The cost-effectiveness of depression screening for the general adult population.

BACKGROUND: Depression is a treatable disease, and untreated depression can lead to serious health complications and decrease the quality of life. Therefore, prevention, early identification, and treatment efforts are essential. Screening has an essential role in preventive medicine in the general population. Ideally, screening tools detect patients early enough to manage the disease and reduce symptoms. We aimed to determine the cost-effectiveness of routine screening schedules. METHODS: We used a discrete-time nonstationary Markov model to simulate the progression of depression. We used Monte Carlo techniques to simulate the stochastic model for 20 years or during the lifetime of individuals. Baseline and screening scenario models with screening frequencies of annual, 2-year, and 5-year strategies were compared based on incremental cost-effectiveness ratios (ICER). Monte Carlo (MC) simulation and one-way sensitivity analysis were conducted to manage uncertainties. RESULTS: In the general population, all screening strategies were cost-effective compared to the baseline. However, male and female populations differed based on cost over quality-adjusted life years (QALY). Females had lower ICERs, and annual screening had the highest ICER for females, with 11,134$/QALY gained. In contrast, males had around three times higher ICER, with annual screening costs of 34,065$/QALY gained. LIMITATIONS: We assumed that the screening frequency was not changing at any time during the screening scenario. In our calculations, false-positive cases were not taking into account. CONCLUSIONS: Considering the high lifetime prevalence and recurrence rates of depression, detection and prevention efforts can be one critical cornerstone to support required care. Our analysis combined the expected benefits and costs of screening and assessed the effectiveness of screening scenarios. We conclude that routine screening is cost-effective for all age groups of females and young, middle-aged males.

Geospatial mapping and resource utilization tool in support of a national smoke-free public housing rule.

OBJECTIVE: To advance public health support for the U.S. Department of Housing and Urban Development's smoke-free rule, the Centers for Disease Control and Prevention collaborated with the Georgia Institute of Technology to develop a geospatial mapping tool. The objective was to create a tool state and local public health agencies could use to tailor smoke-free educational materials and cessation interventions for specific public housing development resident populations. RESULTS: The resulting "Extinguish Tool" includes an interactive map of U.S. public housing developments (PHDs) and healthcare facilities that provides detailed information on individual PHDs, their proximity to existing healthcare facilities, and the demographic characteristics of residents. The tool also estimates the number of PHD residents who smoke cigarettes and calculates crude estimates of the potential economic benefits of providing cessation interventions to these residents. The geospatial mapping tool project serves as an example of a collaborative and innovative public health approach to protecting the health and well-being of the nation's two million public housing residents, including 760,000 children, from the harms of tobacco smoking and secondhand smoke exposure in the places where they live, play, and gather.

Does distance to a cystic fibrosis center impact health outcomes?

INTRODUCTION: This paper considers the impact of geographic distance from cystic fibrosis centers on lung function in children, young adults, and adults with cystic fibrosis. METHODS: Clinical patient-level data on 20 351 patients from 1986 to 2011 were evaluated from the Cystic Fibrosis Foundation National Patient Registry. We measure distance using a patient's zip code centroid to the center where they received care. A heteroscedastic mixed effects model was used to capture the association of distance with longitudinal variation in patients' lung function. RESULTS: Children, young adults, and adults in lower socioeconomic categories had a %FEV1 between 3 and 10 percentage-points lower than those living in higher income areas and those privately insured. For patients who changed distance categories, high distance was associated with lower lung function in young adults (P-value <0.001). For older patients we observed the reverse, suggesting that the choice to move farther away is associated with better health (P-value <0.001). For patients who did not change distance categories, only medium distance in children was significant (P-value = 0.01). Known confounding factors including age and CFTR mutation class were statistically significantly associated to health outcomes (P-value <0.001). CONCLUSIONS: This study shows distance is not found to be associated with health lung function among patients whose distance category remained unchanged during the analysis. For patients who move, the association of health with distance depends on the age of the patient; adult patients further from their care center are healthier. Overall, we find that socioeconomic and genetic factors appear to impact health outcomes to a greater extent.

