Cost-effectiveness and public health impact of typhoid conjugate vaccine introduction strategies in Bangladesh.

PURPOSE: Typhoid fever causes substantial morbidity and mortality in Bangladesh. The government of Bangladesh plans to introduce typhoid conjugate vaccines (TCV) in its expanded program on immunization (EPI) schedule. However, the optimal introduction strategy in addition to the costs and benefits of such a program are unclear. METHODS: We extended an existing mathematical model of typhoid transmission to integrate cost data, clinical incidence data, and recently conducted serosurveys in urban, semi-urban, and rural areas. In our primary analysis, we evaluated the status quo (i.e., no vaccination) and eight vaccine introduction strategies including routine and 1-time campaign strategies, which differed by age groups targeted and geographic focus. Model outcomes included clinical incidence, seroincidence, deaths, costs, disability-adjusted life years (DALYs), and incremental cost-effectiveness ratios (ICERs) for each strategy. We adopted a societal perspective, 10-year model time horizon, and 3 % annual discount rate. We performed probabilistic, one-way, and scenario sensitivity analyses including adopting a healthcare perspective and alternate model time horizons. RESULTS: We projected that all TCV strategies would be cost saving compared to the status quo. The preferred strategy was a nationwide introduction of TCV at 9-12 months of age with a single catch-up campaign for children ages 1-15, which was cost saving compared to all other strategies and the status quo. In the 10 years following implementation, we projected this strategy would avert 3.77 million cases (95 % CrI: 2.60 - 5.18), 11.31 thousand deaths (95 % CrI: 3.77 - 23.60), and save $172.35 million (95 % CrI: -14.29 - 460.59) compared to the status quo. Our findings were broadly robust to changes in parameter values and willingness-to-pay thresholds. CONCLUSIONS: We projected that nationwide TCV introduction with a catch-up campaign would substantially reduce typhoid incidence and very likely be cost saving in Bangladesh.

Dynamics of Respiratory Infectious Diseases in Incarcerated and Free-Living Populations: A Simulation Modeling Study.

BACKGROUND: Historically, correctional facilities have had large outbreaks of respiratory infectious diseases like COVID-19. Hence, importation and exportation of such diseases from correctional facilities raises substantial concern. METHODS: We developed a stochastic simulation model of transmission of respiratory infectious diseases within and between correctional facilities and the community. We investigated the infection dynamics, key governing factors, and relative importance of different infection routes (e.g., incarcerations and releases versus correctional staff). We also developed machine-learning meta-models of the simulation model, which allowed us to examine how our findings depended on different disease, correctional facility, and community characteristics. RESULTS: We find a magnification-reflection dynamic: a small outbreak in the community can cause a larger outbreak in the correction facility, which can then cause a second, larger outbreak in the community. This dynamic is strongest when community size is relatively small as compared with the size of the correctional population, the initial community R-effective is near 1, and initial prevalence of immunity in the correctional population is low. The timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting. Because the release rates from prisons are low, our model suggests correctional staff may be a more important infection entry route into prisons than incarcerations and releases; in jails, where incarceration and release rates are much higher, our model suggests the opposite. CONCLUSIONS: We find that across many combinations of respiratory pathogens, correctional settings, and communities, there can be substantial magnification-reflection dynamics, which are governed by several key factors. Our goal was to derive theoretical insights relevant to many contexts; our findings should be interpreted accordingly. HIGHLIGHTS: We find a magnification-reflection dynamic: a small outbreak in a community can cause a larger outbreak in a correctional facility, which can then cause a second, larger outbreak in the community.For public health decision makers considering contexts most susceptible to this dynamic, we find that the dynamic is strongest when the community size is relatively small, initial community R-effective is near 1, and the initial prevalence of immunity in the correctional population is low; the timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting.We find that correctional staff may be a more important infection entry route into prisons than incarcerations and releases; however, for jails, the relative importance of the entry routes may be reversed.For modelers, we combine simulation modeling, machine-learning meta-modeling, and interpretable machine learning to examine how our findings depend on different disease, correctional facility, and community characteristics; we find they are generally robust.

Personalization of Medical Treatment Decisions: Simplifying Complex Models while Maintaining Patient Health Outcomes.

