{"title":"Insights into suicidality in rural communities: Lessons from Rawat et al. (2025) and perspectives from Alaska and Colorado","authors":"Ezra N. S. Lockhart PhD, LMFT, LAC","doi":"10.1111/jrh.70046","DOIUrl":"https://doi.org/10.1111/jrh.70046","url":null,"abstract":"","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520228","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}
Samuel T. Savitz PhD, Alanna M. Chamberlain PhD, Ruoxiang Jiang BSc, Sheharyar Sarwar DO, Mark D. Williams MD
Purpose
The Collaborative care model (CoCM) is the leading model for integrating behavioral health into primary care for patients with major depressive disorder (MDD). However, CoCM requires engagement and ongoing participation. We aimed to assess whether two area-based measures, the area-deprivation index (ADI) and rurality, were associated with enrollment, participation, and outcomes with CoCM.
Methods
This was an observational analysis of Mayo Clinic patients eligible for CoCM: adults aged ≥18 years, empaneled in primary care, and with a PHQ-9 of ≥10. We operationalized ADI as quintiles with Q1 being least deprived and Q5 being most deprived and rurality using RUCA codes with two categories: urban and rural. We evaluated enrollment in CoCM, drop out defined by leaving the program early, the count and type of contacts with the care coordinator, and clinical improvement measured using the PHQ-9.
Findings
We identified 54,030 individuals with 16,532 (30.6%) residing in rural areas and 11,122 (20.6%) residing in the most deprived ADI quintile (Q5). Living in a rural area was associated with lower enrollment in CoCM (–2.3 percentage points [95% confidence interval (CI): –2.5, 2.2]), longer length in CoCM (18.6 days [95% CI: 5.7, 31.5]), more contacts with the care coordinator (1.1 contacts [95% CI: 0.2, 2.0]), and worse response and remission. In contrast, ADI Q5 was only associated with worse response and remission.
Conclusions
Rurality was associated with lower enrollment, greater engagement, and worse clinical outcomes. More work may be needed to address enrollment barriers for individuals living in rural areas to improve clinical outcomes.
{"title":"Impact of rurality and the area deprivation index on outcomes of collaborative care for depression","authors":"Samuel T. Savitz PhD, Alanna M. Chamberlain PhD, Ruoxiang Jiang BSc, Sheharyar Sarwar DO, Mark D. Williams MD","doi":"10.1111/jrh.70044","DOIUrl":"https://doi.org/10.1111/jrh.70044","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The Collaborative care model (CoCM) is the leading model for integrating behavioral health into primary care for patients with major depressive disorder (MDD). However, CoCM requires engagement and ongoing participation. We aimed to assess whether two area-based measures, the area-deprivation index (ADI) and rurality, were associated with enrollment, participation, and outcomes with CoCM.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This was an observational analysis of Mayo Clinic patients eligible for CoCM: adults aged ≥18 years, empaneled in primary care, and with a PHQ-9 of ≥10. We operationalized ADI as quintiles with Q1 being least deprived and Q5 being most deprived and rurality using RUCA codes with two categories: urban and rural. We evaluated enrollment in CoCM, drop out defined by leaving the program early, the count and type of contacts with the care coordinator, and clinical improvement measured using the PHQ-9.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>We identified 54,030 individuals with 16,532 (30.6%) residing in rural areas and 11,122 (20.6%) residing in the most deprived ADI quintile (Q5). Living in a rural area was associated with lower enrollment in CoCM (–2.3 percentage points [95% confidence interval (CI): –2.5, 2.2]), longer length in CoCM (18.6 days [95% CI: 5.7, 31.5]), more contacts with the care coordinator (1.1 contacts [95% CI: 0.2, 2.0]), and worse response and remission. In contrast, ADI Q5 was only associated with worse response and remission.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Rurality was associated with lower enrollment, greater engagement, and worse clinical outcomes. More work may be needed to address enrollment barriers for individuals living in rural areas to improve clinical outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339502","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}
Courtney Keeler PhD, Alexa Colgrove Curtis PhD, MPH, APRN
Purpose
To explore the geographic impact of California's Proposition 56 (Prop 56) on smoking behaviors.
