Excess Deaths in Mexico City and New York City During the COVID-19 Pandemic, March to August 2020.

Objectives. To estimate all-cause excess deaths in Mexico City (MXC) and New York City (NYC) during the COVID-19 pandemic. Methods. We estimated expected deaths among residents of both cities between March 1 and August 29, 2020, using log-linked negative binomial regression and compared these deaths with observed deaths during the same period. We calculated total and age-specific excess deaths and 95% prediction intervals (PIs). Results. There were 259 excess deaths per 100 000 (95% PI = 249, 269) in MXC and 311 (95% PI = 305, 318) in NYC during the study period. The number of excess deaths among individuals 25 to 44 years old was much higher in MXC (77 per 100 000; 95% PI = 69, 80) than in NYC (34 per 100 000; 95% PI = 30, 38). Corresponding estimates among adults 65 years or older were 1263 (95% PI = 1199, 1317) per 100 000 in MXC and 1581 (95% PI = 1549, 1621) per 100 000 in NYC. Conclusions. Overall, excess mortality was higher in NYC than in MXC; however, the excess mortality rate among young adults was higher in MXC. Public Health Implications. Excess all-cause mortality comparisons across populations and age groups may represent a more complete measure of pandemic effects and provide information on mitigation strategies and susceptibility factors. (Am J Public Health. 2021;111(10): 1847-1850. https://doi.org/10.2105/AJPH.2021.306430).

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.

Surveillance data confirm multiyear predictions of rotavirus dynamics in New York City.

Prediction skill is a key test of models for epidemic dynamics. However, future validation of models against out-of-sample data is rare, partly because of a lack of timely surveillance data. We address this gap by analyzing the response of rotavirus dynamics to infant vaccination. Syndromic surveillance of emergency department visits for diarrhea in New York City reveals a marked decline in diarrheal incidence among infants and young children, in line with data on rotavirus-coded hospitalizations and laboratory-confirmed cases, and a shift from annual to biennial epidemics increasingly affecting older children and adults. A published mechanistic model qualitatively predicted these patterns more than 2 years in advance. Future efforts to increase vaccination coverage may disrupt these patterns and lead to further declines in the incidence of rotavirus-attributable gastroenteritis.

Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City.

The ideal spatial scale, or granularity, at which infectious disease incidence should be monitored and forecast has been little explored. By identifying the optimal granularity for a given disease and host population, and matching surveillance and prediction efforts to this scale, response to emergent and recurrent outbreaks can be improved. Here we explore how granularity and representation of spatial structure affect influenza forecast accuracy within New York City. We develop network models at the borough and neighborhood levels, and use them in conjunction with surveillance data and a data assimilation method to forecast influenza activity. These forecasts are compared to an alternate system that predicts influenza for each borough or neighborhood in isolation. At the borough scale, influenza epidemics are highly synchronous despite substantial differences in intensity, and inclusion of network connectivity among boroughs generally improves forecast accuracy. At the neighborhood scale, we observe much greater spatial heterogeneity among influenza outbreaks including substantial differences in local outbreak timing and structure; however, inclusion of the network model structure generally degrades forecast accuracy. One notable exception is that local outbreak onset, particularly when signal is modest, is better predicted with the network model. These findings suggest that observation and forecast at sub-municipal scales within New York City provides richer, more discriminant information on influenza incidence, particularly at the neighborhood scale where greater heterogeneity exists, and that the spatial spread of influenza among localities can be forecast.

Estimating the hospitalization burden associated with influenza and respiratory syncytial virus in New York City, 2003-2011.

