Cookies?
Library Header Image
LSE Research Online LSE Library Services

Tracking development assistance for mental health: time for better data

Iemmi, Valentina ORCID: 0000-0003-3301-0689 (2023) Tracking development assistance for mental health: time for better data. Health Policy and Planning, 38 (4). 567 - 570. ISSN 1460-2237

[img] Text (Iemmi_tracking-development-assistance--published) - Published Version
Available under License Creative Commons Attribution.

Download (168kB)

Identification Number: 10.1093/heapol/czac108

Abstract

Sustainable mental health financing requires accurate financial data to support funding decisions in the context of limited resources. This is critical in low- and middle-income countries (LMICs) where most people with mental disorders live but resources are extremely limited (Patel et al., 2018). With LMIC governments under substantial fiscal pressure, mental health constitutes as little as 1.8% of governmental health expenditure (WHO, 2018) and is often supplemented by external resources like development assistance for mental health (DAMH) (Chisholm et al., 2019). DAMH includes financial and in-kind contributions disbursed by international organizations for mental health activities in LMICs (Charlson et al., 2017). While crucial for sustainable financing, tracking external resources such as DAMH is complex and current estimates are inaccurate. Three data sources are used: Financial Tracking Service (FTS) database by the United Nations Office for the Coordination of Humanitarian Affairs; Creditor Reporting System (CRS) database by the Organisation for Economic Co-operation and Development (OECD) and Development Assistance for Health (DAH) dataset by the Institute for Health Metrics and Evaluation (IHME) (Iemmi, 2019). Currently, the DAH dataset constitutes the most sustainable source: it is not only publicly available and regularly updated but also pre-coded (IHME, 2021). However, it presents several limitations that lead to imprecise estimates. This Commentary aims to contribute to ameliorate DAMH estimations, using the DAH dataset as example. After introducing the dataset, I identify its limitations and opportunities for improvement.

Item Type: Article
Official URL: https://academic.oup.com/heapol
Additional Information: © 2023 The Author
Divisions: Health Policy
Subjects: R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
H Social Sciences
Date Deposited: 25 Jan 2023 12:15
Last Modified: 12 Dec 2024 03:33
URI: http://eprints.lse.ac.uk/id/eprint/118013

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics