Dataset Information

Introducing riskCommunicator: An R package to obtain interpretable effect estimates for public health.

ABSTRACT: Common statistical modeling methods do not necessarily produce the most relevant or interpretable effect estimates to communicate risk. Overreliance on the odds ratio and relative effect measures limit the potential impact of epidemiologic and public health research. We created a straightforward R package, called riskCommunicator, to facilitate the presentation of a variety of effect measures, including risk differences and ratios, number needed to treat, incidence rate differences and ratios, and mean differences. The riskCommunicator package uses g-computation with parametric regression models and bootstrapping for confidence intervals to estimate effect measures in time-fixed data. We demonstrate the utility of the package using data from the Framingham Heart Study to estimate the effect of prevalent diabetes on the 24-year risk of cardiovascular disease or death. The package promotes the communication of public-health relevant effects and is accessible to a broad range of epidemiologists and health researchers with little to no expertise in causal inference methods or advanced coding.

SUBMITTER: Grembi JA

PROVIDER: S-EPMC9292119 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Introducing riskCommunicator: An R package to obtain interpretable effect estimates for public health.

Grembi Jessica A JA Rogawski McQuade Elizabeth T ET

PloS one 20220718 7

Common statistical modeling methods do not necessarily produce the most relevant or interpretable effect estimates to communicate risk. Overreliance on the odds ratio and relative effect measures limit the potential impact of epidemiologic and public health research. We created a straightforward R package, called riskCommunicator, to facilitate the presentation of a variety of effect measures, including risk differences and ratios, number needed to treat, incidence rate differences and ratios, a ...[more]

PMID: 35849588

Similar Datasets

Project description:BackgroundMany public health programs fail because of an inability to implement tested interventions in diverse, complex settings. The field of implementation science is engaged in developing strategies for successful implementation, but current training is primarily researcher-focused. To tackle the challenges of the twenty-first century, public health leaders are promoting a new model titled Public Health 3.0 where public health practitioners become "chief health strategists" and develop interdisciplinary skills for multisector engagement to achieve impact. This requires broad training for public health practitioners in implementation science that includes the allied fields of systems and design thinking, quality improvement, and innovative evaluation methods. At UNC Chapel Hill's Gillings School of Global Public Health, we created an interdisciplinary set of courses in applied implementation science for Master of Public Health (MPH) students and public health practitioners. We describe our rationale, conceptual approach, pedagogy, courses, and initial results to assist other schools contemplating similar programs.MethodsOur conceptual approach recognized the vital relationship between implementation research and practice. We conducted a literature review of thought leaders in public health to identify skill areas related to implementation science that are priorities for the future workforce. We also reviewed currently available training programs in implementation science to understand their scope and objectives and to assess whether any of these would be a fit for these priorities. We used a design focused implementation framework to create four linked courses drawing from multiple fields such as engineering, management, and the social sciences and emphasizing application through case studies. We validated the course content by mapping them to implementation science competencies in the literature.ResultsTo date, there is no other program that provides comprehensive interdisciplinary skills in applied implementation science for MPH students. As of April 2018, we have offered a total of eleven sections of the four courses, with a total enrollment of 142, of whom 127 have been master's-level students in the school of public health. Using Kirkpatrick's Model, we found positive student reaction, learning, and behavior. Many students have completed applied implementation science focused practicums, master's papers, and special studies.ConclusionsA systematically designed interdisciplinary curriculum in applied implementation science for MPH students has been found by students to be a useful set of skills. Students have demonstrated the capability to master this material and incorporate it into their practicums and master's papers.

Project description:The wealth index based on household assets and amenities is been increasingly used to explain economic variations of health outcomes in the developing countries. While the variables used to compute the wealth index are easy to collect and time- and cost-effective, the wealth index tends to have an urban bias, uses arbitrary weighting, does not provide per capita measures and is a poor measure of inequality. We used micro data from two of the large-scale population-based surveys, the Longitudinal Ageing Study in India, 2017–18 and the India Human Development Survey, 2011–12 that covered over 42,000 households each and collected data on household consumption, assets and amenities in India. We examined the variations and inequality in health estimates by consumption per capita and asset-based measures in India. Descriptive statistics, logistic regression model, concentration index, and concentration curve were used in the analyses. We found a weak association between monthly per capita consumption expenditure (MPCE) and wealth index in both the surveys. Some of the health conditions such as hypertension, cataract, refractive error, and diabetes tended to be underestimated in the bottom 40% of the population when economic well-being was measured using the wealth index compared to consumption. Socio-economic inequality in health outcome, inpatient and outpatient health services were underestimated when measured using the wealth index than when measured using MPCE. We conclude that economic gradients of health by consumption and wealth index are inconsistent and that per capita consumption predicts health estimates better than the wealth index. It is recommended that public health research using population-based surveys that provide data on consumption and wealth index use per capita consumption to explain economic variations in health and health care utilization. We also suggest that the future rounds of the health surveys of National Sample Survey and the National Family and Health Surveys include an abridged version of the consumption schedule to predict better economic variations in health and health care utilization in India. Highlights • Wealth index has been increasingly used to explain economic variations in health outcomes in India.• Association between monthly per capita consumption expenditure and wealth index is weak.• Health estimates by consumption and wealth index are not consistent in India.• Some of the measured health conditions are underestimated for the poor when economic well-being was measured using the wealth index compared to consumption.• Socio-economic inequality in health outcome, inpatient and outpatient health services were underestimated when measured using the wealth index compared to consumption.

Dataset Information

Introducing riskCommunicator: An R package to obtain interpretable effect estimates for public health.

Publications

Introducing riskCommunicator: An R package to obtain interpretable effect estimates for public health.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets