Cost effectiveness of non-drug interventions that reduce nursing home admissions for people living with dementia.

INTRODUCTION: Six million Americans live with Alzheimer's disease and Alzheimer's disease and related dementias (AD/ADRD), a major health-care cost driver. We evaluated the cost effectiveness of non-pharmacologic interventions that reduce nursing home admissions for people living with AD/ADRD. METHODS: We used a person-level microsimulation to model the hazard ratios (HR) on nursing home admission for four evidence-based interventions compared to usual care: Maximizing Independence at Home (MIND), NYU Caregiver (NYU); Alzheimer's and Dementia Care (ADC); and Adult Day Service Plus (ADS Plus). We evaluated societal costs, quality-adjusted life years and incremental cost-effectiveness ratios. RESULTS: All four interventions cost less and are more effective (i.e., cost savings) than usual care from a societal perspective. Results did not materially change in 1-way, 2-way, structural, and probabilistic sensitivity analyses. CONCLUSION: Dementia-care interventions that reduce nursing home admissions save societal costs compared to usual care. Policies should incentivize providers and health systems to implement non-pharmacologic interventions.

Breastfeeding is associated with the intelligence of school-age children in Mexico.

Breastfeeding has been consistently associated with higher intelligence since childhood. However, this relation could be confounded due to maternal selection bias. We estimated the association between predominant breastfeeding and intelligence in school-age children considering potential selection bias and we simulated the intelligence gap reduction between low versus higher socioeconomic status children by increasing breastfeeding. We analysed predominant breastfeeding practices (breastmilk and water-based liquids) of children 0-3 years included in the Mexican Family Life Survey (MxFLS-1). Intelligence was estimated as the z-score of the abbreviated Raven score, measured at 6-12 years in the MxFLS-2 or MxFLS-3. We predicted breastfeeding duration among children with censored data with a Poisson model. We used the Heckman selection model to assess the association between breastfeeding and intelligence, correcting for selection bias and stratified by socioeconomic status. Results show after controlling for selection bias, a 1-month increase in predominant breastfeeding duration was associated with a 0.02 SD increase in the Raven z-score (p < 0.05). The children who were predominantly breastfed for 4-6 months versus <1 month had 0.16 SD higher Raven z-score (p < 0.05). No associations were found using multiple linear regression models. Among low socioeconomic status children, increasing predominantly breastfeeding duration to 6 months would increase their mean Raven z-score from -0.14 to -0.07 SD and reduce by 12.5% the intelligence gap with high socioeconomic status children. In conclusion, predominant breastfeeding duration was significantly associated with childhood intelligence after controlling for maternal selection bias. Increased breastfeeding duration may reduce poverty-driven intelligence inequities.

Effectiveness of Coronavirus Disease 2019 (COVID-19) Vaccines Among Incarcerated People in California State Prisons: Retrospective Cohort Study.

BACKGROUND: Prisons and jails are high-risk settings for coronavirus disease 2019 (COVID-19). Vaccines may substantially reduce these risks, but evidence is needed on COVID-19 vaccine effectiveness for incarcerated people, who are confined in large, risky congregate settings. METHODS: We conducted a retrospective cohort study to estimate effectiveness of messenger RNA (mRNA) vaccines, BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna), against confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections among incarcerated people in California prisons from 22 December 2020 through 1 March 2021. The California Department of Corrections and Rehabilitation provided daily data for all prison residents including demographic, clinical, and carceral characteristics, as well as COVID-19 testing, vaccination, and outcomes. We estimated vaccine effectiveness using multivariable Cox models with time-varying covariates, adjusted for resident characteristics and infection rates across prisons. RESULTS: Among 60 707 cohort members, 49% received at least 1 BNT162b2 or mRNA-1273 dose during the study period. Estimated vaccine effectiveness was 74% (95% confidence interval [CI], 64%-82%) from day 14 after first dose until receipt of second dose and 97% (95% CI, 88%-99%) from day 14 after second dose. Effectiveness was similar among the subset of residents who were medically vulnerable: 74% (95% CI, 62%-82%) and 92% (95% CI, 74%-98%) from 14 days after first and second doses, respectively. CONCLUSIONS: Consistent with results from randomized trials and observational studies in other populations, mRNA vaccines were highly effective in preventing SARS-CoV-2 infections among incarcerated people. Prioritizing incarcerated people for vaccination, redoubling efforts to boost vaccination, and continuing other ongoing mitigation practices are essential in preventing COVID-19 in this disproportionately affected population.

