Association of neighborhood disadvantage with cognitive function and cortical disorganization in an unimpaired cohort.

Neighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity network (MSN) is a promising approach to elucidate cortical network patterns underlying complex cognitive functions. We hypothesized that MSNs could capture changes in cortical patterns related to neighborhood disadvantage and cognitive function. This cross-sectional study included cognitively unimpaired participants from two large Alzheimers studies at University of Wisconsin-Madison. Neighborhood disadvantage status was obtained using the Area Deprivation Index (ADI). Cognitive performance was assessed on memory, processing speed and executive function. Morphological Similarity Networks (MSN) were constructed for each participant based on the similarity in distribution of cortical thickness of brain regions, followed by computation of local and global network features. Association of ADI with cognitive scores and MSN features were examined using linear regression and mediation analysis. ADI showed negative association with category fluency,implicit learning speed, story recall and modified pre-clinical Alzheimers cognitive composite scores, indicating worse cognitive function among those living in more disadvantaged neighborhoods. Local network features of frontal and temporal regions differed based on ADI status. Centrality of left lateral orbitofrontal region showed a partial mediating effect between association of neighborhood disadvantage and story recall performance. Our preliminary findings suggest differences in local cortical organization by neighborhood disadvantage, which partially mediated the relationship between ADI and cognitive performance, providing a possible network-based mechanism to, in-part, explain the risk for poor cognitive functioning associated with disadvantaged neighborhoods.

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.

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.

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.

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.

The Area Deprivation Index: A novel tool for harmonizable risk assessment in Alzheimer's disease research.

INTRODUCTION: Residence in a disadvantaged neighborhood associates with adverse health exposures and outcomes, and may increase risk for cognitive impairment and dementia. Utilization of a publicly available, geocoded disadvantage metric could facilitate efficient integration of social determinants of health into models of cognitive aging. METHODS: Using the validated Area Deprivation Index and two cognitive aging cohorts, we quantified Census block-level poverty, education, housing, and employment characteristics for the neighborhoods of 2119 older adults. We assessed relationships between neighborhood disadvantage and cognitive performance in domains sensitive to age-related change. RESULTS: Participants in the most disadvantaged neighborhoods (n = 156) were younger, more often female, and less often college-educated or white than those in less disadvantaged neighborhoods (n = 1963). Disadvantaged neighborhood residence associated with poorer performance on tests of executive function, verbal learning, and memory. DISCUSSION: This geospatial metric of neighborhood disadvantage may be valuable for exploring socially rooted risk mechanisms, and prioritizing high-risk communities for research recruitment and intervention.

Association of Neighborhood-Level Disadvantage With Alzheimer Disease Neuropathology.

Importance: Social determinants of health, such as income, education, housing quality, and employment, are associated with disparities in Alzheimer disease and health generally, yet these determinants are rarely incorporated within neuropathology research. Objective: To establish the feasibility of linking neuropathology data to social determinants of health exposures using neighborhood disadvantage metrics (the validated Area Deprivation Index) and to evaluate the association between neighborhood disadvantage and Alzheimer disease-related neuropathology. Design, Setting, and Participants: This cross-sectional study consisted of decedents with a known home address who donated their brains to 1 of 2 Alzheimer disease research center brain banks in California and Wisconsin between January 1, 1990, and December 31, 2016. Neither site had preexisting social metrics available for their decedents. Neuropathologic features were obtained from each site for data collected using the standardized Neuropathology Data Set form and from autopsy reports. Data were analyzed from June 7 to October 10, 2019. Exposures: Geocoded decedent addresses linked to neighborhood disadvantage as measured by the Area Deprivation Index calculated for the year of death. Main Outcomes and Measures: Presence of Alzheimer disease neuropathology. The association between neighborhood disadvantage and Alzheimer disease neuropathology was evaluated via logistic regression, adjusting for age, sex, and year of death. Results: The sample consisted of 447 decedents (249 men [56%]; mean [SD] age, 80.3 [9.5] years; median year of death, 2011) spanning 24 years of donation. Fewer decedents (n = 24 [5.4%]) originated from the top 20% most disadvantaged neighborhood contexts. Increasing neighborhood disadvantage was associated with an 8.1% increase in the odds of Alzheimer disease neuropathology for every decile change on the Area Deprivation Index (adjusted odds ratio, 1.08; 95% CI, 1.07-1.09). As such, living in the most disadvantaged neighborhood decile was associated with a 2.18 increased odds of Alzheimer disease neuropathology (adjusted odds ratio, 2.18; 95% CI, 1.99-2.39). Conclusions and Relevance: The findings of this cross-sectional study suggest that social determinants of health data can be linked to preexisting autopsy samples as a means to study sociobiological mechanisms involved in neuropathology. This novel technique has the potential to be applied to any brain bank within the United States. To our knowledge, this is the first time Alzheimer disease neuropathology has been associated with neighborhood disadvantage.

