Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To inform policy supporting the retention of family physicians (FPs) in the perinatal care workforce by identifying physician characteristics that are associated with retention.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>We surveyed FPs who had been in practice for at least 11 years and reported attending deliveries as part of their practice.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We compared the characteristics of FPs who continue to provide perinatal care to those who have ceased and explored their reasons for no longer attending deliveries.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>We estimated a probit regression with the dependent variable: whether the physician currently delivers babies. Open‐ended survey responses were analyzed and close‐coded using a conceptual content analysis approach.</jats:p></jats:sec><jats:sec><jats:title>Principle Findings</jats:title><jats:p>Of the FPs who received a survey, 1505 (37%) responded. Those who continue attending deliveries were more likely to receive a stipend or be paid per hour/shift in addition to their salary versus those paid a salary (percentage point difference = 13), and less likely to work part‐time versus full‐time (percentage point difference = −20). Those who ceased attending deliveries cite lifestyle (<jats:italic>n</jats:italic> = 208), call structure (<jats:italic>n</jats:italic> = 113), and delivery volume (<jats:italic>n</jats:italic> = 89) among the reasons for doing so.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Evidence‐based policies aimed at preventing attrition from the perinatal care workforce, which might include targeting compensation models and work–life balance.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To identify barriers veterans with bipolar disorder face to accessing chronic pain management services within a Veterans Affairs (VA) health care system.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>Veterans (<jats:italic>n</jats:italic> = 15) with chronic pain and bipolar disorder and providers (<jats:italic>n</jats:italic> = 15) working within a mid‐Atlantic VA health care system. Data were collected from August 2017–June 2018.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Veteran interviews focused on their chronic pain experiences and treatment, including barriers that arose when trying to access pain management services. Provider interviews focused on whether they address chronic pain with veteran patients and, if so, what considerations arise when addressing pain in veterans with bipolar disorder and other serious mental illnesses.</jats:p></jats:sec><jats:sec><jats:title>Data Collection</jats:title><jats:p>Veterans were at least 18 years old, had a confirmed bipolar disorder and chronic pain diagnosis, and engaged in outpatient care within the VA health care system. Clinicians provided direct care services to veterans within the same VA. Interviews lasted approximately 60 min and were transcribed and analyzed using a rapid analysis protocol.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Four major themes emerged from veteran and provider interviews: siloed care (unintegrated and uncoordinated mental and physical health care), mental health primacy (prioritization of mental health symptoms at expense of physical health symptoms), lagging expectations (unfamiliarity with comprehensive evidence‐based pain management options), and provider–patient communication concerns (inefficient communication about pain concerns and treatment options).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Veterans with co‐occurring pain and bipolar disorder face unique barriers that compromise equitable access to evidence‐based pain treatment. Our findings suggest that educating providers about bipolar disorder and other serious mental illnesses and the benefit of effective non‐pharmacological pain interventions for this group may improve care coordination and care quality and reduce access disparities.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To understand variation in enrollment in tiered network health plans (TNPs) and the local provider market characteristics associated with TNP penetration.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>We used 2013–2017 Massachusetts three‐digit ZIP code level employer‐sponsored health insurance enrollment data, data on physician horizontal and vertical affiliations from the Massachusetts Provider Database, state hospital reports in 2013, 2015, and 2017, and the 2013–2017 Massachusetts All‐Payer Claims database.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Linear regressions were used to estimate associations between TNP and local provider market characteristics.</jats:p></jats:sec><jats:sec><jats:title>Data Extraction</jats:title><jats:p>We constructed measures of TNP penetration and local provider market characteristics and linked these data using three‐digit ZIP code.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>TNP penetration was at least 10% in all employer market sectors and highest among jumbo sized employers. All state employee health plan enrollees were in a tiered network health plan. Among enrollees not in the state employee health plan, TNP penetration varied from 6.0% to 19.6% across three‐digit ZIP codes in Massachusetts. TNP penetration was higher in areas with less horizontal and vertical physician market concentration.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Market competition, rather than the absolute quantity of physicians in an area, is associated with TNP penetration.