Targeting High-Need Beneficiaries in Medicare Advantage: Opportunities to Address Medical and Social Needs.

Issue: Serving Medicare beneficiaries with complex health care needs requires understanding both the medical and social factors that may affect their health. Goal: Describe the prevalence and characteristics of high-need individuals enrolled in the Medicare Advantage program. Methods: Analysis of the 2015 Medicare Health Outcomes Survey. Key Findings: Thirty-seven percent of enrollees in large Medicare Advantage plans have high needs, requiring both medical and social services. Individuals with high needs are more likely to report having limited financial resources, low levels of education, social isolation, and poor health. Conclusion: Federal policymakers should consider allowing Medicare Advantage plans to identify high-need beneficiaries based on their medical and social risk factors, rather than just medical diagnoses. Doing so would enable plans to deliver better-targeted services that meet their members' needs and facilitate implementation of the CHRONIC Care Act provision that allows plans to offer nonhealth supplemental benefits.

Self-harm: Prevalence estimates from the second Australian Child and Adolescent Survey of Mental Health and Wellbeing.

OBJECTIVE: To (1) estimate the lifetime and 12-month prevalence of self-harm without suicide intent in young people aged 12-17 years, (2) describe the co-morbidity of these behaviours with mental illness and (3) describe their co-variation with key social and demographic variables. METHOD: A nationally representative random sample of households with children aged 4-17 years recruited in 2013-2014. The survey response rate was 55% with 6310 parents and carers of eligible households participating. In addition, 2967 (89%) of young people aged 11-17 completed a self-report questionnaire with 2653 of the 12- to 17-year-olds completing questions about self-harm behaviour. RESULTS: In any 12-month period, about 8% of all 12- to 17-year-olds (an estimated 137,000 12- to 17-year-olds) report engaging in self-harming behaviour without suicide intent. This prevalence increases with age to 11.6% in 16- to 17-year-olds. Eighteen percent (18.8%; 95% confidence interval [CI] = [14.5, 23.0]) of all 12- to 17-year-old young people with any mental health disorder measured by parent or carer report said that they had engaged in self-harm in the past 12 months. Among young people who were measured by self-report and met criteria for the Diagnostic and Statistical Manual of Mental Disorders' major depressive disorder almost half (46.6%; 95% CI = [40.0, 53.1]) also reported that they had engaged in self-harm in the past 12 months. Suicide risk among those who self-harm is significantly elevated relative to the general population. CONCLUSION: The demonstrated higher risks in these young people for continued harm or possible death support the need for ongoing initiatives to reduce self-harm through mental health promotion, improved mental health literacy and continuing mental health reform to ensure services are accessible to, and meet the needs of families and young persons.

Service use by Australian children for emotional and behavioural problems: Findings from the second Australian Child and Adolescent Survey of Mental Health and Wellbeing.

OBJECTIVE: To identify the proportion of children and adolescents in Australia and the proportion of those with mental disorders who used services for emotional and behavioural problems, the type of services used and what characteristics were associated with service use. METHOD: During 2013-2014, a national face-to-face household survey of mental health and wellbeing (Young Minds Matter) was conducted, involving 6310 parents and carers of 4- to 17-year-olds (55% of eligible households) and self-report surveys from 2967 11- to 17-year-olds in these households (89% of eligible youth). The survey identified 12-month mental disorders based on the Diagnostic Interview Schedule for Children-Version IV and asked about service use for emotional or behavioural problems in the previous 12 months. RESULTS: Overall, 17.0% of all 4- to 17-year-olds used services for emotional or behavioural problems in the previous 12 months. Of those with mental disorders, 56.0% used services (48.9% of 4- to 11-year-olds; 65.1% of 12- to 17-year-olds). Service use was highest among 4- to 17-year-olds with major depressive disorder (79.6%) and lowest for those with attention-deficit/hyperactivity disorder (52.7%). Two-fifths (41.2%), 72.5% and 87.6% of those with mild, moderate and severe disorders used services. General practitioners, psychologists, paediatricians and counsellors/family therapists were the most commonly accessed health service providers. Two-fifths with mental disorders had attended school services. About 5% of adolescents reported use of online personal support or counselling for help with their problems. From multivariate models, service use was higher in sole carer families, but also among those living in the least socially and economically disadvantaged compared to the most disadvantaged areas. CONCLUSION: Rates of service use for mental disorders in Australia's children and adolescents appear to have increased substantially. Health services and schools are the major providers of services for emotional and behavioural problems, but telephone counselling and online services have become well-established parts of the service environment.

