Resource Modelling: The Missing Piece of the HTA Jigsaw?

Within health technology assessment (HTA), cost-effectiveness analysis and budget impact analyses have been broadly accepted as important components of decision making. However, whilst they address efficiency and affordability, the issue of implementation and feasibility has been largely ignored. HTA commonly takes place within a deliberative framework that captures issues of implementation and feasibility in a qualitative manner. We argue that only through a formal quantitative assessment of resource constraints can these issues be fully addressed. This paper argues the need for resource modelling to be considered explicitly in HTA. First, economic evaluation and budget impact models are described along with their limitations in evaluating feasibility. Next, resource modelling is defined and its usefulness is described along with examples of resource modelling from the literature. Then, the important issues that need to be considered when undertaking resource modelling are described before setting out recommendations for the use of resource modelling in HTA

A Methodological Approach for Measuring the Impact of HTA

There is a lack of evidence concerning the link between HTA and outcomes in terms of health improvements. This work proposes a framework for assessing the impact of HTA. This impact assessment is a necessary step in then better understanding the value for money of HTA bodies. We emphasis that this is still a work in progress. iDSI has developed a theory of change-based framework in order to evaluate the impact the iDSI has on institutional strengthening – leading to ‘better decisions’ for ‘better health’. This framework recognises that there is a complex translation process between better decisions and better health dependent on many assumptions about local factors and systems, including linkage between decisions and budgets, delivery, implementation, and data accuracy. Work has been undertaken over the last 6 months developing a methodological approach for measuring the impact of health technology assessment (HTA). Two case studies are used to illustrate the approach. At the core of impact assessment is a requirement to link causes and effects, to explain ‘how’ and ‘why’ and to identify – and thus improve or adapt – mechanisms leading to impact. Policy makers also want to know ‘to what extent’ or ‘the magnitude of impact’. The framework developed adopts an economic approach nested in theory of change as a means of both quantifying the magnitude of impact (utilising economic models) as well as explaining why and how impact happens (drawing on theory based approaches) in order to reinforce learning as to how to improve our response and optimise the use of HTA to have the greatest impact in a given context. This should also enable us to capture and explain wider impact – perhaps more intangible aspects which cannot be easily quantified. This may also possibly increase policy-makers’ ‘buy-in’

Challenges in Modeling Complexity of Neglected Tropical Diseases: A Review of Dynamics of Visceral Leishmaniasis in Resource Limited Settings

Objectives: Neglected tropical diseases (NTD), account for a large proportion of the global disease burden, and their control faces several challenges including diminishing human and financial resources for those distressed from such diseases. Visceral leishmaniasis (VL), the second-largest parasitic killer (after malaria) and an NTD affects poor populations and causes considerable cost to the affected individuals. Mathematical models can serve as a critical and cost-effective tool for understanding VL dynamics, however, complex array of socio-economic factors affecting its dynamics need to be identified and appropriately incorporated within a dynamical modeling framework. This study reviews literature on vector-borne diseases and collects challenges and successes related to the modeling of transmission dynamics of VL. Possible ways of creating a comprehensive mathematical model is also discussed. Methods: Published literature in three categories are reviewed: (i) identifying non-traditional but critical mechanisms for VL transmission in resource limited regions, (ii) mathematical models used for dynamics of Leishmaniasis and other related vector borne infectious diseases and (iii) examples of modeling that have the potential to capture identified mechanisms of VL to study its dynamics. Results: This review suggests that VL elimination have not been achieved yet because existing transmission dynamics models for VL fails to capture relevant local socio-economic risk factors. This study identifies critical risk factors of VL and distribute them in six categories (atmosphere, access, availability, awareness, adherence, and accedence). The study also suggests novel quantitative models, parts of it are borrowed from other non-neglected diseases, for incorporating these factors and using them to understand VL dynamics and evaluating control programs for achieving VL elimination in a resource-limited environment. Conclusions: Controlling VL is expensive for local communities in endemic countries where individuals remain in the vicious cycle of disease and poverty. Smarter public investment in control programs would not only decrease the VL disease burden but will also help to alleviate poverty. However, dynamical models are necessary to evaluate intervention strategies to formulate a cost-effective optimal policy for eradication of VL

Assessing the Economics of Obesity and Obesity Interventions

Examines projections for healthcare costs associated with the obesity epidemic; policy solutions and proven cost-effective interventions for addressing it; and the need to improve the Congressional Budget Office's projections

Challenges in modeling complexity of neglected tropical diseases: a review of dynamics of visceral leishmaniasis in resource limited settings

abstract: Neglected tropical diseases (NTD), account for a large proportion of the global disease burden, and their control faces several challenges including diminishing human and financial resources for those distressed from such diseases. Visceral leishmaniasis (VL), the second-largest parasitic killer (after malaria) and an NTD affects poor populations and causes considerable cost to the affected individuals. Mathematical models can serve as a critical and cost-effective tool for understanding VL dynamics, however, complex array of socio-economic factors affecting its dynamics need to be identified and appropriately incorporated within a dynamical modeling framework. This study reviews literature on vector-borne diseases and collects challenges and successes related to the modeling of transmission dynamics of VL. Possible ways of creating a comprehensive mathematical model is also discussed.The electronic version of this article is the complete one and can be found online at: https://ete-online.biomedcentral.com/articles/10.1186/s12982-017-0065-

