Evidence map›Paper›PMID 27398070›Full record

ArticleCost effectiveness and resource allocation : C/E2016

An extended cost-effectiveness analysis of schizophrenia treatment in India under universal public finance.

Neha Raykar, Aditi Nigam, Dan Chisholm

Open access · goldAbstract read
In one paragraph

Article in Cost effectiveness and resource allocation : C/E, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 2 pooled it
0.9field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 3 countries.

Neha RaykarPublic Health Foundation of India, Plot No 47, Sector 44, Gurgaon, Haryana 122002 India.
Aditi NigamCenter for Disease Dynamics, Economics and Policy, 1400 Eye St NW, Suite 500, Washington, DC 20005 USA.
Dan ChisholmDepartment of Mental Health and Substance Abuse, World Health Organization, Geneva, Switzerland.
Center for Disease Dynamics, Economics & Policy · USPublic Health Foundation of India · INWorld Health Organization · CH

Funding

World Health Organization 001
6 · The paper itself

Abstract

backgroundSchizophrenia remains a priority condition in mental health policy and service development because of its early onset, severity and consequences for affected individuals and households. AIMS AND

methodsThis paper reports on an 'extended' cost-effectiveness analysis (ECEA) for schizophrenia treatment in India, which seeks to evaluate through a modeling approach not only the costs and health effects of intervention but also the consequences of a policy of universal public finance (UPF) on health and financial outcomes across income quintiles.

resultsUsing plausible values for input parameters, we conclude that health gains from UPF are concentrated among the poorest, whereas the non-health gains in the form of out-of-pocket private expenditures averted due to UPF are concentrated among the richest income quintiles. Value of insurance is the highest for the poorest quintile and declines with income.

conclusionsUniversal public finance can play a crucial role in ameliorating the adverse economic and social consequences of schizophrenia and its treatment in resource-constrained settings where health insurance coverage is generally poor. This paper shows the potential distributional and financial risk protection effects of treating schizophrenia.

Indexed as

Cost-effectiveness analysisIndiaSchizophreniaValue of insurance

Identifiers

PMID27398070
PMCPMC4938947
OpenAlexW2472846104

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.