Association of school neighbourhood socioeconomic disadvantage and teaching staff's risk of violence at work.

AIM: The aim of this study was to determine the association between neighbourhood socioeconomic disadvantage and teaching staff's risk of workplace violence and whether workplace psychosocial resources can act as effect modifiers. METHODS: Primary school teaching staff in the six largest cities in Finland responded to a survey in 2018 and were linked to information on school neighbourhood disadvantage obtained from the national grid database (n = 3984). RESULTS: After adjustment for confounders, staff working in schools located in the most disadvantaged neighbourhoods had a 1.2-fold (95% confidence interval 1.07-1.35) risk of encountering violence or threat of violence compared with staff working in the most advantaged neighbourhoods. The association was less marked in schools with strong support from colleagues (risk ratio 1.14, 95% confidence interval (95% CI) 0.98-1.32 for high support versus 1.23, 95% CI 1.07-1.43 for low/intermediate support), a strong culture of collaboration (1.08, 95% CI 0.93-1.26 versus 1.31, 95% CI 1.12-1.53), high leadership quality (1.12, 95% CI 0.96-1.31 versus 1.29, 95% CI 1.08-1.54), and high organizational justice (1.09, 95% CI 0.91-1.32 versus 1.29, 95% CI 1.09-1.52). CONCLUSIONS: The association between school neighbourhood and teaching staff's risk of violence was weaker in schools with high workplace psychosocial resources, suggesting that targeting these factors might help in minimizing violence at schools, but future intervention studies are needed to confirm or refute this hypothesis.

Inequality in mortality between Black and White Americans by age, place, and cause and in comparison to Europe, 1990 to 2018.

Although there is a large gap between Black and White American life expectancies, the gap fell 48.9% between 1990 and 2018, mainly due to mortality declines among Black Americans. We examine age-specific mortality trends and racial gaps in life expectancy in high- and low-income US areas and with reference to six European countries. Inequalities in life expectancy are starker in the United States than in Europe. In 1990, White Americans and Europeans in high-income areas had similar overall life expectancy, while life expectancy for White Americans in low-income areas was lower. However, since then, even high-income White Americans have lost ground relative to Europeans. Meanwhile, the gap in life expectancy between Black Americans and Europeans decreased by 8.3%. Black American life expectancy increased more than White American life expectancy in all US areas, but improvements in lower-income areas had the greatest impact on the racial life expectancy gap. The causes that contributed the most to Black Americans' mortality reductions included cancer, homicide, HIV, and causes originating in the fetal or infant period. Life expectancy for both Black and White Americans plateaued or slightly declined after 2012, but this stalling was most evident among Black Americans even prior to the COVID-19 pandemic. If improvements had continued at the 1990 to 2012 rate, the racial gap in life expectancy would have closed by 2036. European life expectancy also stalled after 2014. Still, the comparison with Europe suggests that mortality rates of both Black and White Americans could fall much further across all ages and in both high-income and low-income areas.

Examining area- and individual-level differences in suicide ideation severity and suicide attempt among youth.

Youth suicide is a pressing problem and suicide rates are not equally distributed across geographic areas or socioeconomic status (SES). Death by suicide is often preceded factors including hopelessness and suicide ideation, planning, and attempt. The current study examined area- and individual-level differences in suicide ideation severity and suicide attempt in a state-representative sample of youth from 2019 (N = 78,740) and 2021 (N = 61,396). Youth from higher SES and rural areas showed lower suicide ideation severity and odds of suicide attempt. After including individual-level covariates, SES differences in ideation severity and suicide attempt persisted for 2019 but not 2021. Rural differences for ideation severity persisted across years but not for suicide attempt. Further research on geographic variation in suicide risk is needed.

The Creation of a Multidomain Neighborhood Environmental Vulnerability Index Across New York City.

