Public Surveillance Data Research Articles

Social epidemiology of urban COVID-19 inequalities in Latin America and Canada

BackgroundThe COVID-19 pandemic has spread through pre-existing fault lines in societies, deepening structural barriers faced by precarious workers, low-income populations, and racialized communities in lower income sub-city units. Many studies have quantified the magnitude of inequalities in COVID-19 distribution within cities, but few have taken an international comparative approach to draw inferences on the ways urban epidemics are shaped by social determinants of health.MethodsGuided by critical epidemiology, this study quantifies sub-city unit-level COVID-19 inequalities across eight of the largest metropolitan areas of Latin America and Canada. Leveraging new open-data sources, we use concentration indices to quantify income- and vulnerability-related inequalities in incidence, test positivity, and deaths over the first 125 weeks of the pandemic between January 2020 and May 2022.ResultsOur findings demonstrate that incidence, deaths, and test positivity are all less concentrated in low-income sub-city units than would be expected, with incidence ranging concentration in lower income neighbourhoods in Toronto (CI = -0.07) to concentration in higher income neighbourhoods in Mexico City (CI = 0.33). Drawing on relevant studies and evaluations of data reliability, we conclude that the best available public surveillance data for the largest cities in Latin America are likely not reliable measures of the true COVID-19 disease burden. We also identify recurring trends in the evolution of inequalities across most cities, concluding that higher income sub-city units were frequent early epicentres of COVID-19 transmission across the Latin America and Canada.ConclusionsJust as critical epidemiology points to individuals biologically embodying the material and social conditions in which we live, it may be just as useful to think of cities reifying their material and social inequities in the form of sub-city unit-level infectious disease inequities. By shifting away from a typical vulnerability-based social determinants of health frame, policymakers could act to redress and reduce externalities stemming from sub-city unit-level income inequality through redistributive and equity-promoting policies to shift the centre of gravity of urban health inequalities before the next infectious disease epidemic occurs.

Forecasting infectious diseases in Brazilian cities: Integrating socio-economic and geographic data from related cities through a machine learning approach

Supervised machine learning models and public surveillance data have been employed for infectious disease forecasting in many settings. These models leverage various data sources capturing drivers of disease spread, such as climate conditions or human behavior. However, few models have incorporated the organizational structure of different geographic locations for forecasting. Traveling waves of seasonal outbreaks have been reported for dengue, influenza, and other infectious diseases, and many of the drivers of infectious disease dynamics may be shared across different cities, either due to their geographic or socioeconomic proximity. In this study, we developed a machine learning model to predict case counts of four infectious diseases across Brazilian cities one week ahead by incorporating information from related cities. We compared selecting related cities using both geographic distance and GDP per capita. Incorporating information from geographically proximate cities improved predictive performance for two of the four diseases, specifically COVID-19 and Zika. We also discuss the impact on forecasts in the presence of anomalous contagion patterns and the limitations of the proposed methodology.

A longitudinal mixed effects model for assessing mortality trends during vaccine rollout

The rapid spread of coronavirus disease 2019 (COVID-19) initially presented unprecedented challenges for clinicians, policymakers, and healthcare systems, as there was limited evidence on the efficacy of various control measures. This study endeavors to provide a detailed and comprehensive overview of the global progression of the COVID-19 mortality in the context of vaccine rollout, utilizing public surveillance data from 145 countries sourced from the World Health Organization and the World Bank. The primary focus is to analyze shifts in the trend of new COVID-19 mortality worldwide before and after the introduction of COVID-19 vaccines. To achieve this, we propose a longitudinal mixed effects model aimed at elucidating the relationship between mortality trend and vaccination rollout, alongside other pertinent covariates. Our modeling approach seeks to accommodate variations in the timing of COVID-19 vaccine rollout among countries, as well as the correlation of observations from within the same country. Our findings highlight the significant impact of new cases, cardiovascular death rate, senior population, stringency index, and reproduction rate on mortality. However, we find that the impact of vaccination is not statistically significant, as evidenced by a relatively large p-value. Furthermore, the study reveals substantial disparities in mortality rates among countries across four income groups.

