Using administrative health care system records to recruit a community-based sample for population research

Oakes, J. Michael; MacLehose, Richard F.; McDonald, Kelsey; Harlow, Bernard L.

doi:10.1016/j.annepidem.2015.03.015

Cited by 8 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prior studies demonstrate the utility and limitations of passive use of administrative health records for secondary data analysis and recruitment of population-based samples 14–18. Our study extends this work to evaluate the use of the PHR for data collection activities.…”

Section: Discussionmentioning

confidence: 58%

Active Use of Electronic Health Records (EHRs) and Personal Health Records (PHRs) for Epidemiologic Research: Sample Representativeness and Nonresponse Bias in a Study of Women During Pregnancy

Bower

Bollinger

Foraker

et al. 2017

eGEMs

View full text Add to dashboard Cite

Introduction:With the growing use of electronic medical records, electronic health records (EHRs), and personal health records (PHRs) for health care delivery, new opportunities have arisen for population health researchers. Our objective was to characterize PHR users and examine sample representativeness and nonresponse bias in a study of pregnant women recruited via the PHR.Design:Demographic characteristics were examined for PHR users and nonusers. Enrolled study participants (responders, n=187) were then compared with nonresponders and a representative sample of the target population.Results:PHR patient portal users (34 percent of eligible persons) were older and more likely to be White, have private health insurance, and develop gestational diabetes than nonusers. Of eligible persons (all PHR users), 11 percent (187/1,713) completed a self-administered PHR based questionnaire. Participants in the research study were more likely to be non-Hispanic White (90 percent versus 79 percent) and married (85 percent versus 77 percent), and were less likely to be Non-Hispanic Black (3 percent versus 12 percent) or Hispanic (3 percent versus 6 percent). Responders and nonresponders were similar regarding age distribution, employment status, and health insurance status. Demographic characteristics were similar between responders and nonresponders.Discussion:Demographic characteristics of the study population differed from the general population, consistent with patterns seen in traditional population-based studies. The PHR may be an efficient method for recruiting and conducting observational research with additional benefits of efficiency and cost-cost-effectiveness.

show abstract

Section: Discussionmentioning

confidence: 58%

Active Use of Electronic Health Records (EHRs) and Personal Health Records (PHRs) for Epidemiologic Research: Sample Representativeness and Nonresponse Bias in a Study of Women During Pregnancy

Bower

Bollinger

Foraker

et al. 2017

eGEMs

View full text Add to dashboard Cite

show abstract

“…Many studies suffer from difficulty with patient recruitment or recruitment delays. 4,[11][12][13] Patient recruitment is essential for study success. Without it, studies can be delayed with the potential loss of funding.…”

Section: Discussionmentioning

confidence: 99%

Electronic Health Record Algorithm Development for Research Subject Recruitment Using Colonoscopy Appointment Scheduling

2021

View full text Add to dashboard Cite

Introduction: Electronic health records (EHRs) are often leveraged in medical research to recruit study participants efficiently. The purpose of this study was to validate and refine the logic of an EHR algorithm for identifying potentially eligible participants for a comparative effectiveness study of fecal immunochemical tests (FITs), using colonoscopy as the standard.Methods: An Epic report was built to identify patients who met the eligibility criteria to recruit patients having a screening or surveillance colonoscopy. With the goal of maximizing the number of potentially eligible patients that could be recruited, researchers, with the assistance of information technology and scheduling staff, developed the algorithm for identifying potential subjects in the EHR. Two validation methods, descriptive statistics and manual verification, were used.Results: The algorithm was refined over 3 iterations leading to the following criteria being used for generating the report: Age, Appointment Made On/Cancel Date, Appointment Procedure, Contact Type, Date Range, Encounter Departments, ICD-10 codes, and Patient Type. Appointment Serial Number/ Contact Serial Number were output fields that allowed the tracking of cancellations and reschedules.Conclusion: Development of an EHR algorithm saved time in that most individuals ineligible for the study were excluded before patient medical record review. Running daily reports that included cancellations and rescheduled appointments allowed for maximum recruitment in a time frame appropriate for the use of the FITs. This algorithm demonstrates that refining the algorithm iteratively and adding cancellations and reschedules of colonoscopies increased the accuracy of reaching all potential patients for recruitment. (

show abstract

“…A clinic-based sample would imply women actively seeking care, likely within a specific department. As such, and based on previous research demonstrating that the sample recruited using this methodology is generally reflective of the underlying population, 19 we consider this a community-based sampling strategy, which is closer to population based than clinic based.…”

Section: Study Sample and Designmentioning

confidence: 99%

“…An assessment of this sampling method suggests that it generally produces a sample that is reflective of the underlying population. 19 The fact that this sample was recruited from a health care network likely means that our control group has a higher proportion of careseeking women than would be found in a true populationbased sample. However, given that these women could have been seen as long as 2 years ago for any reason, we believe that our sample is likely closer to a population-based sample than a clinic-based sample, where participants would have to be actively engaged in care.…”

Section: Bond Et Almentioning

confidence: 99%

Potential for Selection Bias in Studies of the Association of Hormonal Contraception and Chronic Vulvar Pain

Bond

Kachura

Fox

et al. 2022

Journal of Women's Health

Self Cite

View full text Add to dashboard Cite

Background: Hormonal contraceptive use is common among reproductive-aged women, but research evaluating its etiological relationship to vulvodynia remains mixed. We sought to evaluate this association and examine the potential for bias due to care-seeking behavior. Materials and Methods: We conducted a case-control study of women recruited from a large health care network database from 2008 to 2011. Of 26,455 eligible respondents, 1168 met the case definition for chronic vulvar pain (CVP). We matched each case to three controls by age and used conditional logistic regression to calculate odds ratios (ORs) for prior hormonal contraception (HC) use and CVP, stratifying cases by whether or not they sought care for their vulvar pain. We also simulated the influence of potential biases due to care seeking, using parameters based on this dataset. Results: HC users had higher odds of CVP (adjusted OR = 2.6, 95% confidence interval [CI]: 2.2-3.2). Effect estimates were stronger when cases were restricted to care seekers (adjusted OR = 2.9, 95% CI: 2.2-3.7). Effect estimates decreased slightly as time increased between HC initiation and pain onset. Our simulations suggested that effect estimates may be spuriously strengthened when cases are restricted to care-seeking women, but controls are recruited from the general population. Conclusions: Our results suggest an association between antecedent HC use and CVP that is potentially spuriously strengthened in case-control studies when cases are restricted to care seekers but controls are not.

show abstract

Using administrative health care system records to recruit a community-based sample for population research

Cited by 8 publications

References 27 publications

Active Use of Electronic Health Records (EHRs) and Personal Health Records (PHRs) for Epidemiologic Research: Sample Representativeness and Nonresponse Bias in a Study of Women During Pregnancy

Active Use of Electronic Health Records (EHRs) and Personal Health Records (PHRs) for Epidemiologic Research: Sample Representativeness and Nonresponse Bias in a Study of Women During Pregnancy

Electronic Health Record Algorithm Development for Research Subject Recruitment Using Colonoscopy Appointment Scheduling

Potential for Selection Bias in Studies of the Association of Hormonal Contraception and Chronic Vulvar Pain

Contact Info

Product

Resources

About