Clinical and sociodemographic characteristics as predictors for quality of life in transmasculine and transfeminine individuals receiving gender-affirming hormone therapy.

Healthcare systems and providers have increasingly acknowledged the role and impact of social determinants in overall health. However, gender-diverse individuals face persistent health disparities due to their identities. There is limited research on the impact of clinical and sociodemographic characteristics on mood and quality of life (QoL) for transgender (TG) individuals. Our study aims to understand and better elucidate social and clinical characteristics of transmasculine (TM) and transfeminine (TF) individuals and their impact on quality of life and depressive symptoms. In this cross-sectional study, 298 TF and TM individuals on gender-affirming hormone therapy (GAHT) were surveyed about their demographic characteristics (age, gender identity, body mass index (BMI), and education), social needs, mood, and quality of life. Multivariable regression modelling was performed to assess the effect of each variable listed above on three domains of QoL (psychological, environmental, and physical) as well as depressive symptoms. We find that QoL scores are similar between TM and TF individuals, with scores in the psychological domain particularly low in both cohorts. TM individuals report higher rates of stress and restroom avoidance than TF individuals. In particular, psychological well-being (measured by the psychological domain of QoL and depressive symptoms) is significantly associated with increased BMI, financial instability, and stress in TM individuals while for TF individuals, psychological well-being is associated with stress and social integration. These data suggest that social circumstances are key drivers of QoL and psychological well-being among gender-diverse individuals receiving GAHT with specific differences between TF and TM individuals. This information may be utilized by healthcare providers and policymakers to address and improve clinical care and social policies to improve health equity for gender-diverse individuals.

A Medicaid-Funded Statewide Diabetes Quality Improvement Collaborative: Ohio 2020‒2022.

We used a collective impact model to form a statewide diabetes quality improvement collaborative to improve diabetes outcomes and advance diabetes health equity. Between 2020 and 2022, in collaboration with the Ohio Department of Medicaid, Medicaid Managed Care Plans, and Ohio's seven medical schools, we recruited 20 primary care practices across the state. The percentage of patients with hemoglobin A1c greater than 9% improved from 25% to 20% over two years. Applying our model more broadly could accelerate improvement in diabetes outcomes. (Am J Public Health. 2023;113(12):1254-1257. https://doi.org/10.2105/AJPH.2023.307410).

Alzheimer`s disease mortality in the United States: Cross-sectional analysis of county-level socio-environmental factors.

BACKGROUND: Geographical disparities in mortality among Alzheimer`s disease (AD) patients have been reported and complex sociodemographic and environmental determinants of health (SEDH) may be contributing to this variation. Therefore, we aimed to explore high-risk SEDH factors possibly associated with all-cause mortality in AD across US counties using machine learning (ML) methods. METHODS: We performed a cross-sectional analysis of individuals ≥65 years with any underlying cause of death but with AD in the multiple causes of death certificate (ICD-10,G30) between 2016 and 2020. Outcomes were defined as age-adjusted all-cause mortality rates (per 100,000 people). We analyzed 50 county-level SEDH and Classification and Regression Trees (CART) was used to identify specific county-level clusters. Random Forest, another ML technique, evaluated variable importance. CART`s performance was validated using a "hold-out" set of counties. RESULTS: Overall, 714,568 individuals with AD died due to any cause across 2,409 counties during 2016-2020. CART identified 9 county clusters associated with an 80.1% relative increase of mortality across the spectrum. Furthermore, 7 SEDH variables were identified by CART to drive the categorization of clusters, including High School Completion (%), annual Particulate Matter 2.5 Level in Air, live births with Low Birthweight (%), Population under 18 years (%), annual Median Household Income in US dollars ($), population with Food Insecurity (%), and houses with Severe Housing Cost Burden (%). CONCLUSION: ML can aid in the assimilation of intricate SEDH exposures associated with mortality among older population with AD, providing opportunities for optimized interventions and resource allocation to reduce mortality among this population.

