Reducing uncertainty in health-care resource allocation.
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ABSTRACT:
SUBMITTER: Simonsson T
PROVIDER: S-EPMC2359979 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cost-effectiveness analysis, routinely used in health care to inform funding decisions, can be extended to consider impact on health inequality. Distributional cost-effectiveness analysis (DCEA) incorporates socioeconomic differences in model parameters to capture how an intervention would affect both overall population health and differences in health between population groups. In DCEA, uncertainty analysis can consider the decision uncertainty around on both impacts (i.e., the probability that an intervention will increase overall health and the probability that it will reduce inequality). Using an illustrative example assessing smoking cessation interventions (2 active interventions and a "no-intervention" arm), we demonstrate how the uncertainty analysis could be conducted in DCEA to inform policy recommendations. We perform value of information (VOI) analysis and analysis of covariance (ANCOVA) to identify what additional evidence would add most value to the level of confidence in the DCEA results. The analyses were conducted for both national and local authority-level decisions to explore whether the conclusions about decision uncertainty based on the national-level estimates could inform local policy. For the comparisons between active interventions and "no intervention," there was no uncertainty that providing the smoking cessation intervention would increase overall health but increase inequality. However, there was uncertainty in the direction of both impacts when comparing between the 2 active interventions. VOI and ANCOVA show that uncertainty in socioeconomic differences in intervention effectiveness and uptake contributes most to the uncertainty in the DCEA results. This suggests potential value of collecting additional evidence on intervention-related inequalities for this evaluation. We also found different levels of decision uncertainty between settings, implying that different types and levels of additional evidence are required for decisions in different localities.
| S-EPMC8295967 | biostudies-literature
Project description:BackgroundJiangsu was one of the first four pilot provinces to engage in comprehensive health care reform in China, which has been on-going for the past 5 years. This study aims to evaluate the equity, efficiency and productivity of health care resource allocation in Jiangsu Province using the most recent data, analyse the causes of deficiencies, and discuss measures to solve these problems.MethodsData were extracted from the Jiangsu Health/Family Planning Statistical Yearbook (2015-2019) and Jiangsu Statistical Yearbook (2015-2019). The Gini coefficient (G), Theil index (T) and health resource density index (HRDI) were chosen to study the fairness of health resource allocation in Jiangsu Province. Data envelopment analysis (DEA) and the Malmquist productivity index (MPI) were used to analyse the efficiency and productivity of this allocation.ResultsFrom 2014 to 2018, the total amount of health resources in Jiangsu Province increased. The G of primary resource allocation by population remained below 0.15, and that by geographical area was between 0.14 and 0.28; additionally, the G of health financial resources was below 0.26, and that by geographical area was above 0.39. T was consistent with the results for G and Lorenz curves. The HRDI shows that the allocated amounts of health care resources were the highest in southern Jiangsu, except for the number of health institutions. The average value of TE was above 0.93, and the DEA results were invalid for only two cities. From 2014 to 2018, the mean TFPC in Jiangsu was less than 1, and the values exceeded 1 for only five cities.ConclusionThe equity of basic medical resources was better than that of financial resources, and the equity of geographical allocation was better than that of population allocation. The overall efficiency of health care resource allocation was high; however, the total factor productivity of the whole province has declined due to technological regression. Jiangsu Province needs to further optimize the allocation and increase the utilization efficiency of health care resources.
| S-EPMC7694921 | biostudies-literature
Project description:BackgroundA variety of approaches have been used to contain escalating hospital costs. One approach is intensifying price competition. The increase in price based competition, which changes the incentives hospitals face, coupled with the fact that consumers can more easily evaluate the quality of hotel services compared with the quality of clinical care, may lead hospitals to allocate more resources into hotel rather than clinical services.MethodsTo test this hypothesis we studied hospitals in California in 1982 and 1989, comparing resource allocations prior to and following selective contracting, a period during which the focus of competition changed from quality to price. We estimated the relationship between clinical outcomes, measured as risk-adjusted-mortality rates, and resources.ResultsIn 1989, higher competition was associated with lower clinical expenditures levels compared with 1982. The trend was stronger for non-profit hospitals. Lower clinical resource use was associated with worse risk adjusted mortality outcomes.ConclusionsThis study raises concerns that cost reductions may be associated with increased mortality.