Compared to previous studies commonly using a single summary score, we aimed to construct a multidomain neighborhood environmental vulnerability index (NEVI) to characterize the magnitude and variability of area-level factors with the potential to modify the association between environmental pollutants and health effects. Using the Toxicological Prioritization Index framework and data from the 2015-2019 U.S. Census American Community Survey and the 2020 CDC PLACES Project, we quantified census tract-level vulnerability overall and in 4 primary domains (demographic, economic, residential, and health status), 24 subdomains, and 54 distinct area-level features for New York City (NYC). Overall and domain-specific indices were calculated by summing standardized feature values within the subdomains and then aggregating and weighting based on the number of features within each subdomain within equally-weighted primary domains. In citywide comparisons, NEVI was correlated with multiple existing indices, including the Neighborhood Deprivation Index (r = 0.91) and Social Vulnerability Index (r = 0.87) but provided additional information on features contributing to vulnerability. Vulnerability varied spatially across NYC, and hierarchical cluster analysis using subdomain scores revealed six patterns of vulnerability across domains: 1) low in all, 2) primarily low except residential, 3) medium in all, 4) high demographic, economic, and residential 5) high economic, residential, and health status, and 6) high demographic, economic and health status. Created using methods that offer flexibility for theory-based construction, NEVI provided detailed vulnerability metrics across domains that can inform targeted research and public health interventions aimed at reducing the health impacts from environmental exposures across urban centers.

Mortality inequality, spatial differences and health care access.

Although Australia maintains relatively high standards of health and healthcare, there exists disparity in health outcomes and longevity among different segments of the population. Internationally, there is growing evidence that life expectancy gains are not being shared equally among the rich and the poor. In this paper we examine the evolution of mortality inequality in Australia between 2001 and 2018. Using a spatial inequality model and combining data from several administrative data sources, we document significant mortality inequality between the rich and the poor in Australia. For most age groups, mortality inequality has remained unchanged over the last 20 years. However, mortality inequality is increasing for middle-aged men and women. In part, this can be explained by improvements in longevity which favor urban over rural Australians. Another contributing factor we identify is differential access to healthcare in rich and poor regions. Although Australia's socioeconomic gradient of mortality is flatter than in the US, due to universal health coverage, the fact that mortality inequality is increasing for some groups accentuates the importance of safeguarding health care accessibility.

Individual and area-level socioeconomic correlates of hypertension prevalence, awareness, treatment, and control in uMgungundlovu, KwaZulu-Natal, South Africa.

BACKGROUND: Hypertension is the second leading risk factor for death in South Africa, and rates have steadily increased since the end of Apartheid. Research on the determinants of hypertension in South Africa has received considerable attention due to South Africa's rapid urbanization and epidemiological transition. However, scant work has been conducted to investigate how various segments of the Black South African population experience this transition. Identifying the correlates of hypertension in this population is critical to the development of policies and targeted interventions to strengthen equitable public health efforts. METHODS: This analysis explores the relationship between individual and area-level socioeconomic status and hypertension prevalence, awareness, treatment, and control within a sample of 7,303 Black South Africans in three municipalities of the uMgungundlovu district in KwaZulu-Natal province: the Msunduzi, uMshwathi, and Mkhambathini. Cross-sectional data were collected on participants from February 2017 to February 2018. Individual-level socioeconomic status was measured by employment status and educational attainment. Ward-level area deprivation was operationalized by the most recent (2011 and 2001) South African Multidimensional Poverty Index scores. Covariates included age, sex, BMI, and diabetes diagnosis. RESULTS: The prevalence of hypertension in the sample was 44.4% (n = 3,240). Of those, 2,324 were aware of their diagnosis, 1,928 were receiving treatment, and 1,051 had their hypertension controlled. Educational attainment was negatively associated with hypertension prevalence and positively associated with its control. Employment status was negatively associated with hypertension control. Black South Africans living in more deprived wards had higher odds of being hypertensive and lower odds of having their hypertension controlled. Those residing in wards that became more deprived from 2001 to 2011 had higher odds of being aware of their hypertension, yet lower odds of receiving treatment for it. CONCLUSIONS: Results from this study can assist policymakers and practitioners in identifying groups within the Black South African population that should be prioritized for public health interventions. Black South Africans who have and continue to face barriers to care, including those with low educational attainment or living in deprived wards had worse hypertension outcomes. Potential interventions include community-based programs that deliver medication to households, workplaces, or community centers.