Go-along interview assessment of community health priorities for neighborhood renewal.

Healthcare systems are increasingly investing in approaches to address social determinants of health and health disparities. Such initiatives dovetail with certain approaches to neighborhood development, such as the EcoDistrict standard for community development, that prioritize both ecologically and socially sustainable neighborhoods. However, healthcare system and community development initiatives can be untethered from the preferences and lived realities of residents in the very neighborhoods upon which they focus. Utilizing the go-along approach to collecting qualitative data in situ, we interviewed 19 adults to delineate residents' community health perspectives and priorities. Findings reveal health priorities distinct from clinical outcomes, with residents emphasizing social connectedness, competing intra- and interneighborhood perceptions that potentially thwart social connectedness, and a neighborhood emplacement of agency, dignity, and self-worth. Priorities of healthcare systems and community members alike must be accounted for to optimize efforts that promote health and social well-being by being valid and meaningful to the community of focus.

Financial Incentives Are Associated with Lower Likelihood of COVID-19 Vaccination in Northeast Ohio.

BACKGROUND: Declining COVID-19 vaccination rates have led to implementation of monetary incentives to increase vaccine uptake. The Ohio Vax-a-Million lottery and subsequent $100 incentives were created to encourage individuals to become vaccinated. The purpose of this survey was to determine the efficacy of these monetary incentives on vaccination rates. METHODS: A 38-item questionnaire was given to outpatients at MetroHealth and Cleveland Veteran Affairs Hospitals between August 2021 and February 2022 who either waited 2 or more months to receive the COVID-19 vaccination or have not yet been vaccinated. The survey contained questions regarding demographics and perceptions of COVID-19 monetary incentives on vaccination likelihood. RESULTS: Of the 471 participants surveyed, 0.95% reported that the Ohio Vax-a-Million lottery increased their vaccination likelihood, while 29.7% reported that it decreased their likelihood. 6.8% of respondents reported the $100 incentive increased their vaccination likelihood while 17.4% reported it decreased their vaccination chances. 20.6% of participants stated news of the Delta (δ) variant increased their vaccination likelihood. CONCLUSION: Our study results suggest that monetary incentives were not associated with increased COVID-19 vaccination rates. Instead, more participants believed that these incentives decreased their vaccination likelihood. Expansion of the survey across a wider sociodemographic range can provide further evidence of the efficacy of these programs before reimplementation.

Evaluating and Modeling Neighborhood Diversity and Health Using Electronic Health Records.

BACKGROUND: Electronic health records (EHRs) provide researchers with abundant sample sizes, detailed clinical data, and other advantages for performing high-quality observational health research on diverse populations. We review and demonstrate strategies for the design and analysis of cohort studies on neighborhood diversity and health, including evaluation of the effects of race, ethnicity, and neighborhood socioeconomic position on disease prevalence and health outcomes, using localized EHR data. METHODS: Design strategies include integrating and harmonizing EHR data across multiple local health systems and defining the population(s) of interest and cohort extraction procedures for a given analysis based on the goal(s) of the study. Analysis strategies address inferential goals, including the mechanistic study of social risks, statistical adjustment for differences in distributions of social and neighborhood-level characteristics between available EHR data and the underlying local population, and inference on individual neighborhoods. We provide analyses of local variation in mortality rates within Cuyahoga County, Ohio. RESULTS: When the goal of the analysis is to adjust EHR samples to be more representative of local populations, sampling and weighting are effective. Causal mediation analysis can inform effects of racism (through racial residential segregation) on health outcomes. Spatial analysis is appealing for large-scale EHR data as a means for studying heterogeneity among neighborhoods even at a given level of overall neighborhood disadvantage. CONCLUSIONS: The methods described are a starting point for robust EHR-derived cohort analysis of diverse populations. The methods offer opportunities for researchers to pursue detailed analyses of current and historical underlying circumstances of social policy and inequality. Investigators can employ combinations of these methods to achieve greater robustness of results. HIGHLIGHTS: EHR data are an abundant resource for studying neighborhood diversity and health.When using EHR data for these studies, careful consideration of the goals of the study should be considered in determining cohort specifications and analytic approaches.Causal mediation analysis, stratification, and spatial analysis are effective methods for characterizing social mechanisms and heterogeneity across localized populations.

