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Spatiotemporal analysis of high-resolution coronavirus disease (COVID-19) data can help health officials monitor disease spread and target interventions (1,2). Publicly available data have been used to detect COVID-19 spatiotemporal clusters [...]

Large outbreaks of Shigella sonnei among children in Haredi Jewish (ultra-Orthodox) communities in Brooklyn, New York have occurred every 3–5 years since at least the mid-1980s. These outbreaks are partially attributable to large numbers of young children in these communities, with transmission highest in child care and school settings, and secondary transmission within households. As these outbreaks have been prolonged and difficult to control, we developed an agent-based model of shigellosis transmission among children in these communities to support New York City Department of Health and Mental Hygiene staff. Simulated children were assigned an initial susceptible, infectious, or recovered (immune) status and interacted and moved between their home, child care program or school, and a community site. We calibrated the model according to observed case counts as reported to the health department. Our goal was to better understand the efficacy of existing interventions and whether limited outreach resources could be focused more effectively. We evaluated how well disseminating hand washing education in child care programs can reduce the number of infected children. The model indicated that intervention efficacy may be as high as 24% when all intervention parameters are at optimal values but only approximately 7% for a more realistic, less stringent scenario. We ranked intervention parameters according to their permutation importance using a random-forest regression analysis. The most important parameter was the minimum number of reported cases in a child care program that triggers a visit to disseminate hand washing education, followed by the use of non-antibacterial soap in hand washing education, the number of additional visits to child care programs, and the probability of successfully obtaining information on child care program attendance via patient interview. Additional strategies should be considered, such as working with community partners to assist with hand hygiene education at facilities during an outbreak. [ABSTRACT FROM AUTHOR]

BackgroundNowcasting approaches enhance the utility of reportable disease data for trend monitoring by correcting for delays, but implementation details affect accuracy. ObjectiveTo support real-time COVID-19 situational awareness, the New York City Department of Health and Mental Hygiene used nowcasting to account for testing and reporting delays. We conducted an evaluation to determine which implementation details would yield the most accurate estimated case counts. MethodsA time-correlated Bayesian approach called Nowcasting by Bayesian Smoothing (NobBS) was applied in real time to line lists of reportable disease surveillance data, accounting for the delay from diagnosis to reporting and the shape of the epidemic curve. We retrospectively evaluated nowcasting performance for confirmed case counts among residents diagnosed during the period from March to May 2020, a period when the median reporting delay was 2 days. ResultsNowcasts with a 2-week moving window and a negative binomial distribution had lower mean absolute error, lower relative root mean square error, and higher 95% prediction interval coverage than nowcasts conducted with a 3-week moving window or with a Poisson distribution. Nowcasts conducted toward the end of the week outperformed nowcasts performed earlier in the week, given fewer patients diagnosed on weekends and lack of day-of-week adjustments. When estimating case counts for weekdays only, metrics were similar across days when the nowcasts were conducted, with Mondays having the lowest mean absolute error of 183 cases in the context of an average daily weekday case count of 2914. ConclusionsNowcasting using NobBS can effectively support COVID-19 trend monitoring. Accounting for overdispersion, shortening the moving window, and suppressing diagnoses on weekends—when fewer patients submitted specimens for testing—improved the accuracy of estimated case counts. Nowcasting ensured that recent decreases in observed case counts were not overinterpreted as true declines and supported officials in anticipating the magnitude and timing of hospitalizations and deaths and allocating resources geographically.

ObjectiveAntibiotic use rates have declined dramatically since the 1990s. We aimed to determine if, when, and at what level the decline in antibiotic-dispensing rates ended and which diagnoses contributed to the trends.MethodsAntibiotic dispensings and diagnoses were obtained from 2 health insurers for 3- to

BackgroundPassive 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.MethodsZip 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.ResultsPotential 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.ConclusionsGI 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.

Shigella bacteria are transmitted through the fecal-oral route by direct contact with an infected person, by ingestion of contaminated food or water, or by fomites. Shigellosis is associated with travel [...]

The Bureau of Communicable Disease (BCD) at the New York City Department of Health and Mental Hygiene (DOHMH) monitors and investigates >70 reportable diseases among the city's 8.49 million residents. [...]

In the United States, meningococcal disease, a nationally reportable bacterial disease caused by Neisseria meningitidis, has a case-fatality rate of 10%-15% (7). Clusters of serogroup C meningococcal disease have been [...]

Detecting aberrant clusters of reportable infectious disease quickly and accurately enough for meaningful action is a central goal of public health institutions (1-3). Clinicians' reports of suspected clusters of illness [...]