ANTICIPATING U.S. POPULATION-LEVEL HEALTH AND ECONOMIC IMPACTS USING DISCRETE-EVENT SIMULATION TO GUIDE HEALTH POLICY DECISIONS

This dissertation presents two applications of discrete-event simulation (DES) to represent clinical processes: (1) a model to quantify the risk of the maternal obese and diabetic intrauterine environment influence on progression to adult obesity and diabetes, and (2) a model to evaluate health and economic outcomes of different smoking cessation strategies. The first application considers the public health impact of the diabetic and obese intrauterine environment\u27s effect on the prevalence of diabetes and obesity across subsequent generations. We first develop a preliminary DES model to investigate and characterize the epidemiology of diabetes during pregnancy and birth outcomes related to maternal obesity and diabetes. Using data from the San Antonio Heart Study (SAHS), the 1980 Census and the NCHS we are able to verify a simplified initial version of our model. Our methodology allows us to quantify the impact of maternal disparities between different racial/ethnic groups on future health disparities at the generational level and to estimate the extent to which intrauterine exposure to diabetes and obesity could be driving these health disparities. The populace of interest in this model is women of child-bearing age. The preliminary model is next modified to accommodate data and assumptions representing the United States population. We use a mixed-methods approach, incorporating both statistical methods and discrete event simulation, to examine trends in weight-gain over time among white and black women of child-bearing age in the US from 1980 to 2008 using United States Census projections and National Health and Nutrition Examination Survey (NHANES) data. We use BMI as a measure of weight adjusted for height. We establish an underlying population representative of the population prior to the onset of the obesity epidemic. Assessing the rate of change in body mass index (BMI) of the population prior to the obesity epidemic allows us to make \u27unadjusted\u27 projections, assuming that subsequent generations carry the same risk as the initial cohort. Unadjusted projections are compared to actual trends in the US population. This comparison allows us to quantify the trends in weight-gain over time. This model is interesting as a first step in understanding the trans-generational impact of obesity during pregnancy at the population level. The aim of the second application is to understand the impact of different pharmacologic interventions for smoking cessation in achieving long-term abstinence from cigarette smoking is an important health and economic issue. We design and develop a clinically-based DES model to provide predictive estimates of health and economic outcomes associated with different smoking cessation interventions. Interventions assessed included nicotine replacement therapy, oral medications (bupropion and varenicline), and abstinence without pharmacologic assistance. We utilized data from multiple sources to simulate patients\u27 actions and associated responses to different interventions along with co-morbidities associated with smoking. Outcomes of interest included estimates of sustained abstinence from smoking, quality adjusted life years, cost of treatment, and additional health-related costs due to long-term effects of smoking (lung cancer, chronic obstructive pulmonary disease, stroke, coronary heart disease). Understanding the comparative effectiveness and intrinsic value of alternative smoking cessation strategies can improve clinical and patient decision-making and subsequent health and economic outcomes at the population level. This dissertation contributes to the field of industrial engineering in healthcare. US population-level data structures are not always available in the desired format and there is not one method for managing the data. The key element is to be able to link the mathematical model with the available data. We illustrate various methods (i.e. bootstrap techniques, mixed-effects regression, application of probability distributions) for extracting information from different types of data (i.e. longitudinal data, cross-sectional data, incidence rates) to make population-level predictions. Methods used in cost-effectiveness evaluations (i.e. incremental cost-effectiveness ratio, bootstrap confidence intervals, cost-effectiveness plane) are applied to output measures obtained from the simulation to compare alternative smoking cessation strategies to deduce additional information. While the estimates resulting from the two models are topic-specific, many of the modules created for these studies are generic and can easily be transferred to other disease models. It is believed that these two models will aid decision makers in recognizing the impact that preventative-care initiatives will have, and to evaluate possible alternatives

System dynamics of diabetes in the presence of social determinants

Diabetes is a chronic and persistent disease that is on the rise in the U.S. in spite of medical advances and current public health efforts to address it. There is a relationship between socioeconomic factors such as poverty and education and the risk of developing diabetes and the progression of the disease. A systems approach is employed to develop a dynamic hypothesis with a stock and flow model will uncover and analyze these relationships to gain a better understanding of the socioeconomic dynamics of diabetes. Social factors have a significant impact on health outcomes and their consideration is essential to developing policies that will help to reduce diabetes incidence

System dynamics of diabetes in the presence of social determinants

Diabetes is a chronic and persistent disease that is on the rise in the U.S. in spite of medical advances and current public health efforts to address it. There is a relationship between socioeconomic factors such as poverty and education and the risk of developing diabetes and the progression of the disease. A systems approach is employed to develop a dynamic hypothesis with a stock and flow model will uncover and analyze these relationships to gain a better understanding of the socioeconomic dynamics of diabetes. Social factors have a significant impact on health outcomes and their consideration is essential to developing policies that will help to reduce diabetes incidence