CDX2 Biomarker Testing and Adjuvant Therapy for Stage II Colon Cancer: An Exploratory Cost-Effectiveness Analysis.

OBJECTIVES: Adjuvant chemotherapy is not recommended for patients with average-risk stage II (T3N0) colon cancer. Nevertheless, a subgroup of these patients who are CDX2-negative might benefit from adjuvant chemotherapy. We evaluated the cost-effectiveness of testing for the absence of CDX2 expression followed by adjuvant chemotherapy (fluorouracil combined with oxaliplatin [FOLFOX]) for patients with stage II colon cancer. METHODS: We developed a decision model to simulate a hypothetical cohort of 65-year-old patients with average-risk stage II colon cancer with 7.2% of these patients being CDX2-negative under 2 different interventions: (1) test for the absence of CDX2 expression followed by adjuvant chemotherapy for CDX2-negative patients and (2) no CDX2 testing and no adjuvant chemotherapy for any patient. We derived disease progression parameters, adjuvant chemotherapy effectiveness and utilities from published analyses, and cancer care costs from the Surveillance, Epidemiology, and End Results (SEER)-Medicare data. Sensitivity analyses were conducted. RESULTS: Testing for CDX2 followed by FOLFOX for CDX2-negative patients had an incremental cost-effectiveness ratio of $5500/quality-adjusted life-years (QALYs) compared with no CDX2 testing and no FOLFOX (6.874 vs 6.838 discounted QALYs and $89 991 vs $89 797 discounted US dollar lifetime costs). In sensitivity analyses, considering a cost-effectiveness threshold of $100 000/QALY, testing for CDX2 followed by FOLFOX on CDX2-negative patients remains cost-effective for hazard ratios of <0.975 of the effectiveness of FOLFOX in CDX2-negative patients in reducing the rate of developing a metastatic recurrence. CONCLUSIONS: Testing tumors of patients with stage II colon cancer for CDX2 and administration of adjuvant treatment to the subgroup found CDX2-negative is a cost-effective and high-value management strategy across a broad range of plausible assumptions.

The Household Secondary Attack Rate of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): A Rapid Review.

BACKGROUND: Although much of the public health effort to combat coronavirus disease 2019 (COVID-19) has focused on disease control strategies in public settings, transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within households remains an important problem. The nature and determinants of household transmission are poorly understood. METHODS: To address this gap, we gathered and analyzed data from 22 published and prepublished studies from 10 countries (20 291 household contacts) that were available through 2 September 2020. Our goal was to combine estimates of the SARS-CoV-2 household secondary attack rate (SAR) and to explore variation in estimates of the household SAR. RESULTS: The overall pooled random-effects estimate of the household SAR was 17.1% (95% confidence interval [CI], 13.7-21.2%). In study-level, random-effects meta-regressions stratified by testing frequency (1 test, 2 tests, >2 tests), SAR estimates were 9.2% (95% CI, 6.7-12.3%), 17.5% (95% CI, 13.9-21.8%), and 21.3% (95% CI, 13.8-31.3%), respectively. Household SARs tended to be higher among older adult contacts and among contacts of symptomatic cases. CONCLUSIONS: These findings suggest that SARs reported using a single follow-up test may be underestimated, and that testing household contacts of COVID-19 cases on multiple occasions may increase the yield for identifying secondary cases.

COVID-19 in the California State Prison System: an Observational Study of Decarceration, Ongoing Risks, and Risk Factors.

