Jonnby S LaGuardia, Madeline G Chin, Sarah Fadich, Katarina B J Morgan, Halena H Ngo, Meiwand Bedar, Shahrzad Moghadam, Kelly X Huang, Christy Mallory, Justine C Lee
Objective: To systematically review Medicaid policies state-by-state for gender-affirming surgery coverage.
Data sources and study setting: Primary data were collected for each US state utilizing the LexisNexis legal database, state legislature publications, and Medicaid manuals.
Study design: A cross-sectional study evaluating Medicaid coverage for numerous gender-affirming surgeries.
Data collection/extraction methods: We previously reported on state health policies that protect gender-affirming care under Medicaid coverage. Building upon our prior work, we systematically assessed the 27 states with protective policies to determine coverage for each type of gender-affirming surgery. We analyzed Medicaid coverage for gender-affirming surgeries in four domains: chest, genital, craniofacial and neck reconstruction, and miscellaneous procedures. Medicaid coverage for each type of surgery was categorized as explicitly covered, explicitly noncovered, or not described.
Principal findings: Among the 27 states with protective Medicaid policies, 17 states (63.0%) provided explicit coverage for at least one gender-affirming chest procedure and at least one gender-affirming genital procedure, while only eight states (29.6%) provided explicit coverage for at least one craniofacial and neck procedure (p = 0.04). Coverage for specific surgical procedures within these three anatomical domains varied. The most common explicitly covered procedures were breast reduction/mastectomy and hysterectomy (n = 17, 63.0%). The most common explicitly noncovered surgery was reversal surgery (n = 12, 44.4%). Several states did not describe the specific surgical procedures covered; thus, final coverage rates are indeterminate.
Conclusions: In 2022, 52.9% of states had health policies that protected gender-affirming care under Medicaid; however, coverage for various gender-affirming surgical procedures remains both variable and occasionally unspecified. When specified, craniofacial and neck reconstruction is the least covered anatomical area compared with chest and genital reconstruction.
{"title":"Medicaid coverage for gender-affirming surgery: A state-by-state review.","authors":"Jonnby S LaGuardia, Madeline G Chin, Sarah Fadich, Katarina B J Morgan, Halena H Ngo, Meiwand Bedar, Shahrzad Moghadam, Kelly X Huang, Christy Mallory, Justine C Lee","doi":"10.1111/1475-6773.14338","DOIUrl":"https://doi.org/10.1111/1475-6773.14338","url":null,"abstract":"<p><strong>Objective: </strong>To systematically review Medicaid policies state-by-state for gender-affirming surgery coverage.</p><p><strong>Data sources and study setting: </strong>Primary data were collected for each US state utilizing the LexisNexis legal database, state legislature publications, and Medicaid manuals.</p><p><strong>Study design: </strong>A cross-sectional study evaluating Medicaid coverage for numerous gender-affirming surgeries.</p><p><strong>Data collection/extraction methods: </strong>We previously reported on state health policies that protect gender-affirming care under Medicaid coverage. Building upon our prior work, we systematically assessed the 27 states with protective policies to determine coverage for each type of gender-affirming surgery. We analyzed Medicaid coverage for gender-affirming surgeries in four domains: chest, genital, craniofacial and neck reconstruction, and miscellaneous procedures. Medicaid coverage for each type of surgery was categorized as explicitly covered, explicitly noncovered, or not described.</p><p><strong>Principal findings: </strong>Among the 27 states with protective Medicaid policies, 17 states (63.0%) provided explicit coverage for at least one gender-affirming chest procedure and at least one gender-affirming genital procedure, while only eight states (29.6%) provided explicit coverage for at least one craniofacial and neck procedure (p = 0.04). Coverage for specific surgical procedures within these three anatomical domains varied. The most common explicitly covered procedures were breast reduction/mastectomy and hysterectomy (n = 17, 63.0%). The most common explicitly noncovered surgery was reversal surgery (n = 12, 44.4%). Several states did not describe the specific surgical procedures covered; thus, final coverage rates are indeterminate.</p><p><strong>Conclusions: </strong>In 2022, 52.9% of states had health policies that protected gender-affirming care under Medicaid; however, coverage for various gender-affirming surgical procedures remains both variable and occasionally unspecified. When specified, craniofacial and neck reconstruction is the least covered anatomical area compared with chest and genital reconstruction.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alice J. Chen PhD, Michael R. Richards MD PhD MPH, Rachel Shriver PhD
Objective
This study aims to examine how variation in physicians' treatment decisions for newborn deliveries responds to changes in the hospital-level norms for obstetric clinical decision-making.
