Health Services and Outcomes Research Methodology This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Characterizing bias due to differential exposure ascertainment in electronic health record data
The objective of this paper was to explore the bias a nd efficiency of these three analytic approaches across a broad range of scenarios motivated by a study of the association between chronic hyperglycemia and 5-year mortality in an EHR-derived cohort of colon cancer survivors. We found that the best available approach tended to mitigate inefficiency and selection bias resulting from exclusion while suffering from less information bias than the common data approach. However, bias in all three approaches can be severe, particularly when both selection bias and information bias are present. When risk of either of these bias...
Source: Health Services and Outcomes Research Methodology - August 24, 2021 Category: Statistics Source Type: research

Chronic Diseases and Multimorbidity in Iran: A Study Protocol for the Use of Iranian Health Insurance Organization ’s Claims Database to Understand Epidemiology, Health Service Utilization, and Patient Costs
AbstractThe burden of chronic diseases and multimorbidity is continue to increase, especially in developing countries, but little comprehensive population-based data exist related to their epidemiology and associated healthcare utilization and costs. Our aim is to estimate incidence rate, prevalence, and trend of common chronic diseases and clusters of multimorbidity among East Azerbaijan ’s Health Insurance Organization (EAHIO) enrollees applying adopted and updated pharmacoepidemiological approach and to analyze outpatient health service utilization patterns and associated costs among this population using outpatient c...
Source: Health Services and Outcomes Research Methodology - August 24, 2021 Category: Statistics Source Type: research

Evaluating efficiency of counties in providing diabetes preventive care using data envelopment analysis
AbstractFor patients with diabetes, annual preventive care is essential to reduce the risk of complications. Local healthcare resources affect the utilization of diabetes preventive care. Our objectives were to evaluate the relative efficiency of counties in providing diabetes preventive care and explore potential to improve efficiencies. The study setting is public and private healthcare providers in US counties with available data. County-level demographics were extracted from the Area Health Resources File using data from 2010 to 2013, and individual-level information of diabetes preventive service use was obtained from...
Source: Health Services and Outcomes Research Methodology - August 24, 2021 Category: Statistics Source Type: research

Improving risk adjustment with machine learning: accounting for service-level propensity scores to reduce service-level selection
In this study, we propose an alternative model using machine learning (ML) techniques to reduce service-level selection by accounting for demographic and diagnostic characteristics as well as service-level propensity scores (SPS) that capture each individual ’s need for each service (the HCC + SPS model). Using the 2013–2014 Truven MarketScan database, we compare the performance of the HCC model (the HCC-only model) and the HCC + SPS model. We first fit both models with ordinary least squares (OLS) because traditional risk adjustment models rely on OLS. We also fit these models with ridge regression, which is a...
Source: Health Services and Outcomes Research Methodology - August 24, 2021 Category: Statistics Source Type: research

Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes
AbstractThe preponderance of large-scale healthcare databases provide abundant opportunities for comparative effectiveness research. Evidence necessary to making informed treatment decisions often relies on comparing effectiveness of multiple treatment options on outcomes of interest observed in a small number of individuals. Causal inference with multiple treatments and rare outcomes is a subject that has been treated sparingly in the literature. This paper designs three sets of simulations, representative of the structure of our healthcare database study, and propose causal analysis strategies for such settings. We inves...
Source: Health Services and Outcomes Research Methodology - August 24, 2021 Category: Statistics Source Type: research

Application of pooled testing in estimating the prevalence of COVID-19
AbstractTesting at a mass scale has been widely accepted as an effective way to contain the spread of the SARS-CoV-2 Virus. In the initial stages, the shortage of test kits severely restricted mass-scale testing. Pooled testing was offered as a partial solution to this problem. However, it is a relatively lesser-known fact that pooled testing can also result in significant gains, both in terms of cost savings as well as measurement accuracy, in prevalence estimation surveys. We review here the statistical theory of pooled testing for screening as well as for prevalence estimation. We study the impact of the diagnostic erro...
Source: Health Services and Outcomes Research Methodology - August 7, 2021 Category: Statistics Source Type: research

Applying random forest in a health administrative data context: a conceptual guide
We describe in detail how RF can be useful in health services research, provide guidance on data set up, modeling decisions and demonstrate how to interpret results. We al so highlight specific considerations for applying RF to health administrative data. In a working example, we compare RF with logistic regression, Ridge regression and LASSO in their ability to predict whether a person has a regular medical doctor. We use survey responses to “do you have a regular medical doctor” from three cycles of the Canadian Community Health Survey (2007, 2009, 2011). Responses are linked with physician claims’ data from 2002 t...
Source: Health Services and Outcomes Research Methodology - July 17, 2021 Category: Statistics Source Type: research

Assessing consistency among indices to measure socioeconomic barriers to health care access
AbstractMany places within rural America lack ready access to health care facilities. Barriers to access can be both spatial and non-spatial. Measurements of spatial access, such as the Enhanced Floating 2-Step Catchment Area and other floating catchment area measures, produce similar patterns of access. However, the extent to which different measurements of socioeconomic barriers to access correspond with each other has not been examined. Using West Virginia as a case study, we compute indices based upon the literature and measure the correlations among them. We find that all indices positively correlate with each other, ...
Source: Health Services and Outcomes Research Methodology - July 17, 2021 Category: Statistics Source Type: research

Causal mediation analysis decomposition of between-hospital variance
AbstractCausal variance decompositions for a given disease-specific quality indicator can be used to quantify differences in performance between hospitals or health care providers. While variance decompositions can demonstrate variation in quality of care, causal mediation analysis can be used to study care pathways leading to the differences in performance between the institutions. This raises the question of whether the two approaches can be combined to decompose between-hospital variation in an outcome type indicator to that mediated through a given process (indirect effect) and remaining variation due to all other path...
Source: Health Services and Outcomes Research Methodology - July 10, 2021 Category: Statistics Source Type: research

Detecting bad actors in value-based payment models
We describe our approach, demonstrate how it can be applied with hypothetical data, and simulate how efficiently it detects participants who are truly bad actors. In our hypothetical case study, the approach correctly identifies a bad actor in the first period in 86% of simulations and by the second period in 96% of simulations. The trade-off is that 9% of honest participants are mistakenly identified as bad actors by the second period. We suggest several ways for researchers to mitigate the rate or consequences of these false positives. Researchers and policymakers can customize and use our approach to appropriately guard...
Source: Health Services and Outcomes Research Methodology - June 28, 2021 Category: Statistics Source Type: research