Background

Passive reporting and laboratory testing delays may limit gastrointestinal (GI) disease outbreak detection. Healthcare systems routinely collect clinical data in electronic medical records (EMRs) that could be used for surveillance. This study's primary objective was to identify data streams from EMRs that may perform well for GI outbreak detection.

Methods

Zip code-specific daily episode counts in 2009 were generated for 22 syndromic and laboratory-based data streams from Kaiser Permanente Northern California EMRs, covering 3.3 million members. Data streams included outpatient and inpatient diagnosis codes, antidiarrheal medication dispensings, stool culture orders, and positive microbiology tests for six GI pathogens. Prospective daily surveillance was mimicked using the space-time permutation scan statistic in single and multi-stream analyses, and space-time clusters were identified. Serotype relatedness was assessed for isolates in two Salmonella clusters.

Results

Potential outbreaks included a cluster of 18 stool cultures ordered over 5 days in one zip code and a Salmonella cluster in three zip codes over 9 days, in which at least five of six cases had the same rare serotype. In all, 28 potential outbreaks were identified using single stream analyses, with signals in outpatient diagnosis codes most common. Multi-stream analyses identified additional potential outbreaks and in one example, improved the timeliness of detection.

Conclusions

GI disease-related data streams can be used to identify potential outbreaks when generated from EMRs with extensive regional coverage. This process can supplement traditional GI outbreak reports to health departments, which frequently consist of outbreaks in well-defined settings (e.g., day care centers and restaurants) with no laboratory-confirmed pathogen. Data streams most promising for surveillance included microbiology test results, stool culture orders, and outpatient diagnoses. In particular, clusters of microbiology tests positive for specific pathogens could be identified in EMRs and used to prioritize further testing at state health departments, potentially improving outbreak detection.

Objective: Our objective was to evaluate the breast cancer risk associated with body burden levels of polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs).Methods: We conducted a hospital-based case-control study among 79 women diagnosed with invasive breast cancer and 52 controls diagnosed with benign breast conditions. We collected breast adipose tissue and analyzed it for all 17 2,3,7,8-substitituted PCDD/PCDFs. We used unconditional logistic regression to calculate age- and race-adjusted exposure-specific odds ratios (ORs) and 95% confidence intervals (CI) for each individual PCDD/PCDF congener as well as for the summary measures (I-TEQ, Adj-TEQ).Results: Dioxin levels were consistent with reports from other small, contemporary studies of body burdens in the U.S. None of the odds ratios for any of the congeners or summary measures differed significantly from one. Especially for the PCDF congeners, point estimates tended to be below one. One notable exception was octachlorodibenzo-p-dioxin (OCDD), for which the odds ratio for the second and third tertiles appeared modestly elevated (OR = 1.22, 95% CI: 0.47:3.16 and OR = 1.62, 95% CI: 0.64:4.12, respectively), though the test for trend was not significant (p = 0.36).Conclusion: Breast cancer risk was not associated with adipose levels of PCDD/PCDFs. More study is suggested among women of color who may have higher body burden levels of these compounds.