BACKGROUND: Hospitalization burden associated with influenza and respiratory syncytial virus (RSV) is uncertain due to ambiguity in the inference methodologies employed for its estimation. OBJECTIVES: Utilization of a new method to quantitate the above burden. METHODS: Weekly hospitalization rates for several principal diagnoses from 2003 to 2011 in New York City by age group were regressed linearly against incidence proxies for the major influenza subtypes and RSV adjusting for temporal trends and seasonal baselines. RESULTS: Average annual rates of influenza-associated respiratory hospitalizations per 100 000 were estimated to be 129 [95% CI (79, 179)] for age <1, 36·3 (21·6, 51·4) for ages 1-4, 10·6 (7·5, 13·7) for ages 5-17, 25·6 (21·3, 29·8) for ages 18-49, 65·5 (54·0, 76·9) for ages 50-64, 125 (105, 147) for ages 65-74, and 288 (244, 331) for ages ≥75. Additionally, influenza had a significant contribution to hospitalization rates with a principal diagnosis of septicemia for ages 5-17 [0·76 (0·1, 1·4)], 18-49 [1·02 (0·3, 1·7)], 50-64 [4·0 (1·7, 6·3)], 65-74 [8·8 (2·2, 15·6)], and ≥75 [38·7 (25·7, 52·9)]. RSV had a significant contribution to the rates of respiratory hospitalizations for age <1 [1900 (1740, 2060)], ages 1-4 [117 (70, 167)], and ≥75 [175 (44, 312)] [including chronic lower respiratory disease, 90 (43, 140)] as well as pneumonia & influenza hospitalizations for ages 18-49 [6·2 (1·1, 11·3)] and circulatory hospitalizations for ages ≥75 [199 (13, 375)]. CONCLUSIONS: The high burden of RSV hospitalizations among young children and seniors age ≥75 suggests the need for additional control measures such as vaccination to mitigate the impact of annual RSV epidemics. Our estimates for influenza-associated hospitalizations provide further evidence of the burden of morbidity associated with influenza, supporting current guidelines regarding influenza vaccination and antiviral treatment.

Enhancing disease surveillance with novel data streams: challenges and opportunities.

Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.

Technical Description of the Distribute Project: A Community-based Syndromic Surveillance System Implementation.

This paper describes the design of a syndromic surveillance system implemented for community-based monitoring of influenza-like illness. The system began as collaboration between colleagues from state and large metropolitan area health jurisdictions, academic institutions, and the non-profit, International Society for Disease Surveillance. Over the six influenza seasons from 2006 to 2012, the system was automated and enhanced, with new features and infrastructure, and the resulting, reliable, enterprise grade system supported peer comparisons between 44 state and local public health jurisdictions who voluntarily contributed summarized data on influenza-like illness and gastrointestinal syndromes. The system was unusual in that it addressed the needs of a widely distributed, voluntary, community engaged in real-time data integration to support operational public health.

Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales.

The goal of influenza-like illness (ILI) surveillance is to determine the timing, location and magnitude of outbreaks by monitoring the frequency and progression of clinical case incidence. Advances in computational and information technology have allowed for automated collection of higher volumes of electronic data and more timely analyses than previously possible. Novel surveillance systems, including those based on internet search query data like Google Flu Trends (GFT), are being used as surrogates for clinically-based reporting of influenza-like-illness (ILI). We investigated the reliability of GFT during the last decade (2003 to 2013), and compared weekly public health surveillance with search query data to characterize the timing and intensity of seasonal and pandemic influenza at the national (United States), regional (Mid-Atlantic) and local (New York City) levels. We identified substantial flaws in the original and updated GFT models at all three geographic scales, including completely missing the first wave of the 2009 influenza A/H1N1 pandemic, and greatly overestimating the intensity of the A/H3N2 epidemic during the 2012/2013 season. These results were obtained for both the original (2008) and the updated (2009) GFT algorithms. The performance of both models was problematic, perhaps because of changes in internet search behavior and differences in the seasonality, geographical heterogeneity and age-distribution of the epidemics between the periods of GFT model-fitting and prospective use. We conclude that GFT data may not provide reliable surveillance for seasonal or pandemic influenza and should be interpreted with caution until the algorithm can be improved and evaluated. Current internet search query data are no substitute for timely local clinical and laboratory surveillance, or national surveillance based on local data collection. New generation surveillance systems such as GFT should incorporate the use of near-real time electronic health data and computational methods for continued model-fitting and ongoing evaluation and improvement.

Perceived usefulness of a distributed community-based syndromic surveillance system: a pilot qualitative evaluation study.

