Charles Gessert

Nursing home quality: a comparative analysis using CMS Nursing Home Compare data to examine differences between rural and nonrural facilities.

BACKGROUND Advances in medicine and an aging US population suggest that there will be an increasing demand for nursing home services. Although nursing homes are highly regulated and scrutinized, their quality remains a concern and may be a greater issue to those living in rural communities. Despite this, few studies have investigated differences in the quality of nursing home care across the rural-urban continuum. The purpose of this study was to compare the quality of rural and nonrural nursing homes by using aggregated rankings on multiple quality measures calculated by the Centers for Medicare and Medicaid Services and reported on their Nursing Home Compare Web site. METHODS Independent-sample t tests were performed to compare the mean ratings on the reported quality measures of rural and nonrural nursing homes. A linear mixed binary logistic regression model controlling for state was performed to determine if the covariates of ownership, number of beds, and geographic locale were associated with a higher overall quality rating. RESULTS Of the 15,177 nursing homes included in the study sample, 69.2% were located in nonrural areas and 30.8% in rural areas. The t test analysis comparing the overall, health inspection, staffing, and quality measure ratings of rural and nonrural nursing homes yielded statistically significant results for 3 measures, 2 of which (overall ratings and health inspections) favored rural nursing homes. Although a higher percentage of nursing homes (44.8%-42.2%) received a 4-star or higher rating, regression analysis using an overall rating of 4 stars or higher as the dependent variable revealed that when controlling for state and adjusting for size and ownership, rural nursing homes were less likely to have a 4-star or higher rating when compared with nonrural nursing homes (OR = .901, 95% CI 0.824-0.986). CONCLUSIONS Mixed model logistic regression analysis suggested that rural nursing home quality was not comparable to that of nonrural nursing homes. When controlling for state and adjusting for nursing home size and ownership, rural nursing homes were not as likely to earn a 4-or higher star quality rating as nonrural nursing homes.

Research in Prisons: An Eye for Equity

In the current issue of Ophthalmic Epidemiology, Tousignant documents a cluster of optic neuropathy in a prison population in Papua New Guinea. In these prisoners, the optic neuropathy was associated with folate deficiency, a condition that can readily be corrected with minor effort and negligible expense. The study serves to illustrate the vital role that epidemiologic research can play in documenting correctable health problems, especially in underserved or marginalized populations. Tousignant and colleagues are to be commended for the special care that they devoted to assuring that the participating prisoners were not exploited in the process of the research – and for describing in detail the precautions that they took. Their study may serve to encourage other investigators to consider prison populations in research plans. There are currently over 10 million prisoners worldwide; the health issues in prison populations are daunting. Around the world, prison populations are drawn from the most impoverished and marginalized sectors of their societies. Many incarcerated persons arrive at prison with histories of inadequate healthcare for acute and chronic health problems. Reviews of the published medical literature consistently reveal that the most common health problems among prisoners are mental health issues, substance abuse and communicable disease. HIV/AIDS, Hepatitis B and C, and drug-resistant tuberculosis are widespread concerns in prison populations, both because of high prevalence at the time of incarceration and due to the risk of transmission in prison. Additional health problems may arise in the course of incarceration, due to overcrowding, isolation, poor nutrition, inadequate exercise, and prison life itself. A recent review of non-communicable diseases in prison populations found broad deficiencies in diet and exercise, including some problems that could be readily addressed, such as energy intake that exceeded recommendations in some populations, and excesses in sodium and fat in the diets of others. The extent to which prison itself raises the risk of illness has only been investigated in relation to infectious diseases and is unknown in relation to most other disorders. Among the health problems in prisons, mental health issues stand out. Serious mental disorders including psychosis, depression, personality disorder, antisocial personality and substance abuse are far more common in prisoners than in the general population. Suicide is the leading cause of death in prisons, accounting for about half of all prison deaths. As many psychiatric institutions have reduced their bed numbers, especially in developed countries, prisons are now serving as modern asylums. As neuroscientist David Eagleman observed recently ‘‘prisons have become our de facto mental health care institutions.’’ In many settings, prison health problems are largely invisible. They remain hidden from the public eye, not only by the walls and barbed wire, but by legal and administrative barriers. In some instances, concerns are aggravated by the suspicion – and sometimes the evidence – that the governments that maintain prisons may not solicit, want or even tolerate additional scrutiny of what goes on behind prison walls. Concerns about the care and treatment of prisoners, such as the use of capital punishment for mentally ill prisoners in some countries, are widespread. In some cases attention may be unwelcome

PS3-47: Rural Health Research Initiative in the HMORN: A New Scientific Interest Group.

