higher per capita household income, median home value, or disproportionately more persons of other races (non-White and non-Black) had lower coverage (p < 0.05 for all three cohorts).
Conclusions
The Arkansas APCD had good coverage of Arkansas population. Coverage was higher in persistent poverty counties than others.
期刊介绍：
Health Services Research (HSR) is a peer-reviewed scholarly journal that provides researchers and public and private policymakers with the latest research findings, methods, and concepts related to the financing, organization, delivery, evaluation, and outcomes of health services. Rated as one of the top journals in the fields of health policy and services and health care administration, HSR publishes outstanding articles reporting the findings of original investigations that expand knowledge and understanding of the wide-ranging field of health care and that will help to improve the health of individuals and communities.
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