Multistage allocation problem for Mexican pension funds.
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ABSTRACT:
SUBMITTER: Garcia-Medina A
PROVIDER: S-EPMC8043405 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Amid the extraordinary economic effects of COVID-19, some policymakers have turned to retirement accounts to support individuals in financial hardship. Given the haste, the long-term impacts and their heterogeneity have scarcely been analyzed. Using Monte Carlo simulations on the Chilean Social Protection Survey linked with administrative data, this study quantifies the effects of a 10% early release of pension funds. Each withdrawn dollar brings losses of 1.59 dollars in future retirement savings, reducing monthly pension benefits by 7.26%. This policy raises income inadequacy and inequality in retirement, increasing government expenditure by 4.33% to counteract these effects for 65-year-old retirees. We propose four policies to mitigate these effects and address the current challenges of most defined contribution pension schemes. Increasing contributions combined with an intragenerational solidarity component shows the biggest impacts. Contribution enforcement, reducing tax evasion, and delaying retirement by at least 1 year via incentives have lower but significant effects.
| S-EPMC8661967 | biostudies-literature
Project description:BackgroundWho benefits from the commercial biomedical research and development (R&D)? Patients-consumers and investors-shareholders have traditionally been viewed as two distinct groups with conflicting interests: shareholders seek maximum profits, patients - maximum clinical benefit. However, what happens when patients are the shareholders? With billions of dollars of public risk capital channeled into the drug development industry, analysing the complex financial architecture and the market for corporate control is essential for understanding industry's characteristics, such as pricing strategies or R&D priorities.ResultsAdding investments by governmentally-mandated retirement schemes, central and promotional banks, and sovereign wealth funds to tax-derived governmental financing shows that the majority of biomedical R&D funding is public in origin. Despite this, even in the high-income countries patients can be denied access to effective treatments due to their high cost. Since these costs are set by the drug development firms that are owned in substantial part by the retirement accounts of said patients, the complex financial architecture of biomedical R&D may be inconsistent with the objectives of the ultimate beneficiaries.ConclusionsThe divergence in economic and public health performance of the drug development industry is resultant from its financial underwriting by enormously expanded pension schemes, governmentally mandated to represent the interests of "captive" beneficiaries, as well as similar policymaker-designed funding flows, whose standards of transparency, accountability and representation are substantially lower than that of governments themselves. Strengthening those elements of institutional design and thus ensuring active responsible shareholding in the interest of the patients-savers is an under-utilised, but potentially high-impact opportunity for advancing public health.
| S-EPMC6833262 | biostudies-literature
Project description:ObjectiveTo estimate the optimal allocation of Centers for Disease Control and Prevention (CDC) HIV prevention funds for health departments in 52 jurisdictions, incorporating Health Resources and Services Administration (HRSA) Ryan White HIV/AIDS Program funds, to improve outcomes along the HIV care continuum and prevent infections.MethodsUsing surveillance data from 2010 to 2012 and budgetary data from 2012, we divided the 52 health departments into 5 groups varying by number of persons living with diagnosed HIV (PLWDH), median annual CDC HIV prevention budget, and median annual HRSA expenditures supporting linkage to care, retention in care, and adherence to antiretroviral therapy. Using an optimization and a Bernoulli process model, we solved for the optimal CDC prevention budget allocation for each health department group. The optimal allocation distributed the funds across prevention interventions and populations at risk for HIV to prevent the greatest number of new HIV cases annually.ResultsBoth the HIV prevention interventions funded by the optimal allocation of CDC HIV prevention funds and the proportions of the budget allocated were similar across health department groups, particularly those representing the large majority of PLWDH. Consistently funded interventions included testing, partner services and linkage to care and interventions for men who have sex with men (MSM). Sensitivity analyses showed that the optimal allocation shifted when there were differences in transmission category proportions and progress along the HIV care continuum.ConclusionThe robustness of the results suggests that most health departments can use these analyses to guide the investment of CDC HIV prevention funds into strategies to prevent the most new cases of HIV.
