Lisham Ashrafioun PhD, Tedra Cobb MSEd, Ken Sayres MA, Christina Cretelle MBA
{"title":"Addressing substance use disorder-related stigma in rural communities using Community Conversations","authors":"Lisham Ashrafioun PhD, Tedra Cobb MSEd, Ken Sayres MA, Christina Cretelle MBA","doi":"10.1111/jrh.12900","DOIUrl":"10.1111/jrh.12900","url":null,"abstract":"","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Melissa Bradley MPH, Daniel Grossman MA, Otto Simonsson PhD, Heith Copes PhD, Peter S. Hendricks PhD
Background
Recent legislative initiatives in the United States have focused on the medical and legal status of psychedelics, prompting interest in understanding public perceptions of their risks. This study investigates rural-urban differences in the perception of LSD and cannabis risks using national survey data.
Methods
Data from the National Survey of Drug Use and Health (NSDUH) between 2015 and 2021 were analyzed. Logistic regression models were used to compare risk perceptions of LSD and cannabis between rural and urban respondents, adjusting for relevant factors.
Results
Rural residents were 1.2 to 1.4 times more likely to perceive using LSD once or twice as of great risk compared to urban residents during the survey period. However, the perception of monthly cannabis use as having great risk was slightly higher among rural residents only until 2019, with no significant differences observed in 2020 and 2021.
Conclusion
The study highlights a significant rural-urban divide in the perception of LSD risk, which could impact policymaking on psychedelic therapies. Understanding these differences is crucial for developing effective and equitable policies regarding psychedelic substances and treatments.
{"title":"Rural-urban divide in risk perception of LSD: Implications for psychedelic-assisted therapy","authors":"Melissa Bradley MPH, Daniel Grossman MA, Otto Simonsson PhD, Heith Copes PhD, Peter S. Hendricks PhD","doi":"10.1111/jrh.12906","DOIUrl":"10.1111/jrh.12906","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Recent legislative initiatives in the United States have focused on the medical and legal status of psychedelics, prompting interest in understanding public perceptions of their risks. This study investigates rural-urban differences in the perception of LSD and cannabis risks using national survey data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Data from the National Survey of Drug Use and Health (NSDUH) between 2015 and 2021 were analyzed. Logistic regression models were used to compare risk perceptions of LSD and cannabis between rural and urban respondents, adjusting for relevant factors.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Rural residents were 1.2 to 1.4 times more likely to perceive using LSD once or twice as of great risk compared to urban residents during the survey period. However, the perception of monthly cannabis use as having great risk was slightly higher among rural residents only until 2019, with no significant differences observed in 2020 and 2021.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The study highlights a significant rural-urban divide in the perception of LSD risk, which could impact policymaking on psychedelic therapies. Understanding these differences is crucial for developing effective and equitable policies regarding psychedelic substances and treatments.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lisa R. LaRowe PhD, Heather K. Hardin PhD, RN, Amy M. Goetzinger PhD, Kristen R. Fox PhD, Rebecca Kilpatrick PhD, Elizabeth K. Seng PhD, Roger Figueroa PhD, MPH, MSc
{"title":"POSITION STATEMENT: Support policies to address opioid use disorder among rural communities","authors":"Lisa R. LaRowe PhD, Heather K. Hardin PhD, RN, Amy M. Goetzinger PhD, Kristen R. Fox PhD, Rebecca Kilpatrick PhD, Elizabeth K. Seng PhD, Roger Figueroa PhD, MPH, MSc","doi":"10.1111/jrh.12899","DOIUrl":"10.1111/jrh.12899","url":null,"abstract":"","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lacey A. McCormack PhD, Debra A. MacKenzie PhD, Arielle Deutsch PhD, Daniel Beene PhD, Christine W. Hockett PhD, Katherine Ziegler MPH, Emily A. Knapp PhD, Amii M. Kress PhD, Zone R. Li BS, Shivani Bakre MHS, Rima Habre ScD, Lisa Jacobson ScD, Margaret R. Karagas PhD, Kaja LeWinn ScD, Sara S. Nozadi PhD, Akram Alshawabkeh PhD, Izzuddin M. Aris PhD, Traci A. Bekelman PhD, Casper G. Bendixsen PhD, Carlos Camargo MD, DrPH, Andrea E. Cassidy-Bushrow PhD, Lisa Croen PhD, Ferrara Assiamira MD, PhD, Rebecca Fry PhD, Tebeb Gebretsadik MPH, Tina Hartert MD, Kelly A. Hirko PhD, Catherine J. Karr MD, PhD, Itai Kloog PhD, Christine Loftus PhD, Kelsey E. Magee PhD, Cindy McEvoy MD, Jenae M. Neiderhiser PhD, Thomas G. O'Connor PhD, Mike O'Shea MD, Jennifer K. Straughen PhD, Audrey Urquhart MPH, Rosalind Wright MD, Amy J. Elliott PhD, for the ECHO Cohort Consortium
Purpose
The Environmental influences on Child Health Outcomes (ECHO) Cohort has enrolled over 60,000 children to examine how early environmental factors (broadly defined) are associated with key child health outcomes. The ECHO Cohort may be well-positioned to contribute to our understanding of rural environments and contexts, which has implications for rural health disparities research. The present study examined the outcome of child obesity to not only illustrate the suitability of ECHO Cohort data for these purposes but also determine how various definitions of rural and urban populations impact the presentation of findings and their interpretation.
