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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.

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 Evidence is accumulating of coronavirus disease 2019 (COVID-19) vaccine effectiveness among persons with prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods We evaluated the effect against incident SARS-CoV-2 infection of (1) prior infection without vaccination, (2) vaccination (2 doses of Pfizer-BioNTech COVID-19 vaccine) without prior infection, and (3) vaccination after prior infection, all compared with unvaccinated persons without prior infection. We included long-term care facility staff in New York City aged <65 years with weekly SARS-CoV-2 testing from 21 January to 5 June 2021. Test results were obtained from state-mandated laboratory reporting. Vaccination status was obtained from the Citywide Immunization Registry. Cox proportional hazards models adjusted for confounding with inverse probability of treatment weights. Results Compared with unvaccinated persons without prior infection, incident SARS-CoV-2 infection risk was lower in all groups: 54.6% (95% confidence interval, 38.0%–66.8%) lower among unvaccinated, previously infected persons; 80.0% (67.6%–87.7%) lower among fully vaccinated persons without prior infection; and 82.4% (70.8%–89.3%) lower among persons fully vaccinated after prior infection. Conclusions Two doses of Pfizer-BioNTech COVID-19 vaccine reduced SARS-CoV-2 infection risk by ≥80% and, for those with prior infection, increased protection from prior infection alone. These findings support recommendations that all eligible persons, regardless of prior infection, be vaccinated against COVID-19. [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]

Using a population-based, representative telephone survey, ~930 000 New York City residents had COVID-19 illness beginning 20 March–30 April 2020, a period with limited testing. For every 1000 persons estimated with COVID-19 illness, 141.8 were tested and reported as cases, 36.8 were hospitalized, and 12.8 died, varying by demographic characteristics. [ABSTRACT FROM AUTHOR]

Background: Guillain-Barré Syndrome (GBS) can be triggered by gastrointestinal or respiratory infections, including influenza. During the 2009 influenza A (H1N1) pandemic in the United States, monovalent inactivated influenza vaccine (MIV) availability coincided with high rates of wildtype influenza infections. Several prior studies suggested an elevated GBS risk following MIV, but adjustment for antecedent infection was limited. Methods: We identified patients enrolled in health plans participating in the Vaccine Safety Datalink and diagnosed with GBS from July 2009 through June 2011. Medical records of GBS cases with 2009–10 MIV, 2010–11 trivalent inactivated influenza vaccine (TIV), and/or a medically-attended respiratory or gastrointestinal infection in the 1 through 141 days prior to GBS diagnosis were reviewed and classified according to Brighton Collaboration criteria for diagnostic certainty. Using a case-centered design, logistic regression models adjusted for patient-level time-varying sources of confounding, including seasonal vaccinations and infections in GBS cases and population-level controls. Results: Eighteen confirmed GBS cases received vaccination in the 6 weeks preceding onset, among 1.27 million 2009–10 MIV recipients and 2.80 million 2010–11 TIV recipients. Forty-four confirmed GBS cases had infection in the 6 weeks preceding onset, among 3.77 million patients diagnosed with medically-attended infection. The observed-versus-expected odds that 2009–10 MIV/2010–11 TIV was received in the 6 weeks preceding GBS onset was odds ratio = 1.54, 95% confidence interval (CI), 0.59–3.99; risk difference = 0.93 per million doses, 95% CI, −0.71–5.16. The association between GBS and medically-attended infection was: odds ratio = 7.73, 95% CI, 3.60–16.61; risk difference = 11.62 per million infected patients, 95% CI, 4.49–26.94. These findings were consistent in sensitivity analyses using alternative infection definitions and risk intervals for prior vaccination shorter than 6 weeks. Conclusions: After adjusting for antecedent infections, we found no evidence for an elevated GBS risk following 2009–10 MIV/2010–11 TIV influenza vaccines. However, the association between GBS and antecedent infection was strongly elevated. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Antibiotic 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. METHODS: Antibiotic dispensings and diagnoses were obtained from 2 health insurers for 3- to <72-month-olds in 16 Massachusetts communities from 2000 to 2009. Population-based antibiotic-dispensing rates per person-year (p-y) were determined according to year (September-August) for 3 age groups. Fit statistics were used to identify the most likely year for a change in trend. Rates for the first and last years were compared according to antibiotic category and associated diagnosis. RESULTS: From 2000-2001 to 2008-2009, the antibiotic-dispensing rate for 3- to <24-month-olds decreased 24% (2.3-1.8 antibiotic dispensings per p-y); for 24- to <48-month-olds, it decreased 18% (1.6-1.3 antibiotic dispensings per p-y); and for 48- to <72-month- olds, it decreased 20% (1.4-1.1 antibiotic dispensings per p-y). For 3- to <48-month-olds, rates declined until 2004-2005 and remained stable thereafter; the downward trend for 48- to <72-month-olds ended earlier in 2001-2002. Among 3- to <24-month-olds, first-line penicillin use declined 26%. For otitis media, the dispensing rate decreased 14% and the diagnosis rate declined 9%, whereas the treatment fraction was stable at 63%. CONCLUSIONS: The downward trend in antibiotic dispensings to young children in these communities ended by 2004-2005. This trend was driven by a declining otitis media diagnosis rate. Continued monitoring of population-based dispensing rates will support efforts to avoid returning to previous levels of antibiotic overuse. [ABSTRACT FROM AUTHOR]