Area-Level Socioeconomic Inequalities in Intracranial Injury-Related Hospitalization in Korea: A Retrospective Analysis of Data From Korea National Hospital Discharge Survey 2008-2015.

BACKGROUND: Although inequality in traumatic brain injury (TBI) by individual socioeconomic status (SES) exists, interventions to modify individual SES are difficult. However, as interventions for area-based SES can affect the individual SES, monitoring or public health intervention can be planned. We analyzed the effect of area-based SES on hospitalization for TBI and revealed yearly inequality trends to provide a basis for health intervention. METHODS: We included patients who were hospitalized due to intracranial injuries (ICIs) between 2008 and 2015 as a measure of severe TBI with data provided by the Korea National Hospital Discharge Survey. Area-based SES was synthesized using the 2010 census data. We assessed inequalities in ICI-related hospitalization rates using the relative index of inequality and the slope index of inequality for the periods 2008-2009, 2010-2011, 2012-2013, and 2014-2015. We analyzed the trends of these indices for the observation period by age and sex. RESULTS: The overall relative indices of inequality for each 2-year period were 1.82 (95% confidence interval, 1.5-2.3), 1.97 (1.6-2.5), 2.01 (1.6-2.5), and 2.01 (1.6-2.5), respectively. The overall slope indices of inequality in each period were 38.74 (23.5-54.0), 36.75 (21.7-51.8), 35.65 (20.7-50.6), and 43.11 (27.6-58.6), respectively. The relative indices of inequality showed a linear trend for men (P = 0.006), which was most evident in the ≥ 65-year age group. CONCLUSION: Inequality in hospitalization for ICIs by area-based SES tended to increase during the observation period. Practical preventive interventions and input in healthcare resources for populations with low area-based SES are likely needed.

Area-level and individual-level socio-economic differences in health-related quality of life trajectories: Results from a 10-year longitudinal stroke study.

BACKGROUND: We examined area-level (aSES) and individual-level (iSES) socio-economic status on trajectories of HRQoL to 10 years following stroke. METHODS: Participants with strokes between 1/5/1996 and 30/4/1999 completed the Assessment of Quality of Life instrument (AQoL, range: -0.04 [worse than death] to 0 [death] to 1 [full health]) at ≥one of 3month, 6-month, 1-year, 2-year, 3-year, 4-year, 5-year, 7-year and 10-year interviews after stroke. Sociodemographic and health information were collected at baseline. We derived aSES from postcode using the Australian Socio-Economic Indexes For Area (2006) (categories: high, medium, low), and iSES from lifetime occupation (categories: non-manual, manual). Multivariable linear mixed effects modelling was used to estimate trajectories of HRQoL over 10 years, by aSES and iSES, adjusting for age, sex, cardiovascular disease, smoking, diabetes, stroke severity, stroke type, and the time influence on age and health conditions. RESULTS: Of 1,686 participants enrolled, we excluded 239 with 'possible' stroke and 284 with missing iSES. Among the remaining 1,163 participants, 1,123 (96.6%) had AQoL assessed at ≥3 timepoints. In multivariable analysis, over time, people in the medium aSES group had mean 0.02 (95% CI -0.06, 0.02) greater reduction in AQoL score, and people in the low aSES group had mean 0.04 (95% CI, -0.07, -0.001) greater reduction, than those in the high aSES group. Manual workers had an average 0.04 (95% CI, -0.07, -0.01) greater reduction in AQoL score over time than non-manual workers. CONCLUSIONS: Over time, HRQoL declines in all people with stroke, declining most rapidly in lower SES groups.

"Residential greenness and site-specific cancer: A registry based cohort of 144,427 participants with a 21-years of follow-up, Tel-Aviv district, Israel".