Data Sources
All hospital-based births in Florida from 2003 through 2017.
Study Design
Difference-in-differences approach is adopted that leverages obstetric unit closures as the source of identifying variation to exogenously shift obstetricians to a new, nearby hospital with different propensities to approach newborn deliveries less intensively.
Data Extraction
Births attributed to physicians continuously observed 2 years before the closure event and 2 years after the closure event (treatment group physicians) or for identical time periods around a randomly assigned placebo closure date (control group physicians).
Principal Findings
All of the physicians meeting our inclusion criteria shifted their births to a new hospital less than 20 miles from the hospital shuttering its obstetric unit. The new hospitals approached newborn births more conservatively, and treatment group physicians sharply became less aggressive in their newborn birth clinical management (e.g., use of C-section). The immediate 11-percentage point (33%) increase in delivering newborns without any procedure behavior change is statistically significant (p value <0.01) and persistent after the closure event; however, the physicians' payer and patient mix are unchanged.
Conclusions
Obstetric physician behavior change appears highly malleable and sensitive to the practice patterns of other physicians delivering newborns at the same hospital. Incentives and policies that encourage more appropriate clinical care norms hospital-wide could sharply improve physician treatment decisions, with benefits for maternal and infant outcomes.
{"title":"Fitting in? Physician practice style after forced relocation","authors":"Alice J. Chen PhD, Michael R. Richards MD PhD MPH, Rachel Shriver PhD","doi":"10.1111/1475-6773.14340","DOIUrl":"10.1111/1475-6773.14340","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study aims to examine how variation in physicians' treatment decisions for newborn deliveries responds to changes in the hospital-level norms for obstetric clinical decision-making.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources</h3>\u0000 \u0000 <p>All hospital-based births in Florida from 2003 through 2017.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Difference-in-differences approach is adopted that leverages obstetric unit closures as the source of identifying variation to exogenously shift obstetricians to a new, nearby hospital with different propensities to approach newborn deliveries less intensively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Extraction</h3>\u0000 \u0000 <p>Births attributed to physicians continuously observed 2 years before the closure event and 2 years after the closure event (treatment group physicians) or for identical time periods around a randomly assigned placebo closure date (control group physicians).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>All of the physicians meeting our inclusion criteria shifted their births to a new hospital less than 20 miles from the hospital shuttering its obstetric unit. The new hospitals approached newborn births more conservatively, and treatment group physicians sharply became less aggressive in their newborn birth clinical management (e.g., use of C-section). The immediate 11-percentage point (33%) increase in delivering newborns without any procedure behavior change is statistically significant (p value <0.01) and persistent after the closure event; however, the physicians' payer and patient mix are unchanged.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Obstetric physician behavior change appears highly malleable and sensitive to the practice patterns of other physicians delivering newborns at the same hospital. Incentives and policies that encourage more appropriate clinical care norms hospital-wide could sharply improve physician treatment decisions, with benefits for maternal and infant outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrew N. Honken BS, Christopher W. Halladay ScM, Lisa E. Wootton LCSW, Alita R. Harmon LCSW, Cassandra L. Hua PhD, James L. Rudolph MD, Portia Y. Cornell PhD
Objective
To evaluate the impact on rural Veterans' access to social work services of a Department of Veterans Affairs (VA) national program to increase social work staffing, by Veterans' rurality, race, and complex care needs.
Data Sources and Study Setting
Data obtained from VA Corporate Data Warehouse, including sites that participated in the social work program between October 1, 2016 and September 30, 2021.
Study Design
The study outcome was monthly number of Veterans per 1000 individuals with 1+ social work encounters. We used difference-in-differences to estimate the program effect on urban, rural, and highly rural Veterans. Among rural and highly rural Veterans, we stratified by race (American Indian or Alaskan Native, Asian, Black, Native Hawaiian or Other Pacific Islander, and White) and complex care needs (homelessness, high hospitalization risk, and dementia).
Data Collection
We defined a cohort of 740,669 Veterans (32,434,001 monthly observations) who received primary care at a participating site.
Principal Findings
Average monthly social work use was 8.7 Veterans per 1000 individuals. The program increased access by 49% (4.3 per 1000; 95% confidence interval, 2.2–6.3). Rural Veterans' social work access increased by 57% (5.0; 3.6–6.3). Among rural/highly rural Veterans, the program increased social work access for those with high hospitalization risk by 63% (24.5; 18.2–30.9), and for Veterans experiencing homelessness, 35% (13.4; 5.2–21.7). By race, the program increased access for Black Veterans by 53% (6.1; 2.1–10.2) and for Asian Veterans by 82% (5.1; 2.2–7.9).
