Invasive Nontypeable Haemophilus influenzae Disease Outbreak at an Elementary School - Michigan, May 2023.

In May 2023, the Detroit Health Department was notified of four cases of invasive nontypeable Haemophilus influenzae (Hi) disease among students attending the same elementary school and grade, all with illness onsets within 7 days. Three patients were hospitalized, and one died. Most U.S. cases of invasive Hi disease are caused by nontypeable strains. No vaccines against nontypeable or non-type b Hi strains are currently available. Chemoprophylaxis is not typically recommended in response to nontypeable Hi cases; however, because of the high attack rate (four cases among 46 students; 8.7%), rifampin prophylaxis was recommended for household contacts of patients with confirmed cases and for all students and staff members in the school wing where confirmed cases occurred. Only 10.8% of students for whom chemoprophylaxis was recommended took it, highlighting gaps in understanding among caregivers and health care providers about persons for whom chemoprophylaxis was recommended. Public health authorities subsequently enhanced communication and education to the school community, improved coordination with health care partners, and established mass prophylaxis clinics at the school. This outbreak highlights the potential for nontypeable Hi to cause serious illness and outbreaks and the need for chemoprophylaxis guidance for nontypeable Hi disease. Achieving high chemoprophylaxis coverage requires education, communication, and coordination with community and health care partners.

Developing an algorithm across integrated healthcare systems to identify a history of cancer using electronic medical records.

OBJECTIVE: Tumor registries in integrated healthcare systems (IHCS) have high precision for identifying incident cancer but often miss recently diagnosed cancers or those diagnosed outside of the IHCS. We developed an algorithm using the electronic medical record (EMR) to identify people with a history of cancer not captured in the tumor registry to identify adults, aged 40-65 years, with no history of cancer. MATERIALS AND METHODS: The algorithm was developed at Kaiser Permanente Colorado, and then applied to 7 other IHCS. We included tumor registry data, diagnosis and procedure codes, chemotherapy files, oncology encounters, and revenue data to develop the algorithm. Each IHCS adapted the algorithm to their EMR data and calculated sensitivity and specificity to evaluate the algorithm's performance after iterative chart review. RESULTS: We included data from over 1.26 million eligible people across 8 IHCS; 55 601 (4.4%) were in a tumor registry, and 44848 (3.5%) had a reported cancer not captured in a registry. The common attributes of the final algorithm at each site were diagnosis and procedure codes. The sensitivity of the algorithm at each IHCS was 90.65%-100%, and the specificity was 87.91%-100%. DISCUSSION: Relying only on tumor registry data would miss nearly half of the identified cancers. Our algorithm was robust and required only minor modifications to adapt to other EMR systems. CONCLUSION: This algorithm can identify cancer cases regardless of when the diagnosis occurred and may be useful for a variety of research applications or quality improvement projects around cancer care.