Methods
We identified 61,193 respondents aged 21+ from the 2012–2018 California Behavioral Risk Factor Surveillance Survey. We constructed county identifiers indicating whether (1) a respondent lived in an urban, suburban, or rural county and (2) whether a respondent lived in a metropolitan statistical area (MSA) or not. Similarly, we created a binary variable indicating whether Prop 56 was in effect (Yes/No). We used a two-part model to estimate the association of Prop 56 with smoking participation among all adults and smoking intensity (average number of cigarettes smoked per day (CPD)) among current smokers. Models were run separately for each geographic subgroup. Additional covariates included sociodemographic characteristics and a time trend variable.
Findings
Smoking prevalence was significantly different across geographic groups, with rates highest among the rural population group (13.8%) and lowest among the urban subgroup (9.1%). Similarly, rates of smoking intensity were significantly higher among non-urban populations, with average CPD highest among the rural population (10.66) and lowest among the urban subgroup (8.32). Regression models highlighted a negative association between Prop 56 and smoking participation only among the urban and MSA subgroups (p < 0.001). We found no evidence of an association between the enactment of Prop 56 with average CPD for any geographic group.
Conclusion
Public health experts, clinicians, and policymakers might consider additional interventions that can be implemented in tandem with pricing tools to help reduce observed geographic disparities in smoking among rural—and even suburban—communities.
{"title":"The impact of California Proposition 56 on smoking behaviors across geographic residence","authors":"Courtney Keeler PhD, Alexa Colgrove Curtis PhD, MPH, APRN","doi":"10.1111/jrh.70041","DOIUrl":"https://doi.org/10.1111/jrh.70041","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To explore the geographic impact of California's Proposition 56 (Prop 56) on smoking behaviors.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We identified 61,193 respondents aged 21+ from the 2012–2018 California Behavioral Risk Factor Surveillance Survey. We constructed county identifiers indicating whether (1) a respondent lived in an urban, suburban, or rural county and (2) whether a respondent lived in a metropolitan statistical area (MSA) or not. Similarly, we created a binary variable indicating whether Prop 56 was in effect (Yes/No). We used a two-part model to estimate the association of Prop 56 with smoking participation among all adults and smoking intensity (average number of cigarettes smoked per day (CPD)) among current smokers. Models were run separately for each geographic subgroup. Additional covariates included sociodemographic characteristics and a time trend variable.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>Smoking prevalence was significantly different across geographic groups, with rates highest among the rural population group (13.8%) and lowest among the urban subgroup (9.1%). Similarly, rates of smoking intensity were significantly higher among non-urban populations, with average CPD highest among the rural population (10.66) and lowest among the urban subgroup (8.32). Regression models highlighted a negative association between Prop 56 and smoking participation only among the urban and MSA subgroups (<i>p</i> < 0.001). We found no evidence of an association between the enactment of Prop 56 with average CPD for any geographic group.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Public health experts, clinicians, and policymakers might consider additional interventions that can be implemented in tandem with pricing tools to help reduce observed geographic disparities in smoking among rural—and even suburban—communities.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281485","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}
Emma Kathryn Boswell MPH, Monique J. Brown PhD, Lorie Donelle PhD, Nicholas Yell PhD, Taryn Farrell MPH, Peiyin Hung PhD, Elizabeth Crouch PhD
Purpose
An updated, nationally representative examination of rural–urban differences in the experiences, health, and well-being of caregivers is needed; previous research on this topic uses older data or has limited generalizability. This study examines rural–urban differences in the characteristics, experiences, and health of caregivers.
Methods
The 2021–2022 Behavioral Risk Factor Surveillance System (n = 44,274 unpaid caregivers) was used, with rurality defined according to the 2013 National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme. Chi-square tests compared rural–urban differences in these caregivers’ characteristics, including demographic factors, caregiving intensity (e.g., weekly hours spent caregiving, reason for caregiving, past-month ADL/IADL assistance), caregiver's health (e.g., general health status and past month physical health, mental health, and limited activity), and caregiver's health behavior (chronic illness, smoking status, binge drinking, and annual checkups).
Findings
Compared to urban caregivers, rural caregivers were more likely to have at least one chronic condition (58.3% vs. 53.2%; p < 0.0001), be obese (42.9% vs. 37.5%; p < 0.0001), be a smoker (24.2% vs. 15.5%; p < 0.0001), and less likely to be a binge drinker (12.7% vs. 15.3%; p = 0.003). Compared to urban caregivers, rural caregivers were more likely to report their general health status as fair/poor (20.3% vs. 17.0%, p = 0.0003) and were more likely to report 14 or more days of poor physical health in the past month (15.6% vs. 12.0%, p < 0.0001).
