Abstract

BackgroundMany administrative data sources are available to study the epidemiology of infectious diseases, including Clostridium difficile infection (CDI), but few publications have compared CDI event rates across databases using similar methodology. We used comparable methods with multiple administrative databases to compare the incidence of CDI in older and younger persons in the United States.MethodsWe performed a retrospective study using three longitudinal data sources (Medicare, OptumInsight LabRx, and Healthcare Cost and Utilization Project State Inpatient Database (SID)), and two hospital encounter-level data sources (Nationwide Inpatient Sample (NIS) and Premier Perspective database) to identify CDI in adults aged 18 and older with calculation of CDI incidence rates/100,000 person-years of observation (pyo) and CDI categorization (onset and association).ResultsThe incidence of CDI ranged from 66/100,000 in persons under 65 years (LabRx), 383/100,000 in elderly persons (SID), and 677/100,000 in elderly persons (Medicare). Ninety percent of CDI episodes in the LabRx population were characterized as community-onset compared to 41 % in the Medicare population. The majority of CDI episodes in the Medicare and LabRx databases were identified based on only a CDI diagnosis, whereas almost ¾ of encounters coded for CDI in the Premier hospital data were confirmed with a positive test result plus treatment with metronidazole or oral vancomycin. Using only the Medicare inpatient data to calculate encounter-level CDI events resulted in 553 CDI events/100,000 persons, virtually the same as the encounter proportion calculated using the NIS (544/100,000 persons).ConclusionsWe found that the incidence of CDI was 35 % higher in the Medicare data and fewer episodes were attributed to hospital acquisition when all medical claims were used to identify CDI, compared to only inpatient data lacking information on diagnosis and treatment in the outpatient setting. The incidence of CDI was 10-fold lower and the proportion of community-onset CDI was much higher in the privately insured younger LabRx population compared to the elderly Medicare population. The methods we developed to identify incident CDI can be used by other investigators to study the incidence of other infectious diseases and adverse events using large generalizable administrative datasets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-016-1501-7) contains supplementary material, which is available to authorized users.

Highlights

  • Many administrative data sources are available to study the epidemiology of infectious diseases, including Clostridium difficile infection (CDI), but few publications have compared CDI event rates across databases using similar methodology

  • In the Medicare data 23 % of inpatient CDI episodes were identified by the CDI diagnosis code together with an outpatient prescription for metronidazole or oral vancomycin within 14 days after hospital discharge; when restricted to patients with Part D coverage this corresponded to 40 % of inpatient CDI episodes (1303/3280)

  • Lessa reported that 53 % of CDI events (159,700 community-associated + 81,300 community-onset, health care facility associated) in persons of all ages were community-onset using the Emerging Infections Program (EIP) data [2], similar to the 41.4 % community-onset CDI episodes we identified in the Medicare data

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Summary

Introduction

Many administrative data sources are available to study the epidemiology of infectious diseases, including Clostridium difficile infection (CDI), but few publications have compared CDI event rates across databases using similar methodology. The results of risk factor studies have not always been consistent [8,9,10,11,12,13,14,15], with potential reasons for discrepancies including differences in patient populations, data availability, and/or study definitions. These differences limit both the ability to compare results across studies and the generalizability of results, making it difficult to identify which populations have the highest CDI burden and how to best target CDI prevention practices

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