Background: The emergence of pandemic H1N1 influenza virus in early 2009 prompted the rapid licensure and use of H1N1 monovalent inactivated (MIV) and live, attenuated (LAMV) vaccines separate from seasonal trivalent inactivated (TIV) and live, attenuated (LAIV) influenza vaccines. A robust influenza immunization program in the U.S. requires ongoing monitoring of potential adverse events associated with vaccination. Purpose: To prospectively conduct safety monitoring of H1N1 and seasonal influenza vaccines during the 2009–2010 season. Methods: The Vaccine Safety Datalink (VSD) Project monitors ∼9.2 million members in eight U.S. medical care organizations. Electronic data on vaccines and pre-specified adverse events were updated and analyzed weekly for signal detection from November 2009 to April 2010 using either a self-controlled design or a current versus historical comparison. Statistical signals were further evaluated using alternative approaches to identify temporal clusters and to control for time-varying confounders. Results: As of May 1, 2010, a total of 1,345,663 MIV, 267,715 LAMV, 2,741,150 TIV, and 157,838 LAIV doses were administered in VSD. No significant associations were noted during sequential analyses for Guillain–Barré syndrome, most other neurologic outcomes, and allergic and cardiac events. For MIV, a statistical signal was observed for Bell''s palsy for adults aged ≥25 years on March 31, 2010, using the self-controlled approach. Subsequent analyses revealed no significant temporal cluster. Case-centered logistic regression adjusting for seasonality demonstrated an OR for Bell''s palsy of 1.26 (95% CI=0.97, 1.63). Conclusions: No major safety problems following H1N1 or seasonal influenza vaccines were detected in the 2009–2010 season in weekly sequential analyses. Seasonality likely contributed to the Bell''s palsy signal following MIV. Prospective safety monitoring followed by rigorous signal refinement is critical to inform decision-making by regulatory and public health agencies. [Copyright &y& Elsevier]

The article presents research which examined the timely detection of localized excess influenza activity across patient prescription, laboratory data and care in northern California. It mentions that the successful identification of clusters of localized influenza activity in addition to background seasonal levels could enhance the situational awareness for public health systems and officials. The authors assessed the performance of 10 kinds of electronic clinical data that can be applied effectively for influenza in 2007 and 2008.

Although influenza is a common disease, the determinants of each season's onset, magnitude, and duration remain poorly understood. Marked seasonality in temperate regions suggests that environmental variables affect transmission. Using deterministic compartmental transmission modeling, we assessed the potential for estimating climatic effects on influenza A epidemic patterns. Two versions of a susceptible-exposed-infectious-removed-susceptible (SEIRS) model were created with multiple immunity levels to represent viral drift. In the first model, seasonal transmission was approximated via a regular sine wave for the probability of transmission (β). In the second model, β varied based on actual values of the Multivariate El Nin˜o Southern Oscillation Index (MEI), an index comprising six weather variables associated with global climate variability. The SEIRS models were each used to generate epidemic patterns, some of which were regular and others irregular, depending on the parameter values used. However, MEI had a notable effect on the epidemic patterns by allowing susceptibles to accumulate, leading to larger seasonal epidemics. This approach allows us to explore how relaxing certain model assumptions affects results, and provides a foundation for detecting climate effects in actual influenza data. Our ultimate goal is to promote interepidemic preparedness by elucidating the contribution of environmental variables to influenza transmission. [Copyright &y& Elsevier]