Conclusions
Understanding geographic disparities in the experiences and context of unpaid caregiving is needed to improve their overall well-being and health. Future research will be necessary to determine factors associated with these outcomes.
需要对农村和城市在护理人员的经历、健康和福祉方面的差异进行最新的、具有全国代表性的调查；以前关于这一主题的研究使用的是较旧的数据，或者泛化能力有限。本研究考察了农村和城市在看护者的特征、经历和健康方面的差异。方法采用2021-2022年行为风险因素监测系统（n = 44,274名无报酬照顾者），农村性根据2013年国家卫生统计中心城乡分类方案定义。卡方检验比较了这些照护者特征的城乡差异，包括人口统计学因素、照护强度（例如，每周照护时间、照护原因、过去一个月的ADL/IADL协助）、照护者健康（例如，一般健康状况和过去一个月的身体健康、心理健康和有限的活动）以及照护者的健康行为（慢性病、吸烟状况、酗酒、残疾和残疾）。以及每年的体检)。与城市护理人员相比，农村护理人员更有可能患有至少一种慢性疾病(58.3%比53.2%；p & lt;0.0001)，肥胖(42.9% vs. 37.5%；p & lt;0.0001)，吸烟(24.2% vs. 15.5%；p & lt;0.0001)，并且不太可能成为酗酒者(12.7% vs. 15.3%；P = 0.003)。与城市护理人员相比，农村护理人员更有可能报告自己的总体健康状况为一般/较差（20.3%比17.0%,p = 0.0003），并且更有可能报告过去一个月有14天或更长时间的身体健康状况不佳(15.6%比12.0%,p <；0.0001)。结论：了解无偿照护经历和背景的地域差异是改善他们整体福祉和健康的必要条件。未来的研究将有必要确定与这些结果相关的因素。
{"title":"Geographic disparities in unpaid caregiving","authors":"Emma Kathryn Boswell MPH, Monique J. Brown PhD, Lorie Donelle PhD, Nicholas Yell PhD, Taryn Farrell MPH, Peiyin Hung PhD, Elizabeth Crouch PhD","doi":"10.1111/jrh.70039","DOIUrl":"https://doi.org/10.1111/jrh.70039","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>An updated, nationally representative examination of rural–urban differences in the experiences, health, and well-being of caregivers is needed; previous research on this topic uses older data or has limited generalizability. This study examines rural–urban differences in the characteristics, experiences, and health of caregivers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The 2021–2022 Behavioral Risk Factor Surveillance System (<i>n</i> = 44,274 unpaid caregivers) was used, with rurality defined according to the 2013 National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme. Chi-square tests compared rural–urban differences in these caregivers’ characteristics, including demographic factors, caregiving intensity (e.g., weekly hours spent caregiving, reason for caregiving, past-month ADL/IADL assistance), caregiver's health (e.g., general health status and past month physical health, mental health, and limited activity), and caregiver's health behavior (chronic illness, smoking status, binge drinking, and annual checkups).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>Compared to urban caregivers, rural caregivers were more likely to have at least one chronic condition (58.3% vs. 53.2%; <i>p</i> < 0.0001), be obese (42.9% vs. 37.5%; <i>p</i> < 0.0001), be a smoker (24.2% vs. 15.5%; <i>p</i> < 0.0001), and less likely to be a binge drinker (12.7% vs. 15.3%; <i>p</i> = 0.003). Compared to urban caregivers, rural caregivers were more likely to report their general health status as fair/poor (20.3% vs. 17.0%, <i>p</i> = 0.0003) and were more likely to report 14 or more days of poor physical health in the past month (15.6% vs. 12.0%, <i>p</i> < 0.0001).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Understanding geographic disparities in the experiences and context of unpaid caregiving is needed to improve their overall well-being and health. Future research will be necessary to determine factors associated with these outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jrh.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281486","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}
Samantha L. Connolly PhD, Amber B. Amspoker PhD, Annette Walder MS, Kathleen M. Grubbs PhD, Liang Chen MD MS, Anthony H. Ecker PhD, Julianna B. Hogan PhD, Jan A. Lindsay PhD
Objective
This study explores factors associated with an increased likelihood of receiving mental health (MH) care exclusively via audio-only phone visits within the Department of Veterans Affairs (VA).
Methods
Included patients had ≥1 VA MH outpatient encounter between October 1, 2021-September 30, 2022 and October 1, 2022-September 30, 2023. Patients were divided into a “phone only” group and an “all other” group, which encompassed all patients who did not exclusively receive phone care, including video and/or in-person care. Logistic regression models evaluated demographic and clinical predictors of receiving MH care via phone only.