Methods
This analysis uses data from children in the ECHO Cohort study who had residential address information between January 2010 and October 2023, including a subset who also had height and weight data. Several rural-urban classification schemes were examined with and without collapsing into binary rural/urban groupings (ie, the Rural-Urban Continuum Codes, 2010 Rural-Urban Commuting Area [RUCA] Codes, and Urban Influence Codes).
Findings
Various rural/urban definitions and classification schemes produce similar obesity prevalence (17%) when collapsed into binary categories (rural vs urban) and for urban participants in general. When all categories within a classification scheme are examined, however, the rural child obesity prevalence ranges from 5.8% to 24%.
Conclusions
Collapsing rural-urban classification schemes into binary groupings erases nuance and context needed for interpreting findings, ultimately impacting health disparities research. Future work should leverage both individual- and community-level datasets to provide context, and all categories of classification schemes should be used when examining rural populations.
{"title":"A descriptive examination of rurality in the Environmental influences on Child Health Outcomes Cohort: Implications, illustrations, and future directions","authors":"Lacey A. McCormack PhD, Debra A. MacKenzie PhD, Arielle Deutsch PhD, Daniel Beene PhD, Christine W. Hockett PhD, Katherine Ziegler MPH, Emily A. Knapp PhD, Amii M. Kress PhD, Zone R. Li BS, Shivani Bakre MHS, Rima Habre ScD, Lisa Jacobson ScD, Margaret R. Karagas PhD, Kaja LeWinn ScD, Sara S. Nozadi PhD, Akram Alshawabkeh PhD, Izzuddin M. Aris PhD, Traci A. Bekelman PhD, Casper G. Bendixsen PhD, Carlos Camargo MD, DrPH, Andrea E. Cassidy-Bushrow PhD, Lisa Croen PhD, Ferrara Assiamira MD, PhD, Rebecca Fry PhD, Tebeb Gebretsadik MPH, Tina Hartert MD, Kelly A. Hirko PhD, Catherine J. Karr MD, PhD, Itai Kloog PhD, Christine Loftus PhD, Kelsey E. Magee PhD, Cindy McEvoy MD, Jenae M. Neiderhiser PhD, Thomas G. O'Connor PhD, Mike O'Shea MD, Jennifer K. Straughen PhD, Audrey Urquhart MPH, Rosalind Wright MD, Amy J. Elliott PhD, for the ECHO Cohort Consortium","doi":"10.1111/jrh.12908","DOIUrl":"10.1111/jrh.12908","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The Environmental influences on Child Health Outcomes (ECHO) Cohort has enrolled over 60,000 children to examine how early environmental factors (broadly defined) are associated with key child health outcomes. The ECHO Cohort may be well-positioned to contribute to our understanding of rural environments and contexts, which has implications for rural health disparities research. The present study examined the outcome of child obesity to not only illustrate the suitability of ECHO Cohort data for these purposes but also determine how various definitions of rural and urban populations impact the presentation of findings and their interpretation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This analysis uses data from children in the ECHO Cohort study who had residential address information between January 2010 and October 2023, including a subset who also had height and weight data. Several rural-urban classification schemes were examined with and without collapsing into binary rural/urban groupings (ie, the Rural-Urban Continuum Codes, 2010 Rural-Urban Commuting Area [RUCA] Codes, and Urban Influence Codes).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>Various rural/urban definitions and classification schemes produce similar obesity prevalence (17%) when collapsed into binary categories (rural vs urban) and for urban participants in general. When all categories within a classification scheme are examined, however, the rural child obesity prevalence ranges from 5.8% to 24%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Collapsing rural-urban classification schemes into binary groupings erases nuance and context needed for interpreting findings, ultimately impacting health disparities research. Future work should leverage both individual- and community-level datasets to provide context, and all categories of classification schemes should be used when examining rural populations.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Young H. Kim MSPH, Kristin L. Reiter PhD, Kristie W. Thompson MA, George H. Pink PhD
Purpose
This study compares 2018–2023 Medicare Advantage (MA) days as a percentage of total Medicare days in rural and urban hospitals, describes 2022–2023 operating profitability of rural and urban hospitals by quartiles of MA days as a percentage of total Medicare days, and explores hospital characteristics that may be important for understanding the relationship between MA and profitability of rural hospitals.