| S-EPMC5955542 | biostudies-literature
Project description:This paper focuses on a continuous-time dynamic mean-variance portfolio selection problem of defined-contribution pension funds with stochastic salary, whose risk comes from both financial market and nonfinancial market. By constructing a special Riccati equation as a continuous (actually a viscosity) solution to the HJB equation, we obtain an explicit closed form solution for the optimal investment portfolio as well as the efficient frontier.
| S-EPMC3981111 | biostudies-other
Project description:The responsiveness of NIH (National Institutes of Health) funding to disease burden is a long-standing issue of policy interest. Previous analyses of this issue have been hindered by data constraints, have not specified channels through which the NIH funding process could be responsive to disease considerations, and have not examined differences across NIH institutes and centers.We collected data from the NIH's new RCDC (Research, Condition, and Disease Categorization) database on funding for 107 diseases in 2008 and linked these to data on deaths and hospitalizations for these diseases. We used RCDC data and information from another NIH database--RePORTER--to determine institute-specific funding for these diseases and also funding by award type. We used these data to examine the overall responsiveness of NIH funding to disease burden, within-institute responsiveness, and the responsiveness of different types of NIH awards.Overall, we found a strong and statistically significant relationship between NIH funding and deaths and hospitalizations associated with a disease. We detected some evidence that more "applied" grant mechanisms--in particular, funding for clinical trials--are more responsive than other types of funding. We also found evidence of differences across institutes in their extent of responsiveness.Overall, the data suggest that NIH funding is responsive to the two measures of disease burden. More applied grant mechanisms also may serve as "safety valves" in the allocation process, allowing Congress, disease advocacy groups, and others to apply pressure to address particular health priorities in a more fine-grained way than is possible through investigator-initiated "basic" research grants alone.
| S-EPMC3607129 | biostudies-other
Project description:BackgroundThis study aimed to investigate the distribution of European Union (EU) healthcare research grants across EU countries, and to study the effect of the potential influencing factors on grant allocation.MethodsWe analysed publicly available data on healthcare research grants from the 7th Framework Programme and the Horizon 2020 Programme allocated to beneficiaries between 2007 and 2016. Grant allocation was analysed at the beneficiary-, country-, and country group-level (EU-15 versus newer Member States, defined as EU-13). The investigated country-level explanatory variables included GDP per capita, population size, overall disease burden, and healthcare research excellence. Grant amounts per 100,000 inhabitants were used as an outcome variable in the regression analyses.ResultsResearch funds were disproportionally allocated to EU-15 versus the EU-13, as 96.9% of total healthcare grants were assigned to EU-15 countries. At the beneficiary level, EU funding was positively influenced by participating in previous grants. The average grant amount per beneficiary was higher for EU-15 organizations. In the multiple regression analysis GDP per capita (p = 0.002) and research excellence (p<0.001) had a significant positive association with EU funding. Population size had an inverted U-shaped relationship with EU funding for healthcare research, having the largest per capita funding in second and the third quartiles (p = 0.03 and p = 0.02).ConclusionThe uneven allocation of healthcare research funds across EU countries was influenced by GDP per capita, medical research excellence and population size. Wealthier countries with an average population size and strong research excellence in healthcare had more EU funding for healthcare research. Higher disease burden apparently was not associated with more EU research funding. While our findings are in line with analyses on previous periods, they suggest that the EU did not implement any effective policy measures to improve the unfair allocation of research grants.
