Screening for Park Access during a Primary Care Social Determinants Screen.

While there is evidence that access to nature and parks benefits pediatric health, it is unclear how low-income families living in an urban center acknowledge or prioritize access to parks.MethodsWe conducted a study about access to parks by pediatric patients in a health system serving low-income families. Adult caregivers of pediatric patients completed a survey to identify and prioritize unmet social and economic needs, including access to parks. Univariate and multivariate analyses were conducted to explore associations between lack of access to parks and sociodemographic variables. We also explored the extent to which access to parks competed with other needs.ResultsThe survey was completed by 890 caregivers; 151 (17%) identified "access to green spaces/parks/playgrounds" as an unmet need, compared to 397 (45%) who endorsed "running out of food before you had money or food stamps to buy more". Being at or below the poverty line doubled the odds ( Odds ratio 1.96, 95% CI 1.16-3.31) of lacking access to a park (reference group: above the poverty line), and lacking a high school degree nearly doubled the odds. Thirty-three of the 151 (22%) caregivers who identified access to parks as an unmet need prioritized it as one of three top unmet needs. Families who faced competing needs of housing, food, and employment insecurity were less likely to prioritize park access (p < 0.001).ConclusionClinical interventions to increase park access would benefit from an understanding of the social and economic adversity faced by patients

An online experiment to assess bias in professional medical coding.

BackgroundMultiple studies have documented bias in medical decision making, but no studies have examined whether this bias extends to medical coding practices. Medical coding is foundational to the US health care enterprise. We evaluate whether bias based on patient characteristics influences specific coding practices of professional medical coders.MethodsThis is an online experimental study of members of a national professional medical coding organization. Participants were randomly assigned a set of six clinical scenarios reflecting common medical conditions and asked to report encounter level of service codes for these clinical scenarios. Clinical scenarios differed by patient demographics (race, age, gender, ability) or social context (food insecurity, housing security) but were otherwise identical. We estimated Ordinary Least Squares regression models to evaluate differences in outcome average visit level of service by patient demographic characteristics described in the clinical scenarios; we adjusted for coders' age, gender, race, and years of coding experience.ResultsThe final analytic sample included 586 respondents who coded at least one clinical scenario. Higher mean level of service was assigned to clinical scenarios describing seniors compared to middle-aged patients in two otherwise identical scenarios, one a patient with type II diabetes mellitus (Coef: 0.28, SE: 0.15) and the other with rheumatoid arthritis (Coef: 0.30, SE: 0.13). Charts describing women were assigned lower level of service than men in patients with asthma exacerbation (Coef: -0.25, SE: 0.13) and rheumatoid arthritis (Coef: -0.20, SE: 0.12). There were no other significant differences in mean complexity score by patient demographics or social needs.ConclusionWe found limited evidence of bias in professional medical coding practice by patient age and gender, though findings were inconsistent across medical conditions. Low levels of observed bias may reflect medical coding workflow and training practices. Future research is needed to better understand bias in coding and to identify effective and generalizable bias prevention practices

Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence.

Population health researchers from different fields often address similar substantive questions but rely on different study designs, reflecting their home disciplines. This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration. In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument (such as instrumental variables, regression discontinuity, or differences-in-differences approaches). Using the Shadish, Cook, and Campbell framework for evaluating threats to validity, we contrast the assumptions, strengths, and limitations of these two approaches and illustrate differences with examples from the literature on education and health. Across disciplines, all methods to test a hypothesized causal relationship involve unverifiable assumptions, and rarely is there clear justification for exclusive reliance on one method. Each method entails trade-offs between statistical power, internal validity, measurement quality, and generalizability. The choice between confounder-control and instrument-based methods should be guided by these tradeoffs and consideration of the most important limitations of previous work in the area. Our goals are to foster common understanding of the methods available for causal inference in population health research and the tradeoffs between them; to encourage researchers to objectively evaluate what can be learned from methods outside one's home discipline; and to facilitate the selection of methods that best answer the investigator's scientific questions

“I would love to see these big institutions… throwing their weight around”: qualitative findings regarding health and social sector collaborations to address community-level socioeconomic adversity