Methods
Financial and hospital data were obtained from the Centers for Medicare & Medicaid Services (CMS) Healthcare Cost Report Information System (HCRIS) for the years 2018 to 2023. Hospitals were assigned to quartiles based on MA days as a percentage of total Medicare days. Descriptive analyses were conducted to compare hospital characteristics and financial performance across quartiles.
Findings
Between 2018–2019 and 2022–2023, the median percentage of total Medicare days from MA grew from 11.3% to 28.0% for rural hospitals. The 2022–2023 median operating margin varied from 0.0% for rural hospitals in Q1 (lowest MA days as a percentage of total Medicare days) to 3.4% for hospitals in Q4 (highest MA days as a percentage of total Medicare days).
Conclusion
Among rural hospitals, higher MA days as a percentage of total Medicare days was found to be associated with higher operating margin. However, results suggest that MA is not randomly distributed: rural hospitals with higher MA days as a percentage of total Medicare days exhibit distinct characteristics. This non-random distribution suggests that descriptive analysis may not fully capture the actual financial impact of MA on rural hospitals. Future research should recognize these complexities.
{"title":"Medicare Advantage and rural hospital profitability","authors":"Young H. Kim MSPH, Kristin L. Reiter PhD, Kristie W. Thompson MA, George H. Pink PhD","doi":"10.1111/jrh.12905","DOIUrl":"10.1111/jrh.12905","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study compares 2018–2023 Medicare Advantage (MA) days as a percentage of total Medicare days in rural and urban hospitals, describes 2022–2023 operating profitability of rural and urban hospitals by quartiles of MA days as a percentage of total Medicare days, and explores hospital characteristics that may be important for understanding the relationship between MA and profitability of rural hospitals.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Financial and hospital data were obtained from the Centers for Medicare & Medicaid Services (CMS) Healthcare Cost Report Information System (HCRIS) for the years 2018 to 2023. Hospitals were assigned to quartiles based on MA days as a percentage of total Medicare days. Descriptive analyses were conducted to compare hospital characteristics and financial performance across quartiles.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>Between 2018–2019 and 2022–2023, the median percentage of total Medicare days from MA grew from 11.3% to 28.0% for rural hospitals. The 2022–2023 median operating margin varied from 0.0% for rural hospitals in Q1 (lowest MA days as a percentage of total Medicare days) to 3.4% for hospitals in Q4 (highest MA days as a percentage of total Medicare days).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Among rural hospitals, higher MA days as a percentage of total Medicare days was found to be associated with higher operating margin. However, results suggest that MA is not randomly distributed: rural hospitals with higher MA days as a percentage of total Medicare days exhibit distinct characteristics. This non-random distribution suggests that descriptive analysis may not fully capture the actual financial impact of MA on rural hospitals. Future research should recognize these complexities.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael J. Hankes MPH, Suzanne E. Judd PhD, Raymond Jones PhD
{"title":"Bridging the rural-urban divide: A commentary on Rural-Urban Commuting Area codes","authors":"Michael J. Hankes MPH, Suzanne E. Judd PhD, Raymond Jones PhD","doi":"10.1111/jrh.12911","DOIUrl":"10.1111/jrh.12911","url":null,"abstract":"","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Redwan Bin Abdul Baten PhD, Fatema Tuz Zohora BDS, Muhammad Umar Hasan Siddiqui MBBS
Purpose
During the COVID-19 pandemic, telehealth services were expanded across the United States to meet the increased demand and safety requirements of care. This observational study aims to understand rural-urban differences in telehealth utilization during the early part of the COVID-19 pandemic.