Using surveillance data for early warning modelling of highly pathogenic avian influenza in Europe reveals a seasonal shift in transmission, 2016–2022

Avian influenza in wild birds and poultry flocks constitutes a problem for animal welfare, food security and public health. In recent years there have been increasing numbers of outbreaks in Europe, with many poultry flocks culled after being infected with highly pathogenic avian influenza (HPAI). Continuous monitoring is crucial to enable timely implementation of control to prevent HPAI spread from wild birds to poultry and between poultry flocks within a country. We here utilize readily available public surveillance data and time-series models to predict HPAI detections within European countries and show a seasonal shift that happened during 2021–2022. The output is models capable of monitoring the weekly risk of HPAI outbreaks, to support decision making.

Invasive pneumococcal disease among the elderly in the later era of paediatric pneumococcal conjugate vaccination-A longitudinal study over 10 years based on public surveillance data in the Nordics.

Pneumococcal conjugate vaccines (PCVs) have proven effective in preventing both non-invasive and invasive pneumococcal disease (IPD) in small children and in older age groups. However, long-term observations and country comparisons of IPD incidence in the elderly following introduction of PCVs in paediatric national immunisation programmes (NIPs) are scarce. We aimed to estimate and compare incidence of IPD in the elderly in Denmark, Finland, Norway, and Sweden over a 10-year time span. During the study period Denmark and Norway used PCV13 in their paediatric NIP, Sweden both PCV10 and PCV13 and Finland used PCV10. Uptake of pneumococcal vaccines for the elderly was low. We collected longitudinal data on confirmed IPD cases and their serotypes among elderly people (aged ≥65 years) 2010-2019 in the four countries of interest. Annual IPD incidence rates were calculated per country, by vaccine-associated serotypes (PCV10, PCV13, PCV15, PCV20 and PPV23) and for non-vaccine serotypes. A regression model was used to estimate average annual change in incidence in each country. Incidence rates of IPD in the elderly in 2019 ranged from 31.4 to 41.8 per 100,000 people across the countries. Denmark and Norway showed an annual average decline in IPD incidence (-3.3; 95% CI: -5.6 to -1.1; p<0.01) and (-3.3; 95% CI: -5.5 to -1.0; p<0.01) respectively from 2010 to 2019, whereas no change was seen for Sweden (-0.5; 95% CI: -1.9 to 0.8; p = 0.39) or Finland (0.9; 95% CI: -1.0 to 2.7; p = 0.32). IPD incidence due to emerging serotypes, e.g., serotypes 8 and 12F, has increased. Serotype 19A remained a major cause of IPD in countries with PCV10 in paediatric NIPs. Despite paediatric PCV programmes, a considerable vaccine preventable IPD burden remains in the elderly. Further, choice of PCV in paediatric programs was associated with differences in serotype distribution and incidence amongst the elderly. Direct vaccination of the elderly with recently approved broad coverage PCVs holds promise for meaningful impact on disease burden with PCV20 covering a majority of IPD amongst the elderly in the four studied countries. Effectiveness of new vaccines in real-life clinical practice should be followed.

Incidence of Lyme Borreliosis in Germany: Exploring Observed Trends Over Time Using Public Surveillance Data, 2016–2020

Background: Public surveillance of Lyme borreliosis (LB) occurs in 9 out of 16 federal states of Germany and remains a critical facet of disease epidemiology and trends. We describe the incidence, time trends, seasonality, and geographic distribution of LB in Germany using publicly reported surveillance data. Methods: We obtained LB cases and incidence (2016-2020) from the online platform SurvStat@RKI 2.0, maintained by the Robert Koch Institute (RKI). Data included clinically diagnosed and laboratory-confirmed LB reported by nine out of 16 federal states of Germany where LB notification is mandatory. Results: During 2016-2020, the nine federal states reported 63,940 LB cases, of which 60,570 (94.7%) were clinically diagnosed, and 3370 (5.3%) also had laboratory confirmation, with an average of 12,789 cases annually. Incidence rates were mostly stable over time. The average annual LB incidence was 37.2/100,000 person-years and varied by spatial level, ranging from 22.9 to 64.6/100,000 person-years among nine states; from 16.8 to 85.6/100,000 person-years among 19 regions; and from 2.9 to 172.8/100,000 person-years among 158 counties. Incidence was lowest among persons 20-24 years old (16.1/100,000 person-years) and highest among those 65-69 years old (60.9/100,000 person-years). Most cases were reported between June and September, with a peak in July of every year. Conclusion: The risk of LB varied substantially at the smallest geographic unit and by age group. Our results underscore the importance of presenting LB data at the most spatially granular unit and by age to allow implementation of efficient preventive interventions and reduction strategies.

