Dataset Information

Development of the health and economic consequences of smoking interactive model.

ABSTRACT:

Objective

To describe the health and economic consequences of smoking model, a user friendly, web based tool, designed to estimate the health and economic outcomes associated with smoking and the benefits of smoking cessation.

Results

An overview of the development of the model equations and user interface is given, and data from the UK are presented as an example of the model outputs. These results show that a typical smoking cessation strategy costs approximately 1200 pounds sterling per life year saved and 22,000 pounds sterling per death averted.

Conclusions

The model successfully captures the complexity required to model smoking behaviour and associated mortality, morbidity, and health care costs. Furthermore, the interface provides the results in a simple and flexible way so as to be useful to a variety of audiences and to simulate a variety of smoking cessation methods.

SUBMITTER: Orme ME

PROVIDER: S-EPMC1763982 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Forgoing healthcare for economic reasons has been previously associated with adverse health outcomes, including a higher risk of hospitalization, a lower quality of life, and worse self-reported health. However, the exact cause-to-effect relation between forgoing healthcare and health-related outcomes has been insufficiently described. Here, we investigate the prospective health consequences of forgoing healthcare for economic reasons using data from "ReBus" (N = 400), a prospective study examining the health consequences of forgoing healthcare (Baseline: 2008-2013, Follow-up: 2014-2016). Using regression models, we explored the baseline determinants of forgoing healthcare, including socioeconomic, demographic, and pre-existing health-risk factors, and examined the associations between forgoing healthcare at baseline and health deterioration at follow-up, using highly pertinent biomarkers (glucose, glycated hemoglobin, lipids, blood pressure) and SF-36 questionnaire data. Low income, low occupation, low education, and smoking were associated with higher odds of forgoing healthcare at baseline. Forgoing healthcare for economic reasons at baseline was subsequently related to detrimental changes in glucose, high-density lipoprotein cholesterol (HDL), and blood pressure (BP) at follow-up, independently of baseline socioeconomic factors (Glucose-β = 0.19, 95%CI[0.03;0.34], HDL-β = -0.07, 95%CI[-0.14;0.01], BP-β = 3.30, 95%CI[-0.01;6.60]). Moreover, we found strong associations between forgoing healthcare and adverse SF-36 health scores at follow-up, with individuals forgoing healthcare systematically displaying worse health scores (6%-11% lower scores). For the first time, we show that forgoing healthcare for economic reasons predicts adverse health-related consequences 2-8 years later. Our findings shall further encourage the implementation of public health measures aimed at identifying individuals who forgo healthcare and preventing the adverse health consequences of unmet medical needs.

Project description:BackgroundHome management of malaria (HMM), promoting presumptive treatment of febrile children in the community, is advocated to improve prompt appropriate treatment of malaria in Africa. The cost-effectiveness of HMM is likely to vary widely in different settings and with the antimalarial drugs used. However, no data on the cost-effectiveness of HMM programmes are available.Methods/principal findingsA Markov model was constructed to estimate the cost-effectiveness of HMM as compared to conventional care for febrile illnesses in children without HMM. The model was populated with data from Uganda, but is designed to be interactive, allowing the user to adjust certain parameters, including the antimalarials distributed. The model calculates the cost per disability adjusted life year averted and presents the incremental cost-effectiveness ratio compared to a threshold value. Model output is stratified by level of malaria transmission and the probability that a child would receive appropriate care from a health facility, to indicate the circumstances in which HMM is likely to be cost-effective. The model output suggests that the cost-effectiveness of HMM varies with malaria transmission, the probability of appropriate care, and the drug distributed. Where transmission is high and the probability of appropriate care is limited, HMM is likely to be cost-effective from a provider perspective. Even with the most effective antimalarials, HMM remains an attractive intervention only in areas of high malaria transmission and in medium transmission areas with a lower probability of appropriate care. HMM is generally not cost-effective in low transmission areas, regardless of which antimalarial is distributed. Considering the analysis from the societal perspective decreases the attractiveness of HMM.ConclusionSyndromic HMM for children with fever may be a useful strategy for higher transmission settings with limited health care and diagnosis, but is not appropriate for all settings. HMM may need to be tailored to specific settings, accounting for local malaria transmission intensity and availability of health services.