Conclusions
At rural VA primary care sites with social work staffing below recommended levels, Black and Asian Veterans and those experiencing homelessness and high hospitalization risk may have unmet needs warranting social work services.
{"title":"Differential effects of a social work staffing intervention on social work access among rural and highly rural Veterans: A cohort study","authors":"Andrew N. Honken BS, Christopher W. Halladay ScM, Lisa E. Wootton LCSW, Alita R. Harmon LCSW, Cassandra L. Hua PhD, James L. Rudolph MD, Portia Y. Cornell PhD","doi":"10.1111/1475-6773.14327","DOIUrl":"10.1111/1475-6773.14327","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To evaluate the impact on rural Veterans' access to social work services of a Department of Veterans Affairs (VA) national program to increase social work staffing, by Veterans' rurality, race, and complex care needs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Data obtained from VA Corporate Data Warehouse, including sites that participated in the social work program between October 1, 2016 and September 30, 2021.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>The study outcome was monthly number of Veterans per 1000 individuals with 1+ social work encounters. We used difference-in-differences to estimate the program effect on urban, rural, and highly rural Veterans. Among rural and highly rural Veterans, we stratified by race (American Indian or Alaskan Native, Asian, Black, Native Hawaiian or Other Pacific Islander, and White) and complex care needs (homelessness, high hospitalization risk, and dementia).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection</h3>\u0000 \u0000 <p>We defined a cohort of 740,669 Veterans (32,434,001 monthly observations) who received primary care at a participating site.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Average monthly social work use was 8.7 Veterans per 1000 individuals. The program increased access by 49% (4.3 per 1000; 95% confidence interval, 2.2–6.3). Rural Veterans' social work access increased by 57% (5.0; 3.6–6.3). Among rural/highly rural Veterans, the program increased social work access for those with high hospitalization risk by 63% (24.5; 18.2–30.9), and for Veterans experiencing homelessness, 35% (13.4; 5.2–21.7). By race, the program increased access for Black Veterans by 53% (6.1; 2.1–10.2) and for Asian Veterans by 82% (5.1; 2.2–7.9).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>At rural VA primary care sites with social work staffing below recommended levels, Black and Asian Veterans and those experiencing homelessness and high hospitalization risk may have unmet needs warranting social work services.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 S2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julia D. Interrante PhD, MPH, Cynthia Pando MA, Alyssa H. Fritz MPH, RD, CLC, Katy B. Kozhimannil PhD, MPA
<div> <section> <h3> Objective</h3> <p>The study aims to examine maternal care among Hispanic birthing people by primary language and state policy environment.</p> </section> <section> <h3> Data Sources and Study Setting</h3> <p>Pooled data from 2016 to 2020 Pregnancy Risk Assessment Monitoring System surveys from 44 states and two jurisdictions.</p> </section> <section> <h3> Study Design</h3> <p>Using multivariable logistic regression, we calculated adjusted predicted probabilities of maternal care utilization (visit attendance, timeliness, adequacy) and quality (receipt of guideline-recommended care components). We examined outcomes by primary language (Spanish, English) and two binary measures of state policy environment: (1) expanded Medicaid eligibility to those <133% Federal Poverty Level, (2) waived five-year waiting period for pregnant immigrants to access Medicaid.</p> </section> <section> <h3> Data Collection/Extraction Methods</h3> <p>Survey responses from 35,779 postpartum individuals with self-reported Hispanic ethnicity who gave birth during 2016–2020.</p> </section> <section> <h3> Principal Findings</h3> <p>Compared to English-speaking Hispanic people, Spanish-speaking individuals reported lower preconception care attendance and worse timeliness and adequacy of prenatal care.</p> <p>In states without Medicaid expansion and immigrant Medicaid coverage, Hispanic birthing people had, respectively, 2.3 (95% CI:0.6, 3.9) and 3.1 (95% CI:1.6, 4.6) percentage-point lower postpartum care attendance and 4.2 (95% CI:2.1, 6.3) and 9.2 (95% CI:7.2, 11.2) percentage-point lower prenatal care quality than people in states with these policies.</p> <p>In states with these policies, Spanish-speaking Hispanic people had 3.3 (95% CI:1.3, 5.4) and 3.0 (95% CI:0.9, 5.1) percentage-point lower prenatal care adequacy, but 1.3 (95% CI:−1.1, 3.6) and 2.7 (95% CI:0.2, 5.1) percentage-point higher postpartum care quality than English-speaking Hispanic people. In states without these policies, those same comparisons were 7.3 (95% CI:3.8, 10.8) and 7.9 (95% CI:4.6, 11.1) percentage-points lower and 9.6 (95% CI:5.5, 13.7) and 5.3 (95% CI:1.8, 8.9) percentage-points higher.</p> </section> <section> <h3> Conclusions</h3> <p>Perinatal care utilization and quality vary among Hispanic birthing people by