BACKGROUND: Correctional institutions nationwide are seeking to mitigate COVID-19-related risks. OBJECTIVE: To quantify changes to California's prison population since the pandemic began and identify risk factors for COVID-19 infection. DESIGN: For California state prisons (March 1-October 10, 2020), we described residents' demographic characteristics, health status, COVID-19 risk scores, room occupancy, and labor participation. We used Cox proportional hazard models to estimate the association between rates of COVID-19 infection and room occupancy and out-of-room labor, respectively. PARTICIPANTS: Residents of California state prisons. MAIN MEASURES: Changes in the incarcerated population's size, composition, housing, and activities. For the risk factor analysis, the exposure variables were room type (cells vs. dormitories) and labor participation (any room occupant participating in the prior 2 weeks) and the outcome variable was incident COVID-19 case rates. KEY RESULTS: The incarcerated population decreased 19.1% (119,401 to 96,623) during the study period. On October 10, 2020, 11.5% of residents were aged ≥60, 18.3% had high COVID-19 risk scores, 31.0% participated in out-of-room labor, and 14.8% lived in rooms with ≥10 occupants. Nearly 40% of residents with high COVID-19 risk scores lived in dormitories. In 9 prisons with major outbreaks (6,928 rooms; 21,750 residents), dormitory residents had higher infection rates than cell residents (adjusted hazard ratio [AHR], 2.51 95% CI, 2.25-2.80) and residents of rooms with labor participation had higher rates than residents of other rooms (AHR, 1.56; 95% CI, 1.39-1.74). CONCLUSION: Despite reductions in room occupancy and mixing, California prisons still house many medically vulnerable residents in risky settings. Reducing risks further requires a combination of strategies, including rehousing, decarceration, and vaccination.

Computing the Expected Value of Sample Information Efficiently: Practical Guidance and Recommendations for Four Model-Based Methods.

Value of information (VOI) analyses can help policy makers make informed decisions about whether to conduct and how to design future studies. Historically a computationally expensive method to compute the expected value of sample information (EVSI) restricted the use of VOI to simple decision models and study designs. Recently, 4 EVSI approximation methods have made such analyses more feasible and accessible. Members of the Collaborative Network for Value of Information (ConVOI) compared the inputs, the analyst's expertise and skills, and the software required for the 4 recently developed EVSI approximation methods. Our report provides practical guidance and recommendations to help inform the choice between the 4 efficient EVSI estimation methods. More specifically, this report provides: (1) a step-by-step guide to the methods' use, (2) the expertise and skills required to implement the methods, and (3) method recommendations based on the features of decision-analytic problems.

Midwife-led care and obstetrician-led care for low-risk pregnancies: A cost comparison.

OBJECTIVE: Low-risk pregnant women cared for by midwives have similar birth outcomes to women cared for by physicians, although experiencing fewer medical procedures. However, limited research has assessed cost implications in the United States. Using national data, we assessed costs and resource use of midwife-led care vs obstetrician-led care for low-risk pregnancies using a decision-analytic approach. METHODS: We developed a decision-analytic model of costs (health plan payments to clinicians) and use of medical procedures during childbirth (epidural analgesia, labor induction, cesarean birth, episiotomy) and outcomes of care (birth at preterm gestation) that may differ with midwife-led vs obstetrician-led care. Model parameters for obstetric procedures were generated using Listening to Mothers III data, a national survey of women who gave birth in US hospitals in 2011-2012 and other published estimates. Cost estimates came from published or publicly available information on health insurance claims payments. RESULTS: The costs of childbirth for low-risk women with midwife-led care were, on average, $2262 less than births to low-risk women cared for by obstetricians. These cost differences derive from lower rates of preterm birth and episiotomy among women with midwife-led care, compared with obstetrician-led care. Across the population of US women with low-risk births each year (approximately 2.6 million), the model predicted substantially fewer preterm births (167 259 vs 219 427 for midwife-led vs obstetrician-led care) and fewer episiotomies (170 504 vs 415 686, for midwife-led vs obstetrician-led care). CONCLUSIONS: A shift from obstetrician-led care to midwife-led care for low-risk pregnancies could be cost saving.

A Value of Information Analysis of Research on the 21-Gene Assay for Breast Cancer Management.