Association of Neighborhood-Level Disadvantage With Cerebral and Hippocampal Volume.

Importance: Identifying risk factors for brain atrophy during the aging process can help direct new preventive approaches for dementia and cognitive decline. The association of neighborhood socioeconomic disadvantage with brain volume in this context is not well known. Objective: To test whether neighborhood-level socioeconomic disadvantage is associated with decreased brain volume in a cognitively unimpaired population enriched for Alzheimer disease risk. Design, Setting, and Participants: This study, conducted from January 6, 2010, to January 17, 2019, at an academic research neuroimaging center, used cross-sectional data on 951 participants from 2 large, ongoing cohort studies of Alzheimer disease (Wisconsin Registry for Alzheimer's Prevention and Wisconsin Alzheimer's Disease Research Center clinical cohort). Participants were cognitively unimpaired based on National Institute on Aging-Alzheimer's Association workgroup diagnostic criteria for mild cognitive impairment and Alzheimer disease, confirmed through a consensus diagnosis panel. The cohort was enriched for Alzheimer disease risk based on family history of dementia. Statistical analysis was performed from April 3 to September 27, 2019. Main Outcomes and Measures: The Area Deprivation Index, a geospatially determined index of neighborhood-level disadvantage, and cardiovascular disease risk indices were calculated for each participant. Linear regression models were fitted to test associations between relative neighborhood-level disadvantage (highest 20% based on state of residence) and hippocampal and total brain tissue volume, as assessed by magnetic resonance imaging. Results: In the primary analysis of 951 participants (637 women [67.0%]; mean [SD] age, 63.9 [8.1] years), living in the 20% most disadvantaged neighborhoods was associated with 4.1% lower hippocampal volume (ß = -317.44; 95% CI, -543.32 to -91.56; P = .006) and 2.0% lower total brain tissue volume (ß = -20 959.67; 95% CI, -37 611.92 to -4307.43; P = .01), after controlling for intracranial volume, individual-level educational attainment, age, and sex. Robust propensity score-matched analyses determined that this association was not due to racial/ethnic or demographic characteristics. Cardiovascular risk score, examined in a subsample of 893 participants, mediated this association for total brain tissue but not for hippocampal volume. Conclusions and Relevance: For cognitively unimpaired individuals, living in the most disadvantaged neighborhoods was associated with significantly lower cerebral volumes, after controlling for maximal premorbid (total intracranial) volume. This finding suggests an association of community socioeconomic context, distinct from individual-level socioeconomic status, with brain volume during aging. Cardiovascular risk mediated this association for total brain tissue volume but not for hippocampal volume, suggesting that neighborhood-level disadvantage may be associated with these 2 outcomes via distinct biological pathways.

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.

Neighborhood disadvantage and chronic disease management.