Mechanisms of socioeconomic differences in COVID-19 screening and hospitalizations.

BACKGROUND: Social and ecological differences in early SARS-CoV-2 pandemic screening and outcomes have been documented, but the means by which these differences have arisen are not well understood. OBJECTIVE: To characterize socioeconomic and chronic disease-related mechanisms underlying these differences. DESIGN: Observational cohort study. SETTING: Outpatient and emergency care. PATIENTS: 12900 Cleveland Clinic Health System patients referred for SARS-CoV-2 testing between March 17 and April 15, 2020. INTERVENTIONS: Nasopharyngeal PCR test for SARS-CoV-2 infection. MEASUREMENTS: Test location (emergency department, ED, vs. outpatient care), COVID-19 symptoms, test positivity and hospitalization among positive cases. RESULTS: We identified six classes of symptoms, ranging in test positivity from 3.4% to 23%. Non-Hispanic Black race/ethnicity was disproportionately represented in the group with highest positivity rates. Non-Hispanic Black patients ranged from 1.81 [95% confidence interval: 0.91-3.59] times (at age 20) to 2.37 [1.54-3.65] times (at age 80) more likely to test positive for the SARS-CoV-2 virus than non-Hispanic White patients, while test positivity was not significantly different across the neighborhood income spectrum. Testing in the emergency department (OR: 5.4 [3.9, 7.5]) and cardiovascular disease (OR: 2.5 [1.7, 3.8]) were related to increased risk of hospitalization among the 1247 patients who tested positive. LIMITATIONS: Constraints on availability of test kits forced providers to selectively test for SARS-Cov-2. CONCLUSION: Non-Hispanic Black patients and patients from low-income neighborhoods tended toward more severe and prolonged symptom profiles and increased comorbidity burden. These factors were associated with higher rates of testing in the ED. Non-Hispanic Black patients also had higher test positivity rates.

Socioeconomic Distress as a Predictor of Missed First Outpatient Newborn Visits.

Objective To determine if the Area Deprivation Index (ADI) can serve as a predictor for patients from geographic regions of high socioeconomic distress as high risk for having no-shows to first-year newborn visits. Methods We assessed the no-show rate per patient from a large public safety-net hospital in Cleveland, Ohio, and the ADI of the census-designated tract for each patient's home geographic identifier (GEOID), aggregated into quintiles, and calculated differences in no-show-rates across census-designated tracts of different ADIs. Results A total of 2944 newborns from an approximate 18-month follow-up period between 2015-2017 were included. Large differences in no-shows per individual patient record (chi-square = 225, p = <0.001, df = 4) were found across quintiles of ADI. Heat-mapping indicated that census tracts with the highest ADIs and highest rates of no-show appointments encompass Cleveland's inner-city region. Conclusion The ADI is demonstrated to identify communities at high risk of no-show newborn appointments. Mapping these communities and their socioeconomic distress levels represented by ADI and missed appointment rate for each community can provide future direction for interventions targeted towards these communities to reduce no-show rates and improve overall community infant health.

Risk-period-cohort approach for averting identification problems in longitudinal models.

