Overweight in adolescents: Implications for health expenditures

We consider a compelling research question raised by the growing prevalence of overweight among adolescents: do overweight adolescents incur greater health care expenditures than adolescents of normal weight? To address this question, we use data from the Medical Expenditure Panel Survey (MEPS) and estimate a two-part, generalized linear model (GLM) of health spending. Considering separate models by gender, we find that overweight females incur $790 more in annual expenditures than those of normal weight but we find no expenditure differences by bodyweight for males. We find that mental health spending is associated with part of the disparity in expenditures for adolescent females but establishing causality between mental health problems and weight-related health expenditure differences is challenging.     

The patient assignment problem in home health care: using a data-driven method to estimate the travel times of care givers

Home health care is one of the recent service systems where human resource planning has a great importance. The assignment of patients to care givers is a relevant issue that the home health care service provider must address before generating the daily routes. The assignment decision is typically made without knowing the visiting sequence, which creates some uncertainties and disparities regarding the effective workload of care givers. However, taking into account travel times in the care giver workload while solving the assignment problem is not straightforward, because travel times can also be affected by clinical conditions of patients and their homes. Providing good travel time estimates that would be used in the assignment decision is the specific topic this paper focuses on. In particular, we propose a data-driven method to estimate the travel times of care givers in the assignment problem when their routes are not available yet. The method, based on the Kernel regression technique, uses the travel times observed from previous periods to estimate the time necessary for visiting a set of patients located in specific geographical locations. The main advantage offered by this technique is the empirical modelling of the travel routes generated by care givers. Numerical results based on realistic problem instances indicate that the proposed estimation method performs better than the average value and k-nearest neighbor search methods and can be successfully used in a two-stage approach that first assigns patients to care givers and then defines their routes.     

Total HIV/AIDS Expenditures in Dehong Prefecture,Yunnan Province in 2010: The First Systematic Evaluation of Both Health and Non-Health Related HIV/AIDS Expenditures in China

Objective

We assessed HIV/AIDS expenditures in Dehong Prefecture, Yunnan Province, one of the highest prevalence regions in China, and describe funding sources and spending for different categories of HIV-related interventions and at-risk populations.

Methods

2010 HIV/AIDS expenditures in Dehong Prefecture were evaluated based on UNAIDS’ National AIDS Spending Assessment methodology.

Results

Nearly 93% of total expenditures for HIV/AIDS was contributed by public sources. Of total expenditures, 52.7% was allocated to treatment and care, 24.5% to program management and administration and 19.8% to prevention. Spending on treatment and care was primarily allocated to the treatment of opportunistic infections. Most (40.4%) prevention spending was concentrated on most-at-risk populations, injection drug users (IDUs), sex workers, and men who have sex with men (MSM), with 5.5% allocated to voluntary counseling and testing. Prevention funding allocated for MSM, partners of people living with HIV and prisoners and other confined populations was low compared to the disproportionate burden of HIV/AIDS in these populations. Overall, people living with HIV accounted for 57.57% of total expenditures, while most-at-risk populations accounted for only 7.99%.

Conclusions

Our study demonstrated the applicability of NASA for tracking and assessing HIV expenditure in the context of China, it proved to be a useful tool in understanding national HIV/AIDS response from financial aspect, and to assess the extent to which HIV expenditure matches epidemic patterns. Limited funding for primary prevention and prevention for MSM, prisoners and partners of people living with HIV, signal that resource allocation to these key areas must be strengthened. Comprehensive analyses of regional and national funding strategies are needed to inform more equitable, effective and cost-effective HIV/AIDS resource allocation.     