| S-EPMC116588 | biostudies-literature
Project description:ImportanceDuring the COVID-19 pandemic, many US states issued or revised pandemic preparedness plans guiding allocation of critical care resources during crises. State plans vary in the factors used to triage patients and have faced criticism from advocacy groups due to the potential for discrimination.ObjectiveTo analyze the role of comorbidities and long-term prognosis in state triage procedures.Design, setting, and participantsThis cross-sectional study used data gathered from parallel internet searches for state-endorsed pandemic preparedness plans for the 50 US states, District of Columbia, and Puerto Rico (hereafter referred to as states), which were conducted between November 25, 2021, and June 16, 2023. Plans available on June 16, 2023, that provided step-by-step instructions for triaging critically ill patients were categorized for use of comorbidities and prognostication.Main outcomes and measuresPrevalence and contents of lists of comorbidities and their stated function in triage and instructions to predict duration of postdischarge survival.ResultsOverall, 32 state-promulgated pandemic preparedness plans included triage procedures specific enough to guide triage in clinical practice. Twenty of these (63%) included lists of comorbidities that excluded (11 of 20 [55%]) or deprioritized (8 of 20 [40%]) patients during triage; one state's list was formulated to resolve ties between patients with equal triage scores. Most states with triage procedures (21 of 32 [66%]) considered predicted survival beyond hospital discharge. These states proposed different prognostic time horizons; 15 of 21 (71%) were numeric (ranging from 6 months to 5 years after hospital discharge), with the remaining 6 (29%) using descriptive terms, such as long-term.Conclusions and relevanceIn this cross-sectional study of state-promulgated critical care triage policies, most plans restricted access to scarce critical care resources for patients with listed comorbidities and/or for patients with less-than-average expected postdischarge survival. This analysis raises concerns about access to care during a public health crisis for populations with high burdens of chronic illness, such as individuals with disabilities and minoritized racial and ethnic groups.
| S-EPMC10466166 | biostudies-literature
Project description:IntroductionPrimary health care is a key element in the structuring and coordination of health systems, contributing to overall coverage and performance. PHC financing is therefore central in this context, with variations in sufficiency and regularity depending on the "political dimension" of health systems. Research that systematically examines the political factors and arrangements influencing PHC financing is justified from a global and multidisciplinary perspective. The scoping review proposed here aims to systematically map the evidence on this topic in the current literature, identifying groups, institutions, priorities and gaps in the research.Methods and analysisA scoping review will be conducted following the method proposed by Arksey and O'Malley to answer the following question: What is known from the literature about political factors and arrangements and their influence on and repercussions for primary health care financing and resource allocation models? The review will include peer-reviewed papers in Portuguese, English or Spanish published between 1978 and 2023. Searches will be performed of the following databases: Medline (PubMed), Embase, BVS Salud, Web of Science, Scopus and Science Direct. The review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. Inclusion and exclusion criteria will be used for literature screening and mapping. Screening and data charting will be conducted by a team of four reviewers.RegistrationThis protocol is registered on the Open Science Framework (OSF) platform, available at https://doi.org/10.17605/OSF.IO/Q9W3P.
| S-EPMC11315285 | biostudies-literature
Project description:Health care resource allocation is key towards attaining equity in the health system. However, health professionals' perceived impact and attitude towards health care resource allocation in Sub-Saharan Africa is unknown; furthermore, they occupy a position which makes them notice the impact of different policies in their health system. This study explored perceptions and attitudes of health professionals in Kenya on health care resource allocation mechanism.We conducted a survey of a representative sample of 341 health professionals in Moi Teaching and Referral Hospital from February to April 2012, consisting of over 3000 employees. We assessed health professionals' perceived impact and attitudes on health care resource allocation mechanism in Kenya. We used structural equation modeling and applied a Confirmatory Factor Analysis using Robust Maximum Likelihood estimation procedure to test the hypothesized model.We found that the allocation mechanism was negatively associated with their perceived positive impact (-1.04, p < .001), health professionals' satisfaction (-0.24, p < .01), and professionals' attitudes (-1.55, p < .001) while it was positively associated with perceived negative impact (1.14, p < .001). Perceived positive impact of the allocation mechanism was negatively associated with their overall satisfaction (-0.08) and attitude (-0.98) at p < .001, respectively. Furthermore, overall satisfaction was negatively associated with attitude (-1.10, p <.001). On the other hand, perceived negative impact of the allocation was positively associated with overall satisfaction (0.29, p <.001) but was not associated with attitude.The result suggests that health care resource allocation mechanism has a negative effect towards perceptions, attitudes and overall satisfaction of health professionals who are at the frontline in health care. These findings can serve as a crucial reference for policymakers as the Kenyan health system move towards devolving the system of governance.
