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

Small area estimation for South African resource distribution and policy impacts during COVID-19

The South African constitutional social justice commitment and equality duty requires that everyone is treated with equal consideration, but also tilts the scales in favour of the most disadvantaged. This paper explores the challenge of utilising publicly available data to promote social justice in resource distribution and fair access to essential services during crisis regulations, and explores Small Area Estimation (SAE) as a method to overcome some of these data challenges. The paper evaluates the strengths and limitations of the primary South African datasets that were available to inform fiscal and resource relief efforts during the COVID-19 pandemic and the ensuing economic crisis. The potential to use SAE was found to be limited due to data constraints but statistics were generated at a district council level from data statistically representative at national level. This demonstrated stark disparities in hunger, access to medical products and piped water - all critical equality considerations during a pandemic. However, the level of disaggregation achieved with SAE is shown to be ineffective to represent the geographical disparities indicative of the true South African population, where extreme inequalities manifest in much closer proximities. This supports the need for improved statistical tools and more targeted and resolved data gathering efforts, to inform fair, social-impact conscious and equality-congruent regulatory impact, as well as just fiscal relief during crisis. Particularly, this work proposes the development of such tools and repositories outside of crisis times, to facilitate awareness of equality and justice issues during the tensions of national crisis.

Province-Level Prevalence of Psychiatric Disorders: Application of Small-Area Methodology to the Iranian Mental Health Survey (IranMHS)

Objective: National surveys revealed a high prevalence of psychiatric disorders in Iran. Province-level estimates are needed to manage the resources and focus on preventive efforts more efficiently. The objective of this study was to provide province-level estimates of psychiatric disorders. Method : In this study, Iranian Mental Health Survey (IranMHS) data (n = 7886) was used to produce province-level prevalence estimates of any psychiatric disorders among 15-64 year old males and females. Psychiatric disorders were diagnosed based on structured diagnostic interview of the Persian version of Composite International Diagnostic Interview (CIDI, version, 2.1). The Hierarchical Bayesian (HB) random effect model was used to calculate the estimates. The mental health status of half of the participants was also measured using a 28-item general health questionnaire (GHQ). Results: A wide variation in the prevalence of psychiatric disorders was found among 31 provinces of Iran. The direct estimates ranged from 3.6% to 62.6%, while the HB estimates ranged from 12.6% to 36.5%. The provincial prevalence among men ranged from 11.9% to 34.5%, while it ranged from 18.4% to 38.8% among women. The Pearson correlation coefficient between HB estimates and GHQ scores was 0.73. Conclusion: The Bayesian small area estimation provides estimation with improved precision at local levels. Detecting high-priority communities with small-area approach could lead to a better distribution of limited facilities and more effective mental health interventions.

The utility of geodemographic indicators in small area estimates of limiting long-term illness

Small area health data are not always available on a consistent and robust routine basis across nations, necessitating the employment of small area estimation methods to generate local-scale data or the use of proxy measures. Geodemographic indicators are widely marketed as a potential proxy for many health indicators. This paper tests the extent to which the inclusion of geodemographic indicators in small area estimation methodology can enhance small area estimates of limiting long-term illness (LLTI). The paper contributes to international debates on small area estimation methodologies in health research and the relevance of geodemographic indicators to the identification of health care needs. We employ a multilevel methodology to estimate small area LLTI prevalence in England, Scotland and Wales. The estimates were created with a standard geographically-based model and with a cross-classified model of individuals nested separately in both spatial groupings and non-spatial geodemographic clusters. LLTI prevalence was estimated as a function of age, sex and deprivation. Estimates from the cross-classified model additionally incorporated residuals relating to the geodemographic classification. Both sets of estimates were compared against direct estimates from the 2011 Census. Geodemographic clusters remain relevant to understanding LLTI even after controlling for age, sex and deprivation. Incorporating a geodemographic indicator significantly improves concordance between the small area estimates and the Census. Small area estimates are however consistently below the equivalent Census measures, with the LLTI prevalence in urban areas characterised as 'blue collar' and 'struggling families' being markedly lower. We conclude that the inclusion of a geodemographic indicator in small area estimation can improve estimate quality and enhance understanding of health inequalities. We recommend the inclusion of geodemographic indicators in public releases of survey data to facilitate better small area estimation but caution against assumptions that geodemographic indicators can, on their own, provide a proxy measure of health status.