BackgroundHealth and social sector organizations are increasingly working together to mitigate socioeconomic adversity within their communities. We sought to learn about the motivations, experiences, and perspectives of organizations engaged in these collaborations.MethodsWe conducted semi-structured, 60-minute interviews with 34 leaders from 25 health and social sector organizations between January-April 2021. Interviews explored motivations, benefits and challenges, and ways in which health sector organizations can most effectively address community-level socioeconomic adversity. Interviews were audio recorded and transcribed; themes were coded using Dedoose software.ResultsPartnerships were primarily motivated by mission-driven organizations and key health sector leaders who were interested in addressing root causes of poor health; policies such as certificate of need laws and value-based care incentives that aligned community-level investments with health sector organizations' financial interests facilitated these efforts. While partnerships were mostly regarded as mutually beneficial ways to increase impact (for the health sector) and resource access (for the social sector), social sector organizations voiced frustrations regarding the outsized expectations, unsustained interest, and lack of partnership from their health sector collaborators. Despite these frustrations, both health and social sector interviewees supported the health sector's continued involvement in community-level socioeconomic initiatives and expansion of policy and systems efforts.ConclusionsCross-sector, community-level socioeconomic initiatives were mutually beneficial, but social sector organizations experienced more frustrations. Policy and organizational changes within the health sector can further mobilize and sustain support for these efforts

Defining functional classes of Barth syndrome mutation in humans

The X-linked disease Barth syndrome (BTHS) is caused by mutations in TAZ; TAZ is the main determinant of the final acyl chain composition of the mitochondrial-specific phospholipid, cardiolipin. To date, a detailed characterization of endogenous TAZ has only been performed in yeast. Further, why a given BTHS-associated missense mutation impairs TAZ function has only been determined in a yeast model of this human disease. Presently, the detailed characterization of yeast tafazzin harboring individual BTHS mutations at evolutionarily conserved residues has identified seven distinct loss-of-function mechanisms caused by patient-associated missense alleles. However, whether the biochemical consequences associated with individual mutations also occur in the context of human TAZ in a validated mammalian model has not been demonstrated. Here, utilizing newly established monoclonal antibodies capable of detecting endogenous TAZ, we demonstrate that mammalian TAZ, like its yeast counterpart, is localized to the mitochondrion where it adopts an extremely protease-resistant fold, associates non-integrally with intermembrane space-facing membranes and assembles in a range of complexes. Even though multiple isoforms are expressed at the mRNA level, only a single polypeptide that co-migrates with the human isoform lacking exon 5 is expressed in human skin fibroblasts, HEK293 cells, and murine heart and liver mitochondria. Finally, using a new genome-edited mammalian BTHS cell culture model, we demonstrate that the loss-of-function mechanisms for two BTHS alleles that represent two of the seven functional classes of BTHS mutation as originally defined in yeast, are the same when modeled in human TAZ

Ixekizumab Efficacy and Safety with and without Concomitant Conventional Disease-modifying Antirheumatic Drugs (cDMARDs) in Biologic DMARD (bDMARD)-naive Patients with Active Psoriatic Arthritis (PsA): Results from SPIRIT-P1

Objective: To evaluate the efficacy and safety of ixekizumab alone or with concomitant conventional disease-modifying antirheumatic drugs (cDMARDs) versus placebo in patients with active psoriatic arthritis (PsA) as part of a SPIRIT-P1 subgroup analysis (NCT01695239). Methods: Patients were stratified by cDMARD use (concomitant cDMARDs use (including methotrexate) or none (past or naive use)) and randomly assigned to treatment groups (ixekizumab 80 mg every 4 weeks (IXEQ4W) or every 2 weeks (IXEQ2W) or placebo). Efficacy was evaluated versus placebo at week 24 by the American College of Rheumatology criteria (ACR20/50/70), modified total Sharp score and Health Assessment Questionnaire-Disability Index (HAQ-DI). Safety was assessed according to cDMARD status. Results: Regardless of concomitant cDMARD usage, ACR20, ACR50 and ACR70 response rates were significantly higher versus placebo with IXEQ4W and IXEQ2W. The proportion of patients achieving HAQ-DI minimal clinically important difference was significantly higher versus placebo with IXEQ4W with concomitant cDMARD use and IXEQ2W, regardless of concomitant cDMARD use. Treatment-emergent adverse events (AE) were more frequent versus placebo for either ixekizumab-dosing regimen, regardless of concomitant cDMARD use. Serious AEs were not higher versus placebo, regardless of concomitant cDMARD use. Conclusion: Ixekizumab treatment improved measures of disease activity and physical function in patients with active PsA relative to placebo, when used with or without concomitant cDMARD therapy

