Efficiency evaluation of public hospitals in Saudi Arabia: an application of data envelopment analysis.

OBJECTIVE:In this study, we assess the performance of public hospitals in Saudi Arabia. We detect the sources of inefficiency and estimate the optimal levels of the resources that provide the current level of health services. We enrich our analysis by employing locations and capacities of the hospitals. DESIGN:We employ data envelopment analysis (DEA) to measure the technical efficiency of 91 public hospitals. We apply the input-oriented Charnes, Cooper and Rhodes, and Banker, Charne, Cooper models under Constant and Variable Returns-to-Scale. The assessment includes four inputs, and six output variables taken from the Ministry of Health databases for 2017. We conducted the assessment via PIM-DEA V.3.2 software. SETTING:Ministry of health-affiliated hospitals in the Kingdom of Saudi Arabia. RESULTS:Findings identified 75.8% (69 of 91) of public hospitals as technically inefficient. The average efficiency score was 0.76, indicating that hospitals could have reduced their inputs by 24% without reduction in health service provision. Small hospitals (efficiency score 0.79) were more efficient than medium-sized and large hospitals. Hospitals in the central region were more efficient (efficiency score 0.83), than those located in other geographical locations. More than half of the hospitals (62.6%) were operating suboptimally in terms of the scale efficiency, implying that to improve efficiency, they need to alter their production capacity. Performance analysis identified overuse of physician's numbers and shortage of health services production, as major causes of inefficiency. CONCLUSION:Most hospitals were technically inefficient and operating at suboptimal scale size and indicate that many hospitals may improve their performance through efficient utilisation of health resources to provide the current level of health services. Changes in the production capacity are required, to facilitate optimal use of medical capacity. The inefficient hospitals could benefit from these findings to benchmarking their system and performance in light of the efficient hospital within their capacity and geographical location

Lifetime health effects and costs of diabetes treatment

BACKGROUND: This article presents cost-effectiveness analyses of the major diabetes interventions as formulated in the revised Dutch guidelines for diabetes type 2 patients in primary and secondary care. The analyses consider two types of care: diabetes control and the treatment of complications, each at current care level and according to the guidelines. METHODS: A validated probabilistic diabetes model describes diabetes and its complications over a lifetime in the Dutch population, computing quality-adjusted life years and medical costs. Effectiveness data and costs of diabetes interventions are from observational current care studies and intensive care experiments. Lifetime consequences of in total sixteen intervention mixes are compared with a baseline glycaemic control of 10% HBA1C. RESULTS: The interventions may reduce the cumulative incidence of blindness, lower-extremity amputation, and end-stage renal disease by >70% in primary care and >60% in secondary care. All primary care guidelines together add 0.8 quality-adjusted life years per lifetime. CONCLUSION: In case of few resources, treating complications according to guidelines yields the most health benefits. Current care of diabetes complications is inefficient. If there are sufficient resources, countries may implement all guidelines, also on diabetes control, and improve efficiency in diabetes care

Socioeconomic benefit to individuals of achieving 2020 targets for four neglected tropical diseases controlled/eliminated by innovative and intensified disease management

__Background__ The control or elimination of neglected tropical diseases (NTDs) has targets defined by the WHO for 2020, reinforced by the 2012 London Declaration. We estimated the economic impact to individuals of meeting these targets for human African trypanosomiasis, leprosy, visceral leishmaniasis and Chagas disease, NTDs controlled or eliminated by innovative and intensified disease management (IDM). __Methods__ A systematic literature review identified information on productivity loss and out-of-pocket payments (OPPs) related to these NTDs, which were combined with projections of the number of people suffering from each NTD, country and year for 2011±2020 and 2021±2030. The ideal scenario in which the WHO's 2020 targets are met was compared with a counterfactual scenario that assumed the situation of 1990 stayed unaltered. Economic benefit equaled the difference between the two scenarios. Values are reported in 2005 US, purchasing power parity-adjusted, discounted at 3% per annum from 2010. Probabilistic sensitivity analyses were used to quantify the degree of uncertainty around the base-case impact estimate. __Results__ The total global productivity gained for the four IDM-NTDs was I 23.1 (I$15.9 ±I$ 34.0) billion in 2011±2020 and I$35.9 (I$ 25.0 ±I$51.9) billion in 2021±2030 (2.5th and 97.5th percentiles in brackets), corresponding to US$ 10.7 billion (US$7.4 ±US$ 15.7) and US$16.6 billion (US$ 11.6 ±US$24.0). Reduction in OPPs was I$ 14 billion (US$6.7 billion) and I$ 18 billion (US$ 10.4 billion) for the same periods. __Conclusions__ We faced important limitations to our work, such as finding no OPPs for leprosy. We had to combine limited data from various sources, heterogeneous background, and of variable quality. Nevertheless, based on conservative assumptions and subsequent uncertainty analyses, we estimate that the benefits of achieving the targets are considerable. Under plausible scenarios, the economic benefits far exceed the necessary investments by endemic country governments and their development partners. Given the higher frequency of NTDs among the poorest households, these investments represent good value for money in the effort to improve well-being, distribute the world's prosperity more equitably and reduce inequity

