Review of the performance of methods to identify diabetes cases among vital statistics, administrative, and survey data
Introduction
Diabetes is a serious and growing public health problem in the United States 1., 2., 3.. Public health surveillance of diabetes and its complications is critical for defining the burden of the disease, identifying high-risk groups, developing strategies to reduce the economic burden and human cost of diabetes, formulating health-care policy, and evaluating progress in disease prevention and control 4., 5., 6.. Health-care organizations, such as managed care, have also developed systems to track and monitor patients with diabetes (4). Such systems were designed to decrease costs, clarify health-care utilization patterns, monitor health-care outcomes, implement case-management programs, evaluate quality of care improvement efforts, and address accountability demands from accreditation agencies, employers, and other purchasers of health care.
Public health and managed-care organizations typically use multiple data sources to track and monitor chronic diseases such as diabetes 4., 7., 8., 9.. These include vital statistics, survey data, and administrative databases (e.g., billing data such as Medicare data, hospitalization data, physician encounter data, and pharmacy data). Because several effective, but underused, preventive-care practices are known to reduce complications related to diabetes, a major focus of diabetes surveillance efforts has been on preventive-care practices and health outcomes among persons with diagnosed diabetes. Understanding the performance of different surveillance methods to accurately identify prevalent cases of diagnosed diabetes is important in these surveillance efforts. However, strategies to identify individuals with diagnosed diabetes from various data sources have not been systematically evaluated. Therefore, we conducted a comprehensive literature review to summarize the validity of various methods for identifying individuals with diagnosed diabetes and to examine the factors influencing the validity within each study.
Section snippets
Methods
We searched Medline for articles published from 1966 through mid-2002 that reported a measure of validity of identifying persons with diabetes, such as sensitivity, specificity, positive predictive value, and kappa, or that contained sufficient data to allow us to calculate those measures. Since this is a review of the available literature, we did not assess the reported validity in each article.
We limited our search to English language articles from developed countries and excluded articles
Death certificates
We found 17 articles on studies that examined the sensitivity of cause-of-death data from death certificates in identifying decedents with diabetes 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37. (Table 1). None of the 17 examined the specificity or positive predictive value (PPV) of the cause-of-death data. The sensitivity of cause-of-death data in these studies ranged from 7.1% to 68.6% with a median of 36%. As would be expected, studies using any listed
Discussion
In this review we found that administrative databases and health surveys did a relatively good job of identifying persons with diagnosed diabetes. We found these data sources adequately sensitive, (i.e., identified the majority of persons with diagnosed diabetes), and highly specific (i.e., did not identify individuals as having diabetes when they did not). In contrast, cause-of-death data from death certificates were not sensitive, failing to identify roughly 60% of decedents with diabetes.
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