Suicidal behaviours: Prevalence estimates from the second Australian Child and Adolescent Survey of Mental Health and Wellbeing.

OBJECTIVE: To (1) estimate the lifetime and 12-month prevalence of suicidal behaviours in Australian young people aged 12-17 years, (2) describe their co-morbidity with mental illness and (3) describe the co-variation of these estimates with social and demographic variables. METHOD: A national random sample of children aged 4-17 years was recruited in 2013-2014. The response rate to the survey was 55% with 6310 parents and carers of eligible households participating. In addition, of the 2967 young people aged 11-17 years in these households, 89% (2653) of the 12- to 17-year-olds completed a self-report questionnaire that included questions about suicidal behaviour. RESULTS: In any 12-month period, about 2.4% or 41,400 young people would have made a suicide attempt. About 7.5% of 12- to 17-year-olds report having suicidal ideation, 5.2% making a plan and less than 1% (0.6%) receiving medical treatment for an attempt. The presence of a mental disorder shows the largest significant association with lifetime and 12-month suicidal behaviour, along with age, gender, sole parent family status and poor family functioning. Of young people with a major depressive disorder, 19.7% reported making a suicide attempt within the previous 12 months. There are also significant elevations in the proportions of young people reporting suicidal behaviour who have anxiety and conduct disorders. CONCLUSION: Mental disorders should be a leading intervention point for suicide prevention both in the primary health sector and in the mental health sector specifically. The associations examined here also suggest that efforts to assist sole parent and/or dysfunctional families would be worthy areas in which to target these efforts.

Methodology of Young Minds Matter: The second Australian Child and Adolescent Survey of Mental Health and Wellbeing.

OBJECTIVE: To describe the study design of Young Minds Matter: The second Australian Child and Adolescent Survey of Mental Health and Wellbeing. The aims of the study, sample design, development of survey content, field procedures and final questionnaires are detailed. METHOD: During 2013-2014, a national household survey of the mental health and wellbeing of young people was conducted involving a sample of 6310 families selected at random from across Australia. The survey included a face-to-face diagnostic interview with parents/carers of 4- to 17-year-olds and a self-report questionnaire completed by young people aged 11-17 years. RESULTS: The overall response rate to the survey was 55% with 6310 parents/carers of eligible households participating in the survey. In addition, 2967 or 89% of young people aged 11-17 years in these participating households completed a questionnaire. The survey sample was found to be broadly representative of the Australian population on major demographic characteristics when compared with data from the Census of Population and Housing. However, adjustments were made for an over-representation of younger children aged 4 to 7 years and also families with more than one eligible child in the household. CONCLUSION: Young Minds Matter provides updated national prevalence estimates of common child and adolescent mental disorders, describes patterns of service use and will help to guide future decisions in the development of policy and provision of mental health services for children and adolescents. Advancements in interviewing methodology, addition of a data linkage component and informed content development contributed to improved breadth and quality of the data collected.

Key findings from the second Australian Child and Adolescent Survey of Mental Health and Wellbeing.

