Can vaccine prioritization reduce disparities in COVID-19 burden for historically marginalized populations?

SARS-CoV-2 vaccination strategies were designed to reduce COVID-19 mortality, morbidity, and health inequities. To assess the impact of vaccination strategies on disparities in COVID-19 burden among historically marginalized populations (HMPs), e.g. Black race and Hispanic ethnicity, we used an agent-based simulation model, populated with census-tract data from North Carolina. We projected COVID-19 deaths, hospitalizations, and cases from 2020 July 1 to 2021 December 31, and estimated racial/ethnic disparities in COVID-19 outcomes. We modeled 2-stage vaccination prioritization scenarios applied to sub-groups including essential workers, older adults (65+), adults with high-risk health conditions, HMPs, or people in low-income tracts. Additionally, we estimated the effects of maximal uptake (100% for HMP vs. 100% for everyone), and distribution to only susceptible people. We found strategies prioritizing essential workers, then older adults led to the largest mortality and case reductions compared to no prioritization. Under baseline uptake scenarios, the age-adjusted mortality for HMPs was higher (e.g. 33.3%-34.1% higher for the Black population and 13.3%-17.0% for the Hispanic population) compared to the White population. The burden on HMPs decreased only when uptake was increased to 100% in HMPs; however, the Black population still had the highest relative mortality rate even when targeted distribution strategies were employed. If prioritization schemes were not paired with increased uptake in HMPs, disparities did not improve. The vaccination strategies publicly outlined were insufficient, exacerbating disparities between racial and ethnic groups. Strategies targeted to increase vaccine uptake among HMPs are needed to ensure equitable distribution and minimize disparities in outcomes.

Return on investment of self-management education and home visits for children with asthma.

OBJECTIVE: Priorities of the Centers for Disease Control and Prevention's 6|18 Initiative include outpatient asthma self-management education (ASME) and home-based asthma visits (home visit) as interventions for children with poorly-controlled asthma. ASME and home visit intervention programs are currently not widely available. This project was to assess the economic sustainability of these programs for state asthma control programs reimbursed by Medicaid. METHODS: We used a simulation model based on parameters from the literature and Medicaid claims, controlling for regression to the mean. We modeled scenarios under various selection criteria based on healthcare utilization and age to forecast the return on investment (ROI) using data from New York. The resulting tool is available in Excel or Python. RESULTS: Our model projected health improvement and cost savings for all simulated interventions. Compared against home visits alone, the simulated ASME alone intervention had a higher ROI for all healthcare utilization and age scenarios. Savings were primarily highest in simulated program participants who had two or more asthma-related emergency department visits or one inpatient visit compared to those participants who had one or more asthma-related emergency department visits. Segmenting the selection criteria by age did not significantly change the results. CONCLUSIONS: This model forecasts reduced healthcare costs and improved health outcomes as a result of ASME and home visits for children with high urgent healthcare utilization (more than two emergency department visits or one inpatient hospitalization) for asthma. Utilizing specific selection criteria, state based asthma control programs can improve health and reduce healthcare costs.

Estimating the impact of self-management education, influenza vaccines, nebulizers, and spacers on health utilization and expenditures for Medicaid-enrolled children with asthma.

INTRODUCTION: We quantify the effect of a set of interventions including asthma self-management education, influenza vaccination, spacers, and nebulizers on healthcare utilization and expenditures for Medicaid-enrolled children with asthma in New York and Michigan. METHODS: We obtained patients' data from Medicaid Analytic eXtract files and evaluated patients with persistent asthma in 2010 and 2011. We used difference-in-difference regression to quantify the effect of the intervention on the probability of asthma-related healthcare utilization, asthma medication, and utilization costs. We estimated the average change in outcome measures from pre-intervention/intervention (2010) to post-intervention (2011) periods for the intervention group by comparing this with the average change in the control group over the same time horizon. RESULTS: All of the interventions reduced both utilization and asthma medication costs. Asthma self-management education, nebulizer, and spacer interventions reduced the probability of emergency department (20.8-1.5%, 95%CI 19.7-21.9% vs. 0.5-2.5%, respectively) and inpatient (3.5-0.8%, 95%CI 2.1-4.9% vs. 0.4-1.2%, respectively) utilizations. Influenza vaccine decreased the probability of primary care physician (6-3.5%, 95%CI 4.4-7.6% vs. 1.5-5.5%, respectively) visit. The reductions varied by state and intervention. CONCLUSIONS: Promoting asthma self-management education, influenza vaccinations, nebulizers, and spacers can decrease the frequency of healthcare utilization and asthma-related expenditures while improving medication adherence.

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.

Balancing investments in Federally Qualified Health Centers and Medicaid for improved access and coverage in Pennsylvania.

Two common health disparities in the US include a lack of access to care and a lack of insurance coverage. To help address these disparities, healthcare reform will provide $11B to expand Federally Qualified Health Centers (FQHCs) over the next 5 years. In 2014, Medicaid rules will be modified so that more people will become eligible. There are, however, important tradeoffs in the investment in these two programs. We find a balanced investment between FQHC expansion and relaxing Medicaid eligibility to improve both access (by increasing the number of FQHCs) and coverage (by FQHC and Medicaid expansion) for the state of Pennsylvania. The comparison is achieved by integrating multi-objective mathematical models with several public data sets that allow for specific estimations of healthcare need. Demand is estimated based on current access and coverage status in order to target groups to be considered preferentially. Results show that for Pennsylvania, FQHCs are more cost effective than Medicaid if we invest all of the resources in just one policy. However, we find a better investment point balancing those two policies. This point is approximately where the additional expenses incurred from relaxing Medicaid eligibility equals the investment in FQHC expansion.

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.

Public health sealant delivery programs: optimal delivery and the cost of practice acts.

BACKGROUND: The greatest unmet health need for US children is dental care. School-based sealant programs target low-income, high-risk second graders and are effective in preventing caries for as long as the sealant material remains in place. However, it is not clear whether such programs make efficient use of available resources and staffing. METHODS: The authors used discrete event simulation to determine the optimal combinations of staffing levels and sealant stations for school-based sealant programs. Using data provided by state programs and the literature, they modeled different-sized programs under different practice act constraints and determined times and associated costs. A detailed economic analysis was done for Wisconsin. RESULTS: For general, direct, or indirect supervision, it is optimal to have only 1 dentist or no dentists for no supervision. For general supervision, it is optimal to have the dentist and dental assistant to come on separate days to screen. The cost savings for adding an assistant and chair averaged over all of the program sizes and travel distances ranged from 4.50% (SE= 0.89) to 10.94% (SE= 0.56). Significant cost savings also result from reducing the required supervision level (8.72% [SE = 1.61] to 29.96% [SE= 1.67]). The cost of the practice act for the state of Wisconsin for 2003 was from USD 83,041 to USD 346,156, significantly more than its annual budget. CONCLUSIONS: States could save money by relaxing restrictions on the type of personnel who can deliver sealants in public health settings and by productivity gains through proper consideration of staffing. The savings could be used to improve access to sealant programs and further reduce disparities in oral health.