Results
The sample included 1,156,146 patients; 49,125 (4.25%) in the phone only group and 1,107,021 (95.75%) in the all other group. The following were associated with greater odds of receiving MH care via phone only in a multivariate model, all Ps<.0001: being highly rural (OR = 1.50), age 65+ (ORs ≥2.17), with fewer than 3 MH diagnoses (OR = 2.03), and >50% of MH visits conducted by a medical MH provider (OR = 1.87).
Conclusions
Patients who were rural and older had greater odds of receiving MH care exclusively by phone. It will be important to assess whether this was by choice or whether they are experiencing barriers to accessing video or in-person care that could be addressed. Patients who were less clinically severe and were seen primarily by a medical MH provider were also more likely to receive phone-only care. Future research should examine the relative effectiveness of audio-only care as compared to video and in-person.
{"title":"Phone-only mental health care within the Department of Veterans Affairs: Associations with rurality, age, sex, and clinical severity","authors":"Samantha L. Connolly PhD, Amber B. Amspoker PhD, Annette Walder MS, Kathleen M. Grubbs PhD, Liang Chen MD MS, Anthony H. Ecker PhD, Julianna B. Hogan PhD, Jan A. Lindsay PhD","doi":"10.1111/jrh.70043","DOIUrl":"https://doi.org/10.1111/jrh.70043","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study explores factors associated with an increased likelihood of receiving mental health (MH) care exclusively via audio-only phone visits within the Department of Veterans Affairs (VA).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Included patients had ≥1 VA MH outpatient encounter between October 1, 2021-September 30, 2022 and October 1, 2022-September 30, 2023. Patients were divided into a “phone only” group and an “all other” group, which encompassed all patients who did not exclusively receive phone care, including video and/or in-person care. Logistic regression models evaluated demographic and clinical predictors of receiving MH care via phone only.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The sample included 1,156,146 patients; 49,125 (4.25%) in the phone only group and 1,107,021 (95.75%) in the all other group. The following were associated with greater odds of receiving MH care via phone only in a multivariate model, all <i>Ps</i><.0001: being highly rural (OR = 1.50), age 65+ (ORs ≥2.17), with fewer than 3 MH diagnoses (OR = 2.03), and >50% of MH visits conducted by a medical MH provider (OR = 1.87).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Patients who were rural and older had greater odds of receiving MH care exclusively by phone. It will be important to assess whether this was by choice or whether they are experiencing barriers to accessing video or in-person care that could be addressed. Patients who were less clinically severe and were seen primarily by a medical MH provider were also more likely to receive phone-only care. Future research should examine the relative effectiveness of audio-only care as compared to video and in-person.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281571","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}
Donna-Jean P. Brock , Lee M. Ritterband , Wen You , Annie L. Reid , Kathleen J. Porter , Theresa Markwalter , Jamie M. Zoellner
Purpose
Digital health studies exploring group disparities across research phases are limited. As a secondary aim of a larger digital health trial, this study explored how rurality and other sociodemographics were associated with enrollment, retention, and engagement in a randomized controlled sugar-sweetened beverage (SSB) reduction trial.
Methods
Participants from a primarily Appalachian sample were randomized into iSIPsmarter (experimental) or static Patient Education (control) websites. Enrollment, retention (6 months), and iSIPsmarter engagement (completion of metered program Core content and SSB and weight diaries) were collected from July 2021 to August 2023. Regression models assessed subgroup associations using Rural Urban Continuum Codes (RUCC), sex, race, age, income, education, and other sociodemographic predictors.
Findings
Of the 509 eligible participants, 249 (49%) enrolled, and 218 (88%) were retained. Participants were predominantly White (89%), college-educated (59%) females (83%) with household incomes <$55,000/year (52%). Rurality varied: RUCC 1-2 (medium-large metro) = 15%, RUCC 3 (small metro) = 45%, and RUCC 4-9 (nonmetro) = 41%. On average, iSIPsmarter participants (n = 127) completed 4.89/6 (SD = 1.69) Cores and 76% (SD = 29%) and 57% (SD = 31%) of SSB and weight diaries. Rurality was a nonsignificant predictor, but higher education and health literacy increased enrollment likelihood by 37% (95% CI = 1.12-1.67) and 23% (95% CI = 1.03-1.47), respectively. Greater education (OR = 1.51, 95% CI = 1.00-2.29), age (OR = 1.04, 95% CI = 1.01-1.07), and income (OR = 1.13, 95% CI = 1.00-1.28) significantly predicted retention. Older age significantly (P<.05) predicted the completion of Cores and diaries.