OBJECTIVE: To estimate the prevalence of mental disorders in children and adolescents in Australia, and the severity and impact of those mental disorders. METHOD: Seven mental disorders were assessed using the parent- or carer-completed version of the Diagnostic Interview Schedule for Children Version IV, and major depressive disorder was also assessed using the youth self-report version of the Diagnostic Interview Schedule for Children Version IV. Severity and impact were assessed using an extended version of the Diagnostic Interview Schedule for Children Version IV impact on functioning questions, and days absent from school due to symptoms of mental disorders. Data were collected in a national face-to-face survey of 6310 parents or carers of children and adolescents aged 4-17 years, with 2969 young people aged 11-17 years also completing a self-report questionnaire. RESULTS: Twelve-month prevalence of mental disorders was 13.9%, with 2.1% of children and adolescents having severe disorders, 3.5% having moderate disorders and 8.3% having mild disorders. The most common class of disorders was attention-deficit/hyperactivity disorder followed by anxiety disorders. Mental disorders were more common in step-, blended- or one-parent families, in families living in rented accommodation and families where one or both carers were not in employment. Mental disorders were associated with a substantial number of days absent from school particularly in adolescents. CONCLUSION: Mental disorders are common in children and adolescents, often have significant impact and are associated with substantial absences from school. Child and adolescent mental disorders remain an important public health problem in Australia. Accurate information about prevalence and severity of child and adolescent mental disorders is an essential prerequisite for effective mental health policy and service planning.

Sparse modeling of spatial environmental variables associated with asthma.

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors.

Estimating treatment rates for mental disorders in Australia.

OBJECTIVE: To estimate the percentage of Australians with a mental disorder who received treatment for that disorder each year between 2006-07 and 2009-10. METHODS: We used: (1) epidemiological survey data to estimate the number of Australians with a mental disorder in any year; (2) a combination of administrative data on people receiving mental health care from the Commonwealth and State and Territories and epidemiological data to estimate the number receiving treatment; and (3) uncertainty modelling to estimate the effects of sampling error and assumptions on these estimates. RESULTS: The estimated population treatment rate for mental disorders in Australia increased from 37% in 2006-07 to 46% in 2009-10. The model estimate for 2006-07 (37%) was very similar to the estimated treatment rate in the 2007 National Survey of Mental Health and Wellbeing (35%), the only data available for external comparison. The uncertainty modelling suggested that the increased treatment rates over subsequent years could not be explained by sampling error or uncertainty in assumptions. CONCLUSIONS: The introduction of the Commonwealth's Better Access initiative in November 2006 has been the driver for the increased the proportion of Australians with mental disorders who received treatment for those disorders over the period from 2006-07 to 2009-10. WHAT IS KNOWN ABOUT THE TOPIC? Untreated mental disorders incur major economic costs and personal suffering. Governments need timely estimates of treatment rates to assess the effects of policy changes aimed at improving access to mental health services. WHAT DOES THIS PAPER ADD? Drawing upon a combination of epidemiological and administrative data sources, the present study estimated that the population treatment rate for mental disorders in Australia increased significantly from 37% in 2006-07 to 46% in 2009-10. WHAT ARE THE IMPLICATIONS FOR PRACTITIONERS? Increased access to services is not sufficient to ensure good outcomes for those with mental disorders. It is also important to ensure that evidence-based treatment is provided to those Australians accessing these services.

Area Deprivation and Disease Severity in Adult Patients With Discoid Lupus Erythematosus.

Importance: The association of area deprivation with outcomes in discoid lupus erythematosus (DLE) remains poorly understood. Objective: To determine the association between US Census block measures of deprivation and disease severity in adult patients with DLE. Design, Setting, and Participants: This cross-sectional study included 154 patients with DLE seen between January 1, 2007, and January 1, 2024, at a single-center referral-based specialty rheumatologic-dermatology clinic in Philadelphia, Pennsylvania. Patients were aged 18 to 73 years and were enrolled in the University of Pennsylvania's Cutaneous Lupus Erythematosus Database study. Data were analyzed between January 1, 2024, and May 8, 2024. Exposures: Residence in a highly disadvantaged area as geocoded by a state area deprivation index (ADI). Main Outcomes and Measures: The main outcome was DLE disease severity as codified by the validated Cutaneous Lupus Erythematosus Disease Area and Severity Index (CLASI) damage and activity scores. Results: A total of 154 adult patients with DLE (128 women [83%] and 26 men [17%]; mean [SD] age, 43 [13] years; 6 [4%] Asian individuals, 98 [64%] Black individuals, 2 [1%] Hispanic individuals, 46 [30%] White individuals, and 2 individuals [1%] with other race or ethnicity; 78 [51%] with an ADI >5; 43 who currently smoked [28%];and 56 [36%] with concurrent systemic lupus erythematosus) were included in the analysis. By multivariable logistic regression, residence within communities with an ADI greater than 5 was associated with nearly 4-fold greater odds of moderate to severe damage (odds ratio [OR], 3.90; 95% CI, 1.27-12.69] and activity (OR, 3.31; 95% CI, 1.27-9.44). Concurrent cigarette smoking was similarly associated with greater odds of moderate to severe damage (OR, 3.15; 95% CI, 1.09-10.29). After controlling for ADI and other confounders, race was not significantly associated with DLE disease severity. Conclusions and Relevance: The results of this cross-sectional study suggest that geospatial disadvantage is associated with DLE disease severity independent of race. This invites a paradigm shift that considers the social context within which racial disparities are observed, highlighting the potential for geographically targeted interventions and policy changes to improve patient outcomes in DLE.

