Pfizer-BioNTech Coronavirus Disease 2019 Vaccine Effectiveness Against Severe Acute Respiratory Syndrome Coronavirus 2 Infection Among Long-term Care Facility Staff With and Without Prior Infection in New York City, January-June 2021.

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.

Reduced Odds of Severe Acute Respiratory Syndrome Coronavirus 2 Reinfection After Vaccination Among New York City Adults, July 2021-November 2021.

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.

Population-Based Estimates of Coronavirus Disease 2019 (COVID-19)-like Illness, COVID-19 Illness, and Rates of Case Ascertainment, Hospitalizations, and Deaths-Noninstitutionalized New York City Residents, March-April 2020.

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.

Detecting COVID-19 Clusters at High Spatiotemporal Resolution, New York City, New York, USA, June-July 2020.

A surveillance system that uses census tract resolution and the SaTScan prospective space-time scan statistic detected clusters of increasing severe acute respiratory syndrome coronavirus 2 test percent positivity in New York City, NY, USA. Clusters included one in which patients attended the same social gathering and another that led to targeted testing and outreach.

Salmonellosis Outbreak Detected by Automated Spatiotemporal Analysis - New York City, May-June 2019.

In May 2019, the New York City Department of Health and Mental Hygiene (NYCDOHMH) detected an unusual cluster of five salmonellosis patients via automated spatiotemporal analysis of notifiable diseases using free SaTScan software (1). Within 1 day of cluster detection, graduate student interviewers determined that three of the patients had eaten prepared food from the same grocery store (establishment A) located inside the cluster area. NYCDOHMH initiated an investigation to identify additional cases, establish the cause, and provide control recommendations. Overall, 15 New York City (NYC) residents with laboratory-diagnosed salmonellosis who reported eating food from establishment A were identified. The most commonly consumed food item was chicken, reported by 10 patients. All 11 clinical isolates available were serotyped as Salmonella Blockley, sequenced, and analyzed by core genome multilocus sequence typing; isolates had a median difference of zero alleles. Environmental assessments revealed food not held at the proper temperature, food not cooled properly, and potential cross-contamination during chicken preparation. Elevated fecal coliform counts were found in two of four ready-to-eat food samples collected from establishment A, and Bacillus cereus was detected in three. The outbreak strain of Salmonella was isolated from one patient's leftover chicken. Establishing automated spatiotemporal cluster detection analyses for salmonellosis and other reportable diseases could aid in the detection of geographically focused, community-acquired outbreaks even before laboratory subtyping results become available.

Detection and Genetic Characterization of Community-Based SARS-CoV-2 Infections - New York City, March 2020.

To limit introduction of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), the United States restricted travel from China on February 2, 2020, and from Europe on March 13. To determine whether local transmission of SARS-CoV-2 could be detected, the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) conducted deidentified sentinel surveillance at six NYC hospital emergency departments (EDs) during March 1-20. On March 8, while testing availability for SARS-CoV-2 was still limited, DOHMH announced sustained community transmission of SARS-CoV-2 (1). At this time, twenty-six NYC residents had confirmed COVID-19, and ED visits for influenza-like illness* increased, despite decreased influenza virus circulation.† The following week, on March 15, when only seven of the 56 (13%) patients with known exposure histories had exposure outside of NYC, the level of community SARS-CoV-2 transmission status was elevated from sustained community transmission to widespread community transmission (2). Through sentinel surveillance during March 1-20, DOHMH collected 544 specimens from patients with influenza-like symptoms (ILS)§ who had negative test results for influenza and, in some instances, other respiratory pathogens.¶ All 544 specimens were tested for SARS-CoV-2 at CDC; 36 (6.6%) tested positive. Using genetic sequencing, CDC determined that the sequences of most SARS-CoV-2-positive specimens resembled those circulating in Europe, suggesting probable introductions of SARS-CoV-2 from Europe, from other U.S. locations, and local introductions from within New York. These findings demonstrate that partnering with health care facilities and developing the systems needed for rapid implementation of sentinel surveillance, coupled with capacity for genetic sequencing before an outbreak, can help inform timely containment and mitigation strategies.

COVID-19 Outbreak - New York City, February 29-June 1, 2020.

New York City (NYC) was an epicenter of the coronavirus disease 2019 (COVID-19) outbreak in the United States during spring 2020 (1). During March-May 2020, approximately 203,000 laboratory-confirmed COVID-19 cases were reported to the NYC Department of Health and Mental Hygiene (DOHMH). To obtain more complete data, DOHMH used supplementary information sources and relied on direct data importation and matching of patient identifiers for data on hospitalization status, the occurrence of death, race/ethnicity, and presence of underlying medical conditions. The highest rates of cases, hospitalizations, and deaths were concentrated in communities of color, high-poverty areas, and among persons aged ≥75 years or with underlying conditions. The crude fatality rate was 9.2% overall and 32.1% among hospitalized patients. Using these data to prevent additional infections among NYC residents during subsequent waves of the pandemic, particularly among those at highest risk for hospitalization and death, is critical. Mitigating COVID-19 transmission among vulnerable groups at high risk for hospitalization and death is an urgent priority. Similar to NYC, other jurisdictions might find the use of supplementary information sources valuable in their efforts to prevent COVID-19 infections.

