Medicaid coverage accuracy in electronic health records

Health insurance coverage facilitates access to preventive screenings and other essential health care services, and is linked to improved health outcomes; therefore, it is critical to understand how well coverage information is documented in the electronic health record (EHR) and which characteristics are associated with accurate documentation. Our objective was to evaluate the validity of EHR data for monitoring longitudinal Medicaid coverage and assess variation by patient demographics, visit types, and clinic characteristics. We conducted a retrospective, observational study comparing Medicaid status agreement between Oregon community health center EHR data linked at the patient-level to Medicaid enrollment data (gold standard). We included adult patients with a Medicaid identification number and ≥1 clinic visit between 1/1/2013–12/31/2014 [>1 million visits (n = 135,514 patients)]. We estimated statistical correspondence between EHR and Medicaid data at each visit (visit-level) and for different insurance cohorts over time (patient-level). Data were collected in 2016 and analyzed 2017–2018. We observed excellent agreement between EHR and Medicaid data for health insurance information: kappa (>0.80), sensitivity (>0.80), and specificity (>0.85). Several characteristics were associated with agreement; at the visit-level, agreement was lower for patients who preferred a non-English language and for visits missing income information. At the patient-level, agreement was lower for black patients and higher for older patients seen in primary care community health centers. Community health center EHR data are a valid source of Medicaid coverage information. Agreement varied with several characteristics, something researchers and clinic staff should consider when using health insurance information from EHR data. Keywords: Electronic health records, Medicaid, Health policy, Health insuranc

Risk Factors for MERS-CoV Seropositivity among Animal Market and Slaughterhouse Workers, Abu Dhabi, United Arab Emirates, 2014–2017

Camel contact is a recognized risk factor for Middle East respiratory syndrome coronavirus (MERS-CoV) infection. Because specific camel exposures associated with MERS-CoV seropositivity are not fully understood, we investigated worker–camel interactions and MERS-CoV seroprevalence. We assessed worker seroprevalence in 2 slaughterhouses and 1 live-animal market in Abu Dhabi, United Arab Emirates, during 2014–2017 and administered an epidemiologic survey in 2016 and 2017. Across 3 sampling rounds during 2014–2017, we sampled 100–235 workers, and 6%–19% were seropositive for MERS-CoV at each sampling round. One (1.4%) of 70 seronegative workers tested at multiple rounds seroconverted. On multivariable analyses, working as a camel salesman, handling live camels or their waste, and having diabetes were associated with seropositivity among all workers, whereas handling live camels and either administering medications or cleaning equipment was associated with seropositivity among market workers. Characterization of high-risk exposures is critical for implementation of preventive measures

Clinical and virologic characteristics of the first 12 patients with coronavirus disease 2019 (COVID-19) in the United States.

Data on the detailed clinical progression of COVID-19 in conjunction with epidemiological and virological characteristics are limited. In this case series, we describe the first 12 US patients confirmed to have COVID-19 from 20 January to 5 February 2020, including 4 patients described previously1,2,3. Respiratory, stool, serum and urine specimens were submitted for SARS-CoV-2 real-time reverse-transcription polymerase chain reaction (rRT-PCR) testing, viral culture and whole genome sequencing. Median age was 53 years (range: 21–68); 8 patients were male. Common symptoms at illness onset were cough (n = 8) and fever (n = 7). Patients had mild to moderately severe illness; seven were hospitalized and demonstrated clinical or laboratory signs of worsening during the second week of illness. No patients required mechanical ventilation and all recovered. All had SARS-CoV-2 RNA detected in respiratory specimens, typically for 2–3 weeks after illness onset. Lowest real-time PCR with reverse transcription cycle threshold values in the upper respiratory tract were often detected in the first week and SARS-CoV-2 was cultured from early respiratory specimens. These data provide insight into the natural history of SARS-CoV-2. Although infectiousness is unclear, highest viral RNA levels were identified in the first week of illness. Clinicians should anticipate that some patients may worsen in the second week of illness