The potential for sociocultural factors in the diagnosis of ADHD in children.

PURPOSE: The nongenetic contributors to attention deficit/hyperactivity disorder (ADHD) remain to be identified. A previous study in eastern Wisconsin (prevalence 13.5%) suggested that male gender, white race, lower block group median household income and population density, and greater distance to the nearest park were factors predictive of ADHD diagnosis. We performed a similar study in Dane County, Wisconsin. METHODS: Cross sectional study of children age 5-17, with and without ADHD diagnosis, who received well child care in Dane County UW Family Medicine clinics (N=7954) 2007-2008. Street addresses were geocoded to 2000 Census block group. Univariate analysis was done by chi-square test or Mann-Whitney U test, multivariate analysis by logistic regression. RESULTS: ADHD diagnosis was present in 309 (3.9%) children (74.1% male; P = 0.000, compared to females) and more frequently diagnosed in black children (6.8% of black children had ADHD diagnosis) than white (4%), Native American (2.7%), Hispanic (1.6%), or Asian (1.3%) children. In contrast to eastern Wisconsin and to Milwaukee County (a subset of the eastern Wisconsin study where black rates were identical to that of Dane County), black race rather than white race was predictive of ADHD in Dane County, while median household income, population density, and distance to nearest park were not associated. The range of ADHD within school district boundaries was 2.4%-7.1% (for N > 100/district). In the group of districts with >4% ADHD diagnosis, the increased rates were largely among whites. CONCLUSION: ADHD diagnosis was much less common in this Dane County cohort than in eastern Wisconsin and was more common among blacks, but not predicted by other geo-demographic factors. Like eastern Wisconsin, ADHD diagnosis prevalence varied with apparent school district boundaries.

Life Course Assessment of Area-Based Social Disadvantage: A Systematic Review.

Area-based social disadvantage, which measures the income, employment, and housing quality in one's community, can impact an individual's health above person-level factors. A life course approach examines how exposure to disadvantage can affect health in later life. This systematic review aimed to summarize the approaches used to assess exposure to area-based disadvantage over a life course, specifically those that define the length and timing of exposure. We reviewed the abstracts of 831 articles based on the following criteria: (1) whether the abstract described original research; (2) whether the study was longitudinal; (3) whether area-based social disadvantage was an exposure variable; (4) whether area-based social disadvantage was assessed at multiple points; and (5) whether exposure was assessed from childhood to older adulthood. Zero articles met all the above criteria, so we relaxed the fifth criterion in a secondary review. Six studies met our secondary criteria and were eligible for data extraction. The included studies followed subjects from childhood into adulthood, but none assessed disadvantages in late life. The approaches used to assess exposure included creating a cumulative disadvantage score, conducting a comparison between life course periods, and modeling the trajectory of disadvantage over time. Additional research was needed to validate the methodologies described here, specifically in terms of measuring the impact of area-based social disadvantage on health.

Planning estimates for the provision of core mental health services in Queensland 2007 to 2017.

OBJECTIVE: To derive planning estimates for the provision of public mental health services in Queensland 2007-2017. METHOD: We used a five-step approach that involved: (i) estimating the prevalence and severity of mental disorders in Queensland, and the number of people at each level of severity treated by health services; (ii) benchmarking the level and mix of specialised mental health services in Queensland against national data; (iii) examining 5-year trends in Queensland public sector mental health service utilisation; (iv) reviewing Australian and international planning benchmarks; and (v) setting resource targets based on the results of the preceding four steps. Best available evidence was used where possible, supplemented by value judgements as required. RESULTS: Recommended resource targets for inpatient service were: 20 acute beds per 100,000 population, consistent with national average service provision but 13% above Queensland provision in 2005; and 10 non-acute beds per 100,000, 65% below Queensland levels in 2005. Growth in service provision was recommended for all other components. Adult residential rehabilitation service targets were 10 clinical 24-hour staffed beds per 100,000, and 18 non-clinical beds per 100,000. Supported accommodation targets were 35 beds per 100,000 in supervised hostels and 35 places per 100,000 in supported public housing. A direct care clinical workforce of 70 FTE per 100,000 for ambulatory care services was recommended. Fifteen per cent of total mental health funding was recommended for community support services provided by non-government organisations. CONCLUSIONS: The recommended targets pointed to specific areas for priority in Queensland, notably the need for additional acute inpatient services for older persons and expansion of clinical ambulatory care, residential rehabilitation and supported accommodation services. The development of nationally agreed planning targets for public mental health services and the mental health community support sector were identified as priorities.

The potential and pitfalls of geocoding electronic health records.

BACKGROUND: Geocoding electronic health records (EHRs) provides novel insights for clinicians, but it is important to understand and address key issues, including privacy and protection of patient records, in order to realize potential benefits. METHODS: This paper discusses the issues surrounding geocoding and illustrates potential benefits through 3 case studies of no-shows to clinical appointments, patient analysis for a merged clinic site, and multi-clinic patient overlap. CONCLUSION: Geocoding EHRs provides a new contextual understanding for clinicians to understand patients and provide targeted interventions that patients can implement. While geocoding EHRs presents a need for high data security, the benefits outweigh the risks when proper protections are observed.

The effect of prenatal support on birth outcomes in an urban midwestern county.

OBJECTIVES: In Dane County, Wisconsin, the black-white infant mortality gap started decreasing from 2000 and was eliminated from 2004 to 2007. Unfortunately, it has reappeared since 2008. This paper examines risk factors and levels of prenatal care to identify key contributors to the dramatic decline and recent increase in black infant mortality and extremely premature birth rates. METHODS: This retrospective cohort study analyzed approximately 100,000 Dane County birth, fetal, and infant death records from 1990 to 2007. Levels of prenatal care received were categorized as "less-than-standard," "standard routine" or "intensive." US Census data analysis identified demographic and socioeconomic changes. Infant mortality rates and extremely premature ( < or = 28 weeks gestation) birth rates were main outcome measures. Contributions to improved outcomes were measured by calculating relative risk, risk difference and population attributable fraction (PAF). Mean income and food stamp use by race were analyzed as indicators of general socioeconomic changes suspected to be responsible for worsening outcomes since 2008. RESULTS: Risk of extremely premature delivery for black women receiving standard routine care and intensive care decreased from 1990-2000 to 2001-2007 by 77.8% (95% CI = 49.9-90.1%) and 57.3% (95% CI = 27.6-74.8%) respectively. Women receiving less-than-standard care showed no significant improvement over time. Racial gaps in mean income and food stamp use narrowed 2002-2007 and widened since 2008. CONCLUSIONS: Prenatal support played an important role in improving black birth outcomes and eliminating the Dane County black-white infant mortality gap. Increasing socioeconomic disparities with worsening US economy since 2008 likely contributed to the gap's reappearance.

The theory and application of UW ehealth-PHINEX, a clinical electronic health record-public health information exchange.

