Comparability of Self Rated Health: Cross Sectional Multi-Country Survey Using Anchoring Vignettes

Objective: To examine differences in expectations for health using anchoring vignettes, which describe fixed levels of health on dimensions such as mobility. Design: Cross sectional survey of adults living in the community. Setting: China, Myanmar, Sri Lanka, Pakistan, Turkey, and United Arab Emirates. Participants: 3012 men and women aged 18 years and older (self ratings); subsample of 406 (vignette ratings). Main outcome measures: Self rated mobility levels and ratings of hypothetical vignettes using the same questions and response categories. Results: Consistent rankings of vignettes are evidence that vignettes are understood in similar ways in different settings, and internal consistency of orderings on two mobility questions indicates good comprehension. Variation in vignette ratings across age groups suggests that expectations for mobility decline with age. Comparison of responses to two different mobility questions supports the assumption that individual ratings of hypothetical vignettes relate to expectations for health in similar ways as self assessments. Conclusions: Anchoring vignettes could provide a powerful tool for understanding and adjusting for the influence of different health expectations on self ratings of health. Incorporating anchoring vignettes in surveys can improve the comparability of self reported measures

Cost-effectiveness of Rotavirus vaccination in Vietnam

Background: Rotavirus is the most common cause of severe diarrhea leading to hospitalization or disease-specific death among young children. New rotavirus vaccines have recently been approved. Some previous studies have provided broad qualitative insights into the health and economic consequences of introducing the vaccines into low-income countries, representing several features of rotavirus infection, such as varying degrees of severity and age-dependency of clinical manifestation, in their model-based analyses. We extend this work to reflect additional features of rotavirus (e.g., the possibility of reinfection and varying degrees of partial immunity conferred by natural infection), and assess the influence of the features on the cost-effectiveness of rotavirus vaccination. Methods: We developed a Markov model that reflects key features of rotavirus infection, using the most recent data available. We applied the model to the 2004 Vietnamese birth cohort and reevaluated the cost-effectiveness (2004 US dollars per disability-adjusted life year [DALY]) of rotavirus vaccination (Rotarix®) compared to no vaccination, from both societal and health care system perspectives. We conducted univariate sensitivity analyses and also performed a probabilistic sensitivity analysis, based on Monte Carlo simulations drawing parameter values from the distributions assigned to key uncertain parameters. Results: Rotavirus vaccination would not completely protect young children against rotavirus infection due to the partial nature of vaccine immunity, but would effectively reduce severe cases of rotavirus gastroenteritis (outpatient visits, hospitalizations, or deaths) by about 67% over the first 5 years of life. Under base-case assumptions (94% coverage and $5 per dose), the incremental cost per DALY averted from vaccination compared to no vaccination would be$540 from the societal perspective and $550 from the health care system perspective. Conclusion: Introducing rotavirus vaccines would be a cost-effective public health intervention in Vietnam. However, given the uncertainty about vaccine efficacy and potential changes in rotavirus epidemiology in local settings, further clinical research and re-evaluation of rotavirus vaccination programs may be necessary as new information emerges

Comparative effectiveness of personalized lifestyle management strategies for cardiovascular disease risk reduction

