Evidence map›Paper›PMID 22828358›Full record

ArticleHealth economics review2011

Efficiency of primary care in rural Burkina Faso. A two-stage DEA analysis.

Paul Marschall, Steffen Flessa

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Article in Health economics review, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 2 pooled it
–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

47 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

2 authors.

Paul MarschallUniversity of Greifswald, Faculty of Law and Economics, Department of Health Care Management, Friedrich-Loeffler-Str, 70, D-17489 Greifswald, Germany. paul.marschall@uni-greifswald.de.
Steffen Flessa

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProviding health care services in Africa is hampered by severe scarcity of personnel, medical supplies and financial funds. Consequently, managers of health care institutions are called to measure and improve the efficiency of their facilities in order to provide the best possible services with their resources. However, very little is known about the efficiency of health care facilities in Africa and instruments of performance measurement are hardly applied in this context.

objectiveThis study determines the relative efficiency of primary care facilities in Nouna, a rural health district in Burkina Faso. Furthermore, it analyses the factors influencing the efficiency of these institutions. METHODOLOGY: We apply a two-stage Data Envelopment Analysis (DEA) based on data from a comprehensive provider and household information system. In the first stage, the relative efficiency of each institution is calculated by a traditional DEA model. In the second stage, we identify the reasons for being inefficient by regression technique.

resultsThe DEA projections suggest that inefficiency is mainly a result of poor utilization of health care facilities as they were either too big or the demand was too low. Regression results showed that distance is an important factor influencing the efficiency of a health care institution

conclusionsCompared to the findings of existing one-stage DEA analyses of health facilities in Africa, the share of relatively efficient units is slightly higher. The difference might be explained by a rather homogenous structure of the primary care facilities in the Burkina Faso sample. The study also indicates that improving the accessibility of primary care facilities will have a major impact on the efficiency of these institutions. Thus, health decision-makers are called to overcome the demand-side barriers in accessing health care.

Identifiers

PMID22828358
PMCPMC3395044

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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.