Comparison of different approaches for the calculation of energy expenditure using doubly labeled water in a small mammal

The doubly labeled water (DLW) method is an isotope-based technique for the estimation of the CO(2) production, and hence energy expenditure, of free-living animals and humans. Several methods are available for the calculation of CO(2) production from the isotope fluxes, depending on different assumptions about the behavior of isotopes during the elimination process. We used the DLW method to estimate the daily energy expenditures (DEE) of 55 field voles (Microtus agrestis) held in a captive facility at 8 degrees C. We calculated DEE using both plateau and intercept approaches for estimating the sizes of the isotope dilution spaces, three different assumptions about fractionation processes, and two ways of treating the different dilution spaces of the oxygen and hydrogen isotopes. We compared the resultant DEE estimates with metabolizable energy intake (MEI) measured during a 3-d feeding trial immediately before the DLW measurements, during which the animals were in energy balance. By making different assumptions about the apparent energy absorption efficiency, we generated a range of direct estimates of MEI. When we compared DEE and MEI, we found that the two-pool model formulations consistently underestimated energy demands by up to 29.8%, depending on the assumptions made in the reference calculation. However, while our data suggest that some correction for fractionation is necessary, with the present data we were unable to separate the two most common treatments of fractionation. These data strongly support the previous suggestion that for small mammals single-pool models provide more accurate estimates of energy demands than two-pool formulation of the DLW method.     

Conception control as a health practice: an emerging concept in government and medicine

The health aspects and benefits of conception control as a justification for U.S. government support of family planning are reviewed. The Family Planning Services Act of 1970 set a goal of serving 5 million indigent women by 1975. It is predicted that the program will far surpass that goal. 340 million dollars were asked for contraceptive research by the federal government in fiscal year 1973, which is an example of the commitment to family planning by the government. Theoretical and sociopolitical reasons for family planning do not justify expenditure of public tax spending on population control assistance, but public health does justify those expenditures. Health and population control issues include the mental, physical, and social health of the parent and child, and broad demographic effects of age group population differentials, food supply, pollution, and health care services. The personal effects of an unwanted child are the most serious consequences of a lack of population control including economic and psychological hardship and damage. Maternal and infant mortality is related to age, economic status, and parity; e.g., toxemic pregnancy is more frequently found in young nulliparas. A correlation has been found between rapid parity and prematurity, though the research is not yet completely conclusive. Family planning through contraception and abortion can prevent unwanted pregnancies and unnecessary health risks from pregnancy by enabling parents to space and limit their families with safety. This is a highly desirable public health service goal and is emerging as a major concern of the federal government.     

Methods for Estimating Population Density in Data-Limited Areas: Evaluating Regression and Tree-Based Models in Peru

Obtaining accurate small area estimates of population is essential for policy and health planning but is often difficult in countries with limited data. In lieu of available population data, small area estimate models draw information from previous time periods or from similar areas. This study focuses on model-based methods for estimating population when no direct samples are available in the area of interest. To explore the efficacy of tree-based models for estimating population density, we compare six different model structures including Random Forest and Bayesian Additive Regression Trees. Results demonstrate that without information from prior time periods, non-parametric tree-based models produced more accurate predictions than did conventional regression methods. Improving estimates of population density in non-sampled areas is important for regions with incomplete census data and has implications for economic, health and development policies.     

Generalized linear latent variable models for repeated measures of spatially correlated multivariate data

Observations of multiple-response variables across space and over time occur often in environmental and ecological studies. Compared to purely spatial models for a single response variable in the exponential family of distributions, fewer statistical tools are available for multiple-response variables that are not necessarily Gaussian. An exception is a common-factor model developed for multivariate spatial data by Wang and Wall (2003, Biostatistics 4, 569-582). The purpose of this article is to extend this multivariate space-only model and develop a flexible class of generalized linear latent variable models for multivariate spatial-temporal data. For statistical inference, maximum likelihood estimates and their standard deviations are obtained using a Monte Carlo EM algorithm. We also use a novel way to automatically adjust the Monte Carlo sample size, which facilitates the convergence of the Monte Carlo EM algorithm. The methodology is illustrated by an ecological study of red pine trees in response to bark beetle challenges in a forest stand of Wisconsin.     

Are the costs of employer-sponsored health insurance passed on to workers at the individual level?