| S-EPMC6464186 | biostudies-literature
Project description:A patient's medical problem list describes his or her current health status and aids in the coordination and transfer of care between providers. Because a problem list is generated once and then subsequently modified or updated, what is not usually observable is the provider-effect. That is, to what extent does a patient's problem in the electronic medical record actually reflect a consensus communication of that patient's current health status? To that end, we report on and analyze a unique interview-based design in which multiple medical providers independently generate problem lists for each of three patient case abstracts of varying clinical difficulty. Due to the uniqueness of both our data and the scientific objectives of our analysis, we apply and extend so-called multistage models for ordered lists and equip the models with variable selection penalties to induce sparsity. Each problem has a corresponding non-negative parameter estimate, interpreted as a relative log-odds ratio, with larger values suggesting greater importance and zero values suggesting unimportant problems. We use these fitted penalized models to quantify and report the extent of consensus. We conduct a simulation study to evaluate the performance of our methodology and then analyze the motivating problem list data. For the three case abstracts, the proportions of problems with model-estimated non-zero log-odds ratios were 10/28, 16/47, and 13/30. Physicians exhibited consensus on the highest ranked problems in the first and last case abstracts but agreement quickly deteriorated; in contrast, physicians broadly disagreed on the relevant problems for the middle - and most difficult - case abstract.
| S-EPMC8345315 | biostudies-literature
Project description:The amendment of the Medical Licensing Act (ÄAppO) in Germany in 2002 led to the introduction of graded assessments in the clinical part of medical studies. This, in turn, lent new weight to the importance of written tests, even though the minimum requirements for exam quality are sometimes difficult to reach. Introducing exam quality as a criterion for the award of performance-based allocation of funds is expected to steer the attention of faculty members towards more quality and perpetuate higher standards. However, at present there is a lack of suitable algorithms for calculating exam quality.In the spring of 2014, the students' dean commissioned the "core group" for curricular improvement at the University Medical Center in Rostock to revise the criteria for the allocation of performance-based funds for teaching. In a first approach, we developed an algorithm that was based on the results of the most common type of exam in medical education, multiple choice tests. It included item difficulty and discrimination, reliability as well as the distribution of grades achieved.This algorithm quantitatively describes exam quality of multiple choice exams. However, it can also be applied to exams involving short assay questions and the OSCE. It thus allows for the quantitation of exam quality in the various subjects and - in analogy to impact factors and third party grants - a ranking among faculty.Our algorithm can be applied to all test formats in which item difficulty, the discriminatory power of the individual items, reliability of the exam and the distribution of grades are measured. Even though the content validity of an exam is not considered here, we believe that our algorithm is suitable as a general basis for performance-based allocation of funds.
| S-EPMC4894354 | biostudies-other
Project description:Concern is growing that business enterprises focus primarily on their economic activities while disregarding the adverse environmental and social effects of these activities. To contribute to the literature on this matter, this study investigates a novel bi-objective inventory allocation planning problem with supplier selection and carbon trading across multiple periods under uncertainty. The concepts of a carbon credit price and a carbon cap are proposed to demonstrate the effect of carbon emissions costs on inventory allocation network costs. Demands of manufacturers, transport price, and defect rate of materials that should be rejected are set as random variables. We combine normalized normal constraint method, differential evolution algorithm, and uncertainty simulation to deal with the complex model. One representative case shows the effectiveness and practicability of this model and proposed method. The Pareto frontier is generated by solving the bi-objective model. We extend the results of numerical examples in large scale problems, and compare the solution method results with exact solutions. The environmental objective across the inventory allocation network varies with changes of the carbon cap and the carbon credit price.
| S-EPMC6261456 | biostudies-literature
Project description:The label switching problem occurs as a result of the nonidentifiability of posterior distribution over various permutations of component labels when using Bayesian approach to estimate parameters in mixture models. In the cases where the number of components is fixed and known, we propose a relabelling algorithm, an allocation variable-based (denoted by AVP) probabilistic relabelling approach, to deal with label switching problem. We establish a model for the posterior distribution of allocation variables with label switching phenomenon. The AVP algorithm stochastically relabel the posterior samples according to the posterior probabilities of the established model. Some existing deterministic and other probabilistic algorithms are compared with AVP algorithm in simulation studies, and the success of the proposed approach is demonstrated in simulation studies and a real dataset.
| S-EPMC4601799 | biostudies-literature