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

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.

Exploring perceptions, motivations, and practices regarding outsourcing support services by general hospitals in Uganda: A mixed methods study.

Research on outsourcing in a developing country using a mixed methods approach can provide insights on outsourcing decisions and practices. This study investigated motivations, practices, perceived benefits, and barriers to outsourcing by general hospitals in Uganda. An explanatory sequential mixed methods design was used. Quantitative data were collected using a self-administered questionnaire from managers in 32 randomly selected hospitals. Qualitative data were latter collected from eight purposively selected managers using an interview guide. Quantitative data were statistically analyzed using SAS 9.3. Qualitative data were managed using ATLAS ti 7 and coded manually, and content analysis was conducted. Quantitative findings indicate that outsourcing of support services was prevalent (72% of hospitals). The key motivation for outsourcing was to gain access to quality service (68%). Limited availability of service providers was a key challenge during outsourcing (57%). Managers perceive improved productivity and better services as key benefits of outsourcing (90%). The main barrier to outsourcing is limited financing. These findings were confirmed and explained by the qualitative data. Findings and recommendations from this study are critical in developing interventions to encourage effective outsourcing by hospitals in Uganda and other developing countries.

To what extent do prescribing practices for hypertension in the private sector in Zimbabwe follow the national treatment guidelines? An analysis of insurance medical claims.

BACKGROUND: Hypertension is the most prevalent cardiovascular disease in Zimbabwe. The prevalence of Hypertension in the country is above 30% regardless of the cut off used. Currently, majority of patients in Zimbabwe seek health care from the private sector due to limited government funding for the public health sector. However, Standard treatment guidelines for hypertension are only available in the public sector and are optional in the private sector. This study assesses compliance of private sector prescribing to Standard Treatment guidelines for hypertension. METHODS: We reviewed hypertension prescription claims to a private health insurance company in Zimbabwe for the period Jan 1-Dec 31 2015. We used the last prescription claimed in the year on the assumption that it represented the patient's current treatment. Prescription data was analyzed by comparing medicines prescribed to those recommended in the Zimbabwe 7th Essential Medicines List and Standard Treatment Guidelines 2015. We used Microsoft Excel© 2010 to conduct the analysis. RESULTS: A total of 1019 prescriptions were reviewed. Most patients were either on mono or dual therapy (76%). The mostly prescribed class of antihypertensive as first line were Angiotensin Converting Enzyme Inhibitors /Angiotensin Receptor Blockers. Regardless of whether they were being used as first, second or third line this class of antihypertensives emerged as the most prescribed (639 times). Only 358 (35%) prescriptions were compliant with standard treatment guidelines; the rest (661) did not meet several criteria. Areas of non-compliance included use of second line medicines as first line, failure to consider patient characteristics when prescribing, use of contraindicated medicines for certain patients, clinically significant interactions among prescribed medicines and illogical combinations that predispose patients to toxicity. CONCLUSION: The poor compliance to standard treatment guidelines observed in our study indicates need to improve prescription practices for Hypertension in the private sector in Zimbabwe for its cost-effective management among the covered patients. However, further investigation is needed to understand the drivers of the prescribing habits and the non-compliance to the Essential Medicines List and Standard Treatment guidelines observed. This will enable design of appropriate educational, managerial and economic interventions to improve compliance.

How efficient are referral hospitals in Uganda? A data envelopment analysis and tobit regression approach.

BACKGROUND: Hospitals represent a significant proportion of health expenditures in Uganda, accounting for about 26 % of total health expenditure. Improving the technical efficiency of hospitals in Uganda can result in large savings which can be devoted to expand access to services and improve quality of care. This paper explores the technical efficiency of referral hospitals in Uganda during the 2012/2013 financial year. METHODS: This was a cross sectional study using secondary data. Input and output data were obtained from the Uganda Ministry of Health annual health sector performance report for the period July 1, 2012 to June 30, 2013 for the 14 public sector regional referral and 4 large private not for profit hospitals. We assumed an output-oriented model with Variable Returns to Scale to estimate the efficiency score for each hospital using Data Envelopment Analysis (DEA) with STATA13. Using a Tobit model DEA, efficiency scores were regressed against selected institutional and contextual/environmental factors to estimate their impacts on efficiency. RESULTS: The average variable returns to scale (Pure) technical efficiency score was 91.4 % and the average scale efficiency score was 87.1 % while the average constant returns to scale technical efficiency score was 79.4 %. Technically inefficient hospitals could have become more efficient by increasing the outpatient department visits by 45,943; and inpatient days by 31,425 without changing the total number of inputs. Alternatively, they would achieve efficiency by for example transferring the excess 216 medical staff and 454 beds to other levels of the health system without changing the total number of outputs. Tobit regression indicates that significant factors in explaining hospital efficiency are: hospital size (p < 0.01); bed occupancy rate (p < 0.01) and outpatient visits as a proportion of inpatient days (p < 0.05). CONCLUSIONS: Hospitals identified at the high and low extremes of efficiency should be investigated further to determine how and why production processes are operating differently at these hospitals. As policy makers gain insight into mechanisms promoting hospital services utilization in hospitals with high efficiency they can develop context-appropriate strategies for supporting hospitals with low efficiency to improve their service and thereby better address unmet needs for hospital services in Uganda.