BACKGROUND: Few longitudinal studies evaluated the beneficial associations between cumulative residential greenness and site-specific cancer. Our objective was to evaluate the associations between cumulative residential greenness exposure and site-specific cancer incidence (lung, bladder, breast, prostate, and skin cancer) within a registry-based cohort study. METHODS: This study was based on 144,427 participants who lived in the Tel Aviv district during 1995-2015. The residential greenness exposure was estimated for every participant, as the weighted mean residential greenness exposure, based on the mean Normalized Difference Vegetation Index (NDVI) in the residential area and the duration of the residence in this area. Cox regression models were used to evaluate the unadjusted and adjusted associations between exposure to greenness and cancer incidence during 1998-2015 (Hazard Ratios (HRs) and 95% Confidence Intervals (CIs)). Covariates included in adjusted models were selected based on prior knowledge and directed acyclic graphs. We imputed missing data and further sensitivity analyses were conducted. RESULTS: After adjustments, beneficial associations between exposure to greenness and cancer incidence were observed. An interquartile range (IQR) increase in NDVI was associated with a lower HRs for lung cancer (HRadj. = 0.75 95% CI: 0.66-0.85), bladder cancer (HRadj. = 0.71, 95% CI: 0.62-0.82), breast cancer (HRadj. = 0.81, 95% CI: 0.74-0.88), prostate cancer (HRadj. = 0.77, 95% CI: 0.70-0.86) and skin cancer (HRadj. = 0.78, 95% CI: 0.69-0.88). Generally, the patterns of associations were consistent between complete-case models and imputed models, when estimated for participants aged 16 years or 40 years and older at baseline, when stratified by area level socioeconomic status, when evaluated for non-movers participants and after further adjustment to social determinants of health. CONCLUSION: Residential greenness may reduce the risk for lung, bladder, breast, prostate, and skin cancers. If our observations will be replicated, it may present a useful avenue for public-health intervention to reduce cancer burden.

Area-level deprivation and adverse childhood experiences among high school students in Maryland.

BACKGROUND: Nearly one-half of Americans have been exposed to at least one adverse childhood experience (ACE) before turning 18, contributing to a broad array of problems spanning physical health, mental and behavioral health, and psychosocial functioning. METHODS: This was a cross-sectional, survey research study, using 2018 data from a state adolescent health surveillance system, i.e., Maryland Youth Risk Behavior Survey/Youth Tobacco Survey. The population-based sample of Maryland high school students (n = 41,091) is representative at the state and county levels. The outcome variables included five binary measures of ACEs (i.e., food insecurity, parental substance use/gambling, parental mental illness, family member in jail/prison, and caregiver verbal abuse), and number of ACEs. The main exposure variable, area-level socioeconomic disadvantage, was assessed at the county level using a continuous measure of the area deprivation index (ADI). Additional covariates included: rural county status, age, race/ethnicity, sex, and sexual or gender minority (SGM) status. We used mixed-effect multivariate logistic regression to estimate the odds of ACEs in association with socioeconomic deprivation. Models were adjusted for all covariates. RESULTS: County-level ADI was associated with 3 of the 5 ACES [i.e., food insecurity (OR = 1.10, 95% CI: 1.07-1.13), parental substance use/gambling (OR = 1.05, 95% CI: 1.02-1.07), and incarceration of a family member (OR = 1.14, 95% CI: 1.09-1.19)]; and with having at least one ACE (i.e., OR = 1.08, 95% CI: 1.05-1.10). Odds of reporting at least one ACE were higher among girls, older adolescents (i.e., aged 16 and ≥ 17 relative to those aged ≤ 14 years), and among SGM, Black, and Latinx students (all ORs > 1.20). CONCLUSIONS: ACEs greatly increase risk for adolescent risk behaviors. We observed an increased likelihood of adversity among youth in more deprived counties and among Black, Latinx, or SGM youth, suggesting that social and structural factors play a role in determining the adversity that youth face. Therefore, efforts to address structural factors (e.g., food access, family financial support, imprisonment as a sanction for criminal behavior) could be a critical strategy for primary prevention of ACEs and promoting adolescent health.

Predicting vaccine hesitancy from area-level indicators: A machine learning approach.