Background-Evidence shows that healthy diet, exercise, smoking interventions, and stress reduction reduce cardiovascular disease risk. We aimed to compare the effectiveness of these lifestyle interventions for individual risk profiles and determine their rank order in reducing 10-year cardiovascular disease risk. Methods and Results-We computed risks using the American College of Cardiology/American Heart Association Pooled Cohort Equations for a variety of individual profiles. Using published literature on risk factor reductions through diverse lifestyle interventions-group therapy for stopping smoking, Mediterranean diet, aerobic exercise (walking), and yoga-we calculated the risk reduction through each of these interventions to determine the strategy associated with the maximum benefit for each profile. Sensitivity analyses were conducted to test the robustness of the results. In the base-case analysis, yoga was associated with the largest 10-year cardiovascular disease risk reductions (maximum absolute reduction 16.7% for the highest-risk individuals). Walking generally ranked second (max 11.4%), followed by Mediterranean diet (max 9.2%), and group therapy for smoking (max 1.6%). If the individual was a current smoker and successfully quit smoking (ie, achieved complete smoking cessation), then stopping smoking yielded the largest reduction. Probabilistic and 1-way sensitivity analysis confirmed the demonstrated trend. Conclusions-This study reports the comparative effectiveness of several forms of lifestyle modifications and found smoking cessation and yoga to be the most effective forms of cardiovascular disease prevention. Future research should focus on patient adherence to personalized therapies, cost-effectiveness of these strategies, and the potential for enhanced benefit when interventions are performed simultaneously rather than as single measures

Clinical Benefits, Costs, and Cost-Effectiveness of Neonatal Intensive Care in Mexico

Joshua Salomon and colleagues performed a cost-effectiveness analysis using health and economic outcomes following preterm birth in Mexico and showed that neonatal intensive care provided high value for the money in this setting

Modeling human papillomavirus and cervical cancer in the United States for analyses of screening and vaccination

Background: To provide quantitative insight into current U.S. policy choices for cervical cancer prevention, we developed a model of human papillomavirus (HPV) and cervical cancer, explicitly incorporating uncertainty about the natural history of disease. Methods: We developed a stochastic microsimulation of cervical cancer that distinguishes different HPV types by their incidence, clearance, persistence, and progression. Input parameter sets were sampled randomly from uniform distributions, and simulations undertaken with each set. Through systematic reviews and formal data synthesis, we established multiple epidemiologic targets for model calibration, including age-specific prevalence of HPV by type, age-specific prevalence of cervical intraepithelial neoplasia (CIN), HPV type distribution within CIN and cancer, and age-specific cancer incidence. For each set of sampled input parameters, likelihood-based goodness-of-fit (GOF) scores were computed based on comparisons between model-predicted outcomes and calibration targets. Using 50 randomly resampled, good-fitting parameter sets, we assessed the external consistency and face validity of the model, comparing predicted screening outcomes to independent data. To illustrate the advantage of this approach in reflecting parameter uncertainty, we used the 50 sets to project the distribution of health outcomes in U.S. women under different cervical cancer prevention strategies. Results: Approximately 200 good-fitting parameter sets were identified from 1,000,000 simulated sets. Modeled screening outcomes were externally consistent with results from multiple independent data sources. Based on 50 good-fitting parameter sets, the expected reductions in lifetime risk of cancer with annual or biennial screening were 76% (range across 50 sets: 69–82%) and 69% (60–77%), respectively. The reduction from vaccination alone was 75%, although it ranged from 60% to 88%, reflecting considerable parameter uncertainty about the natural history of type-specific HPV infection. The uncertainty surrounding the model-predicted reduction in cervical cancer incidence narrowed substantially when vaccination was combined with every-5-year screening, with a mean reduction of 89% and range of 83% to 95%. Conclusion: We demonstrate an approach to parameterization, calibration and performance evaluation for a U.S. cervical cancer microsimulation model intended to provide qualitative and quantitative inputs into decisions that must be taken before long-term data on vaccination outcomes become available. This approach allows for a rigorous and comprehensive description of policy-relevant uncertainty about health outcomes under alternative cancer prevention strategies. The model provides a tool that can accommodate new information, and can be modified as needed, to iteratively assess the expected benefits, costs, and cost-effectiveness of different policies in the U.S

Comparative Effectiveness of Personalized Lifestyle Management Strategies for Cardiovascular Disease Risk Reduction

