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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Nov 16, 2018
Open Peer Review Period: Dec 1, 2018 - Jan 26, 2019
Date Accepted: Jul 19, 2019
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

Golembiewski E, Allen KS, Blackmon AM, Hinrichs RJ, Vest JR

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

JMIR Public Health Surveill 2019;5(4):e12846

DOI: 10.2196/12846

PMID: 31593550

PMCID: 6803891

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

  • Elizabeth Golembiewski; 
  • Katie S Allen; 
  • Amber M Blackmon; 
  • Rachel J Hinrichs; 
  • Joshua R Vest

Background:

Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health.

Objective:

This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources.

Methods:

We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported.

Results:

A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location.

Conclusions:

A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.


 Citation

Please cite as:

Golembiewski E, Allen KS, Blackmon AM, Hinrichs RJ, Vest JR

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

JMIR Public Health Surveill 2019;5(4):e12846

DOI: 10.2196/12846

PMID: 31593550

PMCID: 6803891

Per the author's request the PDF is not available.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.