1354. Incidence, time trends and geographic distribution of Lyme neuroborreliosis in Denmark using public surveillance data, 2015-2019

Abstract Background Lyme neuroborreliosis (LNB) is a potentially severe manifestation of Lyme borreliosis (LB), a spirochetal tick-borne infectious disease caused by Borrelia burgdorferi. We describe the incidence, time trends and geographic distribution of LNB in Denmark, with the aim to inform public health policy, including the potential use of new vaccines under development. Methods Based on the case definition for LNB (clinical diagnosis of LB plus detection of antibodies in blood and cerebral spinal fluid [confirmed case], OR clinical symptoms compatible with LNB and detection of antibodies in blood [probable case]), we obtained cases reported by laboratories and physicians (2015–2019) from the online platform maintained by the Statens Serum Institut (SSI). The LNB incidence (per 100,000 persons) was calculated by dividing LNB cases by the population data (denominator) obtained from Statistics Denmark. Results Between 2015 and 2019, laboratories reported annually 162 to 200 LNB cases, while physicians reported 48 to 68 cases (Figure). The annual LNB incidence notified by laboratories ranged from 2.8 (95% CI: 2.4‒3.3) to 3.4 (95% CI: 3.0‒4.0) per 100,000 persons for the 5 study years. The average annual LNB incidence per 100,000 persons for 5 Danish regions ranged from 2.3 to 3.3; for 11 provinces, from 1.9 to 7.6; and for 98 municipalities, from 0 to 22.1. Incidence peaks occurred in persons 5‒14 and 65‒74 years of age. Higher incidences were observed among males versus females in all age groups. LNB cases were reported throughout the year, with peaks in July to September. Number of LNB cases and annual incidence (per 100,000 population) notified by (A)physicians and (B) laboratories Conclusion LNB incidence in Denmark is moderate with no evidence of decline. Cases occurred across all regions but were focally concentrated among residents of some municipalities. By analyzing and reporting spatially granular data, we could identify areas with the highest LNB incidence. This may assist policy makers with assessing the efficiency of geographically targeted versus national prevention interventions, including vaccines should these be licensed. To this end, expanding the current surveillance system to include other manifestations of LB would be valuable to better understand geographic endemicity. Disclosures Jozica Skufca, Epidemiologist, p95: Paid by Pfizer to perform the study Nick DeSmedt, Masters in Computer Engineering, P95: P95 was paid by Pfizer to perform the study Andreas Pilz, PhD, Pfizer: Employee|Pfizer: Stocks/Bonds|Pfizer: Stocks/Bonds Andrew Vyse, Ph.D., Pfizer: Stocks/Bonds Elizabeth Begier, M.D., M.P.H., Pfizer: Employee|Pfizer: Stocks/Bonds Maxim Blum, Ph.D., P95: Paid by Pfizer to perform the study Margarita Riera, MD, MPH, P95: Paid by Pfizer to perform the study Bradford Gessner, MD, MPH, Pfizer: Stocks/Bonds Mette Skovdal, Ph.D., Pfizer Denmark: Stocks/Bonds James Stark, Ph.D., Pfizer: Stocks/Bonds.