Methods
Individual-level data from the National Health Interview Survey 2020-2021 (age ≥18) were analyzed for this study. The Propensity Score Matching method with multivariable Ordinary Least Square was used to analyze 2 outcome variables—(1) having a medical appointment by video or phone in the past 12 months and (2) having a virtual one for reasons related to the pandemic. Event study models were analyzed to understand the trend of telehealth utilization throughout 6 quarters of the pandemic. Subgroup analysis by health insurance, age, sex, race, citizenship, and disability status was performed to identify underlying disparities between rural and urban residents.
Findings
Analysis reveals that rural respondents (N = 6,984) were 8.7 percentage points (P<.001) less likely than urban respondents (N = 40,207) to have a medical appointment by video or phone. Rural residents were 8.1 percentage points (P<.001) less likely to have had a virtual medical appointment because of reasons related to the COVID-19 pandemic than urban users. The event study showed that rural-urban telehealth utilization disparities persisted throughout the pandemic. Subgroup analysis revealed significant rural-urban disparities in telehealth utilization by demographic characteristics.
Conclusions
Results demonstrate that rural residents were less likely than urban residents to utilize telehealth services during the COVID-19 pandemic, highlighting concerns about access to care for rural residents.
{"title":"Disparities in telehealth utilization between US rural and urban areas during the COVID-19 pandemic","authors":"Redwan Bin Abdul Baten PhD, Fatema Tuz Zohora BDS, Muhammad Umar Hasan Siddiqui MBBS","doi":"10.1111/jrh.12910","DOIUrl":"10.1111/jrh.12910","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>During the COVID-19 pandemic, telehealth services were expanded across the United States to meet the increased demand and safety requirements of care. This observational study aims to understand rural-urban differences in telehealth utilization during the early part of the COVID-19 pandemic.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Individual-level data from the National Health Interview Survey 2020-2021 (age ≥18) were analyzed for this study. The Propensity Score Matching method with multivariable Ordinary Least Square was used to analyze 2 outcome variables—(1) having a medical appointment by video or phone in the past 12 months and (2) having a virtual one for reasons related to the pandemic. Event study models were analyzed to understand the trend of telehealth utilization throughout 6 quarters of the pandemic. Subgroup analysis by health insurance, age, sex, race, citizenship, and disability status was performed to identify underlying disparities between rural and urban residents.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>Analysis reveals that rural respondents (N = 6,984) were 8.7 percentage points (<i>P</i><.001) less likely than urban respondents (N = 40,207) to have a medical appointment by video or phone. Rural residents were 8.1 percentage points (<i>P</i><.001) less likely to have had a virtual medical appointment because of reasons related to the COVID-19 pandemic than urban users. The event study showed that rural-urban telehealth utilization disparities persisted throughout the pandemic. Subgroup analysis revealed significant rural-urban disparities in telehealth utilization by demographic characteristics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Results demonstrate that rural residents were less likely than urban residents to utilize telehealth services during the COVID-19 pandemic, highlighting concerns about access to care for rural residents.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sydney M. Bebus RN, Kristin Palmsten ScD, Heather S. Lipkind MD, MS, Christina M. Ackerman-Banks MD, Katherine A. Ahrens MPH, PhD
Purpose
To estimate the rate of ambulatory care use among postpartum persons by rurality of residence and pregnancy-related conditions.
Methods
We used Maine Health Data Organization's All Payer Claims Data for persons who delivered between 2007 and 2019 (N = 121,905). We estimated rates of ambulatory care (nonemergency department outpatient health care) utilization during the first 24 months’ postpartum by level of rurality (urban, large rural, small rural, and isolated rural) and by pregnancy-related conditions (prenatal depression, hypertensive disorders of pregnancy, and gestational diabetes). To estimate rate ratios (RR), we used Poisson regression with an offset for population at risk, adjusting for potential confounders and restricting the analysis to those with continuous insurance (n = 70,431).