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To determine whether a 6‐ or 12‐month look‐back period affected rates of reported social risks in a social risk survey for use in the Veterans Health Administration and to assess associations of social risks with overall health and mental health.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Cross‐sectional survey of respondents randomized to 6‐ or 12‐month look‐back period.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>Online survey with a convenience sample of Veterans in June and July 2021.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Veteran volunteers were recruited by email to complete a survey assessing social risks, including financial strain, adult caregiving, childcare, food insecurity, housing, transportation, internet access, loneliness/isolation, stress, discrimination, and legal issues. Outcomes included self‐reported overall health and mental health. Chi‐squared tests compared the prevalence of reported social risks between 6‐ and 12‐month look‐back periods. Spearman correlations assessed associations among social risks. Bivariate and multivariable logistic regression models estimated associations between social risks and fair/poor overall and mental health.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Of 3418 Veterans contacted, 1063 (31.10%) responded (87.11% male; 85.61% non‐Hispanic White; median age = 70, interquartile range [IQR] = 61–74). Prevalence of most reported social risks did not significantly differ by look‐back period. Most social risks were weakly intercorrelated (median |<jats:italic>r</jats:italic>| = 0.24, IQR = 0.16‐0.31). Except for legal issues, all social risks were associated with higher odds of fair/poor overall health and mental health in bivariate models. In models containing all significant social risks from bivariate models, adult caregiving and stress remained significant predictors of overall health; food insecurity, housing, loneliness/isolation, and stress remained significant for mental health.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Six‐ and 12‐month look‐back periods yielded similar rates of reported social risks. Although most individual social risks are associated with fair/poor overall and mental health, when examined together, only adult caregiving, stress, loneliness/isolation, food, and housing remain significant.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To describe the national rate of social risk factor screening adoption among federally qualified health centers (FQHCs), examine organizational factors associated with social risk screening adoption, and identify barriers to utilizing a standardized screening tool in 2020.</jats:p></jats:sec><jats:sec><jats:title>Data Source</jats:title><jats:p>2020 Uniform Data System, a 100% sample of all US FQHCs (<jats:italic>N</jats:italic> = 1375).</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We used multivariable linear probability models to assess the association between social risk screening adoption and key FQHC characteristics. We used descriptive statistics to describe variations in screening tool types and barriers to utilizing standardized tools. We thematically categorized open‐ended responses about tools and barriers.</jats:p></jats:sec><jats:sec><jats:title>Data Collection</jats:title><jats:p>None.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>In 2020, 68.9% of FQHCs screened patients for any social risk factors. Characteristics associated with a greater likelihood of screening adoption included having high proportions of patients best served in a language other than English (18.8 percentage point [PP] increase, 95% CI: 6.0, 31.6) and being larger in size (10.3 PP increase, 95% CI: 0.7, 20.0). Having higher proportions of uninsured patients (14.2 PP decrease, 95% CI: −25.5, −0.3) and participating in Medicaid‐managed care contracts (7.3 PP decrease, 95% CI: −14.2, −0.3) were associated with lower screening likelihood. Among screening FQHCs, the Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences (PRAPARE) was the most common tool (47.1%). Among non‐screening FQHCs, common barriers to using a standardized tool included lack of staff training to discuss social issues (25.2%), inability to include screening in patient intake (21.7%), and lack of funding for addressing social needs (19.2%).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Though most FQHCs screened for social risk factors in 2020, various barriers have prevented nearly 1 in 3 FQHCs from adopting a screening tool. Policies that provide FQHCs with resources to support training and workflow changes may increase screening uptake and facilitate engagement with other sectors.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>The aim was to identify hospital and county characteristics associated with variation in breadth and depth of hospital partnerships with a broad range of organizations to improve population health.</jats:p></jats:sec><jats:sec><jats:title>Data Sources</jats:title><jats:p>The American Hospital Association Annual Survey provided data on hospital partnerships to improve population health for the years 2017–2019.</jats:p></jats:sec><jats:sec><jats:title>Design</jats:title><jats:p>The study adopts the dimensional publicness theory and social capital framework to examine hospital and county characteristics that facilitate hospital population health partnerships. The two dependent variables were number of local community organizations that hospitals partner with (breadth) and level of engagement with the partners (depth) to improve population health.