OBJECTIVE: To assess the relationship between a composite measure of neighborhood disadvantage, the Area Deprivation Index (ADI), and control of blood pressure, diabetes, and cholesterol in the Medicare Advantage (MA) population. DATA SOURCES: Secondary analysis of 2013 Medicare Healthcare Effectiveness Data and Information Set, Medicare enrollment data, and a neighborhood disadvantage indicator. STUDY DESIGN: We tested the association of neighborhood disadvantage with intermediate health outcomes. Generalized estimating equations were used to adjust for geographic and individual factors including region, sex, race/ethnicity, dual eligibility, disability, and rurality. DATA COLLECTION: Data were linked by ZIP+4, representing compact geographic areas that can be linked to Census block groups. PRINCIPAL FINDINGS: Compared with enrollees residing in the least disadvantaged neighborhoods, enrollees in the most disadvantaged neighborhoods were 5 percentage points (P < 0.05) less likely to have controlled blood pressure, 6.9 percentage points (P < 0.05) less likely to have controlled diabetes, and 9.9 percentage points (P < 0.05) less likely to have controlled cholesterol. Adjustment attenuated this relationship, but the association remained. CONCLUSIONS: The ADI is a strong, independent predictor of diabetes and cholesterol control, a moderate predictor of blood pressure control, and could be used to track neighborhood-level disparities and to target disparities-focused interventions in the MA population.

Impact Of Risk Adjustment For Socioeconomic Status On Medicare Advantage Plan Quality Rankings.

Sociodemographically disadvantaged patients have worse outcomes on some quality measures that inform Medicare Advantage plan ratings. Performance measurement that does not adjust for sociodemographic factors may penalize plans that disproportionately serve disadvantaged populations. We assessed the impact of adjusting for socioeconomic and demographic factors (sex, race/ethnicity, dual eligibility, disability, rurality, and neighborhood disadvantage) on Medicare Advantage plan rankings for blood pressure, diabetes, and cholesterol control. After adjustment, 20.3 percent, 19.5 percent, and 11.4 percent of Medicare Advantage plans improved by one or more quintiles in rank on the diabetes, cholesterol, and blood pressure measures, respectively. Plans that improved in ranking after adjustment enrolled higher proportions of disadvantaged enrollees. Adjusting quality measures for socioeconomic factors is important for equitable payment and quality reporting. Our study suggests that plans serving disadvantaged populations would have improved relative rankings for three important outcome measures if socioeconomic factors were included in risk-adjustment models.

Linking Neighborhood Context and Health in Community-Dwelling Older Adults in the Medicare Advantage Program.

OBJECTIVES: To examine whether neighborhood context moderates the relationship between multiple chronic conditions (MCCs) and function in Medicare Advantage (MA) beneficiaries. DESIGN: Cross-sectional study. SETTING: Medicare Health Outcome Survey Cohort 16 baseline data from 2013. PARTICIPANTS: MA beneficiaries aged 65 and older (N=187,434). MEASUREMENTS: We defined disadvantaged neighborhoods as those with an Area Deprivation Index greater than the 85th percentile. MCCs was defined as having 2 or more chronic conditions. The primary outcome was any self-reported functional limitations with a basic or instrumental activity of daily living. We used survey-weighted multivariate linear probability regression to examine whether the neighborhood disadvantage moderates the relationship between MCCs and report of a functional limitation. RESULTS: More than one third (35.6%) of the sample reported a functional limitation. Beneficiaries with MCCs were more likely to report a functional limitation those with 0 or 1 chronic condition (odds ratio (OR)=2.63, 95% confidence interval (CI)=2.50-2.77). Beneficiaries in more-disadvantaged neighborhoods were more likely to report a functional limitation than those in less-disadvantaged neighborhoods (OR=1.14, 95% CI=1.08-1.21). In older adults with MCCs, the probability of reporting a functional limitation was 12 percentage points greater in those living in more-disadvantaged neighborhoods than in those living in less-disadvantaged neighborhoods. CONCLUSION: Neighborhood context is a significant predictor of functional limitation and moderates the association between chronic conditions and functional limitations. Health plans and public health officials could use publicly available indicators such as the ADI to target interventions to reduce functional impairment.

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.

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.

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.