In epidemiology, gerontology, human development and the social sciences, age-period-cohort (APC) models are used to study the variability in trajectories of change over time. A well-known issue exists in simultaneously identifying age, period and birth cohort effects, namely that the three characteristics comprise a perfectly collinear system. That is, since age = period-cohort, only two of these effects are estimable at a time. In this paper, we introduce an alternative framework for considering effects relating to age, period and birth cohort. In particular, instead of directly modeling age in the presence of period and cohort effects, we propose a risk modeling approach to characterize age-related risk (i.e., a hybrid of multiple biological and sociological influences to evaluate phenomena associated with growing older). The properties of this approach, termed risk-period-cohort (RPC), are described in this paper and studied by simulations. We show that, except for pathological circumstances where risk is uniquely determined by age, using such risk indices obviates the problem of collinearity. We also show that the size of the chronological age effect in the risk prediction model associates with the correlation between a risk index and chronological age and that the RPC approach can satisfactorily recover cohort and period effects in most cases. We illustrate the advantages of RPC compared to traditional APC analysis on 27496 individuals from NHANES survey data (2005-2016) to study the longitudinal variability in depression screening over time. Our RPC method has broad implications for examining processes of change over time in longitudinal studies.

Barriers and facilitators to epilepsy self-management for patients with physical and psychological co-morbidity.

Objectives This exploratory study identifies barriers and facilitators to self-management to inform future epilepsy self-management interventions for persons who have epilepsy complicated by co-morbid mental health conditions and serious medical events. Methods Focus group methods were used in a series of community advisory board meetings. Analysis was conducted using a thematic, constant comparative approach aiming to describe the range of barriers and facilitators salient to participants. There were a total of 22 participants, including 8 health professionals, 9 patients with epilepsy, and 5 care partners. Mean age was 49.1 (SD = 11.0, range 32-69), 11 (50%) were female, and 11 (50%) were male. For those with epilepsy, mean years having epilepsy was 24.7 (SD = 19.9, range 1-58 years). Results Individual psychological barriers (mental illness, fatigue, and psychological distress) prominently interfered with health behaviors. Community and family barriers included stigma, lack of epilepsy knowledge, and poor social support. Facilitators included planning for seizures, learning about medications, stress management, socializing with others, and talking with other epilepsy patients. Discussion Qualitative evidence in this study suggests a linkage between social integration and positive health behaviors. Future efforts to embed patients with epilepsy and their caregivers into clinical care processes could offset barriers and enhance facilitators.

Accuracy of Cardiovascular Risk Prediction Varies by Neighborhood Socioeconomic Position: A Retrospective Cohort Study.

BACKGROUND: Inequality in health outcomes in relation to Americans' socioeconomic position is rising. OBJECTIVE: First, to evaluate the spatial relationship between neighborhood disadvantage and major atherosclerotic cardiovascular disease (ASCVD)-related events; second, to evaluate the relative extent to which neighborhood disadvantage and physiologic risk account for neighborhood-level variation in ASCVD event rates. DESIGN: Observational cohort analysis of geocoded longitudinal electronic health records. SETTING: A single academic health center and surrounding neighborhoods in northeastern Ohio. PATIENTS: 109 793 patients from the Cleveland Clinic Health System (CCHS) who had an outpatient lipid panel drawn between 2007 and 2010. The date of the first qualifying lipid panel served as the study baseline. MEASUREMENTS: Time from baseline to the first occurrence of a major ASCVD event (myocardial infarction, stroke, or cardiovascular death) within 5 years, modeled as a function of a locally derived neighborhood disadvantage index (NDI) and the predicted 5-year ASCVD event rate from the Pooled Cohort Equations Risk Model (PCERM) of the American College of Cardiology and American Heart Association. Outcome data were censored if no CCHS encounters occurred for 2 consecutive years or when state death data were no longer available (that is, from 2014 onward). RESULTS: The PCERM systematically underpredicted ASCVD event risk among patients from disadvantaged communities. Model discrimination was poorer among these patients (concordance index [C], 0.70 [95% CI, 0.67 to 0.74]) than those from the most affluent communities (C, 0.80 [CI, 0.78 to 0.81]). The NDI alone accounted for 32.0% of census tract-level variation in ASCVD event rates, compared with 10.0% accounted for by the PCERM. LIMITATIONS: Patients from affluent communities were overrepresented. Outcomes of patients who received treatment for cardiovascular disease at Cleveland Clinic were assumed to be independent of whether the patients came from a disadvantaged or an affluent neighborhood. CONCLUSION: Neighborhood disadvantage may be a powerful regulator of ASCVD event risk. In addition to supplemental risk models and clinical screening criteria, population-based solutions are needed to ameliorate the deleterious effects of neighborhood disadvantage on health outcomes. PRIMARY FUNDING SOURCE: The Clinical and Translational Science Collaborative of Cleveland and National Institutes of Health.