Powering population health research: Considerations for plausible and actionable effect sizes

Evidence for Action (E4A), a signature program of the Robert Wood Johnson Foundation, funds investigator-initiated research on the impacts of social programs and policies on population health and health inequities. Across thousands of letters of intent and full proposals E4A has received since 2015, one of the most common methodological challenges faced by applicants is selecting realistic effect sizes to inform power and sample size calculations. E4A prioritizes health studies that are both (1) adequately powered to detect effect sizes that may reasonably be expected for the given intervention and (2) likely to achieve intervention effects sizes that, if demonstrated, correspond to actionable evidence for population health stakeholders. However, little guidance exists to inform the selection of effect sizes for population health research proposals. We draw on examples of five rigorously evaluated population health interventions. These examples illustrate considerations for selecting realistic and actionable effect sizes as inputs to power and sample size calculations for research proposals to study population health interventions. We show that plausible effects sizes for population health inteventions may be smaller than commonly cited guidelines suggest. Effect sizes achieved with population health interventions depend on the characteristics of the intervention, the target population, and the outcomes studied. Population health impact depends on the proportion of the population receiving the intervention. When adequately powered, even studies of interventions with small effect sizes can offer valuable evidence to inform population health if such interventions can be implemented broadly. Demonstrating the effectiveness of such interventions, however, requires large sample sizes.Comment: 24 pages, 1 figur

Building the evidence on Making Health a Shared Value: Insights and considerations for research.

The Robert Wood Johnson Foundation (RWJF)'s Culture of Health Action Framework guides a movement to improve health and advance health equity across the nation. Action Area One of the Framework, Making Health a Shared Value, highlights the role of individual and community factors in achieving a societal commitment to health and health equity, centered around three drivers: Mindset and Expectations, Sense of Community, and Civic Engagement. To stimulate research about how Action Area One and its drivers may impact health, Evidence for Action (E4A), a signature research funding program of RWJF, developed and released a national Call for Proposals (CFP). The process of formulating the CFP and reviewing proposals surfaced important challenges for research on creating and sustaining shared values to foster and maintain a Culture of Health. In this essay, we describe these considerations and provide examples from funded projects regarding how challenges can be addressed

Proinsulin:C-peptide ratio trajectories over time in relatives at increased risk of progression to type 1 diabetes

Objective: Biomarkers are needed to characterize heterogeneity within populations at risk for type 1 diabetes. The ratio of proinsulin to C-peptide (PI:C ratio), has been proposed as a biomarker of beta cell dysfunction and is associated with progression to type 1 diabetes. However, relationships between PI:C ratios and autoantibody type and number have not been examined. We sought to characterize PI:C ratios in multiple islet autoantibody positive, single autoantibody positive and autoantibody negative relatives of individuals with type 1 diabetes. Methods: We measured PI:C ratios and autoantibodies with both electrochemiluminescence (ECL) assays (ECL-IAA, ECL-GADA and ECL-IA2A) and radiobinding (RBA) assays (mIAA, GADA, IA2A and ZnT8A) in 98 relatives of individuals with type 1 diabetes followed in the TrialNet Pathway to Prevention Study at the Barbara Davis Center for a mean of 7.4 ​± ​4.1 years. Of these subjects, eight progressed to T1D, 31 were multiple autoantibody (Ab) positive, 37 were single Ab positive and 22 were Ab negative (by RBA). Results: In cross-sectional analyses, there were no significant differences in PI:C ratios between type 1 diabetes and/or multiple Ab positive subjects (4.16 ​± ​4.06) compared to single Ab positive subjects (4.08 ​± ​4.34) and negative Ab subjects (3.72 ​± ​3.78) (p ​= ​0.92) overall or after adjusting for age, sex and BMI. Higher PI:C ratios were associated with mIAA titers (p ​= ​0.03) and showed an association with ECL-IA2A titers (p ​= ​0.09), but not with ECL-IAA, GADA, ECL-GADA, IA2A nor ZnT8A titers. In mixed-effects longitudinal models, the trajectories of PI:C ratio over time were significantly different between the Ab negative and multiple Ab positive/type 1 diabetes groups, after adjusting for sex, age, and BMI (p ​= ​0.04). Conclusions: PI:C ratio trajectories increase over time in subjects who have multiple Ab or develop type 1 diabetes and may be a helpful biomarker to further characterize and stratify risk of progression to type 1 diabetes over time