Chinese Center for Disease Control and Prevention Reports Findings in Public Health and Epidemiology (Optimal Look-Back Period to Identify True Incident Cases of Diabetes in Medical Insurance Data in the Chinese Population: Retrospective …): Health and Medicine – Public Health and Epidemiology

2023 NOV 16 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New research on Health and Medicine - Public Health and Epidemiology is the subject of a report. According to news reporting originating in Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Accurate estimation of incidence and prevalence is vital for preventing and controlling diabetes. Administrative data (including insurance data) could be a good source to estimate the incidence of diabetes.”

The news reporters obtained a quote from the research from Chinese Center for Disease Control and Prevention, “However, how to determine the look-back period (LP) to remove cases with preceding records remains a problem for administrative data. A short LP will cause overestimation of incidence, whereas a long LP will limit the usefulness of a database. Therefore, it is necessary to determine the optimal LP length for identifying incident cases in administrative data. This study aims to offer different methods to identify the optimal LP for diabetes by using medical insurance data from the Chinese population with reference to other diseases in the administrative data. Data from the insurance database of the city of Weifang, China from between January 2016 and December 2020 were used. To identify the incident cases in 2020, we removed prevalent patients with preceding records of diabetes between 2016 and 2019 (ie, a 4-year LP). Using this 4-year LP as a reference, consistency examination indexes (CEIs), including positive predictive values, the k coefficient, and overestimation rate, were calculated to determine the level of agreement between different LPs and an LP of 4 years (the longest LP). Moreover, we constructed a retrograde survival function, in which survival (ie, incident cases) means not having a preceding record at the given time and the survival time is the difference between the date of the last record in 2020 and the most recent previous record in the LP. Based on the survival outcome and survival time, we established the survival function and survival hazard function. When the survival probability, S(t), remains stable, and survival hazard converges to zero, we obtain the optimal LP. Combined with the results of these two methods, we determined the optimal LP for Chinese diabetes patients. The k agreement was excellent (0.950), with a high positive predictive value (92.2%) and a low overestimation rate (8.4%) after a 2-year LP. As for the retrograde survival function, S(t) dropped rapidly during the first 1-year LP (from 1.00 to 0.11). At a 417-day LP, the hazard function reached approximately zero (h=0.000459), S(t) remained at 0.10, and at 480 days, the frequency of S(t) did not increase. Combining the two methods, we found that the optimal LP is 2 years for Chinese diabetes patients. The retrograde survival method and CEIs both showed effectiveness.”

According to the news reporters, the research concluded: “A 2-year LP should be considered when identifying incident cases of diabetes using insurance data in the Chinese population.”

For more information on this research see: Optimal Look-Back Period to Identify True Incident Cases of Diabetes in Medical Insurance Data in the Chinese Population: Retrospective Analysis Study. JMIR Public Health and Surveillance, 2023;9. JMIR Public Health and Surveillance can be contacted at: Jmir Publications, Inc, 130 Queens Quay East, Unit 1100, Toronto, On M5A 0P6, Canada.

Our news correspondents report that additional information may be obtained by contacting Baohua Wang, Chinese Center for Disease Control and Prevention, National Institute for Prevention and Control of Chronic Noncommunicable Diseases, Beijing, People’s Republic of China. Additional authors for this research include Wenyi Yang, Shaobo Ma, Jingxin Wang, Limei Ai, Zhengyu Li and Xia Wan.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.2196/46708. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

The publisher of the journal JMIR Public Health and Surveillance can be contacted at: Jmir Publications, Inc, 130 Queens Quay East, Unit 1100, Toronto, On M5A 0P6, Canada.

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