Because employer-sponsored health insurance (ESI) is experience rated, employers have an incentive to try to offset its cost by paying lower wages to employees who have greater medical expenditures. The existing evidence on this topic, however, illustrates only that ESI is associated with lower wages for groups of workers who are costlier to cover. In contrast, I use the variation provided by the Affordable Care Act's employer mandate to examine if differences in medical expenditures are passed on to workers at the individual level. My estimates rely on Medical Expenditure Panel Survey data in a dose response difference-in-difference framework that examines how wages change for workers with varying medical expenditures when they must soon be offered ESI. I find that each $1 difference in medical expenditures is associated with a $0.35 to $0.51 wage offset after the employer mandate's announcement wherever ESI must soon be offered to workers. Placebo analyses, focusing on workers whose employers are not affected by the mandate, provide support for a causal interpretation. I also show that my findings are not sensitive to sample selection or data reliability issues and that they cannot be explained by the effects of the Great Recession, demographic characteristics that correlate with medical expenditures, or location- or industry-specific idiosyncratic shocks.     

The statistical analysis of truncated data: application to the Sverdlovsk anthrax outbreak

An outbreak of anthrax occurred in the city of Sverdlovsk in Russia in the spring of 1979. The outbreak was due to the inhalation of spores that were accidentally released from a military microbiology facility. In response to the outbreak a public health intervention was mounted that included distribution of antibiotics and vaccine. The objective of this paper is to develop and apply statistical methodology to analyse the Sverdlovsk outbreak, and in particular to estimate the incubation period of inhalational anthrax and the number of deaths that may have been prevented by the public health intervention. The data available for analysis from this common source epidemic are the incubation periods of reported deaths. The statistical problem is that incubation periods are truncated because some individuals may have had their deaths prevented by the public health interventions and thus are not included in the data. However, it is not known how many persons received the intervention or how efficacious was the intervention. A likelihood function is formulated that accounts for the effects of truncation. The likelihood is decomposed into a binomial likelihood with unknown sample size and a conditional likelihood for the incubation periods. The methods are extended to allow for a phase-in of the intervention over time. Assuming a lognormal model for the incubation period distribution, the median and mean incubation periods were estimated to be 11.0 and 14.2 days respectively. These estimates are longer than have been previously reported in the literature. The death toll from the Sverdlovsk anthrax outbreak could have been about 14% larger had there not been a public health intervention; however, the confidence intervals are wide (95% CI 0-61%). The sensitivity of the results to model assumptions and the parametric model for the incubation period distribution are investigated. The results are useful for determining how long antibiotic therapy should be continued in suspected anthrax cases and also for estimating the ultimate number of deaths in a new outbreak in the absence of any public health interventions.     

Patient admission planning using Approximate Dynamic Programming

Tactical planning in hospitals involves elective patient admission planning and the allocation of hospital resource capacities. We propose a method to develop a tactical resource allocation and patient admission plan that takes stochastic elements into consideration, thereby providing robust plans. Our method is developed in an Approximate Dynamic Programming (ADP) framework and copes with multiple resources, multiple time periods and multiple patient groups with uncertain treatment paths and an uncertain number of arrivals in each time period. As such, the method enables integrated decision making for a network of hospital departments and resources. Computational results indicate that the ADP approach provides an accurate approximation of the value functions, and that it is suitable for large problem instances at hospitals, in which the ADP approach performs significantly better than two other heuristic approaches. Our ADP algorithm is generic, as various cost functions and basis functions can be used in various hospital settings.     

State‐Level Estimates of Annual Medical Expenditures Attributable to Obesity*

Objective: To provide state‐level estimates of total, Medicare, and Medicaid obesity‐attributable medical expenditures. Research Methods and Procedures: We developed an econometric model that predicts medical expenditures. We used this model and state‐representative data to quantify obesity‐attributable medical expenditures. Results: Annual U.S. obesity‐attributable medical expenditures are estimated at $75 billion in 2003 dollars, and approximately one‐half of these expenditures are financed by Medicare and Medicaid. State‐level estimates range from $87 million (Wyoming) to $7.7 billion (California). Obesity‐attributable Medicare estimates range from $15 million (Wyoming) to $1.7 billion (California), and Medicaid estimates range from $23 million (Wyoming) to $3.5 billion (New York). Discussion: These estimates of obesity‐attributable medical expenditures present the best available information concerning the economic impact of obesity at the state level. Policy makers should consider these estimates, along with other factors, in determining how best to allocate scarce public health resources. However, because they are associated with large SE, these estimates should not be used to make comparisons across states or among payers within states.     