OBJECTIVES: The 21-gene assay Oncotype DX (21-GA) shows promise as a guide in deciding when to initiate adjuvant chemotherapy in women with hormone receptor-positive early-stage breast cancer. Nevertheless, its routine use remains controversial, owing to insufficient evidence of its clinical utility and cost-effectiveness. Accordingly, we aim to quantify the value of conducting further research to reduce decision uncertainty in the use of the 21-GA. METHODS: Using value of information methods, we first generated probability distributions of survival and costs for decision making with and without the 21-GA alongside traditional risk prediction. These served as the input to a comparison of 3 alternative study designs: a retrospective observational study to update risk classification from the 21-GA, a prospective observational study to estimate prevalence of chemotherapy use, and a randomized controlled trial (RCT) of the 21-GA predictive value. RESULTS: We found that current evidence strongly supports the use of the 21-GA in intermediate- and high-risk women. Further research should focus on low-risk women, among whom the cost-effectiveness findings remained equivocal. For this population, we identified a high value of reducing uncertainty in the 21-GA use for all proposed research studies. The RCT had the greatest potential to efficiently reduce the likelihood of choosing a suboptimal strategy, providing a value between $162 million and $1.1 billion at willingness-to-pay thresholds of $150 000 to $200 000/quality-adjusted life years. CONCLUSION: Future research to inform 21-GA decision making is of high value. The RCT of the 21-GA predictive value has the greatest potential to efficiently reduce decision uncertainty around 21-GA use in women with low-risk early-stage breast cancer.

"Time Traveling Is Just Too Dangerous" but Some Methods Are Worth Revisiting: The Advantages of Expected Loss Curves Over Cost-Effectiveness Acceptability Curves and Frontier.

BACKGROUND: Cost-effectiveness acceptability curves (CEACs) and the cost-effectiveness acceptability frontier (CEAF) are the recommended graphical representations of uncertainty in a cost-effectiveness analysis (CEA). Nevertheless, many limitations of CEACs and the CEAF have been recognized by others. Expected loss curves (ELCs) overcome these limitations by displaying the expected foregone benefits of choosing one strategy over others, the optimal strategy in expectation, and the value of potential future research all in a single figure. OBJECTIVES: To revisit ELCs, illustrate their benefits using a case study, and promote their adoption by providing open-source code. METHODS: We used a probabilistic sensitivity analysis of a CEA comparing 6 cerebrospinal fluid biomarker test-and-treat strategies in patients with mild cognitive impairment. We showed how to calculate ELCs for a set of decision alternatives. We used the probabilistic sensitivity analysis of the case study to illustrate the limitations of currently recommended methods for communicating uncertainty and then demonstrated how ELCs can address these issues. RESULTS: ELCs combine the probability that each strategy is not cost-effective on the basis of current information and the expected foregone benefits resulting from choosing that strategy (ie, how much is lost if we recommended a strategy with a higher expected loss). ELCs display how the optimal strategy switches across willingness-to-pay thresholds and enables comparison between different strategies in terms of the expected loss. CONCLUSIONS: ELCs provide a more comprehensive representation of uncertainty and overcome current limitations of CEACs and the CEAF. Communication of uncertainty in CEA would benefit from greater adoption of ELCs as a complementary method to CEACs, the CEAF, and the expected value of perfect information.

Incorporating Biomarkers into the Primary Prostate Biopsy Setting: A Cost-Effectiveness Analysis.

PURPOSE: We performed a cost-effectiveness analysis using the PHI (Prostate Health Index), 4Kscore®, SelectMDx™ and the EPI (ExoDx™ Prostate [IntelliScore]) in men with elevated prostate specific antigen to determine the need for biopsy. MATERIALS AND METHODS: We developed a decision analytical model in men with elevated prostate specific antigen (3 ng/ml or greater) in which 1 biomarker test was used to determine which hypothetical individuals required biopsy. In the current standard of care strategy all individuals underwent biopsy. Model parameters were derived from a comprehensive review of the literature. Costs were calculated from a health sector perspective and converted into 2017 United States dollars. RESULTS: The cost and QALYs (quality adjusted life-years) of the current standard of care, which was transrectal ultrasound guided biopsy, was $3,863 and 18.085, respectively. Applying any of the 3 biomarkers improved quality adjusted survival compared to the current standard of care. The cost of SelectMDx, the PHI and the EPI was lower than performing prostate biopsy in all patients. However, the PHI was more costly and less effective than the SelectMDx strategy. The EPI provided the highest QALY with an incremental cost-effectiveness ratio of $58,404 per QALY. The use of biomarkers could reduce the number of unnecessary biopsies by 24% to 34% compared to the current standard of care. CONCLUSIONS: Applying biomarkers in men with elevated prostate specific antigen to determine the need for biopsy improved quality adjusted survival by decreasing the number of biopsies performed and the treatment of indolent disease. Using SelectMDx or the EPI following elevated prostate specific antigen but before proceeding to biopsy is a cost-effective strategy in this setting.