BACKGROUND: Electronic health records (EHRs) hold the promise of improving clinical quality and population health while reducing health care costs. However, it is not clear how these goals can be achieved in practice. METHODS: Clinician-led teams developed EHR data extracts to support chronic disease use cases. EHRs were linked with community-level data to describe disease prevalence and health care quality at the patient, health care system, and community risk factor levels. Software was developed and statistical modeling included multivariate, mixed-model, longitudinal, data mining, and geographic information system (GIS)/spatial regression approaches. RESULTS: A HIPAA-compliant limited data set was created on 192,201 patients seen in University of Wisconsin Family Medicine clinics throughout Wisconsin in 2007-2009. It was linked to a commercially available database of approximately 6000 variables describing community-level risk factors at the census block group. Areas of increased asthma and diabetes prevalence have been mapped, identified, and compared to economic hardship. CONCLUSIONS: A comprehensive framework has been developed for clinical-public health data exchange to develop new evidence and apply it to clinical practice and health policy. EHR data at the neighborhood level can be used for future population studies and may enhance understanding of community-level patterns of illness and care.

Differences in Exclusive Breastfeeding Rates in US Hospitals According to Baby-Friendly Hospital Initiative Designation and Area Deprivation Index Category.

Background: Disparities in breastfeeding persist placing a greater burden of disease on non-Hispanic black and Hispanic women and infants. Targeted implementation of the Baby-Friendly Hospital Initiative (BFHI) in areas at risk for poor breastfeeding outcomes has been shown to improve disparities in breastfeeding. The area deprivation index (ADI), a measure of the relative socioeconomic disadvantage of a neighborhood, may be useful in exploring the accessibility of BFHI hospitals in highly deprived areas and the differences in exclusive breastfeeding (EBF) rates in hospitals with and without the BFHI designation across deprivation categories. Objective: To evaluate the geographical distribution of BFHI and non-BFHI hospitals across ADI categories and explore the differences in EBF rates in BFHI and non-BFHI hospitals across ADI categories. Methods: Hospital EBF rates obtained from the Joint Commission included 414 BFHI and 1,532 non-BFHI hospitals. State ADI rank scores were determined for each hospital's census block group. Descriptive statistics were used to describe the geographic distribution of BFHI hospitals across three ADI categories (low, medium, and high). EBF rates across ADI categories and BFHI designations were compared using multiway analysis of variance. Results: The distribution of BFHI was similar across all ADI categories, ranging from 18% to 24%. EBF rates were 4.9% lower in highly deprived areas compared to areas with lower deprivation (p < 0.01). BFHI was associated with significantly higher EBF rates across all ADI categories (6.9%-11.2%, p < 0.01). Conclusion: ADI may be a useful tool for targeting the implementation of BFHI in hospitals in highly deprived areas to reduce breastfeeding disparities.

A systematic review of geographic indices of disadvantage with implications for older adults.

BACKGROUNDNeighborhood-level socioeconomic disadvantage has wide-ranging impacts on health outcomes, particularly in older adults. Although indices of disadvantage are a widely used tool, research conducted to date has not codified a set of standard variables that should be included in these indices for the United States. The objective of this study was to conduct a systematic review of literature describing the construction of geographic indices of neighborhood-level disadvantage and to summarize and distill the key variables included in these indices. We also sought to demonstrate the utility of these indices for understanding neighborhood-level disadvantage in older adults.METHODSWe conducted a systematic review of existing indices in the English-language literature.RESULTSWe identified 6021 articles, of which 130 met final study inclusion criteria. Our review identified 7 core domains across the surveyed papers, including income, education, housing, employment, neighborhood structure, demographic makeup, and health. Although not universally present, the most prevalent variables included in these indices were education and employment.CONCLUSIONIdentifying these 7 core domains is a key finding of this review. These domains should be considered for inclusion in future neighborhood-level disadvantage indices, and at least 5 domains are recommended to improve the strength of the resulting index. Targeting specific domains offers a path forward toward the construction of a new US-specific index of neighborhood disadvantage with health policy applications. Such an index will be especially useful for characterizing the life-course impact of lived disadvantage in older adults.

Association of Neighborhood Context, Cognitive Decline, and Cortical Change in an Unimpaired Cohort.