Predictors of primary health care pharmaceutical expenditure by districts in Uganda and implications for budget setting and allocation.

BACKGROUND: There is need for the Uganda Ministry of Health to understand predictors of primary health care pharmaceutical expenditure among districts in order to guide budget setting and to improve efficiency in allocation of the set budget among districts. METHODS: Cross sectional, retrospective observational study using secondary data. The value of pharmaceuticals procured by primary health care facilities in 87 randomly selected districts for the Financial Year 2011/2012 was collected. Various specifications of the dependent variable (pharmaceutical expenditure) were used: total pharmaceutical expenditure, Per capita district pharmaceutical expenditure, pharmaceutical expenditure per district health facility and pharmaceutical expenditure per outpatient department visit. Andersen's behaviour model of health services utilisation was used as conceptual framework to identify independent variables likely to influence health care utilisation and hence pharmaceutical expenditure. Econometric analysis was conducted to estimate parameters of various regression models. RESULTS: All models were significant overall (P < 0.01), with explanatory power ranging from 51 to 82%. The log linear model for total pharmaceutical expenditure explained about 80% of the observed variation in total pharmaceutical expenditure (Adjusted R(2) = 0.797) and contained the following variables: Immunisation coverage, Total outpatient department attendance, Urbanisation, Total number of government health facilities and total number of Health Centre IIs. The model based on Per capita Pharmaceutical expenditure explained about 50% of the observed variation in per capita pharmaceutical expenditure (Adjusted R(2) = 0.513) and was more balanced with the following variables: Outpatient per capita attendance, percentage of rural population below poverty line 2005, Male Literacy rate, Whether a district is characterised by MOH as difficult to reach or not and the Human poverty index. CONCLUSIONS: The log-linear model based on total pharmaceutical expenditure works acceptably well and can be considered useful for predicting future total pharmaceutical expenditure following observed trends. It can be used as a simple tool for rough estimation of the potential overall national primary health pharmaceutical expenditure to guide budget setting. The model based on pharmaceutical expenditure per capita is a more balanced model containing both need and enabling factor variables. These variables would be useful in allocating any set budget to districts.

What variables should be considered in allocating Primary health care Pharmaceutical budgets to districts in Uganda?

OBJECTIVES: A key policy question for the government of Uganda is how to equitably allocate primary health care pharmaceutical budgets to districts. This paper seeks to identify variables influencing current primary health care pharmaceutical expenditure and their usefulness in allocating prospective pharmaceutical budgets to districts. METHODS: This was a cross sectional, retrospective observational study using secondary administrative data. We collected data on the value of pharmaceuticals procured by primary health care facilities in each district from National Medical Stores for the financial year 2011/2012. The dependent variable was expressed as per capita district pharmaceutical expenditure. By reviewing literature we identified 26 potential explanatory variables. They include supply, need and demand, and health system organization variables that may influence the demand and supply of health services and the corresponding pharmaceutical expenditure. We collected secondary data for these variables for all the districts in Uganda (n = 112). We performed econometric analysis to estimate parameters of various regression models. RESULTS: There is a significant correlation between per capita district pharmaceutical expenditure and total district population, rural poverty, access to drinking water and outpatient department (OPD) per capita utilisation.(P < 0.01). The percentage of health centre IIIs (HC III) among each district's health facilities is significantly correlated with per capita pharmaceutical expenditure (P < 0.05). OPD per capita utilisation has a relatively strong correlation with per capita pharmaceutical expenditure (r = 0.498); all the other significant factors are weakly correlated with per capita pharmaceutical expenditure (r < 0.5). From several iterations of an initially developed model, the proposed final model for explaining per capita pharmaceutical expenditure explains about 53% of the variation in pharmaceutical expenditure among districts in Uganda (Adjusted R(2) = 0.528). All variables in the model are significant (p < 0.01). CONCLUSIONS: From evaluation of the various models, proposed variables to consider in allocating prospective primary health care pharmaceutical budgets to districts in Uganda are: district outpatient department attendance per capita, total district population, total number of government health facilities in the district and the district human poverty index.