{"title":"Perinatal care among Hispanic birthing people: Differences by primary language and state policy environment","authors":"Julia D. Interrante PhD, MPH, Cynthia Pando MA, Alyssa H. Fritz MPH, RD, CLC, Katy B. Kozhimannil PhD, MPA","doi":"10.1111/1475-6773.14339","DOIUrl":"10.1111/1475-6773.14339","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The study aims to examine maternal care among Hispanic birthing people by primary language and state policy environment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Pooled data from 2016 to 2020 Pregnancy Risk Assessment Monitoring System surveys from 44 states and two jurisdictions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Using multivariable logistic regression, we calculated adjusted predicted probabilities of maternal care utilization (visit attendance, timeliness, adequacy) and quality (receipt of guideline-recommended care components). We examined outcomes by primary language (Spanish, English) and two binary measures of state policy environment: (1) expanded Medicaid eligibility to those <133% Federal Poverty Level, (2) waived five-year waiting period for pregnant immigrants to access Medicaid.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Survey responses from 35,779 postpartum individuals with self-reported Hispanic ethnicity who gave birth during 2016–2020.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Compared to English-speaking Hispanic people, Spanish-speaking individuals reported lower preconception care attendance and worse timeliness and adequacy of prenatal care.</p>\u0000 \u0000 <p>In states without Medicaid expansion and immigrant Medicaid coverage, Hispanic birthing people had, respectively, 2.3 (95% CI:0.6, 3.9) and 3.1 (95% CI:1.6, 4.6) percentage-point lower postpartum care attendance and 4.2 (95% CI:2.1, 6.3) and 9.2 (95% CI:7.2, 11.2) percentage-point lower prenatal care quality than people in states with these policies.</p>\u0000 \u0000 <p>In states with these policies, Spanish-speaking Hispanic people had 3.3 (95% CI:1.3, 5.4) and 3.0 (95% CI:0.9, 5.1) percentage-point lower prenatal care adequacy, but 1.3 (95% CI:−1.1, 3.6) and 2.7 (95% CI:0.2, 5.1) percentage-point higher postpartum care quality than English-speaking Hispanic people. In states without these policies, those same comparisons were 7.3 (95% CI:3.8, 10.8) and 7.9 (95% CI:4.6, 11.1) percentage-points lower and 9.6 (95% CI:5.5, 13.7) and 5.3 (95% CI:1.8, 8.9) percentage-points higher.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Perinatal care utilization and quality vary among Hispanic birthing people by ","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chenghui Li PhD, Cheng Peng PhD, Peter DelNero PhD, Mahima Saini B.Pharm, Mario Schootman PhD
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.
{"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":"10.1111/1475-6773.14342","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources</h3>\u0000 \u0000 <p>Cross-sectional study using 2019 Arkansas APCD member enrollment data and county-level data from various agencies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>An alias identifier linked persons across insurance plans. County FIPS codes were used to extract county-level variables.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The Arkansas APCD had good coverage of Arkansas population. Coverage was higher in persistent poverty counties than others.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To examine the impact of the Affordable Care Act (ACA) health insurance exchanges (Marketplace) on the rate of uninsured discharges in Texas.
Data Source and Study Setting
Secondary discharge data from 2011 to 2019 from Texas.
Study Design
We conducted a retrospective study estimating the effects of the ACA Marketplace using difference-in-difference regressions, with the main outcome being the uninsured discharge rate. We stratified our sample by patient's race, age, gender, urbanicity, major diagnostic categories (MDC), and emergent type of admissions.
Data Collection/Extraction Methods
We used Texas hospital discharge records for non-elderly adults collected by the state of Texas and included acute care hospitals who reported data from 2011 to 2019.
Principal Findings
The expansion of insurance through ACA Marketplaces led to reductions in the uninsured discharge rate by 9.9% (95% CI, −17.5%, −2.3%) relative to the baseline mean. The effects of the ACA were felt strongest in counties with any share of Hispanic, in counties with a larger population of Black, and other racial groups, in counties with a significant share of female and older age individuals, in counties considered to be urban, in high-volume diagnoses, and emergent type of admissions.
Conclusions
These findings indicate that the ACA facilitated a shift in hospital payor mix from uninsured to insured.