Time estimation circadian rhythm in shift workers and diurnally active humans

The ability of shift workers to estimate timer intervals of short duration was examined. The study included 22 shift workers and 10 diurnally working control subjects. A circadian rhythm in time estimates was documented in control subjects, but it was found to bed disrupted in shift workers. Spectral analysis revealed frequency or circadian component in time estimates to be lower among the shift workers. Furthermore, an interesting relationship was marked between time estimates and oral temperature in 4 control subjects and 6 shift workers in that the time of the closest estimation coincided with the peak time of their body temperature.     

Cost-benefit analysis of long-term haemodialysis for chronic renal failure.

A cost-benefit analysis of long-term maintenance haemodialysis indicates that there is a large gulf between the cost of the service and "economic" benefit. The difference may be considered to represent one estimate of the price society is prepared to pay to maintain life. Using "best estimates" from available data we found the implicit social value of maintaining a patient on haemodialysis to be approximately pounds 4720 per annum in hospital or pounds 2600 at home. The analysis would suggest that society must look carefully at alternative uses for health expenditure before extending indiscriminately to large sections of the population these treatment programmes or other similarly expensive.     

Examining inequities in incidence of catastrophic health expenditures on different healthcare services and health facilities in Nigeria

Objective

There is limited evidence about levels of socio-economic and other differences in catastrophic health spending in Nigeria and in many sub-Saharan African countries. The study estimated the level of catastrophic healthcare expenditures for different healthcare services and facilities and their distribution across socioeconomic status (SES) groups.

Methods

The study took place in four Local Government Areas in southeast Nigeria. Data were collected using interviewer-administered questionnaires administered to 4873 households. Catastrophic health expenditures (CHE) were measured using a threshold of 40% of monthly non-food expenditure. We examined both total monthly health expenditure and disaggregated expenditure by source and type of care.

Results

The average total household health expenditure per month was 2354 Naira ($19.6). For outpatient services, average monthly expenditure was 1809 Naira ($15.1), whilst for inpatient services it was 610 Naira ($5.1). Higher health expenditures were incurred by urban residents and the better-off SES groups. Overall, 27% of households incurred CHE, higher for poorer socioeconomic groups and for rural residents. Only 1.0% of households had a member that was enrolled in a health insurance scheme.

Conclusion

The worse-off households (the poorest SES and rural dwellers) experienced the highest burden of health expenditure. There was almost a complete lack of financial risk protection. Health reform mechanisms are needed to ensure universal coverage with financial risk protection mechanisms.     

Economic impacts of non-native forest insects in the continental United States

Reliable estimates of the impacts and costs of biological invasions are critical to developing credible management, trade and regulatory policies. Worldwide, forests and urban trees provide important ecosystem services as well as economic and social benefits, but are threatened by non-native insects. More than 450 non-native forest insects are established in the United States but estimates of broad-scale economic impacts associated with these species are largely unavailable. We developed a novel modeling approach that maximizes the use of available data, accounts for multiple sources of uncertainty, and provides cost estimates for three major feeding guilds of non-native forest insects. For each guild, we calculated the economic damages for five cost categories and we estimated the probability of future introductions of damaging pests. We found that costs are largely borne by homeowners and municipal governments. Wood- and phloem-boring insects are anticipated to cause the largest economic impacts by annually inducing nearly $1.7 billion in local government expenditures and approximately $830 million in lost residential property values. Given observations of new species, there is a 32% chance that another highly destructive borer species will invade the U.S. in the next 10 years. Our damage estimates provide a crucial but previously missing component of cost-benefit analyses to evaluate policies and management options intended to reduce species introductions. The modeling approach we developed is highly flexible and could be similarly employed to estimate damages in other countries or natural resource sectors.