Health Services and Outcomes Research Methodology最新文献

Noncommunicable diseases (NCDs) are the leading causes of mortality and morbidity across the world and factors influencing global poverty and slowing economic development. We summarize how the potential power of real-world data (RWD) and real-world evidence (RWE) can be harnessed to help address the disease burden of NCDs at global, national, regional and local levels. RWE is essential to understand the epidemiology of NCDs, quantify NCD burdens, assist with the early detection of vulnerable populations at high risk of NCDs by identifying the most influential risk factors, and evaluate the effectiveness and cost-benefits of treatments, programs, and public policies for NCDs. To realize the potential power of RWD and RWE, challenges related to data integration, access, interoperability, standardization of analytical methods, quality control, security, privacy protection, and ethical standards for data use must be addressed. Finally, partnerships between academic centers, governments, pharmaceutical companies, and other stakeholders aimed at improving the utilization of RWE can have a substantial beneficial impact in preventing and managing NCDs.

An interrupted time series with a parallel control group (ITS-CG) design is a powerful quasi-experimental design commonly used to evaluate the effectiveness of an intervention, on accelerating uptake of useful public health products, and can be used in the presence of regularly collected data. This paper illustrates how a segmented Poisson model that utilizes general estimating equations (GEE) can be used for the ITS-CG study design to evaluate the effectiveness of a complex social accountability intervention on the level and rate of uptake of modern contraception. The intervention was gradually rolled-out over time to targeted intervention communities in Ghana and Tanzania, with control communities receiving standard of care, as per national guidelines. Two ITS GEE segmented regression models are proposed for evaluating of the uptake. The first, a two-segmented model, fits the data collected during pre-intervention and post-intervention excluding that collected during intervention roll-out. The second, a three-segmented model, fits all data including that collected during the roll-out. A much simpler difference-in-difference (DID) GEE Poisson regression model is also illustrated. Mathematical formulation of both ITS-segmented Poisson models and that of the DID Poisson model, interpretation and significance of resulting regression parameters, and accounting for different sources of variation and lags in intervention effect are respectively discussed. Strengths and limitations of these models are highlighted. Segmented ITS modelling remains valuable for studying the effect of intervention interruptions whether gradual changes, over time, in the level or trend in uptake of public health practices are attributed by the introduced intervention. Trial Registration: The Australian New Zealand Clinical Trials registry. Trial registration number: ACTRN12619000378123. Trial Registration date: 11-March-2019.

As healthcare costs continue to increase, studies assessing costs are becoming increasingly common, but researchers planning for studies that measure costs differences (savings) encounter a lack of literature or consensus among researchers on what constitutes "small" or "large" cost savings for common measures of resource use.  Other fields of research have developed approaches to solve this type of problem. Researchers measuring improvement in quality of life or clinical assessments have defined minimally important differences (MID) which are then used to define magnitudes when planning studies. Also, studies that measure cost effectiveness use benchmarks, such as cost/QALY, but do not provide benchmarks for cost differences. In a review of the literature, we found no publications identifying indicators of magnitude for costs. However, the literature describes three approaches used to identify minimally important outcome differences: (1) anchor-based, (2) distribution-based, and (3) a consensus-based Delphi methods. In this exploratory study, we used these three approaches to derive MID for two types of resource measures common in costing studies for: (1) hospital admissions (high cost); and (2) clinic visits (low cost). We used data from two (unpublished) studies to implement the MID estimation. Because the distributional characteristics of cost measures may require substantial samples, we performed power analyses on all our estimates to illustrate the effect that the definitions of "small" and "large" costs may be expected to have on power and sample size requirements for studies. The anchor-based method, while logical and simple to implement, may be of limited value in cases where it is difficult to identify appropriate anchors. We observed some commonalities and differences for the distribution and consensus-based approaches, which require further examination. We recommend that in cases where acceptable anchors are not available, both the Delphi and the distribution-method of MID for costs be explored for convergence.

Opposition to Universal Basic Income (UBI) is encapsulated by Martinelli's claim that 'an affordable basic income would be inadequate, and an adequate basic income would be unaffordable'. In this article, we present a model of health impact that transforms that assumption. We argue that UBI can affect higher level social determinants of health down to individual determinants of health and on to improvements in public health that lead to a number of economic returns on investment. Given that no trial has been designed and deployed with that impact in mind, we present a methodological framework for assessing prospective costs and returns on investment through modelling to make the case for that trial. We begin by outlining the pathways to health in our model of change in order to present criteria for establishing the size of transfer capable of promoting health. We then consider approaches to calculating cost in a UK context to estimate budgetary burdens that need to be met by the state. Next, we suggest means of modelling the prospective impact of UBI on health before asserting means of costing that impact, using a microsimulation approach. We then outline a set of fiscal options for funding any shortfall in returns. Finally, we suggest that fiscal strategy can be designed specifically with health impact in mind by modelling the impact of reform on health and feeding that data cyclically back into tax transfer module of the microsimulation.

Ecological Momentary Assessment (EMA) studies aim to explore the interaction between subjects' psychological states and real environmental factors. During the EMA studies, participants can receive prompted assessments intensively across days and within each day, which results in three-level longitudinal data, e.g., subject-level (level-3), day-level nested in subject (level-2) and assessment-level nested in each day (level-1). Those three-level data may exhibit complex longitudinal correlation structure but ignoring or mis-specifying the within-subject correlation structure can lead to bias on the estimation of the key effects and the intraclass correlation. Given the three-level EMA data and the time stamps of the responses, we proposed a linear mixed effects model with random effects at each level. In this model, we accounted for level-2 autocorrelation and level-1 autocorrelation and showed how structural information from the three-level data improved the fit of the model. With real time stamps of the assessments, we also provided a useful extension of this proposed model to deal with the issue of irregular-spacing in EMA assessments.