Back

Scale-up costs and societal benefits of psychological interventions for alcohol use and depressive disorders in India

zadey, s.

2023-05-21 public and global health
10.1101/2023.05.15.23289987 medRxiv
Show abstract

There is growing evidence for cost-effective psychological interventions by lay health workers for managing mental health problems. In India, Counseling for Alcohol Problems (CAP) and Healthy Activity Program (HAP) have been shown to have sustained cost-effectiveness for improving harmful alcohol use among males and depression remission among both sexes, respectively. We conducted a retrospective analysis of annual costs and economic benefits of CAP and HAP national scale-up with 2019 as the baseline. The CAP and HAP per capita integration costs were obtained from original studies, prevalence and disability-adjusted life-years for alcohol use disorders (AUD) and depressive disorders for 20-64 years old males and females from Global Burden of Disease study, and treatment gaps from National Mental Health Survey. Scale-up costs were calculated for meeting total or unmet needs Societal benefit estimates based on averted disease burden were calculated using human capital and value of life-year approaches. Net benefits were calculated from combinations of differences between societal benefits and scale-up costs. Values were transformed to 2019 international dollars. CAP scale-up costs ranged from Int$ 2.03 (95%UI: 1.67, 2.44) billion to Int$ 6.34 (5.21, 7.61) billion while HAP ones ranged from Int$ 6.85 (5.61, 8.12) billion to Int$ 23.21 (19.03, 27.52) billion. Societal benefits due to averted AUD burden ranged from Int$ 11.51 (8.75, 14.90) billion to Int$ 38.73 (29.43, 50.11) billion and those due to averted depression burden ranged from Int$ 30.89 (20.77, 43.32) billion to Int$ 105.27 (70.78, 147.61) billion. All scenarios showed net positive benefits for CAP (Int$ 6.05-36.38 billion) and HAP (Int$ 11.12-93.50 billion) scale-up. The novel national-level scale-up estimates have actionable implications for mental health financing in India.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.