The Socioeconomic Benefit to Individuals of Achieving the 2020 Targets for Five Preventive Chemotherapy Neglected Tropical Diseases

Background: Lymphatic filariasis (LF), onchocerciasis, schistosomiasis, soil-transmitted helminths (STH) and trachoma represent the five most prevalent neglected tropical diseases (NTDs). They can be controlled or eliminated by means of safe and cost-effective interventions delivered through programs of Mass Drug Administration (MDA)—also named Preventive Chemotherapy (PCT). The WHO defined targets for NTD control/elimination by 2020, reinforced by the 2012 London Declaration, which, if achieved, would result in dramatic health gains. We estimated the potential economic benefit of achieving these targets, focusing specifically on productivity and out-of-pocket payments. Methods: Productivity loss was calculated by combining disease frequency with productivity loss from the disease, from the perspective of affected individuals. Productivity gain was calculated by deducting the total loss expected in the target achievement scenario from the loss in a counterfactual scenario where it was assumed the pre-intervention situation in 1990 regarding NTDs would continue unabated until 2030. Economic benefits from out-of-pocket payments (OPPs) were calculated similarly. Benefits are reported in 2005 US$(purchasing power parity-adjusted and discounted at 3 Results: The economic benefit from productivity gain was estimated to be I$251 billion in 2011–2020 and I$313 billion in 2021–2030, considerably greater than the total OPPs averted of I$0.72 billion and I$0.96 billion in the same periods. The net benefit is expected to be US$ 27.4 and US$ 42.8 for every dollar invested during the same periods. Impact varies between NTDs and regions, since it is determined by disease prevalence and extent of disease-related p

Mobile phone text-messaging interventions aimed to prevent cardiovascular diseases (Text2PreventCVD): systematic review and individual patient data meta-analysis

Background A variety of small mobile phone text messaging interventions have indicated improvement in risk factors for cardiovascular disease (CVD). Yet the extent of this improvement and whether it impacts multiple risk factors together is uncertain. We aimed to conduct a systematic review and individual patient data (IPD) meta-analysis to investigate the effects of text-messaging interventions for CVD prevention. Methods Electronic databases were searched to identify trials investigating a text-messaging intervention focusing on CVD prevention with the potential to modify at least two CVD risk factors in adults. The main outcome was blood pressure (BP). We conducted standard and IPD meta-analysis on pooled data. We accounted for clustering of patients within studies and the primary analysis used random-effects models. Sensitivity and subgroup analyses were performed. Results Nine trials were included in the systematic review involving 3779 participants and 5 (n=2612) contributed data to the IPD meta-analysis. Standard metaanalysis showed that the weighted mean differences are as follows: systolic blood pressure (SBP), −4.13 mm Hg (95% CI −11.07 to 2.81, p<0.0001); diastolic blood pressure (DBP), −1.11 mm Hg (−1.91 to −0.31, p=0.002); and body mass index (BMI), −0.32 (−0.49 to −0.16, p=0.000). In the IPD meta-analysis, the mean difference are as follows: SBP, −1.3 mm Hg (−5.4 to 2.7, p=0.5236); DBP, −0.8 mm Hg (−2.5 to 1.0, p=0.3912); and BMI, −0.2 (−0.8 to 0.4, p=0.5200) in the random-effects model. The impact on other risk factors is described, but there were insufficient data to conduct meta-analyses. Conclusion Mobile phone text-messaging interventions have modest impacts on BP and BMI. Simultaneous but small impacts on multiple risk factors are likely to be clinically relevant and improve outcome, but there are currently insufficient data in pooled analyses to examine the extent to which simultaneous reduction in multiple risk factors occurs