{"title":"Effects of Affordable Care Act on uninsured hospitalization: Evidence from Texas","authors":"Nima Khodakarami PhD, Benjamin Ukert PhD","doi":"10.1111/1475-6773.14334","DOIUrl":"10.1111/1475-6773.14334","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine the impact of the Affordable Care Act (ACA) health insurance exchanges (Marketplace) on the rate of uninsured discharges in Texas.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Source and Study Setting</h3>\u0000 \u0000 <p>Secondary discharge data from 2011 to 2019 from Texas.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We conducted a retrospective study estimating the effects of the ACA Marketplace using difference-in-difference regressions, with the main outcome being the uninsured discharge rate. We stratified our sample by patient's race, age, gender, urbanicity, major diagnostic categories (MDC), and emergent type of admissions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>We used Texas hospital discharge records for non-elderly adults collected by the state of Texas and included acute care hospitals who reported data from 2011 to 2019.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>The expansion of insurance through ACA Marketplaces led to reductions in the uninsured discharge rate by 9.9% (95% CI, −17.5%, −2.3%) relative to the baseline mean. The effects of the ACA were felt strongest in counties with any share of Hispanic, in counties with a larger population of Black, and other racial groups, in counties with a significant share of female and older age individuals, in counties considered to be urban, in high-volume diagnoses, and emergent type of admissions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>These findings indicate that the ACA facilitated a shift in hospital payor mix from uninsured to insured.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14334","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141236435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Neal Axon MD, Ralph Ward PhD, Ahmed Mohamed PhD, Charlene Pope PhD, Michela Stephens MPH, Patrick D. Mauldin PhD, Mulugeta Gebregziabher PhD
Objective
To examine changes in hospitalization trends and healthcare utilization among Veterans following Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act implementation.
Data Sources and Study Setting
VA Corporate Data Warehouse and Centers for Medicare and Medicaid Services datasets.
Study Design
Retrospective cohort study to compare 7- and 30-day rates for unplanned readmission and emergency department visits following index hospital stays based on payor type (VHA facility stay, VA-funded stay in community facility [CC], or Medicare-funded community stay [CMS]). Segmented regression models were used to compare payors and estimate changes in outcome levels and slopes following MISSION Act implementation.
Data Collection/Extraction Methods
Veterans with active VA primary care utilization and ≥1 acute hospitalization between January 1, 2016 and December 31, 2021.
Principal Findings
Monthly index stays increased for all payors until MISSION Act implementation, when VHA and CMS admissions declined while CC admissions accelerated and overtook VHA admissions. In December 2021, CC admissions accounted for 54% of index admissions, up from 25% in January 2016. From adjusted models, just prior to implementation (May 2019), Veterans with CC admissions had 47% greater risk of 7-day readmission (risk ratio [RR]: 1.47, 95% confidence interval [CI]: 1.43, 1.51) and 20% greater risk of 30-day readmission (RR: 1.20, 95% CI: 1.19, 1.22) compared with those with VHA admissions; both effects persisted post-implementation. Pre-implementation CC admissions were also associated with higher 7- and 30-day ED visits, but both risks were substantially lower by study termination (RR: 0.90, 95% CI: 0.88, 0.91) and (RR: 0.89, 95% CI: 0.87, 0.90), respectively.
Conclusions
MISSION Act implementation was associated with substantial shifts in treatment site and federal payor for Veteran hospitalizations. Post-implementation readmission risk was estimated to be higher for those with CC and CMS index admissions, while post-implementation risk of ED utilization following CC admissions was estimated to be lower compared with VHA index admissions. Reasons for this divergence require further investigation.