Conclusions
Results suggested rurality was not significantly associated with enrollment, retention, or engagement, though this conclusion warrants caution. Future digital health studies targeting similar populations should consider additional sociodemographic differences.
探索不同研究阶段群体差异的数字健康研究是有限的。作为一项更大规模的数字健康试验的次要目标，本研究探讨了在一项随机对照含糖饮料（SSB）减少试验中，乡村性和其他社会人口统计学因素与注册、保留和参与之间的关系。方法主要来自阿巴拉契亚地区的参与者被随机分配到iSIPsmarter（实验）或静态患者教育（对照）网站。从2021年7月到2023年8月，收集了入学人数、保留率（6个月）和iSIPsmarter参与度（完成计量课程核心内容和SSB和体重日记）。回归模型使用城乡连续代码（RUCC）、性别、种族、年龄、收入、教育程度和其他社会人口预测因素评估亚组关联。在509名符合条件的参与者中，249名（49%）入组，218名（88%）保留。参与者主要是白人（89%），受过大学教育（59%）的女性（83%），家庭年收入55,000美元（52%）。农村差异：RUCC 1-2（中型地铁）= 15%,RUCC 3（小型地铁）= 45%,RUCC 4-9（非地铁）= 41%。平均而言，iSIPsmarter参与者（n = 127）完成了4.89/6 （SD = 1.69）个核心，76% （SD = 29%）和57% （SD = 31%）的SSB和体重日记。乡村性是一个不显著的预测因子，但高等教育和健康素养分别使入学可能性增加37% （95% CI = 1.12-1.67）和23% （95% CI = 1.03-1.47）。高等教育（OR = 1.51, 95% CI = 1.00-2.29）、年龄（OR = 1.04, 95% CI = 1.01-1.07）和收入（OR = 1.13, 95% CI = 1.00-1.28）显著预测保留率。年龄对核心和日记的完成有显著的预测作用（p < 0.05）。结论：结果表明，乡村性与入学、保留或参与没有显著关系，尽管这一结论值得谨慎。未来针对类似人群的数字健康研究应考虑更多的社会人口统计学差异。
{"title":"An exploratory study to understand how rurality status and demographic characteristics are associated with enrollment, engagement, and retention in a digital health intervention targeting the Appalachian region","authors":"Donna-Jean P. Brock , Lee M. Ritterband , Wen You , Annie L. Reid , Kathleen J. Porter , Theresa Markwalter , Jamie M. Zoellner","doi":"10.1111/jrh.70042","DOIUrl":"https://doi.org/10.1111/jrh.70042","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Digital health studies exploring group disparities across research phases are limited. As a secondary aim of a larger digital health trial, this study explored how rurality and other sociodemographics were associated with enrollment, retention, and engagement in a randomized controlled sugar-sweetened beverage (SSB) reduction trial.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Participants from a primarily Appalachian sample were randomized into <i>i</i>SIPsmarter (experimental) or static Patient Education (control) websites. Enrollment, retention (6 months), and <i>i</i>SIPsmarter engagement (completion of metered program Core content and SSB and weight diaries) were collected from July 2021 to August 2023. Regression models assessed subgroup associations using Rural Urban Continuum Codes (RUCC), sex, race, age, income, education, and other sociodemographic predictors.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>Of the 509 eligible participants, 249 (49%) enrolled, and 218 (88%) were retained. Participants were predominantly White (89%), college-educated (59%) females (83%) with household incomes <$55,000/year (52%). Rurality varied: RUCC 1-2 (medium-large metro) = 15%, RUCC 3 (small metro) = 45%, and RUCC 4-9 (nonmetro) = 41%. On average, <i>i</i>SIPsmarter participants (n = 127) completed 4.89/6 (SD = 1.69) Cores and 76% (SD = 29%) and 57% (SD = 31%) of SSB and weight diaries. Rurality was a nonsignificant predictor, but higher education and health literacy increased enrollment likelihood by 37% (95% CI = 1.12-1.67) and 23% (95% CI = 1.03-1.47), respectively. Greater education (OR = 1.51, 95% CI = 1.00-2.29), age (OR = 1.04, 95% CI = 1.01-1.07), and income (OR = 1.13, 95% CI = 1.00-1.28) significantly predicted retention. Older age significantly (<i>P</i><.05) predicted the completion of Cores and diaries.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Results suggested rurality was not significantly associated with enrollment, retention, or engagement, though this conclusion warrants caution. Future digital health studies targeting similar populations should consider additional sociodemographic differences.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jrh.70042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281484","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}
Eugenio Paglino PhD, Irma T. Elo PhD, Samuel H. Preston PhD, Katherine Hempstead PhD, Andrew C. Stokes PhD
Purpose
To examine disparities in nonmetropolitan and metropolitan mortality by state and sex from 1999 to 2019.