Detecting Drop-offs in Electronic Laboratory Reporting for Communicable Diseases in New York City.

CONTEXT: The Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene receives an average of more than 1000 reports daily via electronic laboratory reporting. Rapid recognition of any laboratory reporting drop-off of test results for 1 or more diseases is necessary to avoid delays in case investigation and outbreak detection. PROGRAM: We modified our outbreak detection approach using the prospective space-time permutation scan statistic in SaTScan. Instead of searching for spatiotemporal clusters of high case counts, we reconceptualized "space" as "laboratory" and instead searched for clusters of recent low reporting, overall and for each of 52 diseases and 10 hepatitis test types, within individual laboratories. Each analysis controlled for purely temporal trends affecting all laboratories and accounted for multiple testing. IMPLEMENTATION: A SAS program automatically created input files, invoked SaTScan, and further processed SaTScan analysis results and output summaries to a secure folder. Analysts reviewed output weekly and reported concerning drop-offs to coordinators, who liaised with reporting laboratory staff to investigate and resolve issues. EVALUATION: During a 42-week evaluation period, October 2017 to July 2018, we detected 62 unique signals of reporting drop-offs. Of these, 39 (63%) were verified as true drop-offs, including failures to generate or transmit files and programming errors. For example, a hospital laboratory stopped reporting influenza after changing a multiplex panel result from "positive" to "detected." Six drop-offs were detected despite low numbers of expected reports missing (<10 per drop-off). DISCUSSION: Our novel application of SaTScan identified a manageable number of possible electronic laboratory reporting drop-offs for investigation. Ongoing maintenance requirements are minimal but include accounting for laboratory mergers and referrals. Automated analyses facilitated rapid identification and correction of electronic laboratory reporting errors, even with small numbers of expected reports missing, suggesting that our approach might be generalizable to smaller jurisdictions.

Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples.

BACKGROUND: Infectious disease forecasting aims to predict characteristics of both seasonal epidemics and future pandemics. Accurate and timely infectious disease forecasts could aid public health responses by informing key preparation and mitigation efforts. MAIN BODY: For forecasts to be fully integrated into public health decision-making, federal, state, and local officials must understand how forecasts were made, how to interpret forecasts, and how well the forecasts have performed in the past. Since the 2013-14 influenza season, the Influenza Division at the Centers for Disease Control and Prevention (CDC) has hosted collaborative challenges to forecast the timing, intensity, and short-term trajectory of influenza-like illness in the United States. Additional efforts to advance forecasting science have included influenza initiatives focused on state-level and hospitalization forecasts, as well as other infectious diseases. Using CDC influenza forecasting challenges as an example, this paper provides an overview of infectious disease forecasting; applications of forecasting to public health; and current work to develop best practices for forecast methodology, applications, and communication. CONCLUSIONS: These efforts, along with other infectious disease forecasting initiatives, can foster the continued advancement of forecasting science.

Disparities in Zika Virus Testing and Incidence Among Women of Reproductive Age-New York City, 2016.

CONTEXT: The New York City Department of Health and Mental Hygiene (NYC DOHMH) performs surveillance for reportable diseases, including Zika virus (ZIKV) infection and disease, to inform public health responses. Incidence rates of other mosquito-borne diseases related to international travel are associated with census tract poverty level in NYC, suggesting that high poverty areas might be at higher risk for ZIKV infections. OBJECTIVES: We assessed ZIKV testing rates and incidence of travel-associated infection among reproductive age women in NYC to identify areas with high incidence and low testing rates and assess the effectiveness of public health interventions. DESIGN: We analyzed geocoded ZIKV surveillance data collected by NYC DOHMH. Women aged 15 to 44 years tested during January-July 2016 (n = 4733) were assigned to census tracts, which we grouped by poverty level and quartile of the number of persons born in countries or territories with mosquito-borne ZIKV transmission as a proxy for risk of travel to these areas. We calculated crude ZIKV testing rates, incidence rates, and incidence rate ratios (IRRs). SETTING: New York City. RESULTS: Eight percent of patients (n = 376) tested had evidence of ZIKV infection. Cumulative incidence was higher both in areas with higher versus lower poverty levels (IRR = 2.4; 95% confidence interval [CI], 2.0-3.0) and in areas with the largest versus smallest populations of persons born in countries or territories with mosquito-borne ZIKV transmission (IRR = 11.3; 95% CI, 6.2-20.7). Initially, ZIKV testing rates were lowest in higher poverty areas with the largest populations of persons born in countries or territories with mosquito-borne ZIKV transmission (15/100 000), but following targeted interventions, testing rates were highest in these areas (80/100 000). CONCLUSIONS: Geocoded data enabled us to identify communities with low testing but high ZIKV incidence rates, intervene to promote testing and reduce barriers to testing, and measure changes in testing rates.