Vaccine hesitancy (VH) might represent a serious threat to the next COVID-19 mass immunization campaign. We use machine learning algorithms to predict communities at a high risk of VH relying on area-level indicators easily available to policymakers. We illustrate our approach on data from child immunization campaigns for seven nonmandatory vaccines carried out in 6062 Italian municipalities in 2016. A battery of machine learning models is compared in terms of area under the receiver operating characteristics curve. We find that the Random Forest algorithm best predicts areas with a high risk of VH improving the unpredictable baseline level by 24% in terms of accuracy. Among the area-level indicators, the proportion of waste recycling and the employment rate are found to be the most powerful predictors of high VH. This can support policymakers to target area-level provaccine awareness campaigns.

Association Between Area-level Risk of Job Instability and Workers' Health: A Multi-level Analysis Using Population-based Survey Data From Japan.

BACKGROUND: Precarious job status is negatively related with workers' health. Research has yet to address whether and to what extent the area-level risk of precarious employment is associated with workers' health, independently from their job status. We addressed this issue in the present study. METHODS: We estimated multi-level logistic regression models using repeated cross-sectional data comprising 253,048 men and 210,761 women aged 20-59 years who were living in 47 prefectures. This data were obtained from population-based surveys conducted in 2010, 2013, and 2016 in Japan. RESULTS: For male workers, the estimated odds of reporting poor self-rated health, subjective symptoms, and problems in activities of daily living for those residing in the prefectures in the highest tertile of the proportion of precarious employees were 1.10 (95% confidence interval [CI], 1.01-1.18), 1.12 (95% CI, 1.05-1.19), and 1.15 (95% CI, 1.04-1.28) times, respectively, higher than those living in the prefectures in the lowest tertile, even after controlling for individuals' job status and key covariates. The results remained largely similar, despite focusing on the sample with information about household income, which was available from the survey, and controlling for it. In contrast, the results indicated that women's health outcomes were not associated with the prefecture-level proportion of precarious employees. CONCLUSIONS: The area-level risk of precarious employment matters for male workers' health independently from their job status, underscoring the importance of policy measures aimed to reduce the adverse impacts of precarious employment on workers' health.

How does area-level deprivation depress an individual's self-rated health and life satisfaction? Evidence from a nationwide population-based survey in Japan.

BACKGROUND: Area-level deprivation is well known to have an adverse impact on mortality, morbidity, or other specific health outcomes. This study examined how area-level deprivation may affect self-rated health (SRH) and life satisfaction (LS), an issue that is largely understudied. METHODS: We used individual-level data obtained from a nationwide population-based internet survey conducted between 2019 and 2020, as well as municipality-level data obtained from a Japanese government database (N = 12,461 living in 366 municipalities). We developed multilevel regression models to explain an individual's SRH and LS scores using four alternative measures of municipality-level deprivation, controlling for individual-level deprivation and covariates. We also examined how health behavior and interactions with others mediated the impact of area-level deprivation on SRH and LS. RESULTS: Participants in highly deprived municipalities tended to report poorer SRH and lower LS. For example, when living in municipalities falling in the highest tertile of municipality-level deprivation as measured by the z-scoring method, SRH and LS scores worsened by a standard deviation of 0.05 (p < 0.05) when compared with those living in municipalities falling in the lowest tertile of deprivation. In addition, health behavior mediated between 17.6 and 33.1% of the impact of municipality-level deprivation on SRH and LS, depending on model specifications. CONCLUSION: Results showed that area-level deprivation modestly decreased an individual's general health conditions and subjective well-being, underscoring the need for public health policies to improve area-level socioeconomic conditions.

An Automatized Contextual Marketing System Based on a Wi-Fi Indoor Positioning System.

A complete contextual marketing platform including an indoor positioning system (IPS) for smartphones is proposed and evaluated to later be deployed in large infrastructures, such as malls. To this end, we design and implement a novel methodology based on location-as-a-service (LAAS), comprising all the required phases of IPS generation: mall digital map creation, the tools/procedures for offline calibration fingerprint acquisition, the location algorithm, the smartphone app acquiring the fingerprint data, and a validation procedure. To select an appropriate fingerprint location algorithm, a comparison among K-nearest neighbors (KNN), support vector machine (SVM), and Freeloc is accomplished by employing a set of different smartphones in two malls and assessing different occupancy levels. We demonstrate that our solution can be quickly deployed at shop level accuracy in any new location, resulting in a robust and scalable proposal.

A new robust Bayesian small area estimation via α -stable model for estimating the proportion of athletic students in California.

In the last few years, diabetes mellitus and obesity revealed to be one of the fastest-growing chronic diseases in youth in the United States. The number of new diabetes cases is dramatically increasing, and, for the moment, effective therapy does not exist. Experts believe that one of the causes of this increase is the decline in exercise behavior. The California Education Code requires local educational agencies (LEAs) to administer the FITNESSGRAM, the Physical Fitness Test (PFT), to Californian students of public schools. This test evaluates six fitness areas, and experts defined that a passing result on all six areas of the test represents a fitness level that offers some protection against the diseases associated with physical inactivity. We consider 2015-2016 data provided by the California Department of Education (CDE): for each Californian county ( m=57 ), we aim at estimating the county-level proportion of students with a score equal to six. To account for the heterogeneity of the phenomenon and the presence of outlying counties, we extend the standard area-level model by specifying the random effects as a symmetric α -stable (S α S) distribution that can accommodate different types of outlying observations. The model can accurately estimate the county-level proportion of students with a score equal to six. Results highlight some interesting relationships with social and economic situations in each county. The performance of the proposed model is also investigated through an extensive simulation study.

Ethnic disparities in psychotic experiences explained by area-level syndemic effects.

BACKGROUND: Ethnic inequalities in health outcomes are often explained by socioeconomic status and concentrated poverty. However, ethnic disparities in psychotic experiences are not completely attenuated by these factors. AIMS: We investigated whether disparities are better explained by interactions between individual risk factors and place-based clustering of disadvantage, termed a syndemic. METHOD: We performed a cross-sectional survey of 3750 UK men, aged 18-34 years, oversampling Black and minority ethnic (BME) men nationally, together with men residing in London Borough of Hackney. Participants completed questionnaires covering psychiatric symptoms, substance misuse, crime and violence, and risky sexual health behaviours. We included five psychotic experiences and a categorical measure of psychosis based on the Psychosis Screening Questionnaire. RESULTS: At national level, more Black men reported psychotic experiences but disparities disappeared following statistical adjustment for social position. However, large disparities for psychotic experiences in Hackney were not attenuated by adjustment for social factors in Black men (adjusted odds ratio, 3.24; 95% CI 2.14-4.91; P < 0.002), but were for South Asian men. A syndemic model of joint effects, adducing a four-component latent variable (psychotic experiences and anxiety, substance dependence, high-risk sexual behaviour and violence and criminality) showed synergy between components and explained persistent disparities in psychotic experiences. A further interaction confirmed area-level effects (Black ethnicity × Hackney residence, 0.834; P < 0.001). CONCLUSIONS: Syndemic effects result in higher rates of non-affective psychosis among BME persons in certain inner-urban settings. Further research should investigate how syndemics raise levels of psychotic experiences and related health conditions in Black men in specific places with multiple deprivations.

3+ parity in Egypt: a multilevel decomposition of wealth-based inequality.

