Using data envelopment analysis to measure the extent of technical efficiency of public health centres in Ghana

<p>Abstract</p> <p>Background</p> <p>Data Envelopment Analysis (DEA) has been used to analyze the efficiency of the health sector in the developed world for sometime now. However, in developing economies and particularly in Africa only a few studies have applied DEA in measuring the efficiency of their health care systems.</p> <p>Methods</p> <p>This study uses the DEA method, to calculate the technical efficiency of 89 randomly sampled health centers in Ghana. The aim was to determine the degree of efficiency of health centers and recommend performance targets for the inefficient facilities.</p> <p>Results</p> <p>The findings showed that 65% of health centers were technically inefficient and so were using resources that they did not actually need.</p> <p>Conclusion</p> <p>The results broadly point to grave inefficiency in the health care delivery system of public health centers and that significant amounts of resources could be saved if measures were put in place to curb the waste.</p

Technical efficiency of peripheral health units in Pujehun district of Sierra Leone: a DEA application

BACKGROUND: The Data Envelopment Analysis (DEA) method has been fruitfully used in many countries in Asia, Europe and North America to shed light on the efficiency of health facilities and programmes. There is, however, a dearth of such studies in countries in sub-Saharan Africa. Since hospitals and health centres are important instruments in the efforts to scale up pro-poor cost-effective interventions aimed at achieving the United Nations Millennium Development Goals, decision-makers need to ensure that these health facilities provide efficient services. The objective of this study was to measure the technical efficiency (TE) and scale efficiency (SE) of a sample of public peripheral health units (PHUs) in Sierra Leone. METHODS: This study applied the Data Envelopment Analysis approach to investigate the TE and SE among a sample of 37 PHUs in Sierra Leone. RESULTS: Twenty-two (59%) of the 37 health units analysed were found to be technically inefficient, with an average score of 63% (standard deviation = 18%). On the other hand, 24 (65%) health units were found to be scale inefficient, with an average scale efficiency score of 72% (standard deviation = 17%). CONCLUSION: It is concluded that with the existing high levels of pure technical and scale inefficiency, scaling up of interventions to achieve both global and regional targets such as the MDG and Abuja health targets becomes far-fetched. In a country with per capita expenditure on health of about US$7, and with only 30% of its population having access to health services, it is demonstrated that efficiency savings can significantly augment the government's initiatives to cater for the unmet health care needs of the population. Therefore, we strongly recommend that Sierra Leone and all other countries in the Region should institutionalise health facility efficiency monitoring at the Ministry of Health headquarter (MoH/HQ) and at each health district headquarter

Alternative scenarios: harnessing mid-level providers and evidence-based practice in primary dental care in England through operational research

Background: In primary care dentistry, strategies to reconfigure the traditional boundaries of various dental professional groups by task sharing and role substitution have been encouraged in order to meet changing oral health needs. Aim: The aim of this research was to investigate the potential for skill mix use in primary dental care in England based on the undergraduate training experience in a primary care team training centre for dentists and mid-level dental providers. Methods: An operational research model and four alternative scenarios to test the potential for skill mix use in primary care in England were developed, informed by the model of care at a primary dental care training centre in the south of England, professional policy including scope of practice and contemporary evidence-based preventative practice. The model was developed in Excel and drew on published national timings and salary costs. The scenarios included the following: “No Skill Mix”, “Minimal Direct Access”, “More Prevention” and “Maximum Delegation”. The scenario outputs comprised clinical time, workforce numbers and salary costs required for state-funded primary dental care in England. Results: The operational research model suggested that 73% of clinical time in England’s state-funded primary dental care in 2011/12 was spent on tasks that may be delegated to dental care professionals (DCPs), and 45- to 54-year-old patients received the most clinical time overall. Using estimated National Health Service (NHS) clinical working patterns, the model suggested alternative NHS workforce numbers and salary costs to meet the dental demand based on each developed scenario. For scenario 1:“No Skill Mix”, the dentist-only scenario, 81% of the dentists currently registered in England would be required to participate. In scenario 2: “Minimal Direct Access”, where 70% of examinations were delegated and the primary care training centre delegation patterns for other treatments were practised, 40% of registered dentists and eight times the number of dental therapists currently registered would be required; this would save 38% of current salary costs cf. “No Skill Mix”. Scenario 3: “More Prevention”, that is, the current model with no direct access and increasing fluoride varnish from 13.1% to 50% and maintaining the same model of delegation as scenario 2 for other care, would require 57% of registered dentists and 4.7 times the number of dental therapists. It would achieve a 1% salary cost saving cf. “No Skill Mix”. Scenario 4 “Maximum Delegation” where all care within dental therapists’ jurisdiction is delegated at 100%, together with 50% of restorations and radiographs, suggested that only 30% of registered dentists would be required and 10 times the number of dental therapists registered; this scenario would achieve a 52% salary cost saving cf. “No Skill Mix”. Conclusion: Alternative scenarios based on wider expressed treatment need in national primary dental care in England, changing regulations on the scope of practice and increased evidence-based preventive practice suggest that the majority of care in primary dental practice may be delegated to dental therapists, and there is potential time and salary cost saving if the majority of diagnostic tasks and prevention are delegated. However, this would require an increase in trained DCPs, including role enhancement, as part of rebalancing the dental workforce

Sequence of a complete chicken BG haplotype shows dynamic expansion and contraction of two gene lineages with particular expression patterns.

Many genes important in immunity are found as multigene families. The butyrophilin genes are members of the B7 family, playing diverse roles in co-regulation and perhaps in antigen presentation. In humans, a fixed number of butyrophilin genes are found in and around the major histocompatibility complex (MHC), and show striking association with particular autoimmune diseases. In chickens, BG genes encode homologues with somewhat different domain organisation. Only a few BG genes have been characterised, one involved in actin-myosin interaction in the intestinal brush border, and another implicated in resistance to viral diseases. We characterise all BG genes in B12 chickens, finding a multigene family organised as tandem repeats in the BG region outside the MHC, a single gene in the MHC (the BF-BL region), and another single gene on a different chromosome. There is a precise cell and tissue expression for each gene, but overall there are two kinds, those expressed by haemopoietic cells and those expressed in tissues (presumably non-haemopoietic cells), correlating with two different kinds of promoters and 5' untranslated regions (5'UTR). However, the multigene family in the BG region contains many hybrid genes, suggesting recombination and/or deletion as major evolutionary forces. We identify BG genes in the chicken whole genome shotgun sequence, as well as by comparison to other haplotypes by fibre fluorescence in situ hybridisation, confirming dynamic expansion and contraction within the BG region. Thus, the BG genes in chickens are undergoing much more rapid evolution compared to their homologues in mammals, for reasons yet to be understood.This is the final published version. It was originally published by PLOS in PLOS Genetics here: http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004417

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