Evidence map›Paper›PMID 33023598›Full record

ArticleBMC health services research2020

Levels, trends and determinants of technical efficiency of general hospitals in Uganda: data envelopment analysis and Tobit regression analysis.

Rogers Ayiko, Paschal N Mujasi, Joyce Abaliwano, Dickson Turyareeba, Rogers Enyaku, Robert Anguyo, Walter Odoch, Pauline Bakibinga, Tom Aliti

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 3 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

16 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
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

9 authors.

Rogers AyikoThe Foundation for African Empowerment, P. O. Box 116, Arusha, Tanzania. ayikor@gmail.com.ORCID http://orcid.org/0000-0002-5407-895X
Paschal N MujasiDepartment of Economics and Business, Universitat Pompeu Fabra, Barcelona School of Management, Balmes 132, 08001, Barcelona, Spain.
Joyce AbaliwanoMakerere University Business School, Plot 21 A, Port Bell Rd, Kampala, Uganda.
Dickson TuryareebaMakerere University Business School, Plot 21 A, Port Bell Rd, Kampala, Uganda.
Rogers EnyakuCommunity Resource Development Initiative, P. O. Box 6653, Kampala, Uganda.
Robert AnguyoDepartment of International Public Health, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK.
Walter OdochEast Central and Southern Africa Health Community, 157 Olorien, Njiro Road ECSA-HC, P.O. Box 1009, Arusha, Tanzania.
Pauline BakibingaAfrican Population & Health Research Center, Manga Close, Off Kirawa Road, P.O. Box 10787-00100, Nairobi, Kenya.
Tom AlitiMinistry of Health, Plot 6, Lourdel Road, Nakasero, P.O Box 7272, Kampala, Uganda.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGeneral hospitals provide a wide range of primary and secondary healthcare services. They accounted for 38% of government funding to health facilities, 8.8% of outpatient department visits and 28% of admissions in Uganda in the financial year 2016/17. We assessed the levels, trends and determinants of technical efficiency of general hospitals in Uganda from 2012/13 to 2016/17.

methodsWe undertook input-oriented data envelopment analysis to estimate technical efficiency of 78 general hospitals using data abstracted from the Annual Health Sector Performance Reports for 2012/13, 2014/15 and 2016/17. Trends in technical efficiency was analysed using Excel while determinants of technical efficiency were analysed using Tobit Regression Model in STATA 15.1.

resultsThe average constant returns to scale, variable returns to scale and scale efficiency of general hospitals for 2016/17 were 49% (95% CI, 44-54%), 69% (95% CI, 65-74%) and 70% (95% CI, 65-75%) respectively. There was no statistically significant difference in the efficiency scores of public and private hospitals. Technical efficiency generally increased from 2012/13 to 2014/15, and dropped by 2016/17. Some hospitals were persistently efficient while others were inefficient over this period. Hospital size, geographical location, training status and average length of stay were statistically significant determinants of efficiency at 5% level of significance.

conclusionThe 69% average variable returns to scale technical efficiency indicates that the hospitals could generate the same volume of outputs using 31% (3439) less staff and 31% (3539) less beds. Benchmarking performance of the efficient hospitals would help to guide performance improvement in the inefficient ones. There is need to incorporate hospital size, geographical location, training status and average length of stay in the resource allocation formula and adopt annual hospital efficiency assessments.

Indexed as

Data AnalysisEfficiency, OrganizationalHospitals, GeneralHospitals, PrivateHospitals, PublicHumansRegression AnalysisResource AllocationUgandaData envelopment analysisGeneral hospitalTechnical efficiencyTobit regression analysisUganda

Identifiers

PMID33023598
PMCPMC7539474

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.