Evidence map›Paper›PMID 40171145›Full record

ArticleLancet regional health. Americas2025

Impact of conditional cash transfer programs on health outcomes in Argentina: a retrospective, observational analysis based on MICS 2019/2020.

Zlatko Nikoloski, Maria Elisa Zapata, Elias Mossialos

Abstract read
In one paragraph

Article in Lancet regional health. Americas, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Zlatko NikoloskiLSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK.
Maria Elisa ZapataCentro de Estudios Sobre Nutrición Infantil Dr. Alejandro O'Donnell (CESNI), Ciudad Autónoma de Buenos Aires, Argentina.
Elias MossialosLSE Health, Department of Health Policy, London School of Economics and Political Science, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Conditional cash transfers (CCTs) are widely used to combat intergenerational poverty and to invest in human capital. Argentina introduced its own CCT program AUH ( Methods: We utilized data from the Multiple Indicators Cluster Survey (MICS) conducted in Argentina between late 2019 and early 2020. Specifically, we employed different matching techniques to estimate the relationship between AUH and healthcare utilization and high school enrolment. Additionally, we assessed the program's importance in improving nutrition outcomes among children under five years. Findings: Our analysis reveals that the AUH program has not significantly increased healthcare utilization among affiliated children. When accounting for program heterogeneity, the impact of the program was found to be consistent across boys and girls, and across children of different ages, although we found evidence of increased healthcare utilization among adolescents. In addition, there was no statistically significant evidence for a link between program affiliation and reduction in stunting and wasting among children under five years. Furthermore, the program has led to increased high school enrolment among boys, consistent with established findings. Interpretation: The AUH program demonstrates a limited impact, particularly on health and nutrition outcome indicators. Efforts should be made to improve the program by focusing on cash transfer conditionality and amount, as well as strengthening healthcare infrastructure. Funding: None.

Indexed as

ArgentinaCCTsEducationHealthcareNutrition

Identifiers

PMID40171145
PMCPMC11959375

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Registered trials

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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.