Disparities in the geography of serious mental illness in Israel

The impact of geographic isolation on mental disability in the United States

Geographic isolation has long been hypothesized to have a role in the origins and development of mental disabilities. A considerable body of research has established such a correlation. However, study designs have limited researchers’ ability to establish a causal connection and rule out rival hypotheses.

This study, therefore, aims to assess the strength of the geographic isolation - mental disability relationship and to disentangle it from alternative possibilities, namely that it reflects socioeconomic status, social isolation, economic inequality, or reverse causation. The study employs an analysis of variations in the rates of mental disability throughout 2960 U.S. counties using both Census and CDC data. In addition to partial correlation and ordinary least square analyses, the study employs two-stage least squares regression with instrumental variables (2SLS-IV), a procedure that permits resolution of the problem of endogeneity involving the potential effects of unmeasured variables and reverse causation.

Results reveal that the initial bivariate effects of geographic isolation on rates of mental disability are robust after controls for socioeconomic status, income inequality, social isolation, and other predictors are introduced and when tested with the 2SLS-IV procedure. Most variation (54.4%) in county mental disability rates is accounted for by the independent effects of geographic isolation, socioeconomic status, income inequality, and other variables. The results presented, although not conclusive, supports more targeted service planning and more equitable resource investments in rural parts of the United States and other nations.

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Isolation is a moderately strong predictor of mental disability.

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Socioeconomic status has a moderate negative correlation with mental disability.

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Income inequality compounds the effects of isolation and SES on mental disability.

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Most of variation in MI disability rates explained by isolation, SES, and inequality.

'In general, how do you feel today?'--self-rated health in the context of aging in India

This thesis is centered on self-rated health (SRH) as an outcome measure, as a predictor, and as a marker. The thesis uses primary data from the WHO Study on global AGEing and adult health (SAGE) implemented in India in 2007. The structural equation modeling approach is employed to understand the pathways through which the social environment, disability, disease, and sociodemographic characteristics influence SRH among older adults aged 50 years and above. Cox proportional hazard model is used to explore the role of SRH as a predictor for mortality and the role of disability in modifying this effect. The hierarchical ordered probit modeling approach, which combines information from anchoring vignettes with SRH, was used to address the long overlooked methodological concern of interpersonal incomparability. Finally, multilevel model-based small area estimation techniques were used to demonstrate the use of large national surveys and census information to derive precise SRH prevalence estimates at the district and sub-district level. The thesis advocates the use of such a simple measure to identify vulnerable communities for targeted health interventions, to plan and prioritize resource allocation, and to evaluate health interventions in resource-scarce settings. The thesis provides the basis and impetus to generate and integrate similar and harmonized adult health and aging data platforms within demographic surveillance systems in different regions of India and elsewhere.

Self-rated health: small area large area comparisons amongst older adults at the state, district and sub-district level in India

We compared prevalence estimates of self-rated health (SRH) derived indirectly using four different small area estimation methods for the Vadu (small) area from the national Study on Global AGEing (SAGE) survey with estimates derived directly from the Vadu SAGE survey. The indirect synthetic estimate for Vadu was 24% whereas the model based estimates were 45.6% and 45.7% with smaller prediction errors and comparable to the direct survey estimate of 50%. The model based techniques were better suited to estimate the prevalence of SRH than the indirect synthetic method. We conclude that a simplified mixed effects regression model can produce valid small area estimates of SRH.

Modeling the geographic distribution of serious mental illness in New Zealand

PURPOSE

This study aims to estimate, apply, and validate a model of the risk of serious mental illness (SMI) in local service areas throughout New Zealand.

METHODS

The study employs a secondary analysis of data from the Te Rau Hinengaro Mental Health Survey of 12,992 adults aged 16 years and over from the household population. It uses small area estimation (SAE) methods involving: (1) estimation of a logistic model of risk of SMI; (2) use of the foregoing model for computing estimates, using census data, for District Board areas; (3) validation of estimates against an alternative indicator of SMI prevalence.

RESULTS

The model uses age, ethnicity, marital status, employment, and income to predict 92.2 % of respondents' SMI statuses, with a specificity of 95.9 %, sensitivity of 16.9 %, and an AUC of 0.73. The resulting estimates for the District Board areas ranged between 4.1 and 5.7 %, with confidence intervals from ±0.3 to ±1.1 %. The estimates demonstrated a correlation of 0.51 (p = 0.028) with rates of psychiatric hospitalization.

CONCLUSIONS

The use of SAE methods demonstrated the capacity for deriving local prevalence rates of SMI, which can be validated against an available indicator.