{"title":"Trends in Veteran hospitalizations and associated readmissions and emergency department visits during the MISSION Act era","authors":"R. Neal Axon MD, Ralph Ward PhD, Ahmed Mohamed PhD, Charlene Pope PhD, Michela Stephens MPH, Patrick D. Mauldin PhD, Mulugeta Gebregziabher PhD","doi":"10.1111/1475-6773.14332","DOIUrl":"10.1111/1475-6773.14332","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine changes in hospitalization trends and healthcare utilization among Veterans following Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>VA Corporate Data Warehouse and Centers for Medicare and Medicaid Services datasets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Retrospective cohort study to compare 7- and 30-day rates for unplanned readmission and emergency department visits following index hospital stays based on payor type (VHA facility stay, VA-funded stay in community facility [CC], or Medicare-funded community stay [CMS]). Segmented regression models were used to compare payors and estimate changes in outcome levels and slopes following MISSION Act implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Veterans with active VA primary care utilization and ≥1 acute hospitalization between January 1, 2016 and December 31, 2021.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Monthly index stays increased for all payors until MISSION Act implementation, when VHA and CMS admissions declined while CC admissions accelerated and overtook VHA admissions. In December 2021, CC admissions accounted for 54% of index admissions, up from 25% in January 2016. From adjusted models, just prior to implementation (May 2019), Veterans with CC admissions had 47% greater risk of 7-day readmission (risk ratio [RR]: 1.47, 95% confidence interval [CI]: 1.43, 1.51) and 20% greater risk of 30-day readmission (RR: 1.20, 95% CI: 1.19, 1.22) compared with those with VHA admissions; both effects persisted post-implementation. Pre-implementation CC admissions were also associated with higher 7- and 30-day ED visits, but both risks were substantially lower by study termination (RR: 0.90, 95% CI: 0.88, 0.91) and (RR: 0.89, 95% CI: 0.87, 0.90), respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>MISSION Act implementation was associated with substantial shifts in treatment site and federal payor for Veteran hospitalizations. Post-implementation readmission risk was estimated to be higher for those with CC and CMS index admissions, while post-implementation risk of ED utilization following CC admissions was estimated to be lower compared with VHA index admissions. Reasons for this divergence require further investigation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paul R. Shafer PhD, Yingzhe Yuan MPH, Yevgeniy Feyman PhD, Megan E. Price MS, Aigerim Kabdiyeva MPhil, Stuart M. Figueroa MSW, Yi-Jung Shen MS, Jonathan R. Nebeker MD, MS, Merry C. Ward PhD, Kiersten L. Strombotne PhD, Steven D. Pizer PhD
Objective
To estimate a causal relationship between mental health staffing and time to initiation of mental health care for new patients.
Data Sources and Study Setting
As the largest integrated health care delivery system in the United States, the Veterans Health Administration (VHA) provides a unique setting for isolating the effects of staffing on initiation of mental health care where demand is high and out-of-pocket costs are not a relevant confounder. We use data from the Department of Defense and VHA to obtain patient and facility characteristics and health care use.
Study Design
To isolate exogenous variation in mental health staffing, we used an instrumental variables approach—two-stage residual inclusion with a discrete time hazard model. Our outcome is time to initiation of mental health care after separation from active duty (first appointment) and our exposure is mental health staffing (standardized clinic time per 1000 VHA enrollees per pay period).
Data Collection/Extraction Methods
Our cohort consists of all Veterans separating from active duty between July 2014 and September 2017, who were enrolled in the VHA, and had at least one diagnosis of post-traumatic stress disorder, major depressive disorder, and/or substance use disorder in the year prior to separation from active duty (N = 54,209).
Principal Findings
An increase of 1 standard deviation in mental health staffing results in a higher likelihood of initiating mental health care (adjusted hazard ratio: 3.17, 95% confidence interval: 2.62, 3.84, p < 0.001). Models stratified by tertile of mental health staffing exhibit decreasing returns to scale.
Conclusions
Increases in mental health staffing led to faster initiation of care and are especially beneficial in facilities where staffing is lower, although initiation of care appears capacity-limited everywhere.
{"title":"Effect of mental health staffing inputs on initiation of care among recently separated Veterans","authors":"Paul R. Shafer PhD, Yingzhe Yuan MPH, Yevgeniy Feyman PhD, Megan E. Price MS, Aigerim Kabdiyeva MPhil, Stuart M. Figueroa MSW, Yi-Jung Shen MS, Jonathan R. Nebeker MD, MS, Merry C. Ward PhD, Kiersten L. Strombotne PhD, Steven D. Pizer PhD","doi":"10.1111/1475-6773.14333","DOIUrl":"10.1111/1475-6773.14333","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To estimate a causal relationship between mental health staffing and time to initiation of mental health care for new patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>As the largest integrated health care delivery system in the United States, the Veterans Health Administration (VHA) provides a unique setting for isolating the effects of staffing on initiation of mental health care where demand is high and out-of-pocket costs are not a relevant confounder. We use data from the Department of Defense and VHA to obtain patient and facility characteristics and health care use.