Project description:IntroductionPursuant to the Tobacco Control Act (TCA), the US Food and Drug Administration (FDA) is developing new cigarette health warnings to convey the negative health consequences of cigarette smoking.Aims and methodsThis study assessed which of 15 revised warning statements (10 on topics similar to TCA statements and 5 on other topics) promoted greater understanding of cigarette smoking risks relative to TCA statements. In February 2018, adolescent and adult smokers and adolescents susceptible to smoking (n = 2505) completed an online experiment. Control condition participants viewed TCA statements; treatment condition participants viewed combinations of TCA and revised statements. Analyses compared revised statements to TCA statements on the same health topic or to randomly selected TCA statements if there were no statements on the same topic.ResultsRelative to TCA statements, 12 of 15 revised statements were more likely to be considered new information, and 12 resulted in more self-reported learning. Three revised statements made participants think more about health risks than TCA statements; the reverse was true for one revised statement. Participants rated most TCA and revised statements as moderately believable and informative. Seven revised statements were found to be less believable and factual, and one revised statement more believable and factual. Treatment condition participants correctly selected more smoking-related health conditions than control condition participants (13.79 versus 12.42 of 25).ConclusionsFindings suggest that revised statements can promote greater understanding of cigarette smoking risks. Results informed FDA's selection of warning text that was paired with images for testing in a follow-up study.ImplicationsThe US FDA may adjust the text of the cigarette warning statements provided in the TCA if the revised statements promote greater public understanding of the negative health consequences of cigarette smoking. Most of the revised warning statements tested were more likely to be considered new information and resulted in more self-reported learning compared with paired TCA statements, providing support for using revised statements as part of cigarette health warnings. These results informed the development of pictorial cigarette warnings by FDA that were tested in a follow-up study and included in a proposed rule.

Project description:Routine childhood vaccination is declining in some regions of the United States due to vaccine hesitancy, which risks the resurgence of many infectious diseases with public health and economic consequences. There are ongoing policy debates on the state and national level, including legislation around nonmedical (personal-belief) exemptions for childhood vaccination and possibly a special government commission on vaccine safety, which may affect vaccine coverage.To estimate the number of measles cases in US children and the associated economic costs under scenarios of different levels of vaccine hesitancy, using the case example of measles, mumps, and rubella (MMR) vaccination and measles.Publicly available data from the US Centers for Disease Control and Prevention were used to simulate county-level MMR vaccination coverage in children (age 2-11 years) in the United States. A stochastic mathematical model was adapted for infectious disease transmission that estimated a distribution for outbreak size as it relates to vaccine coverage. Economic costs per measles case were obtained from the literature. The predicted effects of increasing the prevalence of vaccine hesitancy as well as the removal of nonmedical exemptions were estimated. The model was calibrated to annual measles cases in US children over recent years, and the model prediction was validated using an independent data set from England and Wales.Annual measles cases in the United States and the associated public sector costs.A 5% decline in MMR vaccine coverage in the United States would result in an estimated 3-fold increase in measles cases for children aged 2 to 11 years nationally every year, with an additional $2.1 million in public sector costs. The numbers would be substantially higher if unvaccinated infants, adolescents, and adult populations were also considered. There was variation around these estimates due to the stochastic elements of measles importation and sensitivity of some model inputs, although the trend was robust.This analysis predicts that even minor reductions in childhood vaccination, driven by vaccine hesitancy (nonmedical and personal belief exemptions), will have substantial public health and economic consequences. The results support an urgent need to address vaccine hesitancy in policy dialogues at the state and national level, with consideration of removing personal belief exemptions of childhood vaccination.

