Do incentives matter when working for god? The impact of performance-based financing on faith-based healthcare in Uganda

Duchoslav, Jan; Cecchi, Francesco

doi:10.1016/j.worlddev.2018.09.011

Cited by 16 publications

(11 citation statements)

References 57 publications

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“…Whereas higher-level referral hospitals registered improvements in the Standard Unit of Output (SUO) from 9,837,521 in 2015/16 to 9,956,067 in 2016/17, the overall SUO for general hospitals reduced from 17,692,056 in 2015/16 to 17,418,29 (32). The SUO is a weighted average output of the most commonly performed procedures and services, considering their relative time and cost requirements (33). A recent study of TE was carried out for referral hospitals in Uganda using the DEA approach, however, we could not find any for general hospitals yet they account for a huge part of the recurrent and capital expenditure (17,31).…”

Section: Introductionmentioning

confidence: 99%

Levels, Trends and Determinants of Technical Efficiency of General Hospitals in Uganda: Data Envelopment Analysis and Tobit Regression Analysis

Ayiko

Mujasi

Abaliwano

et al. 2020

Preprint

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Background: General 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. Methods: We 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. Results: The 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. Conclusion: The 69% average variable returns to scale technical efficiency indicates that the hospitals could generate the same volume of outputs using 31% (3,439) less staff and 31% (3,539) 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.

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Section: Introductionmentioning

confidence: 99%

Levels, Trends and Determinants of Technical Efficiency of General Hospitals in Uganda: Data Envelopment Analysis and Tobit Regression Analysis

Ayiko

Mujasi

Abaliwano

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Whereas higher-level referral hospitals registered improvements in the Standard Unit of Output (SUO) from 9,837,521 in 2015/16 to 9, 956,067 in 2016/17, the overall SUO for general hospitals reduced from 17,692,056 in 2015/16 to 17,418,29 [32]. The SUO is a weighted average output of the most commonly performed procedures and services, considering their relative time and cost requirements [33]. A recent study of TE was carried out for referral hospitals in Uganda using the DEA approach, however, we could not find any for general hospitals yet they account for a huge part of the recurrent and capital expenditure [17,31].…”

Section: Introductionmentioning

confidence: 99%

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

Ayiko

Mujasi

Abaliwano

et al. 2020

BMC Health Serv Res

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Background General 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. Methods We 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. Results The 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. Conclusion The 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.

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“…The current literature, however, shows relatively little attention to efficiency which combines inputs and outputs. The little available evidence shows that P4P increased provider productivity in Rwanda, 26 increased efficiency of faith-based facilities in Uganda, 27 and efficiency improved over time among P4P implementing health centres in Rwanda. 28 However, these studies focused on a single type of facilities (eg, faith-based or health centres only 27 28 ), used only one input and output 26 or relied on a small sample of facilities and assessed trend over time without comparing them with control facilities.…”

Section: Introductionmentioning

confidence: 99%

Understanding efficiency and the effect of pay-for-performance across health facilities in Tanzania

Binyaruka

Anselmi

2020

BMJ Glob Health

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BackgroundEnsuring efficient use and allocation of limited resources is crucial to achieving the UHC goal. Performance-based financing that provides financial incentives for health providers reaching predefined targets would be expected to enhance technical efficiency across facilities by promoting an output-oriented payment system. However, there is no study which has systematically assessed efficiency scores across facilities before and after the introduction of pay-for-performance (P4P). This paper seeks to fill this knowledge gap.MethodsWe used data of P4P evaluation related to healthcare inputs (staff, equipment, medicines) and outputs (outpatient consultations and institutional deliveries) from 75 health facilities implementing P4P in Pwani region, and 75 from comparison districts in Tanzania. We measured technical efficiency using Data Envelopment Analysis and obtained efficiency scores across facilities before and after P4P scheme. We analysed which factors influence technical efficiency by regressing the efficiency scores over a number of contextual factors. We also tested the impact of P4P on efficiency through a difference-in-differences regression analysis.ResultsThe overall technical efficiency scores ranged between 0.40 and 0.65 for hospitals and health centres, and around 0.20 for dispensaries. Only 21% of hospitals and health centres were efficient when outpatient consultations and deliveries were considered as output, and <3% out of all facilities were efficient when outpatient consultations only were considered as outputs. Higher efficiency scores were significantly associated with the level of care (hospital and health centre) and wealthier catchment populations. Despite no evidence of P4P effect on efficiency on average, P4P might have improved efficiency marginally among public facilities.ConclusionMost facilities were not operating at their full capacity indicating potential for improving resource usage. A better understanding of the production process at the facility level and of how different healthcare financing reforms affects efficiency is needed. Effective reforms should improve inputs, outputs but also efficiency.

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Do incentives matter when working for god? The impact of performance-based financing on faith-based healthcare in Uganda

Cited by 16 publications

References 57 publications

Levels, Trends and Determinants of Technical Efficiency of General Hospitals in Uganda: Data Envelopment Analysis and Tobit Regression Analysis

Levels, Trends and Determinants of Technical Efficiency of General Hospitals in Uganda: Data Envelopment Analysis and Tobit Regression Analysis

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

Understanding efficiency and the effect of pay-for-performance across health facilities in Tanzania

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