Increasing Antibiotic Resistance in Shigella spp. from Infected New York City Residents, New York, USA.

Approximately 20% of Shigella isolates tested in New York City, New York, USA, during 2013-2015 displayed decreased azithromycin susceptibility. Case-patients were older and more frequently male and HIV infected than those with azithromycin-susceptible Shigella infection; 90% identified as men who have sex with men. Clinical interpretation guidelines for azithromycin resistance and outcome studies are needed.

Daily Reportable Disease Spatiotemporal Cluster Detection, New York City, New York, USA, 2014-2015.

Each day, the New York City Department of Health and Mental Hygiene uses the free SaTScan software to apply prospective space-time permutation scan statistics to strengthen early outbreak detection for 35 reportable diseases. This method prompted early detection of outbreaks of community-acquired legionellosis and shigellosis.

Reportable Bacterial Infections among New York City-Born Infants, 2001-2009.

OBJECTIVE: To determine rates of reportable bacterial infections among infants in New York City and identify populations at risk and preventable causes of morbidity. STUDY DESIGN: This retrospective cohort study matched live births in New York City from 2001-2009 to reported cases of bacterial infections among infants less than 1 year of age. Characteristics recorded on birth certificates were compared between infants with bacterial enteric infection, bacterial nonenteric infection, and no reportable bacterial infection. Multinomial logistic regression and multivariable logistic regression were used to identify risk factors for infection. RESULTS: Bacterial infection was reported in 4.6 cases per 1000 live births. Of 4524 infants with a reportable infection, the majority (2880, 63%) had an enteric infection. Asian/Pacific Islanders in Brooklyn were the borough-level race/ethnic group with the highest enteric infection rate (8.5 per 1000 live births). Citywide, infants with enteric infections were disproportionately male, from higher poverty neighborhoods, born to foreign-born mothers, and enrolled in Special Supplemental Food Program for Women, Infants, and Children or Medicaid. In contrast, infants with nonenteric infections were more likely to have low birthweight and mothers characterized by US birth and black race or white Hispanic race/ethnicity. CONCLUSIONS: Distinct patterns of risk factors for enteric and nonenteric bacterial infections among infants were identified. The results suggest that infants born to Asian/Pacific Islander mothers residing in Brooklyn should be a focus of enteric disease prevention. More research is necessary to better understand what behaviors increase the risk of enteric disease in this population.

Risk Factors for Serogroup C Meningococcal Disease during Outbreak among Men who Have Sex with Men, New York City, New York, USA.

Risk factors for illness during a serogroup C meningococcal disease outbreak among men who have sex with men in New York City, New York, USA, in 2012-2013 included methamphetamine and cocaine use and sexually transmitted infections. Outbreak investigations should consider routinely capturing information regarding drug use and sex-related risk factors.

Refining historical limits method to improve disease cluster detection, New York City, New York, USA.

Since the early 2000s, the Bureau of Communicable Disease of the New York City Department of Health and Mental Hygiene has analyzed reportable infectious disease data weekly by using the historical limits method to detect unusual clusters that could represent outbreaks. This method typically produced too many signals for each to be investigated with available resources while possibly failing to signal during true disease outbreaks. We made method refinements that improved the consistency of case inclusion criteria and accounted for data lags and trends and aberrations in historical data. During a 12-week period in 2013, we prospectively assessed these refinements using actual surveillance data. The refined method yielded 74 signals, a 45% decrease from what the original method would have produced. Fewer and less biased signals included a true citywide increase in legionellosis and a localized campylobacteriosis cluster subsequently linked to live-poultry markets. Future evaluations using simulated data could complement this descriptive assessment.

Disparities in Reportable Communicable Disease Incidence by Census Tract-Level Poverty, New York City, 2006-2013.