Background/Aims Rural health describes a set of health issues, health care challenges and research priorities driven by a single geo-demographic factor: low population density. Rural areas compared to urbanized areas have fewer providers per capita, longer distance to care, lower socioeconomic status, higher rates of untreated illness, greater exposure to agricultural chemicals, and higher rates of alcohol use, fatal motor vehicle crashes, and suicide. Accessing clinical data for large numbers of rural residents can be challenging. To meet this challenge, seven sites formed the HMORN Rural Health Scientific Interest Group (SIG). Methods VDW data from seven HMORN sites were analyzed. Rural-Urban Commuting Area (RUCA) codes describe commuting flow but include data on urban, town, and rural tracts; RUCA codes were used to categorize areas as urban, large rural town, small rural town, or isolated rural area. We determined prevalence of chronic conditions by rural status and age group (child, adult, seniors). Results Common diseases were hypertension, obesity, dyslipidemia, diabetes, alcohol/drug use, depression, and cancers. Most sites saw stable rates of rural vs. urban patients over the years. Rates of pediatric obesity increased at all sites. Adult obesity increased markedly among seniors while dyslipidemia and diabetes increased in all age groups. Cancer among adults also trended upward over time and exceeded national averages. Hypertension among adults appeared lower than US national average in 2010 (32%). Conclusions Economic challenges and other factors may further accentuate existing health and health care disparities experienced by many Americans living in rural areas. The Rural Health SIG of the HMORN is poised to conduct meaningful, multi-site research addressing health care issues, health care delivery and care follow-up for this special patient population. Future analyses will explore variation in chronic disease by rural status and the influence of economic factors within geographies.

Differences in breast tissue oxygenation following radiotherapy

Tissue perfusion and oxygenation changes following radiotherapy may result from and/or contribute to the toxicity of treatment. Breast tissue oxygenation levels were determined in the treated and non-treated breast 1 year after radiotherapy for breast conserving treatment. Transcutaneous oxygenation varied between subjects in both treated and non-treated breast. Subjects without diabetes mellitus (n=16) had an average oxygenation level of 64.8 ± 19.9mmHg in the irradiated breast and an average of 72.3 ± 18.1mmHg (p=0.018) at the corresponding location in the control breast. Patients with diabetes (n=4) showed a different oxygenation pattern, with lower oxygenation levels in control tissue and no decrease in the irradiated breast. This study suggests oxygenation levels in normal tissues vary between patients and may respond differently after radiotherapy.

CC4-04: An Application of Doubly Robust Estimation

Background/Aims Observational data often has treatment exposure confounded with baseline covariates. Doubly robust estimation utilizes both a regression model and an additional model for the exposure, often the propensity score, to estimate the causal effect of an exposure on an outcome. Methods In a study designed to evaluate change in hemoglobin (Hb) with use of angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB), in a primary care patient population, we found that receiving ARB vs. ACEI was associated with several of the baseline covariates. These baseline covariates, Hb, treatment start date, chronic kidney disease, congestive heart failure, diabetes mellitus, hypertension, sex, and age, initially thought to be informative in estimating follow-up (F/U) Hb, were chosen a priori. No medical explanation for the differential ordering of ACEI and ARB based on the level of these covariates was identified, so the treatment effect of ARB relative to ACEI is considered to be truly confounded. We therefore adopted the doubly robust semiparametric efficient estimator of Robins et al. (1994) in a causal analysis of the treatment effect of ARB vs. ACEI on F/U Hgb. The method produces an estimate of the treatment effect by simultaneously incorporating the propensity of a subject to receive ACEI or ARB, given their levels of covariates, and the effects of the covariates upon the response of interest, F/U Hb. It is doubly robust in the sense that it produces an unbiased estimate of the treatment effect if either the outcome or propensity model is correct. A complete-case ANCOVA was conducted to estimate the treatment effect. Results We found the estimated F/U Hb and bootstrap bias-corrected accelerated (BCa) 95% confidence interval (CI) of ACEI and ARB to be 14.31 (14.21, 14.42) gm/dL and 14.48 (14.33, 14.62) gm/dL, respectively. The causal effect of ARB relative to ACEI and associated BCa CI is estimated to be 0.17 (0.00, 0.31) gm/dL (p = 0.0310). Discussion The use of doubly robust estimation documented a significant difference between the effects of ACEI and ARB on F/U Hb, despite the association of several of the baseline covariates with the differential ordering of these drugs.