Economic evaluations through decision-analytical models have played a limited role in shaping healthcare resource optimisation and reimbursement decisions in the Middle East.
This review aims to systematically examine economic evaluation studies focusing on decision-analytical models of medicines in the Middle East, defining methodological characteristics and appraising the quality of the identified models.
A systematic review approach was employed to identify published decision-analytical models of medicines in the Middle East. Six databases were searched (MEDLINE, EMBASE, Econlit, Web of Science, Global Health Cost-Effectiveness Analysis Registry and the Global Index Medicus) from 1998 to July 2024. Studies meeting the inclusion criteria—full economic evaluations of medicines using decision-analytical models in the Middle East—were considered. Data were extracted and tabulated to include study characteristics and methodological specifications, and data were narratively analysed. The Philips checklist was used to assess the quality of studies.
Sixty-three decision-analytical modelling studies of medicines were identified and reviewed, from eight Middle Eastern countries, with the majority (90%) conducted in Iran, Saudi Arabia, Qatar and Egypt. The cost-effectiveness of medications for non-communicable diseases was explored in 77% of the models. Gross domestic product-based cost-effectiveness thresholds were commonly used, and international sources provided data on intervention effectiveness and health outcomes, while national sources were mainly used for the costs of resource use. Most models incorporated an assessment of parameter uncertainty, whereas other types of uncertainty were not explored. Studies from high-income countries were generally of higher quality than those from middle-income countries.
The number of published decision-analytical models in the Middle East was low, considering the available medicinal products and disease burden. Key elements related to the quality of decision-analytical models, including analysis of the model structure, appropriateness of model inputs and uncertainty assessment, were not consistently fulfilled. Recommendations are provided to enhance the quality of future economic evaluation studies. This includes strengthening the existing health economics capacities, establishing country-specific health technology assessment systems (where possible), and initiating collaborations to generate national cost and outcome data.
PROSPERO registration: CRD42021283904.
Private hospitals account for 46% of all hospitals empanelled in India’s national health insurance scheme and contribute to 54% of all the hospitalizations under it. However, insufficient package prices are often cited as a constraint to viable hospital operations. This study assesses the financial viability of establishing such hospitals at district level, with a focus on determining the break-even threshold by forecasting the financial trajectory of hospitals.
By utilizing primary data from 27 district hospitals across nine states in India on cost of providing healthcare services, a blend of bottom-up and top-down micro-costing methods was used to estimate financial cost across input resource categories, including land procurement, building construction, human resources, equipment, drugs, consumables, maintenance, and overheads. Revenue from inpatient services was estimated using healthcare provider payment rates under India’s largest tax-funded health insurance scheme, coupled with patient volume data stratified by distinct diseases across different specialties. Revenue projections from outpatient services were extrapolated as a fixed proportion of their inpatient counterparts. A 10-year evaluation framework was employed to forecast the hospital operations using revenue–expenditure perspective. Sensitivity analyses were undertaken to assess the extent of variations in the output owing to varying bed-occupancy levels and doctor-to-bed ratios.
For a model 100-bed private hospital operating at district level, the average annual expenditure and revenue are projected to be at Indian Rupee (₹)85.27 million (US $1.03 million) and ₹104.36 million (US $1.26 million), respectively, for the initial 10 years. Human resources constitute the primary share (40%) of total expenditure, followed by spending on drugs and consumables (20%). A sequential evaluation of annual revenue and expenditure reveals that hospitals reach breakeven by their fourth operational year, subsequently transitioning into a profitable phase.
The study suggests a viable financial trajectory for private hospitals at district level, following the pricing structure of government-sponsored health insurance scheme.
Limited healthcare resources necessitate a strategic approach to their allocation. This paper highlights the importance of population net health benefit (NHB) metric as a means of aligning two existing concepts used for resource prioritization in health: burden of disease and cost effectiveness. By explicitly incorporating health opportunity costs and eligible patient population size, NHB provides a clearer understanding of the likely scale of impact of interventions on population health. Moreover, when expressed in disability-adjusted life years (DALYs) averted, NHB enables policymakers to effectively communicate the population-level health gains from interventions relative to the existing disease burden. Using a stylized example, we demonstrate the estimation of population NHB for four alternative health interventions and its use in resource allocation decisions. The analysis reveals how variations in patient population size and health opportunity costs can significantly impact NHB estimates, ultimately influencing resource allocation decisions. The results further illustrate how NHB can be expressed as a proportion of the total disease burden, allowing for the consideration of the percentage of the overall burden addressed by each intervention. The paper demonstrates how population NHB combines cost effectiveness with components of disease burden, offering a more comprehensive approach to health intervention selection and implementation. As countries move towards universal health coverage, this metric can aid policymakers in making informed, evidence-based decisions.
