Evidence map›Paper›PMID 37202022›Full record

ArticleThe Lancet. Global health2023

Health service quality in 2929 facilities in six low-income and middle-income countries: a positive deviance analysis.

Todd P Lewis, Margaret McConnell, Amit Aryal, Grace Irimu, Suresh Mehata, Mwifadhi Mrisho, Margaret E Kruk

Abstract read
In one paragraph

Article in The Lancet. Global health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 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

30 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Observational
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Todd P LewisDepartment of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA. Electronic address: tlewis@hsph.harvard.edu.
Margaret McConnellDepartment of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA.
Amit AryalSwiss TPH, University of Basel, Basel, Switzerland.
Grace IrimuDepartment of Paediatrics and Child Health, University of Nairobi, Nairobi, Kenya.
Suresh MehataPolicy, Planning and Public Health Division, Ministry of Health, Biratnagar, Nepal.
Mwifadhi MrishoDepartment of Health Systems, Impact Evaluation, and Policy, Ifakara Health Institute, Dar es Salaam, Tanzania.
Margaret E KrukDepartment of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrimary care is of insufficient quality in many low-income and middle-income countries. Some health facilities perform better than others despite operating in similar contexts, although the factors that characterise best performance are not well known. Existing best-performance analyses are concentrated in high-income countries and focus on hospitals. We used the positive deviance approach to identify the factors that differentiate best from worst primary care performance among health facilities across six low-resource health systems.

methodsThis positive deviance analysis used nationally representative samples of public and private health facilities from Service Provision Assessments of the Democratic Republic of the Congo, Haiti, Malawi, Nepal, Senegal, and Tanzania. Data were collected starting June 11, 2013, in Malawi and ending Feb 28, 2020, in Senegal. We assessed facility performance through completion of the Good Medical Practice Index (GMPI) of essential clinical actions (eg, taking a thorough history, conducting an adequate physical examination) according to clinical guidelines and measured with direct observations of care. We identified hospitals and clinics in the top decile of performance (defined as best performers) and conducted a quantitative, cross-national positive deviance analysis to compare them with facilities performing below the median (defined as worst performers) and identify facility-level factors that explain the gap between best and worst performance.

findingsWe identified 132 best-performing and 664 worst-performing hospitals, and 355 best-performing and 1778 worst-performing clinics based on clinical performance across countries. The mean GMPI score was 0·81 (SD 0·07) for the best-performing hospitals and 0·44 (0·09) for the worst-performing hospitals. Among clinics, mean GMPI scores were 0·75 (0·07) for the best performers and 0·34 (0·10) for the worst performers. High-quality governance, management, and community engagement were associated with best performance compared with worst performance. Private facilities out-performed government-owned hospitals and clinics.

interpretationOur findings suggest that best-performing health facilities are characterised by good management and leaders who can engage staff and community members. Governments should look to best performers to identify scalable practices and conditions for success that can improve primary care quality overall and decrease quality gaps between health facilities.

fundingBill & Melinda Gates Foundation.

Indexed as

Developing CountriesHealth ServicesHealth FacilitiesHumansMalawiQuality of Health Care

Identifiers

PMID37202022
PMCPMC10205971

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.