Reading Between the Lines: A Pursuit of Estimating the Population Prevalence of Mental Illness Using Multiple Data Sources

Abstract

Population-based prevalence estimates of mental illness are foundational to health service planning, strategic resource allocation, and the development and evaluation of public mental health policy. Generating valid, reliable, and context-specific population-level estimates is of utmost importance and can be achieved by combining various data sources. This pursuit benefits from the right combination of theory, applied statistics, and the conceptualization of available data sources as a collective rather than in isolation. We believe there is a need to read between the lines as theory, methodology, and context (i.e., strengths and limitations) are what determines the meaningfulness of a combined prevalence estimate. Currently lacking is a gold standard approach to combining estimates from multiple data sources. Here, we compare and contrast various approaches to combining data and introduce an idea that leverages the strengths of pre-existing individually linked population-based survey and health administrative data sources currently available in Canada.

Investigating the spatiotemporal patterns and clustering of attendances for mental health services to inform policy and resource allocation in Thailand

“…Contributing to this underestimation are socio-economic factors, including stigma and prejudice, particularly pronounced in LMICs [ 8 , 52 ]. An alternative approach involves combining various data sources to enhance the accuracy of estimates, although there is currently no standardized method for this [ 53 ]. Nonetheless, it is important to note that the main analysis focused on the relative spatial distribution rather than comparing absolute measures of disease occurrence between diagnoses, potentially limiting the impact of this aspect on the findings.…”

Representativeness of survey participants in relation to mental disorders: a linkage between national registers and a population-representative survey

“…Therefore, misclassification is possible in both directions. However, it should be considered that by combining the surveys and the registers, we are trying to improve our understanding of the limitations of these data sourcesresearch can, and should, continue to consider these, with the aim of providing estimates of mental disorder prevalence [23,24]. Third, selection bias could also arise from excess mortality if a disorder is associated with increased risk of premature mortality.…”

“…Furthermore, the standardization of self-reported measures can be more culturally sensitive for measuring mental disorders [22]. Combining register data with survey data can fill these gaps [23,24]. Register data can be supplemented by data from surveys on topics that registers do not capture well (e.g.…”

Surveillance of Child and Youth Mental Disorders and Associated Service Use in Canada

“…15 In recent years, collaborations between StatCan and CIHI have led to linkages between surveys and various health administrative databases. Though these linkages provide unique advantages over individual data sources, 12 they are not yet used in Canadian surveillance.…”