Roads to health : multi-state modelling of population health and resource use

The book has described the dynamics of disease occurrence in populations and gives an overview of the major known health determinants of mortality decline, health risk factors and health services, and studies the health interventions options in two example diseases i.e. diabetes and stroke. We postulated that a lifetime multi-state modelling approach can be useful to describe disease processes and health care costs in populations and outlined the approach in the second chapter. After describing various case studies in the five application sections, a number of conclusions can be drawn. The multi-state models allow for analysing dynamic disease processes throughout a lifetime in relation to the actual stage of the health transition in a country. The dynamic components are threefold: 1) the substitution, clustering and synergism of health determinants and diseases, 2) the effectiveness and efficiency of health services, and 3) the effect of ageing of populations in quantitative and qualitative terms as both early and late survival improves. At all stages, there is a trade-off with other diseases when a first disease is treated. The model approach quantifies these effects and allows for an analysis in time. Chapter 4 shows that substitution and competition of multiple health risks, at all ages, may partly explain the lack of results of the introduction of health programmes as in international co-operation in health. The same chapter shows that multiple, also low cost, roads to population health exist by elimination of health risks and by improvement of disease survival. Computation of optimal pathways is possible. The chapters on stroke and diabetes show the relationship of health intervention mixes options, available resources, health benefits, and optimisation options. The stroke analysis shows that, in case of high available budgets, costly clinical interventions for all patients can be more cost-effective than low cost clinical interventions for small groups of high-risk patients. The diabetes chapter shows that, in case of low available budgets, low cost clinical interventions for small groups of high-risk patients can be more cost-effective than prevention. The general conclusion is that multi-state models allow for computation of multiple, optimum paths to health throughout a lifetime, depending on the societal resources available. Health policy relevance As multi-state models allow for quantification of the health and costs influences of each health determinant, including health interventions, they facilitate rational policy making. Broad policy questions in relation to the role of health determinants and health care provision can be specified, modelled and explored like e.g. the use of a zero health expenditure scenarios (chapter 4) and in the use of interventions proposed in guidelines (chapter 5-8). For other broad or detailed policy questions the model approach can be expanded or simplified according depending on the nature of the question. Ethical and political choices will have to remain with the domain of politicians and the public. A lot of the evidence-based approaches, prominent on the national and international agendas for health policy and health research, frequently and increasingly make use of health modelling approaches. It is unclear what the implications of this policy approach are for the production and distribution of health in populations, given the notion of multiple determinants in health. It is equally unclear what kind of barriers there are to the adoption of evidence-based approaches in health care practice. Chapter 9 outlines the ways in which health policy is informed by the results from health research and health modelling. It summarises approaches in health at three impact levels: inter-sectoral assessment, national health care policy, and evidence-based medicine in everyday practice. Consensus is growing on the role of broad and specific health determinants, including health care, as well as on priority setting based on the burden of diseases. In spite of methodological constraints, there is a demand for inter-sectoral assessments, especially in health sector reform. Initiators of policy changes in other sectors may be held responsible for providing the evidence related to health. There are limited possibilities for priority setting at the national health care policy level. Hence, there is a decentralisation of responsibilities for resource use towards providers and health insurance companies. They are encouraged to assume agency roles for both patients and society and ask to promote and deliver effective and efficient health care. Governments need to design national frameworks to strengthen their organisation to enhance their roles. The formulation of national health guidelines supported by evidence on effectiveness and efficiency will be one essential element in this process. With the increasing number of advocates for the enhancement of population health in the policy arenas, evidence-based approaches provide the insights, information, and tools to help with priority setting. RESEARCH RECOMMENDATIONS Model validation The book observes the start of multi-state modelling of population health in epidemiology, demography, public health, and health economics research. Up to now there are relatively few related research efforts. The designs, implementation, and application of generic multi-state approaches have been initiated. Important is the validity of the results. More model calibration and validation can and should take place. Validation can be structural validation (chapter 3) or external validation, using external time series (chapters 4 and 5). The developed models have relatively few free variables that can be used for calibration and reproduction of population-based time series of morbidity and mortality. An important free variable to be use for calibration is the non-attributable incidence of diseases. In combination with the risk-attributable fraction, it results in the observed disease incidence. The population attributive risk approach should be developed further to account for regression dilution bias and the occurrence of multiple diseases and multiple determinants. Another important calibration parameter is the effectiveness of prevention and curative services in daily settings. Related parameters, such as coverage, are usually based on cross-sectional studies. Incidentally, longitudinal follow-up may be able to give data on day-to-day effectiveness. Also comparison with special population groups that have remained without an intervention may give supportive evidence like studies on religious, cultural groups, on the uninsured, or on ‘natural’ experiments like war or strikes. A third group of calibration parameters is disease-specific mortality and morbidity. This group can be used for the large disease categories that we used for the applications (chapter 3-5). This would be for Mexico for the period 1950-1990 and for India for the period 1980-1990 based on the Federal Sample Registration Survey. Last, disease-specific calibration is possible for The Netherlands 1900-1990 and also, but with more uncertainties, from 1860 onwards. Expert validation of model structure and assumptions could be more explored and transparency increased. Examples are review procedures and panel discussions with researchers, policy makers and the public or its representatives. This would also give more room to account for the more subjective or political choices to be a priori made. Future research The main characteristic of the multi-state approach is a comprehensive consideration of disease occurrence, disability and the cost of disease through a lifetime at the population