Methods
We calculate age-standardized mortality rates for nonmetropolitan and metropolitan areas by state and sex and compute age-standardized differences in these rates within each state and relative to the national average. We further estimate the number of excess deaths in nonmetropolitan areas by state. These are deaths that would have been avoided if nonmetropolitan areas had the same age-specific death rates as metropolitan areas in the same state.
Findings
We document increasing nonmetropolitan mortality disadvantage since 1999 along with significant variation in the magnitude and timing of its emergence by state. Although stagnation in mortality was observed nationally in the mid-2010s, this was not true in all states or in all metropolitan and nonmetropolitan areas. Additionally, mortality trends became progressively more discordant across and within states. Despite this heterogeneity, we document a steady increase in the number of nonmetropolitan excess deaths from 8,400 in 1999 to 47,000 in 2019, representing 9.0% of all nonmetropolitan deaths.
Conclusions
National-level mortality trends mask geographic variation by nonmetropolitan and metropolitan areas within and across states. Further research is needed to identify factors that contribute to these divergent patterns.
{"title":"Evolution of the US nonmetropolitan mortality disadvantage by sex, state, and year, 1999-2019","authors":"Eugenio Paglino PhD, Irma T. Elo PhD, Samuel H. Preston PhD, Katherine Hempstead PhD, Andrew C. Stokes PhD","doi":"10.1111/jrh.70040","DOIUrl":"https://doi.org/10.1111/jrh.70040","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To examine disparities in nonmetropolitan and metropolitan mortality by state and sex from 1999 to 2019.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We calculate age-standardized mortality rates for nonmetropolitan and metropolitan areas by state and sex and compute age-standardized differences in these rates within each state and relative to the national average. We further estimate the number of excess deaths in nonmetropolitan areas by state. These are deaths that would have been avoided if nonmetropolitan areas had the same age-specific death rates as metropolitan areas in the same state.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>We document increasing nonmetropolitan mortality disadvantage since 1999 along with significant variation in the magnitude and timing of its emergence by state. Although stagnation in mortality was observed nationally in the mid-2010s, this was not true in all states or in all metropolitan and nonmetropolitan areas. Additionally, mortality trends became progressively more discordant across and within states. Despite this heterogeneity, we document a steady increase in the number of nonmetropolitan excess deaths from 8,400 in 1999 to 47,000 in 2019, representing 9.0% of all nonmetropolitan deaths.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>National-level mortality trends mask geographic variation by nonmetropolitan and metropolitan areas within and across states. Further research is needed to identify factors that contribute to these divergent patterns.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jrh.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281570","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}
Saleema A. Karim PhD, J. Mick Tilford PhD, Cari A. Bogulski PhD, Corey J. Hayes MPH, PharmD, PhD, Hari Eswaran PhD
Purpose
To examine factors associated with rural hospital telehealth adoption during the COVID-19 public health emergency (PHE), and evaluate its relationship with rural hospital financial performance before and during the PHE.
Methods
This panel study used retrospective data (2017–2021) from the American Hospital Association Annual Survey, the Centers for Medicare & Medicaid Services Healthcare Cost Report Information Systems, and the Area Health Resource File. Rural hospitals were categorized as persistent adopters, persistent nonadopters, or switchers based on telehealth adoption status. Bivariate analyses assessed differences in subgroup means and frequencies, while a difference-in-difference model estimated the impact of telehealth adoption on rural hospital financial performance.
Findings
Telehealth adoption varied among rural hospitals. Before the PHE, 75% (751) of rural hospitals had adopted telehealth, while 25% (247) were nonadopters. Despite efforts to promote remote care delivery during the PHE, 58% (144) of pre-PHE nonadopters did not adopt telehealth. Among the 42% (103) that did adopt telehealth during the PHE, no statistically significant effect was observed on operating or total margins.