An optimization framework for measuring spatial access over healthcare networks.

BACKGROUND: Measurement of healthcare spatial access over a network involves accounting for demand, supply, and network structure. Popular approaches are based on floating catchment areas; however the methods can overestimate demand over the network and fail to capture cascading effects across the system. METHODS: Optimization is presented as a framework to measure spatial access. Questions related to when and why optimization should be used are addressed. The accuracy of the optimization models compared to the two-step floating catchment area method and its variations is analytically demonstrated, and a case study of specialty care for Cystic Fibrosis over the continental United States is used to compare these approaches. RESULTS: The optimization models capture a patient's experience rather than their opportunities and avoid overestimating patient demand. They can also capture system effects due to change based on congestion. Furthermore, the optimization models provide more elements of access than traditional catchment methods. CONCLUSIONS: Optimization models can incorporate user choice and other variations, and they can be useful towards targeting interventions to improve access. They can be easily adapted to measure access for different types of patients, over different provider types, or with capacity constraints in the network. Moreover, optimization models allow differences in access in rural and urban areas.

System factors to explain 2009 pandemic H1N1 state vaccination rates for children and high-risk adults in US emergency response to pandemic.

INTRODUCTION: During the 2009-2010 H1N1 pandemic, children and high-risk adults had priority for vaccination. Vaccine in short supply was allocated to states pro-rata by population, but vaccination rates as of January 2010 varied among states from 21.3% to 84.7% for children and 10.4% to 47.2% for high-risk adults. States had different campaign processes and decisions. OBJECTIVE: To determine program and system factors associated with higher state pandemic vaccination coverage for children and high-risk adults during an emergency response with short supply of vaccine. METHODS: Regression analysis of factors predicting state-specific H1N1 vaccination coverage in children and high-risk adults, including state campaign information, demographics, preventive or health-seeking behavior, preparedness funding, providers, state characteristics, and surveillance data. RESULTS: Our modeling explained variation in state-specific vaccination coverage with an adjusted R-squared of 0.82 for children and 0.78 for high-risk adults. We found that coverage of children was positively associated with programs focusing on school clinics and with a larger proportion of doses administered in public sites; negatively with the proportion of children in the population, and the proportion not visiting a doctor because of cost. The coverage for high-risk adults was positively associated with shipments of vaccine to "general access" locations, including pharmacy and retail, with the percentage of women with a Pap smear within the past 3 years and with past seasonal influenza vaccination. It was negatively associated with the expansion of vaccination to the general public by December 4, 2009. For children and high-risk adults, coverage was positively associated with the maximum number of ship-to-sites and negatively associated with the proportion of medically underserved population. CONCLUSION: Findings suggest that distribution and system decisions such as vaccination venues and providers targeted can positively impact vaccination rates for children and high-risk adults. Additionally, existing health infrastructure, health-seeking behaviors, and access affected coverage.

System factors to explain H1N1 state vaccination rates for adults in US emergency response to pandemic.

INTRODUCTION: During the 2009-2010 H1N1 pandemic, vaccine in short supply was allocated to states pro rata by population, yet the vaccination rates of adults differed by state. States also differed in their campaign processes and decisions. Analyzing the campaign provides an opportunity to identify specific approaches that may result in higher vaccine uptake in a future event of this nature. OBJECTIVE: To determine supply chain and system factors associated with higher state H1N1 vaccination coverage for adults in a system where vaccine was in short supply. METHODS: Regression analysis of factors predicting state-specific H1N1 vaccination coverage in adults. Independent variables included state campaign information, demographics, preventive or health-seeking behavior, preparedness funding, providers, state characteristics, and H1N1-specific state data. RESULTS: The best model explained the variation in state-specific adult vaccination coverage with an adjusted R-squared of 0.76. We found that higher H1N1 coverage of adults is associated with program aspects including shorter lead-times (i.e., the number of days between when doses were allocated to a state and were shipped, including the time for states to order the doses) and less vaccine directed to specialist locations. Higher vaccination coverage is also positively associated with the maximum number of ship-to locations, past seasonal influenza vaccination coverage, the percentage of women with a Pap smear, the percentage of the population that is Hispanic, and negatively associated with a long duration of the epidemic peak. CONCLUSION: Long lead-times may be a function of system structure or of efficiency and may suggest monitoring or redesign of distribution processes. Sending vaccine to sites with broad access could be useful when covering a general population. Existing infrastructure may be reflected in the maximum number of ship-to locations, so strengthening routine influenza vaccination programs may help during emergency vaccinations also. Future research could continue to inform program decisions.