</jats:p><jats:p>The independent variables include three dimensions of publicness: Regulative, Normative and Cultural‐cognitive measured by various hospital factors and presence of social capital present at county level. Covariates in the multivariate analysis included hospital factors such as bed‐size and system membership.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We used hierarchical linear regression models to assess various hospital and county factors associated with breadth and depth of hospital‐community partnerships, adjusting for covariates.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Nonprofit and public hospitals provided a greater breadth (coefficient, 1.61; SE, 0.11; <jats:italic>p</jats:italic> &lt; 0.001 and coefficient, 0.95; SE, 0.14; <jats:italic>p</jats:italic> &lt; 0.001) and depth (coefficient, 0.26, SE, 0.04; <jats:italic>p</jats:italic> &lt; 0.001 &amp; coefficient, 0.13; SE, 0.05; <jats:italic>p</jats:italic> &lt; 0.05) of partnerships than their for‐profit counterparts, partially supporting regulative dimension of publicness. At a county level, we found community social capital positively associated with breadth of partnerships (coefficient, 0.13; SE, 0.08; <jats:italic>p</jats:italic> &lt; 0.001).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>An environment that promotes collaboration between hospitals and organizations to improve population health may impact the health of the community by identifying health needs of the community, targeting social determinants of health, or by addressing patient social needs. However, findings suggest that publicness dimensions at an organizational level, which involves a culture of public value, maybe more important than county factors to achieve community building through partnerships.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objectives</jats:title><jats:p>To describe the impact of Accountable Communities of Health (ACHs) on organizational and community partnerships and explore how ACHs contribute to systems change.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>The California Accountable Communities of Health Initiative (CACHI) was a 5‐year, $17 M investment in community health transformation in 13 ACH sites. Data sources include two surveys, key informant interviews, small group conversations, and ACH meeting observations and document review.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>This was a mixed‐methods, observational study. Surveys conducted in 2021 and 2022 focused on ACH progress in building organizational and community partnerships and ACH impact on partners and systems, respectively. Interviews and small group conversations were conducted toward the end of the CACHI grant period and designed to complement the surveys.</jats:p></jats:sec><jats:sec><jats:title>Data Collection</jats:title><jats:p>Survey respondents included ACH backbone agency staff and partner organization representatives (<jats:italic>n</jats:italic> = 141 in 2021 and 88 in 2022). Semistructured individual interviews and group conversations were conducted with 40 ACH backbone staff and partners. Documents were collected via grant reporting and directly from ACH staff. Data were analyzed descriptively and thematically.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>ACHs appear to have supported organizational partnerships and collaboration. Seventy‐six percent of survey respondents reported that their ACH had strengthened organizations' ability to work together and 65% reported developing new or deepened connections. While ACH participants reported a better understanding of community needs and priorities, progress on community relationships, and greater attention to equity and racial justice, many saw room for improvement on meaningful community engagement. Systems changes and precursors of systems change observed across ACH sites included strengthened partnerships, enhanced knowledge, increased capacity, more collaborative ways of working, and new funding streams.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The ACH model is effective at strengthening organizational partnerships and catalyzing other systems changes and precursors including enhanced knowledge, increased capacity, more collaborative ways of working, and new funding.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>Identify factors affecting the use of a community resource referral platform among local community‐based organizations (CBOs) and test strategies to increase platform use.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>Data sources included platform usage data and semistructured interviews. The study took place in a small city in the Northeastern United States from 2020 to 2022.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We analyzed platform data and conducted interviews with local organizations and organizations in other communities to understand barriers to CBOs' use of the referral platform and identify strategies that might increase use. We then tested 4 strategies and assessed impacts via time trend analysis of platform usage and qualitative interviews.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Platform usage data were obtained from the platform. Semistructured interviews were conducted with staff and leaders of 36 local CBOs and 9 external organizations.