BACKGROUND: We conducted a pilot utility evaluation and information needs assessment of the Distribute Project at the 2010 Washington State Public Health Association (WSPHA) Joint Conference. Distribute is a distributed community-based syndromic surveillance system and network for detection of influenza-like illness (ILI). Using qualitative methods, we assessed the perceived usefulness of the Distribute system and explored areas for improvement. Nine state and local public health professionals participated in a focus group (n = 6) and in semi-structured interviews (n = 3). Field notes were taken, summarized and analyzed. FINDINGS: Several emergent themes that contribute to the perceived usefulness of system data and the Distribute system were identified: 1) Standardization: a common ILI syndrome definition; 2) Regional Comparability: views that support county-by-county comparisons of syndromic surveillance data; 3) Completeness: complete data for all expected data at a given time; 4) Coverage: data coverage of all jurisdictions in WA state; 5) CONTEXT: metadata incorporated into the views to provide context for graphed data; 6) Trusted Data: verification that information is valid and timely; and 7) Customization: the ability to customize views as necessary. As a result of the focus group, a new county level health jurisdiction expressed interest in contributing data to the Distribute system. CONCLUSION: The resulting themes from this study can be used to guide future information design efforts for the Distribute system and other syndromic surveillance systems. In addition, this study demonstrates the benefits of conducting a low cost, qualitative evaluation at a professional conference.

Applying a New Model for Sharing Population Health Data to National Syndromic Influenza Surveillance: DiSTRIBuTE Project Proof of Concept, 2006 to 2009.

The Distributed Surveillance Taskforce for Real-time Influenza Burden Tracking and Evaluation (DiSTRIBuTE) project began as a pilot effort initiated by the International Society for Disease Surveillance (ISDS) in autumn 2006 to create a collaborative electronic emergency department (ED) syndromic influenza-like illness (ILI) surveillance network based on existing state and local systems and expertise. DiSTRIBuTE brought together health departments that were interested in: 1) sharing aggregate level data; 2) maintaining jurisdictional control; 3) minimizing barriers to participation; and 4) leveraging the flexibility of local systems to create a dynamic and collaborative surveillance network. This approach was in contrast to the prevailing paradigm for surveillance where record level information was collected, stored and analyzed centrally. The DiSTRIBuTE project was created with a distributed design, where individual level data remained local and only summarized, stratified counts were reported centrally, thus minimizing privacy risks. The project was responsive to federal mandates to improve integration of federal, state, and local biosurveillance capabilities. During the proof of concept phase, 2006 to 2009, ten jurisdictions from across North America sent ISDS on a daily to weekly basis year-round, aggregated data by day, stratified by local ILI syndrome, age-group and region. During this period, data from participating U.S. state or local health departments captured over 13% of all ED visits nationwide. The initiative focused on state and local health department trust, expertise, and control. Morbidity trends observed in DiSTRIBuTE were highly correlated with other influenza surveillance measures. With the emergence of novel A/H1N1 influenza in the spring of 2009, the project was used to support information sharing and ad hoc querying at the state and local level. In the fall of 2009, through a broadly collaborative effort, the project was expanded to enhance electronic ED surveillance nationwide.

Mortality and morbidity burden associated with A/H1N1pdm influenza virus: Who is likely to be infected, experience clinical symptoms, or die from the H1N1pdm 2009 pandemic virus ?

Here we use lessons from past influenza pandemics and recent information about the H1N1pdm pandemic to discuss variations in H1N1pdm disease burden with age, underlying risk factors, and geography.

Prioritization of influenza pandemic vaccination to minimize years of life lost.

BACKGROUND: How to allocate limited vaccine supplies in the event of an influenza pandemic is currently under debate. Conventional vaccination strategies focus on those at highest risk for severe outcomes, including seniors, but do not consider (1) the signature pandemic pattern in which mortality risk is shifted to younger ages, (2) likely reduced vaccine response in seniors, and (3) differences in remaining years of life with age. METHODS: We integrated these factors to project the age-specific years of life lost (YLL) and saved in a future pandemic, on the basis of mortality patterns from 3 historical pandemics, age-specific vaccine efficacy, and the 2000 US population structure. RESULTS: For a 1918-like scenario, the absolute mortality risk is highest in people <45 years old; in contrast, seniors (those >or=65 years old) have the highest mortality risk in the 1957 and 1968 scenarios. The greatest YLL savings would be achieved by targeting different age groups in each scenario; people <45 years old in the 1918 scenario, people 45-64 years old in the 1968 scenario, and people >45 years old in the 1957 scenario. CONCLUSIONS: Our findings shift the focus of pandemic vaccination strategies onto younger populations and illustrate the need for real-time surveillance of mortality patterns in a future pandemic. Flexible setting of vaccination priority is essential to minimize mortality.