Trade-offs Between Efficacy and Cardiac Toxicity of Adjuvant Chemotherapy in Early-Stage Breast Cancer Patients: Do Competing Risks Matter?

Evidence about treatment efficacy and long-term toxicities for adjuvant chemotherapy in patients with early-stage breast cancer is often presented in different formats and studies. This leads to challenges for patients and their physicians to adequately weigh the trade-offs between effectiveness and long-term cardiac toxicity when making decisions about adjuvant chemotherapy. We used a decision-analytic framework to quantify these trade-offs by combining the available evidence into a single, comparable metric. We developed a Markov model to simulate a hypothetical cohort of newly diagnosed breast cancer patients under three scenarios: no treatment, anthracycline (AC)-based adjuvant chemotherapy (more effective but also more cardiotoxic), and non-AC-based adjuvant chemotherapy. We derived the model parameters from medical literature (e.g., clinical trials). Our primary outcome is 10-year mortality, and other metrics such as cause of death; life years (LYs) and quality-adjusted LYs over 10 years were evaluated in sensitivity analysis. For 55-year-old women with a 10-year risk of metastatic recurrence <12.5% no chemotherapy resulted in the preferred strategy. In general, non-AC-based adjuvant chemotherapy resulted in lower 10-year mortality than AC-based chemotherapy. Patients with low risk of metastatic recurrence are better off without adjuvant chemotherapy regardless of the outcome considered (i.e., the risks of cardiac toxicity from chemotherapy outweighed the benefits). Trade-offs between effectiveness and induced cardiac toxicity impact health outcomes. The choice of adjuvant treatment must consider the patient's risk of distant recurrence and the quality of life associated with different health outcomes.

Modeling the Cost-Effectiveness of Doula Care Associated with Reductions in Preterm Birth and Cesarean Delivery.

BACKGROUND: One in nine US infants is born before 37 weeks' gestation, incurring medical costs 10 times higher than full-term infants. One in three infants is born by cesarean; cesarean births cost twice as much as vaginal births. We compared rates of preterm and cesarean birth among Medicaid recipients with prenatal access to doula care (nonmedical maternal support) with similar women regionally. We used data on this association to mathematically model the potential cost-effectiveness of Medicaid coverage of doula services. METHODS: Data came from two sources: all Medicaid-funded, singleton births at hospitals in the West North Central and East North Central US (n = 65,147) in the 2012 Nationwide Inpatient Sample, and all Medicaid-funded singleton births (n = 1,935) supported by a community-based doula organization in the Upper Midwest from 2010 to 2014. We analyzed routinely collected, de-identified administrative data. Multivariable regression analysis was used to estimate associations between doula care and outcomes. A probabilistic decision-analytic model was used for cost-effectiveness estimates. RESULTS: Women who received doula support had lower preterm and cesarean birth rates than Medicaid beneficiaries regionally (4.7 vs 6.3%, and 20.4 vs 34.2%). After adjustment for covariates, women with doula care had 22 percent lower odds of preterm birth (AOR 0.77 [95% CI 0.61-0.96]). Cost-effectiveness analyses indicate potential savings associated with doula support reimbursed at an average of $986 (ranging from $929 to $1,047 across states). CONCLUSIONS: Based on associations between doula care and preterm and cesarean birth, coverage reimbursement for doula services would likely be cost saving or cost-effective for state Medicaid programs.

A computationally efficient nonparametric sampling (NPS) method of time to event for individual-level models.