BACKGROUND: Evidence shows that healthy diet, exercise, smoking interventions, and stress reduction reduce cardiovascular disease risk. We aimed to compare the effectiveness of these lifestyle interventions for individual risk profiles and determine their rank order in reducing 10-year cardiovascular disease risk. METHODS AND RESULTS: We computed risks using the American College of Cardiology/American Heart Association Pooled Cohort Equations for a variety of individual profiles. Using published literature on risk factor reductions through diverse lifestyle interventions-group therapy for stopping smoking, Mediterranean diet, aerobic exercise (walking), and yoga-we calculated the risk reduction through each of these interventions to determine the strategy associated with the maximum benefit for each profile. Sensitivity analyses were conducted to test the robustness of the results. In the base-case analysis, yoga was associated with the largest 10-year cardiovascular disease risk reductions (maximum absolute reduction 16.7% for the highest-risk individuals). Walking generally ranked second (max 11.4%), followed by Mediterranean diet (max 9.2%), and group therapy for smoking (max 1.6%). If the individual was a current smoker and successfully quit smoking (ie, achieved complete smoking cessation), then stopping smoking yielded the largest reduction. Probabilistic and 1-way sensitivity analysis confirmed the demonstrated trend. CONCLUSIONS: This study reports the comparative effectiveness of several forms of lifestyle modifications and found smoking cessation and yoga to be the most effective forms of cardiovascular disease prevention. Future research should focus on patient adherence to personalized therapies, cost-effectiveness of these strategies, and the potential for enhanced benefit when interventions are performed simultaneously rather than as single measures

All-cause versus cause-specific excess deaths for estimating influenza-associated mortality in Denmark, Spain, and the United States

Background: Seasonal influenza-associated excess mortality estimates can be timely and provide useful information on the severity of an epidemic. This methodology can be leveraged during an emergency response or pandemic. Method: For Denmark, Spain, and the United States, we estimated age-stratified excess mortality for (i) all-cause, (ii) respiratory and circulatory, (iii) circulatory, (iv) respiratory, and (v) pneumonia, and influenza causes of death for the 2015/2016 and 2016/2017 influenza seasons. We quantified differences between the countries and seasonal excess mortality estimates and the death categories. We used a time-series linear regression model accounting for time and seasonal trends using mortality data from 2010 through 2017. Results: The respective periods of weekly excess mortality for all-cause and cause-specific deaths were similar in their chronological patterns. Seasonal all-cause excess mortality rates for the 2015/2016 and 2016/2017 influenza seasons were 4.7 (3.3-6.1) and 14.3 (13.0-15.6) per 100,000 population, for the United States; 20.3 (15.8-25.0) and 24.0 (19.3-28.7) per 100,000 population for Denmark; and 22.9 (18.9-26.9) and 52.9 (49.1-56.8) per 100,000 population for Spain. Seasonal respiratory and circulatory excess mortality estimates were two to three times lower than the all-cause estimates. Discussion: We observed fewer influenza-associated deaths when we examined cause-specific death categories compared with all-cause deaths and observed the same trends in peaks in deaths with all death causes. Because all-cause deaths are more available, these models can be used to monitor virus activity in near real time. This approach may contribute to the development of timely mortality monitoring systems during public health emergencies.This study was conducted as part of Sebastian Schmidt's research fellowship, which was financially supported by the Novo Nordic Foundation and A.P. Møller Fonden. The EuroMOMO network has received financial support from the European Centre for Disease Prevention and Control (ECDC) and from the World Health Organization (WHO) Regional Office for Europe.S

HIV Treatment as Prevention: Systematic Comparison of Mathematical Models of the Potential Impact of Antiretroviral Therapy on HIV Incidence in South Africa