Scientific advancements offer significant opportunities for better patient outcomes, but also present new challenges for value assessment, affordability and access. Alternative payment models (APMs) can offer solutions to the ensuing payer challenges. However, a comprehensive framework that matches the spectrum of challenges with the right solution, and places them within a framework for implementation, is currently missing. To fill this gap, we propose evidence-based steps for the effective selection and implementation of APMs. First, contracting challenges should be identified and mapped to potential APM solutions. We developed a decision guide that can serve as a starting point to articulate core problems and map these to APM solutions. The main problem categories identified are: budget impact and uncertainty, value uncertainty, and the scope of value assessment and negotiation. Sub-categories include affordability, uncertainty of effectiveness, and patient heterogeneity, which map onto APM solutions such as outcome-based agreements, instalments, and subscription models. Just as important are the subsequent identification and assessment of the feasibility of potential solutions as well as collaboration to reach agreement on the terms of the APM and lay the groundwork for effective implementation. We adduce recent examples of APM implementation as evidence of how commonly cited implementation barriers can be overcome by applying pragmatic design choices and collaboration. This step-by-step framework can aid payers and manufacturers in the process of effectively identifying, agreeing on, and implementing APMs to advance patient access to cost-effective medicines, while at the same time providing appropriate incentives to support future innovation.
To sustain positive progress toward sustainable development goals as envisioned in goal 3 and beyond, safe and affordable care during pregnancy and birth for women, their families, and health facilities and professionals is essential. In this systematic review, we report the best available evidence regarding the cost and cost-effectiveness of birth in various settings, including hospitals, birth centres, and homes for women at low risk of complications from high-, middle-, and low-income countries.
We conducted a systematic review of cost and economic evaluation papers, following the comprehensive search of online databases, including Medline, CINAHL, Embase, Scopus, and Google Scholar, and grey literature, using predetermined search strategies. Both partial and full economic evaluation studies were included, and we appraised them using Joanna Briggs Institute’s (JBI’s) critical appraisal checklists for economic evaluation studies. Although we attempted to pool total incremental net benefit, the results were synthesised narratively without a meta-analysis due to the high heterogeneity between primary studies.
From 2307 identified studies, 11 studies (13 country level records from 11 countries) were included. Both direct and indirect costs of childbirth at home, midwife-led birth units (MLBUs), and hospitals were reported. Ten studies showed that births in MLBUs were less costly than hospital births, while home births were also reported to be less costly than hospital births in seven studies. Regarding cost-effectiveness, in Bangladesh, MLBUs generally showed better outcomes at lower costs than hospital births, while one site had higher costs. In Pakistan and Uganda, MLBUs displayed mixed results, with some being cost-effective and others more costly with poorer outcomes. In the Netherlands, MLBUs were less costly but had poorer outcomes, whereas home births were less costly and more effective. In Belgium, MLBUs were less costly but less effective in reducing caesarean and instrumental births, though they did reduce epidural analgesia use cost-effectively.
Most studies found that births in MLBUs and at home were less costly than births in hospital. There is the potential for these settings to provide a cost-effective option for women through reduced intervention rates and favourable outcomes in high-income countries and could offer birthing options to women in low- and middle-income countries that includes care by skilled maternity practitioners in potentially more affordable settings.
Genomic medicine offers an unprecedented opportunity to improve cancer outcomes through prevention, early detection and precision therapy. Health policy makers worldwide are developing strategies to embed genomic medicine in routine cancer care. Successful translation of genomic medicine, however, remains slow. This systematic review aims to identify and synthesise published evidence on the cost effectiveness of genomic medicine in cancer control. The insights could support efforts to accelerate access to cost-effective applications of human genomics.
The study protocol was registered with PROSPERO (CRD42024480842), and the review was conducted in line with Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) Guidelines. The search was run in four databases: MEDLINE, Embase, CINAHL and EconLit. Full economic evaluations of genomic technologies at any stage of cancer care, and published after 2018 and in English, were included for data extraction.