A return-to-office mandate did not result in a relative increase in COVID-19 cases among New York City municipal employees. Reopening public schools resulted in a relative increase in cases among Department of Education employees. A vaccination mandate resulted in a relative decrease in cases and hospitalizations among municipal employees. Objective: On September 13, 2021, teleworking ended for New York City municipal employees, and Department of Education employees returned to reopened schools. On October 29, COVID-19 vaccination was mandated. We assessed these mandates' short-term effects on disease transmission. Methods: Using difference-in-difference analyses, we calculated COVID-19 incidence rate ratios (IRRs) among residents 18 to 64 years old by employment status before and after policy implementation. Results: IRRs after (September 23–October 28) versus before (July 5–September 12) the return-to-office mandate were similar between office-based City employees and non-City employees. Among Department of Education employees, the IRR after schools reopened was elevated by 28.4% (95% confidence interval, 17.3%–40.3%). Among City employees, the IRR after (October 29–November 30) versus before (September 23–October 28) the vaccination mandate was lowered by 20.1% (95% confidence interval, 13.7%–26.0%). Conclusions: Workforce mandates influenced disease transmission, among other societal effects. [ABSTRACT FROM AUTHOR]

Background Belief that vaccination is not needed for individuals with prior infection contributes to coronavirus disease 2019 (COVID-19) vaccine hesitancy. Among individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) before vaccines became available, we determined whether vaccinated individuals had reduced odds of reinfection. Methods We conducted a case-control study among adult New York City residents who tested positive for SARS-CoV-2 infection in 2020 and had not died or tested positive again >90 days after an initial positive test as of 1 July 2021. Case patients with reinfection during July 2021–November 2021 and controls with no reinfection were matched (1:3) on age, sex, timing of initial positive test in 2020, and neighborhood poverty level. Matched odds ratios (mORs) and 95% confidence intervals (CIs) were calculated using conditional logistic regression. Results Of 349 827 eligible adults, 2583 were reinfected during July 2021–November 2021. Of 2401 with complete matching criteria data, 1102 (45.9%) were known to be symptomatic for COVID-19-like illness, and 96 (4.0%) were hospitalized. Unvaccinated individuals, compared with individuals fully vaccinated within the prior 90 days, had elevated odds of reinfection (mOR, 3.21; 95% CI, 2.70 to 3.82), of symptomatic reinfection (mOR, 2.97; 95% CI, 2.31 to 3.83), and of reinfection with hospitalization (mOR, 2.09; 95% CI,.91 to 4.79). Conclusions Vaccination reduced odds of reinfections when the Delta variant predominated. Further studies should assess risk of severe outcomes among reinfected persons as new variants emerge, infection- and vaccine-induced immunity wanes, and booster doses are administered. [ABSTRACT FROM AUTHOR]

Background: Comparing disease severity between SARS‐CoV‐2 variants among populations with varied vaccination and infection histories can help characterize emerging variants and support healthcare system preparedness. Methods: We compared COVID‐19 hospitalization risk among New York City residents with positive laboratory‐based SARS‐CoV‐2 tests when ≥98% of sequencing results were Delta (August–November 2021) or Omicron (BA.1 and sublineages, January 2022). A secondary analysis defined variant exposure using patient‐level sequencing results during July 2021–January 2022, comprising 1–18% of weekly confirmed cases. Results: Hospitalization risk was lower among patients testing positive when Omicron (16,025/488,053, 3.3%) than when Delta predominated (8268/158,799, 5.2%). In multivariable analysis adjusting for demographic characteristics and prior diagnosis and vaccination status, patients testing positive when Omicron predominated, compared with Delta, had 0.72 (95% CI: 0.63, 0.82) times the hospitalization risk. In a secondary analysis of patients with sequencing results, hospitalization risk was similar among patients infected with Omicron (2042/29,866, 6.8%), compared with Delta (1780/25,272, 7.0%), and higher among the subset who received two mRNA vaccine doses (adjusted relative risk 1.64; 95% CI: 1.44, 1.87). Conclusions: Hospitalization risk was lower among patients testing positive when Omicron predominated, compared with Delta. This finding persisted after adjusting for prior diagnosis and vaccination status, suggesting intrinsic virologic properties, not population‐based immunity, explained the lower severity. Secondary analyses demonstrated collider bias from the sequencing sampling frame changing over time in ways associated with disease severity. Representative data collection is necessary to avoid bias when comparing disease severity between previously dominant and newly emerging variants. [ABSTRACT FROM AUTHOR]