Purpose: Individual-level simulation models often require sampling times to events, however efficient parametric distributions for many processes may often not exist. For example, time to death from life tables cannot be accurately sampled from existing parametric distributions. We propose an efficient nonparametric method to sample times to events that does not require any parametric assumption on the hazards. Methods: We developed a nonparametric sampling (NPS) approach that simultaneously draws multiple time-to-event samples from a categorical distribution. This approach can be applied to univariate and multivariate processes. The probabilities for each time interval are derived from the time interval-specific constant hazards. The times to events can then be used directly in individual-level simulation models. We compared the accuracy of our approach in sampling time-to-events from common parametric distributions, including exponential, Gamma, and Gompertz. In addition, we evaluated the method's performance in sampling age to death from US life tables and sampling times to events from parametric baseline hazards with time-dependent covariates. Results: The NPS method estimated similar expected times to events from 1 million draws for the three parametric distributions, 100,000 draws for the homogenous cohort, 200,000 draws from the heterogeneous cohort, and 1 million draws for the parametric distributions with time-varying covariates, all in less than a second. Conclusion: Our method produces accurate and computationally efficient samples for time-to-events from hazards without requiring parametric assumptions. This approach can substantially reduce the computation time required to simulate individual-level models.

Effects of Mitigation and Control Policies in Realistic Epidemic Models Accounting for Household Transmission Dynamics.

BACKGROUND: Compartmental infectious disease (ID) models are often used to evaluate nonpharmaceutical interventions (NPIs) and vaccines. Such models rarely separate within-household and community transmission, potentially introducing biases in situations in which multiple transmission routes exist. We formulated an approach that incorporates household structure into ID models, extending the work of House and Keeling. DESIGN: We developed a multicompartment susceptible-exposed-infectious-recovered-susceptible-vaccinated (MC-SEIRSV) modeling framework, allowing nonexponentially distributed duration in exposed and infectious compartments, that tracks within-household and community transmission. We simulated epidemics that varied by community and household transmission rates, waning immunity rate, household size (3 or 5 members), and numbers of exposed and infectious compartments (1-3 each). We calibrated otherwise identical models without household structure to the early phase of each parameter combination's epidemic curve. We compared each model pair in terms of epidemic forecasts and predicted NPI and vaccine impacts on the timing and magnitude of the epidemic peak and its total size. Meta-analytic regressions characterized the relationship between household structure inclusion and the size and direction of biases. RESULTS: Otherwise similar models with and without household structure produced equivalent early epidemic curves. However, forecasts from models without household structure were biased. Without intervention, they were upward biased on peak size and total epidemic size, with biases also depending on the number of exposed and infectious compartments. Model-estimated NPI effects of a 60% reduction in community contacts on peak time and size were systematically overestimated without household structure. Biases were smaller with a 20% reduction NPI. Because vaccination affected both community and household transmission, their biases were smaller. CONCLUSIONS: ID models without household structure can produce biased outcomes in settings in which within-household and community transmission differ. HIGHLIGHTS: Infectious disease models rarely separate household transmission from community transmission. The pace of household transmission may differ from community transmission, depends on household size, and can accelerate epidemic growth.Many infectious disease models assume exponential duration distributions for infected states. However, the duration of most infections is not exponentially distributed, and distributional choice alters modeled epidemic dynamics and intervention effectiveness.We propose a mathematical framework for household and community transmission that allows for nonexponential duration times and a suite of interventions and quantified the effect of accounting for household transmission by varying household size and duration distributions of infected states on modeled epidemic dynamics.Failure to include household structure induces biases in the modeled overall course of an epidemic and the effects of interventions delivered differentially in community settings. Epidemic dynamics are faster and more intense in populations with larger household sizes and for diseases with nonexponentially distributed infectious durations. Modelers should consider explicitly incorporating household structure to quantify the effects of non-pharmaceutical interventions (e.g., shelter-in-place).

Using Age-Specific Rates for Parametric Survival Function Estimation in Simulation Models.