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Four years after launch, platform use remained relatively low. None of the tested strategies (data insight reports, a referral hub, tailored training, and a communication campaign) noticeably increased platform use. The main barrier to the use of the platform was the lack of perceived usefulness, mostly because existing processes for identifying resources and referring clients worked well enough and because many organizations were already required to use a client management or referral tool. Additional barriers included the lack of comfort with and, in some cases, active dislike of e‐referrals, and lack of comfort with technology tools overall. Organizations that were most likely to find the platform useful and to use it were those that provided referrals for a wide range of needs and whose staff were not already familiar with local resources.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Organizations seeking to implement referral platforms should not assume that local CBOs will automatically take up these platforms. For these platforms to succeed, much more attention needs to be paid to ensuring the platforms provide value to the CBOs they seek to engage.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To measure key characteristics of the Veterans Health Administration's (VHA) Community Care (CC) referral network for screening colonoscopy and identify market and institutional factors associated with network size.</jats:p></jats:sec><jats:sec><jats:title>Data Sources</jats:title><jats:p>VHA electronic health records, CC claim data, and National Plan and Provider Enumeration System.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>In this retrospective cross‐sectional study, we measure the size of the VHA's CC referral networks over time and by VHA parent facility (<jats:italic>n</jats:italic> = 137). We used a multivariable linear regression to identify factors associated with network size at the market‐year level. Network size was measured as the number of physicians who performed at least one VHA‐purchased screening colonoscopy per 1000 enrollees at baseline.</jats:p></jats:sec><jats:sec><jats:title>Data Extraction</jats:title><jats:p>Data were extracted for all Veterans (<jats:italic>n</jats:italic> = 102,119) who underwent a screening colonoscopy purchased by the VHA from a non‐VHA physician from 2018 to 2021.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>From 2018 to 2021, median network volume of screening colonoscopies per 1000 enrollees grew from 1.6 (IQR: 0.6, 4.6) to 3.6 (IQR: 1.6, 6.6). The median network size grew from 0.63 (IQR: 0.30, 1.26) to 0.92 (IQR: 0.57, 1.63). Finally, the median procedures per physician increased from 2.5 (IQR: 1.6, 4.2) to 3.2 (IQR: 2.4, 4.7). After adjusting for baseline market characteristics, volume of screening colonoscopies was positively related to network size (<jats:italic>β</jats:italic> = 0.15, 95% CI: [0.10, 0.20]), negatively related to procedures per physician (<jats:italic>β</jats:italic> = −0.12, 95% CI: [−0.18, −0.05]), and positively associated with the percent of rural enrollees (<jats:italic>β</jats:italic> = 0.01, 95% CI: [0.00, 0.01]).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>VHA facilities with a higher volume of VHA‐purchased screening colonoscopies and more rural enrollees had more non‐VHA physicians providing care. Geographic variation in referral networks may also explain differences in the effects of the MISSION Act on access to care and patient outcomes.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>The aim was to quantify changes in the market structure of primary care physicians and examine its relationship with access to care.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>We created measures of market structure from a 5% sample of Medicare fee‐for‐service claims and examined access to care using nationally representative data from the Medical Expenditure Panel Survey (MEPS). Our study spanned from 2008 to 2019.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We used a linear probability model to estimate the relationship between access to care and two measures of market structure: concentration, measured by the Herfindahl–Hirschman Index (HHI), and vertical integration, measured by the market share of multispecialty firms. Our model controlled for year and ZIP code fixed effects, respondents' demographics and health status, and other measures of market structure.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>All adult respondents in the MEPS were included.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>The percentage of people living in concentrated ZIP codes (HHI above 1500) increased from 37% in 2008 to 53% in 2019. During the same period, the median market share of multispecialty firms rose from 30% to 48%. Respondents in highly concentrated ZIP codes (HHI over 2500) were 5.9 percentage points (95% CI: −1.4 to −10.4) less likely to report having access to immediate care than respondents in unconcentrated ZIP codes. The association was largest among Medicaid beneficiaries, a 17.3 percentage point reduction (95% CI: −5.1 to −29.4). When we applied a model that was robust to biases from treatments with staggered timing, the estimated association remained negative but was not statistically significant. We found no association between HHI and indicators for having a usual source of care and annual checkups. The multispecialty market share was negatively associated with checkups, but not other measures of access.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Increases in concentration may reduce some types of access to healthcare. These effects appear most pronounced among Medicaid beneficiaries.</jats:p></jats:sec>