Patient portals and broadband internet inequality.

BACKGROUND: Patient portals have shown potential for increasing health care quality and efficiency. Internet access and other factors influencing patient portal use could worsen health disparities. METHODS: Observational study of adults with 1 or more visits to the outpatient clinics of an urban public health care system from 2012 to 2015. We used mixed effects logistic regression to evaluate the association between broadband internet access and (1) patient portal initiation (whether a patient logged in at least 1 time) and (2) messaging, controlling for demographic and neighborhood characteristics. RESULTS: There were 243 248 adults with 1 or more visits during 2012-2015 and 70 835 (29.1%) initiated portal use. Portal initiation was 34.1% for whites, 23.4% for blacks, and 23.8% for Hispanics, and was lower for Medicaid (26.5%), Medicare (23.4%), and uninsured patients (17.4%) than commercially insured patients (39.3%). In multivariate analysis, both initiation of portal use (odds ratio [OR] = 1.24 per quintile, 95% confidence interval [CI], 1.23-1.24, P < .0001) and sending messages to providers (OR = 1.15, 95%CI, 1.09-1.14, P < .0001) were associated with neighborhood broadband internet access. CONCLUSIONS: The majority of adults with outpatient visits to a large urban health care system did not use the patient portal, and initiation of use was lower for racial and ethnic minorities, persons of lower socioeconomic status, and those without neighborhood broadband internet access. These results suggest the emergence of a digital divide in patient portal use. Given the scale of investment in patient portals and other internet-dependent health information technologies, efforts are urgently needed to address this growing inequality.

Addressing Disparities in Diabetes Management Through Novel Approaches to Encourage Technology Adoption and Use.

Type 2 diabetes (T2D) is one of the nation's leading drivers of disability and health care utilization, with elevated prevalence among individuals with lower education, income, and racial/ethnic minorities. Health information technology (HIT) holds vast potential for helping patients, providers, and payers to address T2D and the skyrocketing rates of chronic illness and associated health care costs. Patient portals to electronic health records (EHRs) serve as a gateway to consumer use of HIT. We found that disparities in portal use portend growing T2D disparities. Little progress has been made in addressing identified barriers to technology adoption, especially among populations with elevated risk of T2D. Patients often lack digital literacy skills and continuous connectivity and fear loss of the relationship with providers. Providers may experience structural disincentives to promoting patient use of HIT and apply hidden biases that inhibit portal use. Health care systems often provide inadequate training to patients and providers in use of HIT, and lack resources devoted to obtaining and optimizing use of data generated by HIT. Lastly, technology-related barriers include inadequate consideration of user perspectives, lack of evidence for patient-focused apps, and lack of features to enable providers and health care systems to readily obtain aggregate data to improve care and facilitate research. After discussing these barriers in detail, we propose possible solutions and areas where further research is needed to ensure that individuals and health care systems obtain the full benefit of the nation's planned $38 billion HIT investment. A digital inclusion framework sheds new light on barriers posed for patients with social health inequalities. We have determined that partnerships with community organizations focused on digital inclusion could help health systems explore and study new approaches, such as universal screening and referral of patients for digital skills, health literacy, and Internet connectivity.

Modeling Individual Patient Preferences for Colorectal Cancer Screening Based on Their Tolerance for Complications Risk.