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>To isolate exogenous variation in mental health staffing, we used an instrumental variables approach—two-stage residual inclusion with a discrete time hazard model. Our outcome is time to initiation of mental health care after separation from active duty (first appointment) and our exposure is mental health staffing (standardized clinic time per 1000 VHA enrollees per pay period).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Our cohort consists of all Veterans separating from active duty between July 2014 and September 2017, who were enrolled in the VHA, and had at least one diagnosis of post-traumatic stress disorder, major depressive disorder, and/or substance use disorder in the year prior to separation from active duty (<i>N</i> = 54,209).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>An increase of 1 standard deviation in mental health staffing results in a higher likelihood of initiating mental health care (adjusted hazard ratio: 3.17, 95% confidence interval: 2.62, 3.84, <i>p</i> < 0.001). Models stratified by tertile of mental health staffing exhibit decreasing returns to scale.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Increases in mental health staffing led to faster initiation of care and are especially beneficial in facilities where staffing is lower, although initiation of care appears capacity-limited everywhere.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 S2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Edward C. Norton PhD, Bryan E. Dowd PhD, Melissa M. Garrido PhD, Matthew L. Maciejewski PhD
<p><i>Health Services Research</i> encourages authors to report marginal effects instead of odds ratios for logistic regression with a binary outcome. Specifically, in the instructions for authors, Manuscript Formatting and Submission Requirements, section 2.4.2.2 Structured abstract and keywords, it reads “Reporting of odds ratios is discouraged (marginal effects preferred) except in case-control studies” (see the <i>HSR</i> website https://www.hsr.org/authors/manuscript-formatting-submission-requirements).</p><p>We applaud this decision. We also encourage other journals to make the same decision. It is time to end the reporting of odds ratios in the scientific literature for most research studies, except for case–control studies with matched samples.</p><p><i>HSR</i>'s decision is due to increasing recognition that odds ratios are not only confusing to non-researchers,<span><sup>1, 2</sup></span> but that researchers themselves often misinterpret them.<span><sup>3, 4</sup></span> Odds ratios are also of limited utility in meta-analyses. Marginal effects, which represent the difference in the probability of a binary outcome between comparison groups, are more straightforward to interpret and compare. Below, we illustrate the difficulties in interpreting odds ratios, outline the conditions that must be met for odds ratios to be compared directly, and explain how marginal effects overcome these difficulties.</p><p>Consider a hypothetical prospective cohort study of whether a new hospital-based discharge program affects the 30-day readmission rate, a binary outcome, observed for each patient who is discharged alive. The program's goal is to help eligible patients avoid unnecessary readmissions, and patients are randomized into participating in the program or not. Suppose that a carefully designed study estimates the logistic regression coefficient (the log odds) on the discharge program to be <span></span><math> <mrow> <mo>−</mo> <mn>0.2</mn> </mrow></math>, indicating that readmission rates are lower for patients who participate in the discharge program than patients who do not. When writing about the results, the researcher must decide how to report the magnitude of the change and has several choices for how to do so.</p><p>One option is to report the odds ratio, which in this case is <span></span><math> <mrow> <mn>0.82</mn> <mo>=</mo> <mi>exp</mi> <mfenced> <mrow> <mo>−</mo> <mn>0.2</mn> </mrow> </mfenced> </mrow></math>, and then compare it with other published odds ratios in the literature. However, this estimated odds ratio of 0.82 depends on an unobservable scaling factor that makes its interpretation conditional on the data and on the model specification.<span><sup>3, 5
{"title":"Requiem for odds ratios","authors":"Edward C. Norton PhD, Bryan E. Dowd PhD, Melissa M. Garrido PhD, Matthew L. Maciejewski PhD","doi":"10.1111/1475-6773.14337","DOIUrl":"10.1111/1475-6773.14337","url":null,"abstract":"<p><i>Health Services Research</i> encourages authors to report marginal effects instead of odds ratios for logistic regression with a binary outcome. Specifically, in the instructions for authors, Manuscript Formatting and Submission Requirements, section 2.4.2.2 Structured abstract and keywords, it reads “Reporting of odds ratios is discouraged (marginal effects preferred) except in case-control studies” (see the <i>HSR</i> website https://www.hsr.org/authors/manuscript-formatting-submission-requirements).</p><p>We applaud this decision. We also encourage other journals to make the same decision. It is time to end the reporting of odds ratios in the scientific literature for most research studies, except for case–control studies with matched samples.</p><p><i>HSR</i>'s decision is due to increasing recognition that odds ratios are not only confusing to non-researchers,<span><sup>1, 2</sup></span> but that researchers themselves often misinterpret them.<span><sup>3, 4</sup></span> Odds ratios are also of limited utility in meta-analyses. Marginal effects, which represent the difference in the probability of a binary outcome between comparison groups, are more straightforward to interpret and compare. Below, we illustrate the difficulties in interpreting odds ratios, outline the conditions that must be met for odds ratios to be compared directly, and explain how marginal effects overcome these difficulties.