| S-EPMC4454489 | biostudies-literature
Project description:BackgroundPandemics including COVID-19 have disproportionately affected socioeconomically vulnerable populations.ObjectiveOur objective was to create a repeatable modeling process to identify regional population centers with pandemic vulnerability.MethodsUsing readily available COVID-19 and socioeconomic variable data sets, we used stepwise linear regression techniques to build predictive models during the early days of the COVID-19 pandemic. The models were validated later in the pandemic timeline using actual COVID-19 mortality rates in high population density states. The mean sample size was 43 and ranged from 8 (Connecticut) to 82 (Michigan).ResultsThe New York, New Jersey, Connecticut, Massachusetts, Louisiana, Michigan, and Pennsylvania models provided the strongest predictions of top counties in densely populated states with a high likelihood of disproportionate COVID-19 mortality rates. For all of these models, P values were less than .05.ConclusionsThe models have been shared with the Department of Health Commissioners of each of these states with strong model predictions as input into a much needed "pandemic playbook" for local health care agencies in allocating medical testing and treatment resources. We have also confirmed the utility of our models with pharmaceutical companies for use in decisions pertaining to vaccine trial and distribution locations.
| S-EPMC7924701 | biostudies-literature
Project description:An open question in the literature is whether families compensate or reinforce the impact of child health shocks. Discussions usually focus on one dimension of child investment. This paper examines multiple dimensions using household survey data on Chinese child twins whose average age is 11. We find that, compared with a twin sibling who did not suffer from negative early health shocks at ages 0-3, the other twin sibling who did suffer negative health shocks received RMB 305 more in terms of health investments, but received RMB 182 less in terms of educational investments in the 12 months prior to the survey. In terms of financial transfers over all dimensions of investment, the family acts as a net equalizer in response to early health shocks for children. We estimate a human capital production function and establish that, for this sample, early health shocks negatively affect child human capital, including health, education, and socioemotional skills. Compensating investments in health as measured by BMI reduce the adverse effects of health shocks by 50%, but exacerbate the adverse impact of shocks on educational attainment by 30%.
| S-EPMC4803089 | biostudies-literature
Project description:International institutions provide well over US$10 billion in development assistance for health (DAH) annually and between 1990 and 2014, DAH disbursements totaled $458 billion but how do they decide who gets what, and for what purpose? In this article, we explore how allocation decisions were made by the nine convening agencies of the Equitable Access Initiative. We provide clear, plain language descriptions of the complete process from resource mobilization to allocation for the nine multilateral agencies with prominent agendas in global health. Then, through a comparative analysis we illuminate the choices and strategies employed in the nine international institutions. We find that resource allocation in all reviewed institutions follow a similar pattern, which we categorized in a framework of five steps: strategy definition, resource mobilization, eligibility of countries, support type and funds allocation. All the reviewed institutions generate resource allocation decisions through well-structured and fairly complex processes. Variations in those processes seem to reflect differences in institutional principles and goals. However, these processes have serious shortcomings. Technical problems include inadequate flexibility to account for or meet country needs. Although aid effectiveness and value for money are commonly referenced, we find that neither performance nor impact is a major criterion for allocating resources. We found very little formal consideration of the incentives generated by allocation choices. Political issues include non-transparent influence on allocation processes by donors and bureaucrats, and the common practice of earmarking funds to bypass the normal allocation process entirely. Ethical deficiencies include low accountability and transparency at international institutions, and limited participation by affected citizens or their representatives. We find that recipient countries have low influence on allocation processes themselves, although within these processes they have some influence in relatively narrow areas.
| S-EPMC5886160 | biostudies-literature
Project description:BackgroundHealthcare spending has grown over the last decades in all developed countries. Making hard choices for investments in a rational, evidence-informed, systematic, transparent and legitimate manner constitutes an important objective. Yet, most scientific work in this area has focused on developing/improving prescriptive approaches for decision making and presenting case studies. The present work aimed to describe existing practices of priority setting and resource allocation (PSRA) within the context of publicly funded health care systems of high-income countries and inform areas for further improvement and research.MethodsAn online qualitative survey, developed from a theoretical framework, was administered with decision-makers and academics from 18 countries. 450 individuals were invited and 58 participated (13% of response rate).ResultsWe found evidence that resource allocation is still largely carried out based on historical patterns and through ad hoc decisions, despite the widely held understanding that decisions should be based on multiple explicit criteria. Health technology assessment (HTA) was the tool most commonly indicated by respondents as a formal priority setting strategy. Several approaches were reported to have been used, with special emphasis on Program Budgeting and Marginal Analysis (PBMA), but limited evidence exists on their evaluation and routine use. Disinvestment frameworks are still very rare. There is increasing convergence on the use of multiple types of evidence to judge the value of investment options.ConclusionsEfforts to establish formal and explicit processes and rationales for decision-making in priority setting and resource allocation have been still rare outside the HTA realm. Our work indicates the need of development/improvement of decision-making frameworks in PSRA that: 1) have well-defined steps; 2) are based on multiple criteria; 3) are capable of assessing the opportunity costs involved; 4) focus on achieving higher value and not just on adoption; 5) engage involved stakeholders and the general public; 6) make good use and appraisal of all evidence available; and 6) emphasize transparency, legitimacy, and fairness.
| S-EPMC7839200 | biostudies-literature