BACKGROUND: Wealth disparities in levels of fertility are well documented in Egypt. Data from the Egypt Demographic and Health Survey (2014) showed that 61% of births to mothers from the poorest wealth quintile were of the third order or higher compared to 33% among mothers from the richest wealth quintile. The current paper investigates the main individual and socio-contextual level determinants of having more than two living children among women aged 30 and older, and decomposes its wealth-based inequality. METHODS: The secondary analysis was based on the 2014 Egypt Demographic and Health Survey. Multilevel linear regression was used to model the number of additional living children a woman has after her first two living children. A decomposition analysis of the wealth-based concentration index was applied using a multilevel model. RESULTS: Individual level and area level attributes explained 83 and 17% of the variation in 3+ parity, respectively. Only areas not well served by the health system were significantly related to 3+ parity. Decomposition of the wealth-based concentration index revealed that 55.7 and 44.3% of the 3+ parity inequality were attributed to individual level attributes and area level attributes, respectively. At the individual level, early marriage accounted for 26% of the inequality in 3+ parity inequality. At the area level, living in areas not well served by the health system accounted for 22.3% of the 3+ parity wealth- based inequality, while highly gendered areas contributed 5.8% to this inequality. Areas' random effects contributed 7.1% to the 3+ parity inequality, assist in identifying specific areas that require targeted policies. CONCLUSION: Multilevel decomposition allowed the contributions of both the individual and area level attributes to be quantified. The decomposition highlighted the need for more tailored policies that target different social groups and different areas. Decomposition analysis also pinpointed specific areas that require additional targeted policies. This targeted approach can be used to support the efficient use of limited resources within any society.

Small Area Estimation for Disease Prevalence Mapping.

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area-level and at the unit-level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015-2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to COVID-19 is discussed.

Area-Level Variation in Children's Unmet Need for Community-Based Mental Health Services: Findings from the 2014 Ontario Child Health Study.

There is limited empirical evidence documenting the magnitude and correlates of area-level variability in unmet need for children's mental health services. Research is needed that identifies area-level characteristics that can inform strategies for reducing unmet need in the population. The study purpose is to: (1) estimate area-level variation in children's unmet need for mental health services (using Service Areas as defined by the Ontario Ministry of Children and Youth Services), and (2) identify area-level service arrangements, and geographic and population characteristics associated with unmet need. Using individual-level general population data, area-level government administrative data and Census data from Ontario, Canada, we use multilevel regression models to analyze unmet need for mental health services among children (level 1) nested within Service Areas (level 2). The study finds that 1.64% of the reliable variance in unmet need for mental health services is attributable to between-area differences. Across areas, we find that Service Areas with more agencies had a lower likelihood of unmet need for mental health services. Compared to other Service Areas, Toronto had much lower likelihood of unmet need compared to the rest of Ontario. Rural areas, areas with unsatisfactory public transport, and areas with higher levels of socio-economic disadvantage had a higher likelihood of unmet need for mental health services. These findings identify challenges in service provision that researchers, policymakers and administrators in children's mental health services need to better understand. Policy implications and potential Service Area strategies that could address equitable access to mental health services are discussed.

Geographic and area-level socioeconomic variation in cardiometabolic risk factor distribution: a systematic review of the literature.

INTRODUCTION: A growing number of publications report variation in the distribution of cardiometabolic risk factors (CMRFs) at different geographic scales. A review of these variations may help inform policy and health service organisation. AIM: To review studies reporting variation in the geographic distribution of CMRFs and its association with various proxy measures of area-level socioeconomic disadvantage (ASED) among the adult ( ≥ 18 years) population across the world. METHODS: A systematic search for published articles was conducted in four databases (MEDLINE (Ovid), PubMed, Scopus and Web of Science) considering the interdisciplinary nature of the review question. Population-based cross-sectional and cohort studies on geographic variations of one or more biological proxies of CMRFs with/without an analysed contextual association with ASED were included. Two independent reviewers screened the studies and PRISMA guidelines were followed in the study selection and reporting. RESULT: A total of 265 studies were retrieved and screened, resulting in 24 eligible studies. The review revealed reports of variation in the distribution of CMRFs, at varying geographic scales, in multiple countries. In addition, consistent associations between ASED and higher prevalence of CMRFs were demonstrated. The reports were mainly from industrialised nations and small area geographic units were frequently used. CONCLUSION: Geographic variation in cardiometabolic risk exists across multiple spatial scales and is positively associated with ASED. This association is independent of individual-level factors and provides an imperative for area-based approaches to informing policy and health service organisation. The study protocol is registered in International prospective register of systematic reviews (Register No: CRD42018115294) PROSPERO 2018.