Monitoring the impact of influenza by age: emergency department fever and respiratory complaint surveillance in New York City.

BACKGROUND: The importance of understanding age when estimating the impact of influenza on hospitalizations and deaths has been well described, yet existing surveillance systems have not made adequate use of age-specific data. Monitoring influenza-related morbidity using electronic health data may provide timely and detailed insight into the age-specific course, impact and epidemiology of seasonal drift and reassortment epidemic viruses. The purpose of this study was to evaluate the use of emergency department (ED) chief complaint data for measuring influenza-attributable morbidity by age and by predominant circulating virus. METHODS AND FINDINGS: We analyzed electronically reported ED fever and respiratory chief complaint and viral surveillance data in New York City (NYC) during the 2001-2002 through 2005-2006 influenza seasons, and inferred dominant circulating viruses from national surveillance reports. We estimated influenza-attributable impact as observed visits in excess of a model-predicted baseline during influenza periods, and epidemic timing by threshold and cross correlation. We found excess fever and respiratory ED visits occurred predominantly among school-aged children (8.5 excess ED visits per 1,000 children aged 5-17 y) with little or no impact on adults during the early-2002 B/Victoria-lineage epidemic; increased fever and respiratory ED visits among children younger than 5 y during respiratory syncytial virus-predominant periods preceding epidemic influenza; and excess ED visits across all ages during the 2003-2004 (9.2 excess visits per 1,000 population) and 2004-2005 (5.2 excess visits per 1,000 population) A/H3N2 Fujian-lineage epidemics, with the relative impact shifted within and between seasons from younger to older ages. During each influenza epidemic period in the study, ED visits were increased among school-aged children, and each epidemic peaked among school-aged children before other impacted age groups. CONCLUSIONS: Influenza-related morbidity in NYC was highly age- and strain-specific. The impact of reemerging B/Victoria-lineage influenza was focused primarily on school-aged children born since the virus was last widespread in the US, while epidemic A/Fujian-lineage influenza affected all age groups, consistent with a novel antigenic variant. The correspondence between predominant circulating viruses and excess ED visits, hospitalizations, and deaths shows that excess fever and respiratory ED visits provide a reliable surrogate measure of incident influenza-attributable morbidity. The highly age-specific impact of influenza by subtype and strain suggests that greater age detail be incorporated into ongoing surveillance. Influenza morbidity surveillance using electronic data currently available in many jurisdictions can provide timely and representative information about the age-specific epidemiology of circulating influenza viruses.

Biomechanical comparison of a novel multilevel hex-head pedicle screw design with a conventional head design.

The objective of the study was to determine the biomechanical effect during insertion of multilevel hex-head design pedicle screws compared to a conventional screw-head design. Eighteen lumbar vertebrae and thoracic vertebrae from human cadavers were instrumented with a novel, multilevel hexagonal head pedicle screw on one side and a conventional head pedicle screw on the contralateral side. Screws were inserted at a constant rate and insertion and removal torques were recorded. A further 14 lumbar and thoracic vertebrae were used to test alterability of screw direction and operational effort required. Electromagnetic sensors recorded the change in angular direction for both screw and screwdriver. The force applied through the insertion screwdriver required to produce the directional change was also recorded. No significant differences were found between the two screw types for insertion or removal torque in either lumbar or thoracic vertebrae. Multilevel hex-head screws had significantly greater directional alterability than conventional head screws in both lumbar and thoracic specimens. Multilevel hex-head screws also required less force applied through the screwdriver than conventional screws to alter direction of screw insertion in both lumbar and thoracic specimens. The multilevel hex-head design did not affect the insertion or removal torque in comparison to a conventional head design.

Epidemiological evidence of an early wave of the 1918 influenza pandemic in New York City.

The 1918 "Spanish flu" was the fastest spreading and most deadly influenza pandemic in recorded history. Hypotheses of its origin have been based on a limited collection of case and outbreak reports from before its recognized European emergence in the summer of 1918. These anecdotal accounts, however, remain insufficient for determining the early diffusion and impact of the pandemic virus. Using routinely collected monthly age-stratified mortality data, we show that an unmistakable shift in the age distribution of epidemic deaths occurred during the 1917/1918 influenza season in New York City. The timing, magnitude, and age distribution of this mortality shift provide strong evidence that an early wave of the pandemic virus was present in New York City during February-April 1918.