INTRODUCTION: Recommendations for colorectal cancer screening encourage patients to choose among various screening methods based on individual preferences for benefits, risks, screening frequency, and discomfort. We devised a model to illustrate how individuals with varying tolerance for screening complications risk might decide on their preferred screening strategy. METHODS: We developed a discrete-time Markov mathematical model that allowed hypothetical individuals to maximize expected lifetime utility by selecting screening method, start age, stop age, and frequency. Individuals could choose from stool-based testing every 1 to 3 years, flexible sigmoidoscopy every 1 to 20 years with annual stool-based testing, colonoscopy every 1 to 20 years, or no screening. We compared the life expectancy gained from the chosen strategy with the life expectancy available from a benchmark strategy of decennial colonoscopy. RESULTS: For an individual at average risk of colorectal cancer who was risk neutral with respect to screening complications (and therefore was willing to undergo screening if it would actuarially increase life expectancy), the model predicted that he or she would choose colonoscopy every 10 years, from age 53 to 73 years, consistent with national guidelines. For a similar individual who was moderately averse to screening complications risk (and therefore required a greater increase in life expectancy to accept potential risks of colonoscopy), the model predicted that he or she would prefer flexible sigmoidoscopy every 12 years with annual stool-based testing, with 93% of the life expectancy benefit of decennial colonoscopy. For an individual with higher risk aversion, the model predicted that he or she would prefer 2 lifetime flexible sigmoidoscopies, 20 years apart, with 70% of the life expectancy benefit of decennial colonoscopy. CONCLUSION: Mathematical models may formalize how individuals with different risk attitudes choose between various guideline-recommended colorectal cancer screening strategies.

Ecologic Momentary Assessment: Perspectives on Applications and Opportunities in Research and Practice Regarding Nutrition Behaviors.

Retrospective self-reported data have limitations, making it important to evaluate alternative forms of measurement for nutrition behaviors. Ecological momentary assessment (EMA) attempts to overcome the challenges of recalled data with real-time data collection in a subject's natural environment, often leveraging technology. This perspective piece 1) introduces the concepts and terminology of EMA, 2) provides an overview of the methodological and analytical considerations, 3) gives examples of past research using EMA, and 4) suggests new opportunities (including combining assessment and intervention) and limitations (including the need for technology) for the application of EMA to research and practice regarding nutrition behaviors.

No moment wasted: the primary-care visit for adults with diabetes and low socio-economic status.

AIM: To better understand the type and range of health issues initiated by patients and providers in 'high-quality' primary-care for adults with diabetes and low socio-economic status (SES). BACKGROUND: Although quality of care guidelines are straightforward, diabetes visits in primary care are often more complex than adhering to guidelines, especially in adults with low SES who experience many financial and environmental barriers to good care. METHODS: We conducted a qualitative study using direct observation of primary-care diabetes visits at an exemplar safety net practice in 2009-2010. Findings In a mainly African American (93%) low-income population with fair cardiovascular control (mean A1c 7.5%, BP 134/81 mmHg, and low-density lipoprotein cholesterol 100 mg/dL), visits addressed a variety of bio-psychosocial health issues [median: 25 problems/visit (range 13-32)]. Physicians most frequently initiated discussions about chronic diseases, prevention, and health behavior. Patients most frequently initiated discussions about social environment and acute symptoms followed by prevention and health behavior. CONCLUSIONS: Primary-care visits by diabetes patients with low SES address a surprising number and diversity of problems. Emerging new models of primary-care delivery and quality measurement should allow adequate time and resources to address the range of tasks necessary for integrating biomedical and psychosocial concerns to improve the health of socio-economically disadvantaged patients.

Associations between comorbid anxiety, diabetes control, and overall medical burden in patients with serious mental illness and diabetes.