</p><p>Consider a hypothetical prospective cohort study of whether a new hospital-based discharge program affects the 30-day readmission rate, a binary outcome, observed for each patient who is discharged alive. The program's goal is to help eligible patients avoid unnecessary readmissions, and patients are randomized into participating in the program or not. Suppose that a carefully designed study estimates the logistic regression coefficient (the log odds) on the discharge program to be <span></span><math>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>0.2</mn>\u0000 </mrow></math>, indicating that readmission rates are lower for patients who participate in the discharge program than patients who do not. When writing about the results, the researcher must decide how to report the magnitude of the change and has several choices for how to do so.</p><p>One option is to report the odds ratio, which in this case is <span></span><math>\u0000 <mrow>\u0000 <mn>0.82</mn>\u0000 <mo>=</mo>\u0000 <mi>exp</mi>\u0000 <mfenced>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>0.2</mn>\u0000 </mrow>\u0000 </mfenced>\u0000 </mrow></math>, and then compare it with other published odds ratios in the literature. However, this estimated odds ratio of 0.82 depends on an unobservable scaling factor that makes its interpretation conditional on the data and on the model specification.<span><sup>3, 5","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141187228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianhui Xu PhD, Kelly E. Anderson PhD, Angela Liu PhD, Daniel Polsky PhD
Objective
To examine whether certain Medicare Advantage (MA) plan characteristics are associated with driving beneficiaries to providers that generate fewer avoidable hospital stays.
Data Sources
This paper primarily used 2018–2019 MA encounter data and traditional Medicare (TM) claims data for a nationally representative 20% sample of Medicare beneficiaries.
Study Design
For each plan design aspect—plan type, carrier, star rating, and network breadth—we estimated two adjusted Poisson regressions of avoidable hospital stays: one without clinician fixed effects and the other with. We calculated the difference between the coefficients to evaluate the extent to which patient sorting affected avoidable hospital stays relative to TM.
Data Extraction Methods
Our sample included Medicare beneficiaries 65 years and older who were continuously enrolled in either MA or TM during 2018–2019. Beneficiaries in our sample had one or more chronic, ambulatory care-sensitive conditions.
Principal Findings
Patient sorting can be attributed to certain characteristics of plan design aspects. For plan type, HMOs account for 86%, with PPOs accounting for only 14%. For carriers, Humana and smaller carriers account for 89%. For star ratings, high-star contracts account for 94%, with other stars only accounting for 6%. By network design, narrow network plan-counties explained 20% of the patient sorting effect.
Conclusions
While MA plans were found to be associated with driving beneficiaries to providers that generate fewer avoidable hospital stays, the effect is not homogeneous across the characteristics of MA plans. HMOs and high-star contracts are drivers of this MA phenomenon.
{"title":"Medicare Advantage plan characteristics associated with sorting their beneficiaries to providers that generate fewer avoidable hospital stays","authors":"Jianhui Xu PhD, Kelly E. Anderson PhD, Angela Liu PhD, Daniel Polsky PhD","doi":"10.1111/1475-6773.14335","DOIUrl":"10.1111/1475-6773.14335","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine whether certain Medicare Advantage (MA) plan characteristics are associated with driving beneficiaries to providers that generate fewer avoidable hospital stays.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources</h3>\u0000 \u0000 <p>This paper primarily used 2018–2019 MA encounter data and traditional Medicare (TM) claims data for a nationally representative 20% sample of Medicare beneficiaries.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>For each plan design aspect—plan type, carrier, star rating, and network breadth—we estimated two adjusted Poisson regressions of avoidable hospital stays: one without clinician fixed effects and the other with. We calculated the difference between the coefficients to evaluate the extent to which patient sorting affected avoidable hospital stays relative to TM.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Extraction Methods</h3>\u0000 \u0000 <p>Our sample included Medicare beneficiaries 65 years and older who were continuously enrolled in either MA or TM during 2018–2019. Beneficiaries in our sample had one or more chronic, ambulatory care-sensitive conditions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Patient sorting can be attributed to certain characteristics of plan design aspects. For plan type, HMOs account for 86%, with PPOs accounting for only 14%. For carriers, Humana and smaller carriers account for 89%. For star ratings, high-star contracts account for 94%, with other stars only accounting for 6%. By network design, narrow network plan-counties explained 20% of the patient sorting effect.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>While MA plans were found to be associated with driving beneficiaries to providers that generate fewer avoidable hospital stays, the effect is not homogeneous across the characteristics of MA plans. HMOs and high-star contracts are drivers of this MA phenomenon.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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