BACKGROUND: Personalizing medical treatments based on patient-specific risks and preferences can improve patient health. However, models to support personalized treatment decisions are often complex and difficult to interpret, limiting their clinical application. METHODS: We present a new method, using machine learning to create meta-models, for simplifying complex models for personalizing medical treatment decisions. We consider simple interpretable models, interpretable ensemble models, and noninterpretable ensemble models. We use variable selection with a penalty for patient-specific risks and/or preferences that are difficult, risky, or costly to obtain. We interpret the meta-models to the extent permitted by their model architectures. We illustrate our method by applying it to simplify a previously developed model for personalized selection of antipsychotic drugs for patients with schizophrenia. RESULTS: The best simplified interpretable, interpretable ensemble, and noninterpretable ensemble models contained at most half the number of patient-specific risks and preferences compared with the original model. The simplified models achieved 60.5% (95% credible interval [crI]: 55.2-65.4), 60.8% (95% crI: 55.5-65.7), and 83.8% (95% crI: 80.8-86.6), respectively, of the net health benefit of the original model (quality-adjusted life-years gained). Important variables in all models were similar and made intuitive sense. Computation time for the meta-models was orders of magnitude less than for the original model. LIMITATIONS: The simplified models share the limitations of the original model (e.g., potential biases). CONCLUSIONS: Our meta-modeling method is disease- and model- agnostic and can be used to simplify complex models for personalization, allowing for variable selection in addition to improved model interpretability and computational performance. Simplified models may be more likely to be adopted in clinical settings and can help improve equity in patient outcomes.

Partial Personalization of Medical Treatment Decisions: Adverse Effects and Possible Solutions.

BACKGROUND: Personalizing medical treatment decisions based on patient-specific risks and/or preferences can improve health outcomes. Decision makers frequently select treatments based on partial personalization (e.g., personalization based on risks but not preferences or vice versa) due to a lack of data about patient-specific risks and preferences. However, partially personalizing treatment decisions based on a subset of patient risks and/or preferences can result in worse population-level health outcomes than no personalization and can increase the variance of population-level health outcomes. METHODS: We develop a new method for partially personalizing treatment decisions that avoids these problems. Using a case study of antipsychotic treatment for schizophrenia, as well as 4 additional illustrative examples, we demonstrate the adverse effects and our method for avoiding them. RESULTS: For the schizophrenia treatment case study, using a previously proposed modeling approach for personalizing treatment decisions and using only a subset of patient preferences regarding treatment efficacy and side effects, mean population-level health outcomes decreased by 0.04 quality-adjusted life-years (QALYs; 95% credible interval [crI]: 0.02-0.06) per patient compared with no personalization. Using our new method and considering the same subset of patient preferences, mean population-level health outcomes increased by 0.01 QALYs (95% crI: 0.00-0.03) per patient as compared with no personalization, and the variance decreased. LIMITATIONS: We assumed a linear and additive utility function. CONCLUSIONS: Selecting personalized treatments for patients should be done in a way that does not decrease expected population-level health outcomes and does not increase their variance, thereby resulting in worse risk-adjusted, population-level health outcomes compared with treatment selection with no personalization. Our method can be used to ensure this, thereby helping patients realize the benefits of treatment personalization without the potential harms.

Personalizing Medical Treatment Decisions: Integrating Meta-analytic Treatment Comparisons with Patient-Specific Risks and Preferences.

Background. Network meta-analyses (NMAs) that compare treatments for a given condition allow physicians to identify which treatments have higher or lower probabilities of reducing the risks of disease complications or increasing the risks of treatment side effects. Translating these data into personalized treatment plans requires integration of NMA data with patient-specific pretreatment risk estimates and preferences regarding treatment objectives and acceptable risks. Methods. We introduce a modeling framework to integrate data probabilistically from NMAs with data on individualized patient risk estimates for disease outcomes, treatment preferences (such as willingness to incur greater side effects for increased life expectancy), and risk preferences. We illustrate the modeling framework by creating personalized plans for antipsychotic drug treatment and evaluating their effectiveness and cost-effectiveness. Results. Compared with treating all patients with the drug that yields the greatest quality-adjusted life-years (QALYs) on average (amisulpride), personalizing the selection of antipsychotic drugs for schizophrenia patients over the next 5 years would be expected to yield 0.33 QALYs (95% credible interval [crI]: 0.30-0.37) per patient at an incremental cost of $4849/QALY gained (95% crI: dominant-$12,357), versus 0.29 and 0.04 QALYs per patient when accounting for only risks or preferences, respectively, but not both. Limitations. The analysis uses a linear, additive utility function to reflect patient treatment preferences and does not consider potential variations in patient time discounting. Conclusions. Our modeling framework rigorously computes what physicians normally have to do mentally. By integrating 3 key components of personalized medicine-evidence on efficacy, patient risks, and patient preferences-the modeling framework can provide personalized treatment decisions to improve patient health outcomes.

Anticipated burden and mitigation of carbon-dioxide-induced nutritional deficiencies and related diseases: A simulation modeling study.