OBJECTIVES: We described disparities in selected communicable disease incidence across area-based poverty levels in New York City, an area with more than 8 million residents and pronounced household income inequality. METHODS: We geocoded and categorized cases of 53 communicable diseases diagnosed during 2006 to 2013 by census tract-based poverty level. Age-standardized incidence rate ratios (IRRs) were calculated for areas with 30% or more versus fewer than 10% of residents below the federal poverty threshold. RESULTS: Diseases associated with high poverty included rickettsialpox (IRR = 3.69; 95% confidence interval [CI] = 2.29, 5.95), chronic hepatitis C (IRR for new reports = 3.58; 95% CI = 3.50, 3.66), and malaria (IRR = 3.48; 95% CI = 2.97, 4.08). Diseases associated with low poverty included domestic tick-borne diseases acquired through travel to areas where infected vectors are prevalent, such as human granulocytic anaplasmosis (IRR = 0.08; 95% CI = 0.03, 0.19) and Lyme disease (IRR = 0.34; 95% CI = 0.32, 0.36). CONCLUSIONS: Residents of high poverty areas were disproportionately affected by certain communicable diseases that are amenable to public health interventions. Future work should clarify subgroups at highest risk, identify reasons for the observed associations, and use findings to support programs to minimize disparities.

Association between Guillain-Barré syndrome and influenza A (H1N1) 2009 monovalent inactivated vaccines in the USA: a meta-analysis.

BACKGROUND: The influenza A (H1N1) 2009 monovalent vaccination programme was the largest mass vaccination initiative in recent US history. Commensurate with the size and scope of the vaccination programme, a project to monitor vaccine adverse events was undertaken, the most comprehensive safety surveillance agenda in the USA to date. The adverse event monitoring project identified an increased risk of Guillain-Barré syndrome after vaccination; however, some individual variability in results was noted. Guillain-Barré syndrome is a rare but serious health disorder in which a person's own immune system damages their nerve cells, causing muscle weakness, sometimes paralysis, and infrequently death. We did a meta-analysis of data from the adverse event monitoring project to ascertain whether influenza A (H1N1) 2009 monovalent inactivated vaccines used in the USA increased the risk of Guillain-Barré syndrome. METHODS: Data were obtained from six adverse event monitoring systems. About 23 million vaccinated people were included in the analysis. The primary analysis entailed calculation of incidence rate ratios and attributable risks of excess cases of Guillain-Barré syndrome per million vaccinations. We used a self-controlled risk-interval design. FINDINGS: Influenza A (H1N1) 2009 monovalent inactivated vaccines were associated with a small increased risk of Guillain-Barré syndrome (incidence rate ratio 2·35, 95% CI 1·42-4·01, p=0·0003). This finding translated to about 1·6 excess cases of Guillain-Barré syndrome per million people vaccinated. INTERPRETATION: The modest risk of Guillain-Barré syndrome attributed to vaccination is consistent with previous estimates of the disorder after seasonal influenza vaccination. A risk of this small magnitude would be difficult to capture during routine seasonal influenza vaccine programmes, which have extensive, but comparatively less, safety monitoring. In view of the morbidity and mortality caused by 2009 H1N1 influenza and the effectiveness of the vaccine, clinicians, policy makers, and those eligible for vaccination should be assured that the benefits of inactivated pandemic vaccines greatly outweigh the risks. FUNDING: US Federal Government.

Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks.

Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using SaTScan. SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease's systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve.

Adapting group sequential methods to observational postlicensure vaccine safety surveillance: results of a pentavalent combination DTaP-IPV-Hib vaccine safety study.

To address gaps in traditional postlicensure vaccine safety surveillance and to promote rapid signal identification, new prospective monitoring systems using large health-care database cohorts have been developed. We newly adapted clinical trial group sequential methods to this observational setting in an original safety study of a combination diphtheria and tetanus toxoids and acellular pertussis adsorbed (DTaP), inactivated poliovirus (IPV), and Haemophilus influenzae type b (Hib) conjugate vaccine (DTaP-IPV-Hib) among children within the Vaccine Safety Datalink population. For each prespecified outcome, we conducted 11 sequential Poisson-based likelihood ratio tests during September 2008-January 2011 to compare DTaP-IPV-Hib vaccinees with historical recipients of other DTaP-containing vaccines. No increased risk was detected among 149,337 DTaP-IPV-Hib vaccinees versus historical comparators for any outcome, including medically attended fever, seizure, meningitis/encephalitis/myelitis, nonanaphylactic serious allergic reaction, anaphylaxis, Guillain-Barré syndrome, or invasive Hib disease. In end-of-study prespecified subgroup analyses, risk of medically attended fever was elevated among 1- to 2-year-olds who received DTaP-IPV-Hib vaccine versus historical comparators (relative risk = 1.83, 95% confidence interval: 1.34, 2.50) but not among infants under 1 year old (relative risk = 0.83, 95% confidence interval: 0.73, 0.94). Findings were similar in analyses with concurrent comparators who received other DTaP-containing vaccines during the study period. Although lack of a controlled experiment presents numerous challenges, implementation of group sequential monitoring methods in observational safety surveillance studies is promising and warrants further investigation.