The review identified 137 articles that met the inclusion criteria. Most economic evaluations focused on the prevention and early detection stage (n = 44; 32%), the treatment stage (n = 36; 26%), and managing relapsed, refractory or progressive disease (n = 51, 37%). Convergent cost-effectiveness evidence was identified for the prevention and early detection of breast and ovarian cancer, and for colorectal and endometrial cancers. For cancer treatment, the use of genomic testing for guiding therapy was highly likely to be cost effective for breast and blood cancers. Studies reported that genomic medicine was cost effective for advanced and metastatic non-small cell lung cancer. There was insufficient or mixed evidence regarding the cost effectiveness of genomic medicine in the management of other cancers.
This review mapped out the cost-effectiveness evidence of genomic medicine across the cancer care continuum. Gaps in the literature mean that potentially cost-effective uses of genomic medicine in cancer control, for example rare cancers or cancers of unknown primary, may be being overlooked. Evidence on the value of information and budget impact are critical, and advancements in methods to include distributional effects, system capacity and consumer preferences will be valuable. Expanding the current cost-effectiveness evidence base is essential to enable the sustainable and equitable translation of genomic medicine.
Anxiety and depression are prevalent in young people. Web-based mental health interventions (W-MHIs) have the potential to reduce anxiety and depression, yet the level of engagement remains low. This study aims to elicit young people’s preferences towards W-MHIs and the relative importance of intervention attributes in influencing choice.
A discrete choice experiment (DCE) was conducted online among young people aged 18–25 years who lived in Australia, self-reported experiences of anxiety and/or depression in the past 12 months and had an intention to use W-MHIs and/or previous experience with W-MHIs for managing anxiety and/or depression. Participants were recruited via social media and Deakin University notice boards. The DCE design comprised six attributes, including out-of-pocket cost, access to trained instructors (e.g., therapists, coaches) to help users stay engaged with the intervention, total time required to complete the intervention, initial screening, quizzes within the W-MHIs to check user’s understanding about the intervention content, and communication with other users. The DCE design consisted of three blocks, each with eight unlabelled choice tasks, each with two alternatives. Data were analysed using a mixed logit model.
One hundred ninety-nine participants completed the DCE (mean age: 21.43 ± 2.29 years, 64.32% female). Lower cost, access to instructors, and moderate time required to complete the intervention (5 h) were significant facilitators. The W-MHIs including audio- or video-call access to instructors were 23 percentage points more likely to be chosen than those without and W-MHI with a moderate completion time (5 h) was 18 percentage points more likely to be chosen than one with a shorter time (2 h).
Our results highlight that low-cost W-MHIs with access to trained instructors and moderate completion time could increase uptake. More research is required to confirm these findings and examine whether these preferences vary across different population characteristics.
The growth of scientific literature in health economics and policy represents a challenge for researchers conducting literature reviews. This study explores the adoption of a machine learning (ML) tool to enhance title and abstract screening. By retrospectively assessing its performance against the manual screening of a recent scoping review, we aimed to evaluate its reliability and potential for streamlining future reviews.
ASReview was utilised in ‘Simulation Mode’ to evaluate the percentage of relevant records found (RRF) during title/abstract screening. A dataset of 10,246 unique records from three databases was considered, with 135 relevant records labelled. Performance was assessed across three scenarios with varying levels of prior knowledge (PK) (i.e., 5, 10, or 15 records), using both sampling and heuristic stopping criteria, with 100 simulations conducted for each scenario.
The ML tool demonstrated strong performance in facilitating the screening process. Using the sampling criterion, median RRF values stabilised at 97% with 25% of the sample screened, saving reviewers approximately 32 working days. The heuristic criterion showed similar median values, but greater variability due to premature conclusions upon reaching the threshold. While higher PK levels improved early-stage performance, the ML tool’s accuracy stabilised as screening progressed, even with minimal PK.
This study highlights the potential of ML tools to enhance the efficiency of title and abstract screening in health economics and policy literature reviews. To fully realise this potential, it is essential for regulatory bodies to establish comprehensive guidelines that ensure ML-assisted reviews uphold rigorous evidence quality standards, thereby enhancing their integrity and reliability.
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