BACKGROUND: Rising atmospheric carbon dioxide concentrations are anticipated to decrease the zinc and iron concentrations of crops. The associated disease burden and optimal mitigation strategies remain unknown. We sought to understand where and to what extent increasing carbon dioxide concentrations may increase the global burden of nutritional deficiencies through changes in crop nutrient concentrations, and the effects of potential mitigation strategies. METHODS AND FINDINGS: For each of 137 countries, we incorporated estimates of climate change, crop nutrient concentrations, dietary patterns, and disease risk into a microsimulation model of zinc and iron deficiency. These estimates were obtained from the Intergovernmental Panel on Climate Change, US Department of Agriculture, Statistics Division of the Food and Agriculture Organization of the United Nations, and Global Burden of Disease Project, respectively. In the absence of increasing carbon dioxide concentrations, we estimated that zinc and iron deficiencies would induce 1,072.9 million disability-adjusted life years (DALYs) globally over the period 2015 to 2050 (95% credible interval [CrI]: 971.1-1,167.7). In the presence of increasing carbon dioxide concentrations, we estimated that decreasing zinc and iron concentrations of crops would induce an additional 125.8 million DALYs globally over the same period (95% CrI: 113.6-138.9). This carbon-dioxide-induced disease burden is projected to disproportionately affect nations in the World Health Organization's South-East Asia and African Regions (44.0 and 28.5 million DALYs, respectively), which already have high existing disease burdens from zinc and iron deficiencies (364.3 and 299.5 million DALYs, respectively), increasing global nutritional inequalities. A climate mitigation strategy such as the Paris Agreement (an international agreement to keep global temperatures within 2°C of pre-industrial levels) would be expected to avert 48.2% of this burden (95% CrI: 47.8%-48.5%), while traditional public health interventions including nutrient supplementation and disease control programs would be expected to avert 26.6% of the burden (95% CrI: 23.8%-29.6%). Of the traditional public health interventions, zinc supplementation would be expected to avert 5.5%, iron supplementation 15.7%, malaria mitigation 3.2%, pneumonia mitigation 1.6%, and diarrhea mitigation 0.5%. The primary limitations of the analysis include uncertainty regarding how food consumption patterns may change with climate, how disease mortality rates will change over time, and how crop zinc and iron concentrations will decline from those at present to those in 2050. CONCLUSIONS: Effects of increased carbon dioxide on crop nutrient concentrations are anticipated to exacerbate inequalities in zinc and iron deficiencies by 2050. Proposed Paris Agreement strategies are expected to be more effective than traditional public health measures to avert the increased inequality.

Cost-Effectiveness of HIV Preexposure Prophylaxis for People Who Inject Drugs in the United States.

BACKGROUND: The total population health benefits and costs of HIV preexposure prophylaxis (PrEP) for people who inject drugs (PWID) in the United States are unclear. OBJECTIVE: To evaluate the cost-effectiveness and optimal delivery conditions of PrEP for PWID. DESIGN: Empirically calibrated dynamic compartmental model. DATA SOURCES: Published literature and expert opinion. TARGET POPULATION: Adult U.S. PWID. TIME HORIZON: 20 years and lifetime. INTERVENTION: PrEP alone, PrEP with frequent screening (PrEP+screen), and PrEP+screen with enhanced provision of antiretroviral therapy (ART) for individuals who become infected (PrEP+screen+ART). All scenarios are considered at 25% coverage. OUTCOME MEASURES: Infections averted, deaths averted, change in HIV prevalence, discounted costs (in 2015 U.S. dollars), discounted quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios. RESULTS OF BASE-CASE ANALYSIS: PrEP+screen+ART dominates other strategies, averting 26 700 infections and reducing HIV prevalence among PWID by 14% compared with the status quo. Achieving these benefits costs $253 000 per QALY gained. At current drug prices, total expenditures for PrEP+screen+ART could be as high as $44 billion over 20 years. RESULTS OF SENSITIVITY ANALYSIS: Cost-effectiveness of the intervention is linear in the annual cost of PrEP and is dependent on PrEP drug adherence, individual transmission risks, and community HIV prevalence. LIMITATION: Data on risk stratification and achievable PrEP efficacy levels for U.S. PWID are limited. CONCLUSION: PrEP with frequent screening and prompt treatment for those who become infected can reduce HIV burden among PWID and provide health benefits for the entire U.S. population, but, at current drug prices, it remains an expensive intervention both in absolute terms and in cost per QALY gained. PRIMARY FUNDING SOURCE: National Institute on Drug Abuse.

Comparison study of biomimetic strontium-doped calcium phosphate coatings by electrochemical deposition and air plasma spray: morphology, composition and bioactive performance.

In this study, strontium-doped calcium phosphate coatings were deposited by electrochemical deposition and plasma spray under different process parameters to achieve various coating morphologies. The coating composition was investigated by energy dispersive X-ray spectroscopy and X-ray diffraction. The surface morphologies of the coatings were studied through scanning electron microscopy while the cytocompatibility and bioactivity of the strontium-doped calcium phosphate coatings were evaluated using bone cell culture using MC3T3-E1 osteoblast-like cells. The addition of strontium leads to enhanced proliferation suggesting the possible benefits of strontium incorporation in calcium phosphate coatings. The morphology and composition of deposited coatings showed a strong influence on the growth of cells.