Project description:Patient self-management interventions for smoking cessation are effective but underused. Health care providers do not routinely refer smokers to these interventions.The objective of our study was to uncover barriers and facilitators to the use of an e-referral system that will be evaluated in a community-based randomized trial. The e-referral system will allow providers to refer smokers to an online smoking intervention during routine clinical care.We devised a four-step development and pilot testing process: (1) system conceptualization using Delphi to identify key functionalities that would overcome barriers in provider referrals for smoking cessation, (2) Web system programming using agile software development and best programming practices with usability refinement using think-aloud testing, (3) implementation planning using the nominal group technique for the effective integration of the system into the workflow of practices, and (4) pilot testing to identify practice recruitment and system-use barriers in real-world settings.Our Delphi process (step 1) conceptualized three key e-referral functions: (1) Refer Your Smokers, allowing providers to e-refer patients at the point of care by entering their emails directly into the system, (2) practice reports, providing feedback regarding referrals and impact of smoking-cessation counseling, and (3) secure messaging, facilitating provider-patient communication. Usability testing (step 2) suggested the system was easy to use, but implementation planning (step 3) suggested several important approaches to encourage use (eg, proactive email cues to encourage practices to participate). Pilot testing (step 4) in 5 practices had limited success, with only 2 patients referred; we uncovered important recruitment and system-use barriers (eg, lack of study champion, training, and motivation, registration difficulties, and forgetting to refer).Implementing a system to be used in a clinical setting is complex, as several issues can affect system use. In our ongoing large randomized trial, preliminary analysis with the first 50 practices using the system for 3 months demonstrated that our rigorous preimplementation evaluation helped us successfully identify and overcome these barriers before the main trial.Clinicaltrials.gov NCT00797628; http://clinicaltrials.gov/ct2/show/NCT00797628 (Archived by WebCite at http://www.webcitation.org/61feCfjCy).

Project description:BackgroundFew simulation models have incorporated the interplay of diabetes, obesity, and cardiovascular disease (CVD); their upstream lifestyle and biological risk factors; and their downstream effects on health disparities and economic consequences.MethodsWe developed and validated a US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) model that incorporates demographic, clinical, and lifestyle risk factors to jointly predict overall and racial-ethnic groups-specific obesity, diabetes, CVD, and cause-specific mortality for the US adult population aged 40 to 79 y at baseline. An individualized health care cost prediction model was further developed and integrated. This model incorporates nationally representative data on baseline demographics, lifestyle, health, and cause-specific mortality; dynamic changes in modifiable risk factors over time; and parameter uncertainty using probabilistic distributions. Validation analyses included assessment of 1) population-level risk calibration and 2) individual-level risk discrimination. To illustrate the application of the DOC-M model, we evaluated the long-term cost-effectiveness of a national produce prescription program.ResultsComparing the 15-y model-predicted population risk of primary outcomes among the 2001-2002 National Health and Nutrition Examination Survey (NHANES) cohort with the observed prevalence from age-matched cross-sectional 2003-2016 NHANES cohorts, calibration performance was strong based on observed-to-expected ratio and calibration plot analysis. In most cases, Brier scores fell below 0.0004, indicating a low overall prediction error. Using the Multi-Ethnic Study of Atherosclerosis cohorts, the c-statistics for assessing individual-level risk discrimination were 0.85 to 0.88 for diabetes, 0.93 to 0.95 for obesity, 0.74 to 0.76 for CVD history, and 0.78 to 0.81 for all-cause mortality, both overall and in three racial-ethnic groups. Open-source code for the model was posted at https://github.com/food-price/DOC-M-Model-Development-and-Validation.ConclusionsThe validated DOC-M model can be used to examine health, equity, and the economic impact of health policies and interventions on behavioral and clinical risk factors for obesity, diabetes, and CVD.HighlightsWe developed a novel microsimula'tion model for obesity, diabetes, and CVD, which intersect together and - critically for prevention and treatment interventions - share common lifestyle, biologic, and demographic risk factors.Validation analyses, including assessment of (1) population-level risk calibration and (2) individual-level risk discrimination, showed strong performance across the overall population and three major racial-ethnic groups for 6 outcomes (obesity, diabetes, CVD, and all-cause mortality, CVD- and DM-cause mortality)This paper provides a thorough explanation and documentation of the development and validation process of a novel microsimulation model, along with the open-source code (https://github.com/food-price/ DOCM_validation) for public use, to serve as a guide for future simulation model assessments, validation, and implementation.