Evaluation of telemedicine for screening of diabetic retinopathy in the Veterans Health Administration.

OBJECTIVE: To explore the cost-effectiveness of telemedicine for the screening of diabetic retinopathy (DR) and identify changes within the demographics of a patient population after telemedicine implementation. DESIGN: A retrospective medical chart review (cohort study) was conducted. PARTICIPANTS: A total of 900 type 1 and type 2 diabetic patients enrolled in a medical system with a telemedicine screening program for DR. METHODS: The cost-effectiveness of the DR telemedicine program was determined by using a finite-horizon, discrete time, discounted Markov decision process model populated by parameters and testing frequency obtained from patient records. The model estimated the progression of DR and determined average quality-adjusted life years (QALYs) saved and average additional cost incurred by the telemedicine screening program. MAIN OUTCOME MEASURES: Diabetic retinopathy, macular edema, blindness, and associated QALYs. RESULTS: The results indicate that telemedicine screening is cost-effective for DR under most conditions. On average, it is cost-effective for patient populations of >3500, patients aged <80 years, and all racial groups. Observable trends were identified in the screening population since the implementation of telemedicine screening: the number of known DR cases has increased, the overall age of patients receiving screenings has decreased, the percentage of nonwhites receiving screenings has increased, the average number of miles traveled by a patient to receive a screening has decreased, and the teleretinal screening participation is increasing. CONCLUSIONS: The current teleretinal screening program is effective in terms of being cost-effective and increasing population reach. Future screening policies should give consideration to the age of patients receiving screenings and the system's patient pool size because our results indicate it is not cost-effective to screen patients aged older than 80 years or in populations with <3500 patients.

Timing of testing and treatment for asymptomatic diseases.

Many papers in the medical literature analyze the cost-effectiveness of screening for diseases by comparing a limited number of a priori testing policies under estimated problem parameters. However, this may be insufficient to determine the best timing of the tests or incorporate changes over time. In this paper, we develop and solve a Markov Decision Process (MDP) model for a simple class of asymptomatic diseases in order to provide the building blocks for analysis of a more general class of diseases. We provide a computationally efficient method for determining a cost-effective dynamic intervention strategy that takes into account (i) the results of the previous test for each individual and (ii) the change in the individual's behavior based on awareness of the disease. We demonstrate the usefulness of the approach by applying the results to screening decisions for Hepatitis C (HCV) using medical data, and compare our findings to current HCV screening recommendations.

Bias in Markov models of disease.

We examine bias in Markov models of diseases, including both chronic and infectious diseases. We consider two common types of Markov disease models: ones where disease progression changes by severity of disease, and ones where progression of disease changes in time or by age. We find sufficient conditions for bias to exist in models with aggregated transition probabilities when compared to models with state/time dependent transition probabilities. We also find that when aggregating data to compute transition probabilities, bias increases with the degree of data aggregation. We illustrate by examining bias in Markov models of Hepatitis C, Alzheimer's disease, and lung cancer using medical data and find that the bias is significant depending on the method used to aggregate the data. A key implication is that by not incorporating state/time dependent transition probabilities, studies that use Markov models of diseases may be significantly overestimating or underestimating disease progression. This could potentially result in incorrect recommendations from cost-effectiveness studies and incorrect disease burden forecasts.