Conclusion
Rural hospitals in economically disadvantaged and sparsely populated areas, which stand to benefit the most from telehealth adoption, often face substantial barriers that limit their ability to adopt this technology. Financial constraints and limited resources continue to hinder adoption, underscoring the need for targeted policies and investments to expand telehealth access and improve health care outcomes in rural communities.
{"title":"Exploring telehealth adoption and financial outcomes for rural hospitals during the COVID-19 public health emergency","authors":"Saleema A. Karim PhD, J. Mick Tilford PhD, Cari A. Bogulski PhD, Corey J. Hayes MPH, PharmD, PhD, Hari Eswaran PhD","doi":"10.1111/jrh.70038","DOIUrl":"https://doi.org/10.1111/jrh.70038","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To examine factors associated with rural hospital telehealth adoption during the COVID-19 public health emergency (PHE), and evaluate its relationship with rural hospital financial performance before and during the PHE.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This panel study used retrospective data (2017–2021) from the American Hospital Association Annual Survey, the Centers for Medicare & Medicaid Services Healthcare Cost Report Information Systems, and the Area Health Resource File. Rural hospitals were categorized as persistent adopters, persistent nonadopters, or switchers based on telehealth adoption status. Bivariate analyses assessed differences in subgroup means and frequencies, while a difference-in-difference model estimated the impact of telehealth adoption on rural hospital financial performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>Telehealth adoption varied among rural hospitals. Before the PHE, 75% (751) of rural hospitals had adopted telehealth, while 25% (247) were nonadopters. Despite efforts to promote remote care delivery during the PHE, 58% (144) of pre-PHE nonadopters did not adopt telehealth. Among the 42% (103) that did adopt telehealth during the PHE, no statistically significant effect was observed on operating or total margins.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Rural hospitals in economically disadvantaged and sparsely populated areas, which stand to benefit the most from telehealth adoption, often face substantial barriers that limit their ability to adopt this technology. Financial constraints and limited resources continue to hinder adoption, underscoring the need for targeted policies and investments to expand telehealth access and improve health care outcomes in rural communities.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148581","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}
Annie L. Glover PhD, MPH, MPA, Diane Brown MPH, Carly Holman MS, Megan Nelson MSW
Purpose
Pregnancy-related mortality has increased steadily over the last 30 years in the United States; during the same period, rural communities have lost access to care as rural hospitals and obstetric units have shut their doors. Rural critical access hospitals (CAHs) are often the only option for a pregnant person in a rural community needing emergency care. This study aimed to apply a uniform assessment of the capacity of hospitals that do not have obstetric units to meet the emergency obstetric care needs of the rural communities they serve, with the goal of facilitating ongoing obstetric emergency readiness assessments that can be used in the rural context.
Methods
The study team conducted facility assessments across Montana's statewide system of hospital care. The Centers for Disease Control and Prevention (CDC) Levels of Care Assessment Tool (LOCATe) was used in hospitals with an obstetrics unit (N = 25). The team adapted the World Health Organization (WHO) Emergency Obstetric Care (EmOC) framework to assess readiness in hospitals without an obstetrics unit (N = 34) but with Emergency Medical Treatment and Labor Act (EMTALA)-based obligations to patients presenting to emergency departments with obstetric emergencies.
Findings
None of the responding hospitals without obstetric units met criteria indicating readiness to provide comprehensive emergency obstetric care (CEmOC), and only one hospital met criteria indicating readiness to provide basic emergency obstetric care (BEmOC).
Conclusion
Significant work must be done to bring CAHs up to a level of readiness where they can safely and effectively screen, stabilize, and transfer or accept an obstetric emergency. The WHO EmOC framework can provide a starting point for assessing the capacity of hospitals without obstetric units, but a standardized assessment, such as LOCATe, should be developed to improve readiness for obstetric emergencies.