Background: Many mathematical models have investigated the impact of expanding access to antiretroviral therapy (ART) on new HIV infections. Comparing results and conclusions across models is challenging because models have addressed slightly different questions and have reported different outcome metrics. This study compares the predictions of several mathematical models simulating the same ART intervention programmes to determine the extent to which models agree about the epidemiological impact of expanded ART. Methods and Findings: Twelve independent mathematical models evaluated a set of standardised ART intervention scenarios in South Africa and reported a common set of outputs. Intervention scenarios systematically varied the CD4 count threshold for treatment eligibility, access to treatment, and programme retention. For a scenario in which 80% of HIV-infected individuals start treatment on average 1 y after their CD4 count drops below 350 cells/µl and 85% remain on treatment after 3 y, the models projected that HIV incidence would be 35% to 54% lower 8 y after the introduction of ART, compared to a counterfactual scenario in which there is no ART. More variation existed in the estimated long-term (38 y) reductions in incidence. The impact of optimistic interventions including immediate ART initiation varied widely across models, maintaining substantial uncertainty about the theoretical prospect for elimination of HIV from the population using ART alone over the next four decades. The number of person-years of ART per infection averted over 8 y ranged between 5.8 and 18.7. Considering the actual scale-up of ART in South Africa, seven models estimated that current HIV incidence is 17% to 32% lower than it would have been in the absence of ART. Differences between model assumptions about CD4 decline and HIV transmissibility over the course of infection explained only a modest amount of the variation in model results. Conclusions: Mathematical models evaluating the impact of ART vary substantially in structure, complexity, and parameter choices, but all suggest that ART, at high levels of access and with high adherence, has the potential to substantially reduce new HIV infections. There was broad agreement regarding the short-term epidemiologic impact of ambitious treatment scale-up, but more variation in longer term projections and in the efficiency with which treatment can reduce new infections. Differences between model predictions could not be explained by differences in model structure or parameterization that were hypothesized to affect intervention impact

Prioritizing CD4 Count Monitoring in Response to ART in Resource-Constrained Settings: A Retrospective Application of Prediction-Based Classification

Luis Montaner and colleagues retrospectively apply a potential capacity-saving CD4 count prediction tool to a cohort of HIV patients on antiretroviral therapy

Reconsidering the use of rankings in the valuation of health states: a model for estimating cardinal values from ordinal data

BACKGROUND: In survey studies on health-state valuations, ordinal ranking exercises often are used as precursors to other elicitation methods such as the time trade-off (TTO) or standard gamble, but the ranking data have not been used in deriving cardinal valuations. This study reconsiders the role of ordinal ranks in valuing health and introduces a new approach to estimate interval-scaled valuations based on aggregate ranking data. METHODS: Analyses were undertaken on data from a previously published general population survey study in the United Kingdom that included rankings and TTO values for hypothetical states described using the EQ-5D classification system. The EQ-5D includes five domains (mobility, self-care, usual activities, pain/discomfort and anxiety/depression) with three possible levels on each. Rank data were analysed using a random utility model, operationalized through conditional logit regression. In the statistical model, probabilities of observed rankings were related to the latent utilities of different health states, modeled as a linear function of EQ-5D domain scores, as in previously reported EQ-5D valuation functions. Predicted valuations based on the conditional logit model were compared to observed TTO values for the 42 states in the study and to predictions based on a model estimated directly from the TTO values. Models were evaluated using the intraclass correlation coefficient (ICC) between predictions and mean observations, and the root mean squared error of predictions at the individual level. RESULTS: Agreement between predicted valuations from the rank model and observed TTO values was very high, with an ICC of 0.97, only marginally lower than for predictions based on the model estimated directly from TTO values (ICC = 0.99). Individual-level errors were also comparable in the two models, with root mean squared errors of 0.503 and 0.496 for the rank-based and TTO-based predictions, respectively. CONCLUSIONS: Modeling health-state valuations based on ordinal ranks can provide results that are similar to those obtained from more widely analyzed valuation techniques such as the TTO. The information content in aggregate ranking data is not currently exploited to full advantage. The possibility of estimating cardinal valuations from ordinal ranks could also simplify future data collection dramatically and facilitate wider empirical study of health-state valuations in diverse settings and population groups