The Impact of Socioeconomic and Clinical Factors on Mental Healthcare Expenditure: Enabling Quality Managed Care Decisions
Author(s)
Shelley McGee, BEc, BSc, MSc1, Preenan Pillay, PhD2, Teboho Letsoalo, BSc (Hons)3, Kagiso Seripe, MD4, Felicia Mhlanga, BAcSci5, Fatima Patel, BAcSci5, Tebogo Phaleng, MBCHB6.
1Health Economist, Insight Health Solutions, Midrand, South Africa, 2Research and Development and Department of Environmental and Life Science, Insight Health Solutions and University of South Africa, Midrand, South Africa, 3Research and Development, Insight Health Solutions, Midrand, South Africa, 4Insight Actuaries & Consultants, Midrand, South Africa, 5Actuarial Consulting, Insight Actuarial Solutions, Midrand, South Africa, 6Department Head, Insight Health Solutions, Midrand, South Africa.
1Health Economist, Insight Health Solutions, Midrand, South Africa, 2Research and Development and Department of Environmental and Life Science, Insight Health Solutions and University of South Africa, Midrand, South Africa, 3Research and Development, Insight Health Solutions, Midrand, South Africa, 4Insight Actuaries & Consultants, Midrand, South Africa, 5Actuarial Consulting, Insight Actuarial Solutions, Midrand, South Africa, 6Department Head, Insight Health Solutions, Midrand, South Africa.
OBJECTIVES: The management of mental healthcare expenditure has disrupted global healthcare systems due to the complexity of evolving socioeconomic factors. The presented research aims to provide a framework for stratified, risk-adjusted clinical management based on claims data.
METHODS: A South African 2024 health records database was systematically mined for Mental Health Diseases and Disorders using the Insight Diagnosis-Related Grouper (DRG). De-identified patient claims data were stratified according to age, gender, socioeconomic determinants, and clinical diagnosis. Statistical comparisons were done using One-Way ANOVA (non-parametric), Friedman Test, and a two-stage linear set-up procedure of Benjamin, Krieger, and Yekutieli to control False Discovery Rate.
RESULTS: A total of 1,978,538 mental health claims records were extracted with a total expenditure of 138,756,648 USD (approximately 18% of annual mental health expenditure in South Africa). Female claims expenditure was significantly higher than males relative to the total mental health expenditure (68 vs 56%, p < 0.0062). The 40-59 year age group contributed to a significantly higher mental health expenditure within both gender groups. Depressive disorders within this age group were significantly higher in comparison to other mental disorders (80% and 67%, respectively, p < 0.0001). Claims from high-income earners were statistically significantly greater than lower and middle-income earners (43 vs 32 & 26% respectively, p < 0.0253). Importantly, a significant proportion (>80%) of the claimants were found to have underlying chronic disorders, mainly Diabetes, HIV, and Hypertension. This data is currently being used to develop a value-based healthcare program and related reimbursement model for a healthcare fund.
CONCLUSIONS: Identifying clinical and socio-economic risk factors within a population enables the development of personalised, value-based healthcare programs tailored to the needs of high-risk patient groups. Importantly, statistically validated analyses inform reimbursement models that promote high-quality, cost-effective, and integrated care, which ultimately facilitates enhanced healthcare decision-making.
METHODS: A South African 2024 health records database was systematically mined for Mental Health Diseases and Disorders using the Insight Diagnosis-Related Grouper (DRG). De-identified patient claims data were stratified according to age, gender, socioeconomic determinants, and clinical diagnosis. Statistical comparisons were done using One-Way ANOVA (non-parametric), Friedman Test, and a two-stage linear set-up procedure of Benjamin, Krieger, and Yekutieli to control False Discovery Rate.
RESULTS: A total of 1,978,538 mental health claims records were extracted with a total expenditure of 138,756,648 USD (approximately 18% of annual mental health expenditure in South Africa). Female claims expenditure was significantly higher than males relative to the total mental health expenditure (68 vs 56%, p < 0.0062). The 40-59 year age group contributed to a significantly higher mental health expenditure within both gender groups. Depressive disorders within this age group were significantly higher in comparison to other mental disorders (80% and 67%, respectively, p < 0.0001). Claims from high-income earners were statistically significantly greater than lower and middle-income earners (43 vs 32 & 26% respectively, p < 0.0253). Importantly, a significant proportion (>80%) of the claimants were found to have underlying chronic disorders, mainly Diabetes, HIV, and Hypertension. This data is currently being used to develop a value-based healthcare program and related reimbursement model for a healthcare fund.
CONCLUSIONS: Identifying clinical and socio-economic risk factors within a population enables the development of personalised, value-based healthcare programs tailored to the needs of high-risk patient groups. Importantly, statistically validated analyses inform reimbursement models that promote high-quality, cost-effective, and integrated care, which ultimately facilitates enhanced healthcare decision-making.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
RWD180
Topic
Epidemiology & Public Health, Patient-Centered Research, Real World Data & Information Systems
Topic Subcategory
Health & Insurance Records Systems
Disease
Mental Health (including addition), No Additional Disease & Conditions/Specialized Treatment Areas