PURPOSE: To describe a procedure for incorporating parametric functions into individual-level simulation models to sample time to event when age-specific rates are available but not the individual data. METHODS: Using age-specific event rates, regression analysis was used to parametrize parametric survival distributions (Weibull, Gompertz, log-normal, and log-logistic), select the best fit using the R2 statistic, and apply the corresponding formula to assign random times to events in simulation models. We used stroke rates in the Spanish population to illustrate our procedure. RESULTS: The 3 selected survival functions (Gompertz, Weibull, and log-normal) had a good fit to the data up to 85 y of age. We selected Gompertz distribution as the best-fitting distribution due to its goodness of fit. CONCLUSIONS: Our work provides a simple procedure for incorporating parametric risk functions into simulation models without individual-level data. HIGHLIGHTS: We describe the procedure for sampling times to event for individual-level simulation models as a function of age from parametric survival functions when age-specific rates are available but not the individual dataWe used linear regression to estimate age-specific hazard functions, obtaining estimates of parameter uncertainty.Our approach allows incorporating parameter (second-order) uncertainty in individual-level simulation models needed for probabilistic sensitivity analysis in the absence of individual-level survival data.

Incentivizing adherence to pre-exposure prophylaxis for HIV prevention: a randomized pilot trial among male sex workers in Mexico.

Low adherence to preventative medications against life-long health conditions is a major contributor to global morbidity and mortality. We implemented a pilot randomized controlled trial in Mexico to measure the extent to which conditional economic incentives help male sex workers increase their adherence to pre-exposure prophylaxis (PrEP) for HIV prevention. We followed n = 110 male sex workers over 6 months. At each quarterly visit (at months 0, 3, and 6), all workers received a $10 transport reimbursement, a free 3-month PrEP supply, and completed socio-behavioral surveys. The primary outcome was an objective biomarker of medication adherence based on tenofovir (TFV) drug concentration levels in hair collected at each visit. Individuals randomized to the intervention received incentives based on a grading system as a function of PrEP adherence: those with high (> 0.043 ng/mg TFV concentration), medium (0.011 to 0.042 ng/mg), or low (< 0.011 ng/mg) adherence received $20, $10, or $0, respectively. Six-month pooled effects of incentives on PrEP adherence were analyzed using population-averaged gamma generalized estimating equation models. We estimated heterogeneous treatment effects by sex worker characteristics. The incentive intervention led to a 28.7% increase in hair antiretroviral concentration levels over 6 months consistent with increased PrEP adherence (p = 0.05). The effect of incentives on PrEP adherence was greater for male sex workers who were street-based (vs. internet) workers (p < 0.10). These pilot findings suggest that modest conditional economic incentives could be effective, at scale, for improving PrEP adherence among male sex workers, and should be tested in larger implementation trials. ClinicalTrials.gov Identifier: NCT03674983.

Emulator-based Bayesian calibration of the CISNET colorectal cancer models.

Purpose: To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET) 's SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets. Methods: We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANN) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets. Results: The optimal ANN for SimCRC had four hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had one hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 hours for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN. Conclusions: Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, like the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating three realistic CRC individual-level models using a Bayesian approach.

State-level disparities in cervical cancer prevention and impact on outcomes in the U.S.: A modeling study.

Background: Despite the availability of HPV vaccines for over a decade, coverage across the United States (US) is varied. While some states have made concerted efforts to increase HPV vaccination coverage, most model-based analyses have estimated vaccine impact on the US. We estimated the impact of hypothetical changes in HPV vaccination coverage at the state level for three states with varying levels of HPV vaccination coverage and cervical cancer incidence (California, New York, Texas) using a mathematical model. Methods: We developed a new mathematical model of HPV transmission and cervical cancer tailored to state-level cancer incidence and mortality. We quantified the public health impact of increasing HPV vaccination coverage to 80% by 2025 or 2030 and the effect on time to elimination in the three states. Results: Increasing vaccination coverage to 80% in Texas in 10 years could reduce cervical cancer incidence by 50.9% (95%-CrI: 46.6-56.1%) by 2100. In New York and California, achieving the same coverage could reduce incidence by 27.3% (95%-CrI: 23.9-31.5%) and 24.4% (95%-CrI: 20.0-30.0%), respectively. Achieving 80% coverage in 5 years will slightly increase the reduction. If 2019 vaccination coverage continues, cervical cancer elimination would be reached in the US by 2051 (95%-Crl: 2034-2064). However, the timeline by which individual states reach elimination could vary by decades. Conclusion: Achieving an HPV vaccination coverage target of 80% by 2030 will benefit states with low vaccination coverage and high cervical cancer incidence the most. Our results highlight the value of more geographically focused analyses to inform priorities.