{"title":"Obstetric care in rural critical access hospitals: A domestic application of the World Health Organization Emergency Obstetric Care framework in rural communities","authors":"Annie L. Glover PhD, MPH, MPA, Diane Brown MPH, Carly Holman MS, Megan Nelson MSW","doi":"10.1111/jrh.70037","DOIUrl":"https://doi.org/10.1111/jrh.70037","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Pregnancy-related mortality has increased steadily over the last 30 years in the United States; during the same period, rural communities have lost access to care as rural hospitals and obstetric units have shut their doors. Rural critical access hospitals (CAHs) are often the only option for a pregnant person in a rural community needing emergency care. This study aimed to apply a uniform assessment of the capacity of hospitals that do not have obstetric units to meet the emergency obstetric care needs of the rural communities they serve, with the goal of facilitating ongoing obstetric emergency readiness assessments that can be used in the rural context.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The study team conducted facility assessments across Montana's statewide system of hospital care. The Centers for Disease Control and Prevention (CDC) Levels of Care Assessment Tool (LOCATe) was used in hospitals with an obstetrics unit (<i>N</i> = 25). The team adapted the World Health Organization (WHO) Emergency Obstetric Care (EmOC) framework to assess readiness in hospitals without an obstetrics unit (<i>N</i> = 34) but with Emergency Medical Treatment and Labor Act (EMTALA)-based obligations to patients presenting to emergency departments with obstetric emergencies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>None of the responding hospitals without obstetric units met criteria indicating readiness to provide comprehensive emergency obstetric care (CEmOC), and only one hospital met criteria indicating readiness to provide basic emergency obstetric care (BEmOC).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Significant work must be done to bring CAHs up to a level of readiness where they can safely and effectively screen, stabilize, and transfer or accept an obstetric emergency. The WHO EmOC framework can provide a starting point for assessing the capacity of hospitals without obstetric units, but a standardized assessment, such as LOCATe, should be developed to improve readiness for obstetric emergencies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jrh.70037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125964","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}
Feinuo Sun PhD, Yulin Yang PhD, Richard L. Nahin MPH, PhD
Purpose
Rural health disadvantages are well documented in previous literature; however, research on rural–urban disparities in chronic pain outcomes is scarce. This paper fills this gap by examining pain prevalences and longitudinal transitions across the rural–urban continuum (i.e., large central metro, large fringe metro, medium and small metro, and nonmetropolitan).
Methods
Based on the 2019–2020 National Health Interview Survey Longitudinal Cohort (NHIS-LC) data, we examined the disparities in pain prevalences and transitions among different pain statuses, including no pain, nonchronic pain, chronic pain, and high-impact chronic pain (HICP), across the rural–urban continuum and by age, sex, race/ethnicity, and region. A test for linear trend was conducted to examine the significance of linear changes across the rural–urban continuum.
Findings
The findings reveal significant linear increases in the prevalence of chronic pain and HICP, as well as transitions from no pain to nonchronic pain and from nonchronic pain to more severe pain conditions, along the continuum from metropolitan to nonmetropolitan areas. Subgroup analyses indicate that rural–urban gaps are most pronounced among middle-aged (45–64) groups and non-Hispanic Whites.
Conclusions
This longitudinal analysis provides new evidence on rural–urban health disparities by focusing on pain, highlighting the urgent need to enhance health care services in remote and rural areas for effective pain prevention and management.
{"title":"Development of chronic pain and high-impact chronic pain across the US rural–urban continuum, 2019–2020","authors":"Feinuo Sun PhD, Yulin Yang PhD, Richard L. Nahin MPH, PhD","doi":"10.1111/jrh.70036","DOIUrl":"https://doi.org/10.1111/jrh.70036","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Rural health disadvantages are well documented in previous literature; however, research on rural–urban disparities in chronic pain outcomes is scarce. This paper fills this gap by examining pain prevalences and longitudinal transitions across the rural–urban continuum (i.e., large central metro, large fringe metro, medium and small metro, and nonmetropolitan).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Based on the 2019–2020 National Health Interview Survey Longitudinal Cohort (NHIS-LC) data, we examined the disparities in pain prevalences and transitions among different pain statuses, including no pain, nonchronic pain, chronic pain, and high-impact chronic pain (HICP), across the rural–urban continuum and by age, sex, race/ethnicity, and region. A test for linear trend was conducted to examine the significance of linear changes across the rural–urban continuum.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>The findings reveal significant linear increases in the prevalence of chronic pain and HICP, as well as transitions from no pain to nonchronic pain and from nonchronic pain to more severe pain conditions, along the continuum from metropolitan to nonmetropolitan areas. Subgroup analyses indicate that rural–urban gaps are most pronounced among middle-aged (45–64) groups and non-Hispanic Whites.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This longitudinal analysis provides new evidence on rural–urban health disparities by focusing on pain, highlighting the urgent need to enhance health care services in remote and rural areas for effective pain prevention and management.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"41 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jrh.70036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117888","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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