Dataset Information

Differentiating anomalous disease intensity with confounding variables in space.

ABSTRACT:

Background

The investigation of perceived geographical disease clusters serves as a preliminary step that expedites subsequent etiological studies and analysis of epidemicity. With the identification of disease clusters of statistical significance, to determine whether or not the detected disease clusters can be explained by known or suspected risk factors is a logical next step. The models allowing for confounding variables permit the investigators to determine if some risk factors can explain the occurrence of geographical clustering of disease incidence and to investigate other hidden spatially related risk factors if there still exist geographical disease clusters, after adjusting for risk factors.

Methods

We propose to develop statistical methods for differentiating incidence intensity of geographical disease clusters of peak incidence and low incidence in a hierarchical manner, adjusted for confounding variables. The methods prioritize the areas with the highest or lowest incidence anomalies and are designed to recognize hierarchical (in intensity) disease clusters of respectively high-risk areas and low-risk areas within close geographic proximity on a map, with the adjustment for known or suspected risk factors. The data on spatial occurrence of sudden infant death syndrome with a confounding variable of race in North Carolina counties were analyzed, using the proposed methods.

Results

The proposed Poisson model appears better than the one based on SMR, particularly at facilitating discrimination between the 13 counties with no cases. Our study showed that the difference in racial distribution of live births explained, to a large extent, the 3 previously identified hierarchical high-intensity clusters, and a small region of 4 mutually adjacent counties with the higher race-adjusted rates, which was hidden previously, emerged in the southwest, indicating that unobserved spatially related risk factors may cause the elevated risk. We also showed that a large geographical cluster with the low race-adjusted rates, which was hidden previously, emerged in the mid-east.

Conclusion

With the information on hierarchy in adjusted intensity levels, epidemiologists and public health officials can better prioritize the regions with the highest rates for thorough etiologic studies, seeking hidden spatially related risk factors and precisely moving resources to areas with genuine highest abnormalities.

SUBMITTER: Wu CC

PROVIDER: S-EPMC7489047 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

Differentiating anomalous disease intensity with confounding variables in space.

Wu Chih-Chieh CC Shete Sanjay S

International journal of health geographics 20200914 1

<h4>Background</h4>The investigation of perceived geographical disease clusters serves as a preliminary step that expedites subsequent etiological studies and analysis of epidemicity. With the identification of disease clusters of statistical significance, to determine whether or not the detected disease clusters can be explained by known or suspected risk factors is a logical next step. The models allowing for confounding variables permit the investigators to determine if some risk factors can ...[more]

PMID: 32928225

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Project description:BackgroundIn 2016, orthopaedic surgeons received nearly USD 300 million from industry, with the top 10% of recipients making more than 95% of the total amount. The degree to which gender may be associated with industry compensation has not been well explored; however, this may be confounded by a number of variables, including academic productivity, experience, and other factors. We wished to explore the variability in payment distribution by gender after controlling for these factors.Questions/purposes(1) Do men or women academic orthopaedic surgeons receive more payments from industry? (2) To what degree do any observed differences between the genders persist, even after accounting for identifiable factors, including academic rank, scholarly productivity, regional location of university, subspecialty selection as identified by fellowships completed, and years since completion of residency?MethodsThis study was a cross-sectional retrospective analysis of surgeons practicing in orthopaedic surgery academic departments in the United States. Academic orthopaedic surgery departments were identified using the Fellowship and Residency Electronic Interactive Database. Publicly available data on gender, academic rank, scholarly productivity, regional location of university, fellowships completed, and years since residency graduation were collected from institutional websites. Industry funding data for 2016 were obtained from the Centers for Medicare & Medicaid Services Open Payments Database, and scholarly productivity data through 2017 were collected from Scopus. A total of 2939 academic orthopaedic surgeons, 2620 (89%) men and 319 (11%) women from 126 programs were identified. Men and women surgeons were different in most of the variables collected, and all except region of university were associated with differences in industry payments.ResultsThe median payment for men surgeons was greater than that for women (USD 1027 [interquartile range USD 125-USD 9616] versus USD 177 [IQR USD 47-USD 1486]; difference of medians, USD 850; p < 0.001]. After accounting for potentially confounding variables like faculty rank, years since residency, H-index and subspecialty choice, women faculty members still received only 29% of payments received by otherwise comparable men orthopaedists (beta coefficient for gender = 0.29 [95% CI 0.20 to 0.44; p < 0.001]).ConclusionsWomen academic orthopaedic surgeons received only 29% of the industry payments received by men, even after controlling for faculty rank, years since residency, H-index, and subspecialty selection. This gender-related disparity may hinder the career advancement of women orthopaedic surgeons.Clinical relevanceIncreased transparency by companies can help guide orthopaedic surgeons who wish to receive industry funding.

Dataset Information

Differentiating anomalous disease intensity with confounding variables in space.

Background

Methods

Results

Conclusion

Publications

Differentiating anomalous disease intensity with confounding variables in space.

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