Findings
The mean monthly rate per 100 deliveries of ambulatory care visits was 86.1; the median number of visits was 12 (interquartile range = 6, 25). Persons living in rural areas had lower monthly rates of visits than persons living in urban areas (adjusted RR ranged from 0.87 [95% CI: 0.85, 0.89] in isolated rural areas to 0.91 [95% CI: 0.90, 0.93] in large rural areas). Persons with prenatal depression (aRR = 2.07; 95% CI: 2.04, 2.11), hypertensive disorders of pregnancy (aRR = 1.07; 95% CI: 1.05, 1.10), and gestational diabetes (aRR = 1.11; 95% CI: 1.08, 1.14) had higher rates of visits than those without these conditions.
Conclusions
New practices and policies may be needed to improve postpartum ambulatory care access and utilization in rural areas. Postpartum persons with pregnancy-related conditions are accessing ambulatory care at higher rates after delivery, which may reduce their need for acute health care use.
{"title":"Ambulatory care utilization in the first 24 months’ postpartum by rurality and pregnancy-related conditions: A prospective cohort study from Maine","authors":"Sydney M. Bebus RN, Kristin Palmsten ScD, Heather S. Lipkind MD, MS, Christina M. Ackerman-Banks MD, Katherine A. Ahrens MPH, PhD","doi":"10.1111/jrh.12912","DOIUrl":"10.1111/jrh.12912","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To estimate the rate of ambulatory care use among postpartum persons by rurality of residence and pregnancy-related conditions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We used Maine Health Data Organization's All Payer Claims Data for persons who delivered between 2007 and 2019 (N = 121,905). We estimated rates of ambulatory care (nonemergency department outpatient health care) utilization during the first 24 months’ postpartum by level of rurality (urban, large rural, small rural, and isolated rural) and by pregnancy-related conditions (prenatal depression, hypertensive disorders of pregnancy, and gestational diabetes). To estimate rate ratios (RR), we used Poisson regression with an offset for population at risk, adjusting for potential confounders and restricting the analysis to those with continuous insurance (n = 70,431).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>The mean monthly rate per 100 deliveries of ambulatory care visits was 86.1; the median number of visits was 12 (interquartile range = 6, 25). Persons living in rural areas had lower monthly rates of visits than persons living in urban areas (adjusted RR ranged from 0.87 [95% CI: 0.85, 0.89] in isolated rural areas to 0.91 [95% CI: 0.90, 0.93] in large rural areas). Persons with prenatal depression (aRR = 2.07; 95% CI: 2.04, 2.11), hypertensive disorders of pregnancy (aRR = 1.07; 95% CI: 1.05, 1.10), and gestational diabetes (aRR = 1.11; 95% CI: 1.08, 1.14) had higher rates of visits than those without these conditions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>New practices and policies may be needed to improve postpartum ambulatory care access and utilization in rural areas. Postpartum persons with pregnancy-related conditions are accessing ambulatory care at higher rates after delivery, which may reduce their need for acute health care use.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bobbie L. Johannes PhD MPH, Arch G. Mainous III PhD, Alex R. Chang MD MS, H. Lester Kirchner PhD, G. Craig Wood MS, Christopher D. Still DO, Lisa Bailey-Davis DEd RD
Purpose
To assess trends in continuity of care (COC) by geographic context (i.e., rural vs urban) among a cohort of persons with prediabetes prior to and after diagnosis of prediabetes.
Methods
We use cross-sectional data from Geisinger's electronic health record between 1997 and 2017. Our dependent variable is the Modified Modified Continuity Index (MMCI), a measure of dispersion among primary care providers seen. Our primary independent variable is a binary indicator variable for rurality constructed from the 2010 Census Bureau's Urban and Rural Classification. We control for age, sex, race/ethnicity, and baseline clinical characteristics. We use fractional logistic regression with bootstrapped standard errors.
Findings
Urban residing patients had greater odds of increased COC in the 3-year period prior to a diagnosis of prediabetes (aOR = 1.10, 95% CI = 1.03, 1.18; P = .007). However, there were no significant differences in COC among rural and urban residing patients upon diagnosis of prediabetes in unadjusted and fully adjusted regression models. Other factors significantly associated with COC across the observed time periods (pre- and post-diagnosis of prediabetes) include age, male, and hypertension in the patients’ problem list at baseline.
Conclusions
Among persons diagnosed with prediabetes, rurality was associated with decreased COC in the 3-year period prior to being diagnosed. However, in the 3-year period after diagnosis of prediabetes, geographic disparities in COC were not observed. Rural residing patients need enhanced continuity of primary care to potentially improve opportunistic screening for prediabetes.
目的：评估按地理环境（即农村与城市）划分的前驱糖尿病患者在前驱糖尿病诊断前后的连续性护理（COC）趋势。方法：我们使用1997年至2017年Geisinger电子健康记录的横截面数据。我们的因变量是修正修正连续性指数（MMCI），这是一种衡量初级保健提供者之间分散程度的指标。我们的主要自变量是根据2010年人口普查局的城乡分类构建的农村二元指标变量。我们控制了年龄、性别、种族/民族和基线临床特征。我们使用带有自举标准误差的分数逻辑回归。结果：城市居民在诊断为前驱糖尿病前的3年内COC升高的几率更高(aOR = 1.10, 95% CI = 1.03, 1.18；P = .007)。然而，在未调整和完全调整的回归模型中，诊断为糖尿病前期的农村和城市居民的COC没有显著差异。其他与COC显著相关的因素（前驱糖尿病诊断前后）包括年龄、男性和基线时患者问题清单中的高血压。结论：在诊断为前驱糖尿病的人群中，农村生活与诊断前3年期间COC下降有关。然而，在诊断为前驱糖尿病后的3年期间，未观察到COC的地理差异。农村居民需要加强初级保健的连续性，以潜在地改善对前驱糖尿病的机会性筛查。
{"title":"Association of rurality and decreased continuity of care prior to a diagnosis of prediabetes","authors":"Bobbie L. Johannes PhD MPH, Arch G. Mainous III PhD, Alex R. Chang MD MS, H. Lester Kirchner PhD, G. Craig Wood MS, Christopher D. Still DO, Lisa Bailey-Davis DEd RD","doi":"10.1111/jrh.12907","DOIUrl":"10.1111/jrh.12907","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To assess trends in continuity of care (COC) by geographic context (i.e., rural vs urban) among a cohort of persons with prediabetes prior to and after diagnosis of prediabetes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We use cross-sectional data from Geisinger's electronic health record between 1997 and 2017. Our dependent variable is the Modified Modified Continuity Index (MMCI), a measure of dispersion among primary care providers seen. Our primary independent variable is a binary indicator variable for rurality constructed from the 2010 Census Bureau's Urban and Rural Classification. We control for age, sex, race/ethnicity, and baseline clinical characteristics. We use fractional logistic regression with bootstrapped standard errors.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>Urban residing patients had greater odds of increased COC in the 3-year period prior to a diagnosis of prediabetes (aOR = 1.10, 95% CI = 1.03, 1.18; <i>P</i> = .007). However, there were no significant differences in COC among rural and urban residing patients upon diagnosis of prediabetes in unadjusted and fully adjusted regression models. Other factors significantly associated with COC across the observed time periods (pre- and post-diagnosis of prediabetes) include age, male, and hypertension in the patients’ problem list at baseline.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Among persons diagnosed with prediabetes, rurality was associated with decreased COC in the 3-year period prior to being diagnosed. However, in the 3-year period after diagnosis of prediabetes, geographic disparities in COC were not observed. Rural residing patients need enhanced continuity of primary care to potentially improve opportunistic screening for prediabetes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mairead Moloney PhD, Jasmine Rubio BS, Israel Palencia BS, Laronda Hollimon MS, Dunia Mejia BS, Azizi Seixas PhD
<p>Rural health research, fundamental to US public health, has faced significant challenges including inconsistencies in defining rural areas, methodological constraints in studying dispersed populations, and complex social and cultural factors.<span><sup>1, 2</sup></span> This commentary reexamines these enduring issues and proposes innovative solutions leveraging digital technologies. While acknowledging the potential of these technological approaches, we also address barriers to digital equity in rural settings and suggest practical strategies to overcome them.</p><p>Defining “rural” poses significant challenges.<span><sup>1, 2</sup></span> Current classification methods typically consider population density, proximity to urban centers, and infrastructure availability. However, these approaches often lead to inconsistencies.<span><sup>1, 3</sup></span> The US Census, for instance, identifies urban areas based on population density, with non-urban areas classified as rural.<span><sup>1</sup></span> This method, while systematic, often overlooks crucial factors like commuting patterns, employment nature, land use, and access to essential services—including internet connectivity and advanced medical care.</p><p>Online tools have emerged to address these limitations by incorporating multiple definitions of rurality. The Rural Health Information Hub's “Am I Rural?” tool exemplifies this approach, integrating seven distinct definitions including data from the US Census, Rural-Urban Commuting Areas, and Federal Office of Rural Health Policy classifications.