No abstract

We present discrete-event simulation models of the operations of primary health centers (PHCs) in the Indian context. Our PHC simulation models incorporate four types of patients seeking medical care: outpatients, inpatients, childbirth cases, and patients seeking antenatal care. A generic modeling approach was adopted to develop simulation models of PHC operations. This involved developing an archetype PHC simulation, which was then adapted to represent two other PHC configurations, differing in numbers of resources and types of services provided, encountered during PHC visits. A model representing a benchmark configuration conforming to government-mandated operational guidelines, with demand estimated from disease burden data and service times closer to international estimates (higher than observed), was also developed. Simulation outcomes for the three observed configurations indicate negligible patient waiting times and low resource utilization values at observed patient demand estimates. However, simulation outcomes for the benchmark configuration indicated significantly higher resource utilization. Simulation experiments to evaluate the effect of potential changes in operational patterns on reducing the utilization of stressed resources for the benchmark case were performed. Our analysis also motivated the development of simple analytical approximations of the average utilization of a server in a queueing system with characteristics similar to the PHC doctor/patient system. Our study represents the first step in an ongoing effort to establish the computational infrastructure required to analyze public health operations in India and can provide researchers in other settings with hierarchical health systems, a template for the development of simulation models of their primary healthcare facilities.

BAckground: In Mexico breast cancer reported an incidence of 13,939 cases and 5,217 mortality cases in 2008. Between 40-50% of those cases are diagnosed in stages III and IV. oBjectives: The aim of this study was to assess a cost-effectiveness analysis for the use of fulvestrant 500mg as a therapy for postmenopausal women with locally advanced or metastatic breast cancer with ER+ receptor and prior progression on endocrine therapy, from Public Health Sector perspective in Mexico. Methods: A cost-effectiveness analysis was performed using a Markov model with a time horizon of 1 and 5 years according to a prior economic evaluation and based on the results from CONFIRM study. Two cohorts of treatment were compared; Cohort A contemplates the addition of fulvestrant to the standard treatment and Cohort B the standard treatment. The effectiveness was measured as Life years Gained (LY). The use of resources was determined from the clinical practice in Mexico and costs were obtained from institutional sources. results: The ICER per LY for 1 year time horizon was $9,609 for Cohort A versus Cohort B. The fifth year implies an ICER of $6,581, being in both cases an ICER below the willingness to pay in Mexico (1 GDP: $10,000USD). conclusions: Based on the findings the use of fulvestrant is a cost-effective strategy that provides a longer period of stable disease, an improvement in LY, diminish the use of chemotherapy and optimize institutional resources.

A discrete-event simulation (DES) of the network of primary health centers (PHCs) in a region can be used to evaluate the effect of changes in patient flow on operational outcomes across the network, and can also form the base simulation to which simulations of secondary and tertiary care facilities can be added. We present a DES of a network of PHCs using stochastic metamodels developed from more detailed DES models of PHCs ('parent' simulations), which were developed separately for comprehensively analyzing individual PHC operations. The stochastic metamodels are DESs in their own right. They are simplified versions of the parent simulation with full-featured representations of only those components relevant to the analysis at hand. We show that the outputs of interest from the metamodels and the parent simulations (including the network simulations) are statistically similar and that our metamodel-based network simulation yields reductions of up to 80% in runtimes.

No abstract

In this study, we investigate differences in tuberculosis (TB) treatment outcomes between urban and rural India and estimate their impact on epidemiological outcomes such as TB incidence, prevalence and mortality using a mathematical model of TB transmission dynamics. Publicly available district-level treatment outcomes data for new and previously treated TB cases was analyzed in conjunction with census data providing the proportion of urban population in each district to determine the effect of urbanity/rurality on treatment outcomes. Districts were grouped in clusters based on the proportion of urban population in each district, wherein the clusters were identified by applying machine learning methods. Regression analyses revealed that average treatment success rates among both new and previously treated cases decline with increase in the proportion of urban population in a district cluster, with substantially sharper declines in treatment success rates with degree of urbanity observed for previously treated cases. The impact of differences in treatment outcomes on epidemiological outcomes was estimated using a dynamic transmission model developed for this purpose. For example, the cluster with highest treatment success rates is projected to have an average of 3.2% fewer deaths per 100,000 population in comparison with the national average across 2019-24, and the cluster with the lowest treatment success rates has an average of 4.5% more deaths per 100,000 in comparison with the national average. We anticipate that these disparities in TB treatment outcomes and epidemiology between urban and rural India may motivate investigations into the associated causes and their redressal.

BackgroundThe treatment failure rate for Helicobacter pylori eradication therapy is ~20% due to poor patient compliance and increased antibiotic resistance. This analysis assessed the cost-effectiveness of universal post-treatment testing to confirm eradication of H. pylori infection in adults.MethodsDecision-analytic models evaluated the cost-effectiveness of universal post-treatment testing (urea breath test [UBT] or monoclonal fecal antigen test [mFAT]) vs no testing (Model 1), and UBT vs mFAT after adjusting for patient adherence to testing (Model 2) in adults who previously received first-line antimicrobial therapy. Patients testing positive received second-line quadruple therapy; no further action was taken for those testing negative or with no testing (Model 1) or for those nonadherent to testing (Model 2). In addition to testing costs, excess lifetime costs and reduced quality-adjusted life-years (QALYs) due to continuing H. pylori infection were considered in the model.ResultsExpected total costs per patient were higher for post-treatment testing (UBT: US$325.76; mFAT: US$242.12) vs no testing (US$182.41) in Model 1 and for UBT (US$336.75) vs mFAT (US$326.24) in Model 2. Expected QALYs gained per patient were 0.71 and 0.72 for UBT and mFAT, respectively, vs no testing (Model 1), and the same was 0.37 for UBT vs mFAT (Model 2). The estimated incremental costs per QALY gained for post-treatment testing vs no testing were US$82.90–US$202.45 and, after adjusting for adherence, US$28.13 for UBT vs mFAT.ConclusionUniversal post-treatment testing was found to be cost-effective for confirming eradication of H. pylori infection following first-line therapy. Better adherence to UBT relative to mFAT was the key to its cost-effectiveness.

The model can be used to evaluate the utility of laboratory protocols and establish realistic assay performance targets. The model also can help instrument manufacturers and laboratorians identify major contributors to assay measurement uncertainty, which helps improve performance in future assay systems.