OBJECTIVE: While previous work has demonstrated elevation of both comorbid anxiety disorders and diabetes mellitus type II in individuals with serious mental illness, little is known regarding the impact of comorbid anxiety on diabetes mellitus type II outcomes in serious mental illness populations. We analyzed baseline data from patients with serious mental illness and diabetes mellitus type II to examine relationships between comorbid anxiety, glucose control as measured by hemoglobin A1c score, and overall illness burden. METHODS: Using baseline data from an ongoing prospective treatment study involving 157 individuals with serious mental illness and diabetes mellitus type II, we compared individuals with and without a comorbid anxiety disorder and compared hemoglobin A1c levels between these groups to assess the relationship between anxiety and management of diabetes mellitus type II. We conducted a similar analysis using cumulative number of anxiety diagnoses as a proxy for anxiety load. Finally, we searched for associations between anxiety and overall medical illness burden as measured by Charlson score. RESULTS: Anxiety disorders were seen in 33.1% (N=52) of individuals with serious mental illness and diabetes mellitus type II and were associated with increased severity of depressive symptoms and decreased function. Hemoglobin A1c levels were not significantly different in those with or without anxiety, and having multiple anxiety disorders was not associated with differences in diabetes mellitus type II control. However, depressive symptoms were significantly associated with higher hemoglobin A1c levels. Neither comorbid anxiety nor anxiety load was significantly associated with overall medical burden. CONCLUSION: One in three people with serious mental illness and diabetes mellitus type II had anxiety. Depressive symptoms were significantly associated with Hb1Ac levels while anxiety symptoms had no relation to hemoglobin A1c; this is consistent with previously published work. More studies are needed to better understand the relationship between depression, anxiety, and health management in people with serious mental illness and diabetes mellitus type II.

Neighborhood socioeconomic status and barriers to peritoneal dialysis: a mixed methods study.

BACKGROUND AND OBJECTIVES: The objective of this study was to evaluate the association between neighborhood socioeconomic status and barriers to peritoneal dialysis eligibility and choice. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This study was a mixed methods parallel design study using quantitative and qualitative data from a prospective clinical database of ESRD patients. The eligibility and choice cohorts were assembled from consecutive incident chronic dialysis patients entering one of five renal programs in the province of Ontario, Canada, between January 1, 2004 and December 31, 2010. Socioeconomic status was measured as median household income and percentage of residents with at least a high school education using Statistics Canada dissemination area-level data. Multivariable models described the relationship between socioeconomic status and likelihood of peritoneal dialysis eligibility and choice. Barriers to peritoneal dialysis eligibility and choice were classified into qualitative categories using the thematic constant comparative approach. RESULTS: The peritoneal dialysis eligibility and choice cohorts had 1314 and 857 patients, respectively; 65% of patients were deemed eligible for peritoneal dialysis, and 46% of eligible patients chose peritoneal dialysis. Socioeconomic status was not a significant predictor of peritoneal dialysis eligibility or choice in this study. Qualitative analyses identified 16 barriers to peritoneal dialysis choice. Patients in lower- versus higher-income Statistics Canada dissemination areas cited built environment or space barriers to peritoneal dialysis (4.6% versus 2.7%) and family or social support barriers (8.3% versus 3.5%) more frequently. CONCLUSIONS: Peritoneal dialysis eligibility and choice were not associated with socioeconomic status. However, socioeconomic status may influence specific barriers to peritoneal dialysis choice. Additional studies to determine the effect of targeting interventions to specific barriers to peritoneal dialysis choice in low socioeconomic status patients on peritoneal dialysis use are needed.

Priorities for oral health goals in a sample of older adults.

This study was conducted to identify oral health factors that are important to people who are elderly. By identifying factors valued by patients who are older, dental professionals can gain better insights into the preferences and priorities that patients use in making dental treatment decisions. To determine which factors are important to this population, we compiled a list of oral health goals identified through a literature review. To supplement this initial list, we undertook line-by-line (open) coding of transcripts of two sets of semi-structured interviews. The first set of interviews explored the decision of older adults to seek treatment in response tooth pain. The second set of interviews elicited anticipated responses to three scenarios involving extensive restoration and one scenario involving extraction and prosthetic replacement among older patients at a dental school.