OBJECTIVE: To test the hypothesis that neighborhood-level disadvantage is associated with longitudinal measures of neurodegeneration and cognitive decline in an unimpaired cohort. METHODS: Longitudinal MRI and cognitive testing data were collected from 601 cognitively unimpaired participants in the Wisconsin Registry for Alzheimer's Prevention Study and the Wisconsin Alzheimer's Disease Research Center clinical cohort. Area Deprivation Index was geospatially determined based on participant residence geocode and ranked relative to state of residence. Linear regression models were fitted to test associations between neighborhood-level disadvantage and longitudinal change in cortical thickness and cognitive test performance. Mediation tests were used to assess whether neurodegeneration and cognitive decline were associated with neighborhood-level disadvantage along the same theoretical causal path. RESULTS: In our middle- to older-aged study population (mean baseline age 59 years), living in the 20% most disadvantaged neighborhoods (n = 19) relative to state of residence was associated with cortical thinning in Alzheimer signature regions (p = 0.002) and decline in the Preclinical Alzheimer's Disease Cognitive Composite (p = 0.04), particularly the Trail-Making Test, part B (p < 0.001), but not Rey Auditory Verbal Learning Test (p = 0.77) or Story Memory Delayed Recall (p = 0.49) subtests. Associations were attenuated but remained significant after controlling for racial and demographic differences between neighborhood-level disadvantage groups. Cortical thinning partially mediated the association between neighborhood-level disadvantage and cognitive decline. CONCLUSIONS: In this longitudinal study of cognitively unimpaired adults, living in the most highly disadvantaged neighborhoods was associated with accelerated degeneration in Alzheimer signature regions and cognitive decline. This study provides further evidence for neighborhood-level disadvantage as a risk factor for preclinical neurodegeneration and cognitive decline in certain populations. Limitations of the present study, including a small number of participants from highly disadvantaged neighborhoods and a circumscribed geographic setting, should be explored in larger and more diverse study cohorts.

Thirty-Day Re-observation, Chronic Re-observation, and Neighborhood Disadvantage.

OBJECTIVE: To determine whether neighborhood socioeconomic disadvantage, as determined by the Area Deprivation Index, increases 30-day hospital re-observation risk. PARTICIPANTS AND METHODS: This retrospective study of 20% Medicare fee-for-service beneficiary observation stays from January 1, 2014, to November 30, 2014, included 319,980 stays among 273,308 beneficiaries. We evaluated risk for a 30-day re-observation following an index observation stay for those living in the 15% most disadvantaged compared with the 85% least disadvantaged neighborhoods. RESULTS: Overall, 4.5% (270,600 of 6,080,664) of beneficiaries had index observation stays, which varied by disadvantage (4.3% [232,568 of 5,398,311] in the least disadvantaged 85% compared with 5.6% [38,032 of 682,353] in the most disadvantaged 15%). Patients in the most disadvantaged neighborhoods had a higher 30-day re-observation rate (2857 of 41,975; 6.8%) compared with least disadvantaged neighborhoods (13,543 of 278,005; 4.9%); a 43% increased risk (unadjusted odds ratio [OR], 1.43; 95% CI, 1.31 to 1.55). After adjustment, this risk remained (adjusted OR, 1.13; 95% CI, 1.04 to 1.22). Discharge to a skilled nursing facility reduced 30-day re-observation risk (OR, 0.63; 95% CI, 0.57 to 0.69), whereas index observation length of stay of 4 or more days (3 midnights) conferred increased risk (OR, 1.29; 95% CI, 1.09 to 1.52); those living in disadvantaged neighborhoods were less likely to discharge to skilled nursing facilities and more likely to have long index stays. Beneficiaries with more than one 30-day re-observation (chronic re-observation) had progressively greater disadvantage by number of stays (adjusted incident rate ratio, 1.08; 95% CI, 1.02 to 1.14). Observation prevalence varied nationally. CONCLUSION: Thirty-day re-observation, especially chronic re-observation, is highly associated with socioeconomic neighborhood disadvantage, even after accounting for factors such as race, disability, and Medicaid eligibility. Beneficiaries least able to pay are potentially most vulnerable to costs from serial re-observations and challenges of Medicare observation policy, which may discourage patients from seeking necessary care.