<span><sup>4</sup></span> This tool also considers federal grant eligibility and health care professional shortages, providing a more comprehensive assessment of rural status.</p><p>The “Am I Rural?” tool illustrates how technological advancements can enhance rural area definition precision.<span><sup>4</sup></span> By employing a multifaceted approach, these tools enable more accurate representations of rurality in health research. Consequently, this can inform policy decisions and resource allocation more effectively, ultimately benefiting rural communities' health and wellness.</p><p>Smaller population size, low population density, and limited access to transportation often pose methodological challenges for rural participant recruitment and retention, particularly if in-person data collection is required.<span><sup>2, 5</sup></span> Additionally, research questions or scales that are urban-normative (i.e., urban lifestyles or values are viewed as the default/ideal) may alienate respondents, leading to reduced response rates and questionable validity.<span><sup>2</sup></span> Dissemination of rural research findings is often more challenging due to confidentiality concerns in smaller communities.<span><sup>6, 7</sup></span></p><p>To address these challenges, researchers are increasingly turning to innovative digital approaches. For instance, Vos et al.<span><sup>8</sup></span> created a standalo
{"title":"Rural health research in the 21st century: A commentary on challenges and the role of digital technology","authors":"Mairead Moloney PhD, Jasmine Rubio BS, Israel Palencia BS, Laronda Hollimon MS, Dunia Mejia BS, Azizi Seixas PhD","doi":"10.1111/jrh.12903","DOIUrl":"10.1111/jrh.12903","url":null,"abstract":"<p>Rural health research, fundamental to US public health, has faced significant challenges including inconsistencies in defining rural areas, methodological constraints in studying dispersed populations, and complex social and cultural factors.<span><sup>1, 2</sup></span> This commentary reexamines these enduring issues and proposes innovative solutions leveraging digital technologies. While acknowledging the potential of these technological approaches, we also address barriers to digital equity in rural settings and suggest practical strategies to overcome them.</p><p>Defining “rural” poses significant challenges.<span><sup>1, 2</sup></span> Current classification methods typically consider population density, proximity to urban centers, and infrastructure availability. However, these approaches often lead to inconsistencies.<span><sup>1, 3</sup></span> The US Census, for instance, identifies urban areas based on population density, with non-urban areas classified as rural.<span><sup>1</sup></span> This method, while systematic, often overlooks crucial factors like commuting patterns, employment nature, land use, and access to essential services—including internet connectivity and advanced medical care.</p><p>Online tools have emerged to address these limitations by incorporating multiple definitions of rurality. The Rural Health Information Hub's “Am I Rural?” tool exemplifies this approach, integrating seven distinct definitions including data from the US Census, Rural-Urban Commuting Areas, and Federal Office of Rural Health Policy classifications.<span><sup>4</sup></span> This tool also considers federal grant eligibility and health care professional shortages, providing a more comprehensive assessment of rural status.</p><p>The “Am I Rural?” tool illustrates how technological advancements can enhance rural area definition precision.<span><sup>4</sup></span> By employing a multifaceted approach, these tools enable more accurate representations of rurality in health research. Consequently, this can inform policy decisions and resource allocation more effectively, ultimately benefiting rural communities' health and wellness.</p><p>Smaller population size, low population density, and limited access to transportation often pose methodological challenges for rural participant recruitment and retention, particularly if in-person data collection is required.<span><sup>2, 5</sup></span> Additionally, research questions or scales that are urban-normative (i.e., urban lifestyles or values are viewed as the default/ideal) may alienate respondents, leading to reduced response rates and questionable validity.<span><sup>2</sup></span> Dissemination of rural research findings is often more challenging due to confidentiality concerns in smaller communities.<span><sup>6, 7</sup></span></p><p>To address these challenges, researchers are increasingly turning to innovative digital approaches. For instance, Vos et al.<span><sup>8</sup></span> created a standalo","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jrh.12903","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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