ArticleHealth care science2024
Evaluating rural health outcomes: A methodological approach using population-level data.
Article in Health care science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Summative evaluation of the rural surgical obstetrical networks initiative: Findings from a five year retrospective qualitative study.PloS one · 2026Article
- 'I Am Not Going, So You're Doing It': Management of Pregnant People With High BMI at Rural Hospitals in British Columbia: A Mixed Methods Analysis.The Australian journal of rural health · 2025Article
- Examining Changes in Compassion Satisfaction, Burnout, Secondary Traumatic Stress and Work-Related Quality-of-Life of Rural Surgical and Obstetrical Nurses in British Columbia During the COVID-19 Pandemic.The Canadian journal of nursing research = Revue canadienne de recherche en sciences infirmieres · 2025Article
- Evaluating rural health outcomes: A methodological approach using population-level data.Health care science · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The sustainability of rural surgical and obstetrical facilities depends on their efficacy and quality of care, which are difficult to measure in a rural context. In an evaluation of rural practice, it is often the case that the only comparators are larger referral facilities, for which facility-level comparisons are difficult due to differences in population demographics, acuity of patients, and services offered. This publication outlines these limitations and highlights a best-practice approach to making facility-level comparisons using population-level data, risk stratification, tests of noninferiority, and Firth logistic regression analysis. This includes an investigation of minimum sample-size requirements through Monte Carlo power analysis in the context of low-acuity rural surgical care. Methods: Monte Carlo power analysis was used to estimate the minimum sample size required to achieve a power of 0.8 for both logistic regression and Firth logistic regression models that compare the proportion of surgical adverse events against facility type, among other confounders. We provide guidelines for the implementation of a recommended methodology that uses risk stratification, Firth penalized logistic regression, and tests of noninferiority. Results: We illustrate limitations in facility-level comparison of surgical quality among patients undergoing one of four index procedures including hernia repair, colonoscopy, appendectomy, and cesarean delivery. We identified minimum sample sizes for comparison of each index procedure that fluctuate depending on the level of risk stratification used. Conclusion: The availability of administrative data can provide an adequate sample size to allow for facility-level comparisons in surgical quality, at the rural level and elsewhere. When they are made appropriately, these comparisons can be used to evaluate the efficacy of general practitioners and nurse practitioners in performing low-acuity procedures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.