Incidence of Lyme neuroborreliosis in Denmark: Exploring observed trends using public surveillance data, 2015–2019

Lyme neuroborreliosis (LNB) can be a serious manifestation of Lyme borreliosis. We describe the incidence, time trends and geographic distribution of LNB in Denmark.We obtained LNB cases reported by laboratories and physicians (2015–2019) from the online platform maintained by the Statens Serum Institut (SSI) (2021b). The LNB incidence (per 100,000 persons) was calculated by dividing LNB cases by the population data (denominator) obtained from Statistics Denmark (2021).Between 2015 and 2019, laboratories reported annually 162 to 200 LNB cases, while physicians reported 48 to 68 cases. The annual LNB incidence notified by laboratories ranged from 2.8 (95% CI: 2.4‒3.3) to 3.4 (95% CI: 3.0‒4.0) per 100,000 persons for the 5 study years. The average annual LNB incidence per 100,000 persons for 5 Danish regions ranged from 2.3 to 3.3; for 11 provinces, from 1.9 to 7.6; and for 98 municipalities, from 0 to 22.1. Incidence peaks occurred in persons 5‒14 and 65‒74 years of age. Higher incidences were observed among males versus females in all age groups. LNB cases were reported throughout the year, with peaks in July to September.Notified LNB incidence in Denmark was moderate with no evidence of decline. Cases occurred across all regions but were focally concentrated among residents of some municipalities. Expanding the current surveillance system to include other manifestations of LB would be valuable to better understand geographic endemicity to inform targeted preventive measures.

The Missing COVID-19 Demographic Data: A Statewide Analysis of COVID-19-Related Demographic Data From Local Government Sources and a Comparison With Federal Public Surveillance Data.

Objectives. To collect and standardize COVID-19 demographic data published by local public-facing Web sites and analyze how this information differs from Centers for Disease Control and Prevention (CDC) public surveillance data. Methods. We aggregated and standardized COVID-19 data on cases and deaths by age, gender, race, and ethnicity from US state and territorial governmental sources between May 24 and June 4, 2021. We describe the standardization process and compare it with the CDC's process for public surveillance data. Results. As of June 2021, the CDC's public demographic data set included 80.9% of total cases and 46.7% of total deaths reported by states, with significant variation across jurisdictions. Relative to state and territorial data sources, the CDC consistently underreports cases and deaths among African American and Hispanic or Latino individuals and overreports deaths among people older than 65 years and White individuals. Conclusions. Differences exist in amounts of data included and demographic composition between the CDC's public surveillance data and state and territory reporting, with large heterogeneity across jurisdictions. A lack of standardization and reporting mechanisms limits the production of complete real-time demographic data.

Modelling COVID-19 outbreak on the Diamond Princess ship using the public surveillance data.

The novel coronavirus disease 2019 (COVID-19) outbreak on the Diamond Princess (DP) ship has caused over 634 cases as of February 20, 2020. We model the transmission process on DP ship as a stochastic branching process, and estimate the reproduction number at the innitial phase of 2.9 (95%CrI: 1.7–7.7). The epidemic doubling time is 3.4 days, and thus timely actions on COVID-19 control were crucial. We estimate the COVID-19 transmissibility reduced 34% after the quarantine program on the DP ship which was implemented on February 5. According to the model simulation, relocating the population at risk may sustainably decrease the epidemic size, postpone the timing of epidemic peak, and thus relieve the tensive demands in the healthcare. The lesson learnt on the ship should be considered in other similar settings.

PIN50 Leveraging Publicly Available Data to Estimate a Real-Time Reproduction Number (Rt) for Covid-19: A Comparison of Two Methods

R0, the number of cases resulting from one infectious person in a susceptible population, is a common metric in infectious disease research. Newly-developed methods for real-time approximations (Rt) offer promise for monitoring COVID-19 trends using real-world data. The objective was to compare available methods of calculating Rt and understand drivers in variability, using available regional public surveillance data.

Estimating the serial interval of the novel coronavirus disease (COVID-19) based on the public surveillance data in Shenzhen, China, from 19 January to 22 February 2020.

The novel coronavirus disease (COVID‐19) poses a serious threat to global public health and economics. Serial interval (SI), time between the onset of symptoms of a primary case and a secondary case, is a key epidemiological parameter. We estimated SI of COVID‐19 in Shenzhen, China based on 27 records of transmission chains. We adopted three parametric models: Weibull, lognormal and gamma distributions, and an interval‐censored likelihood framework. The three models were compared using the corrected Akaike information criterion (AICc). We also fitted the epidemic curve of COVID‐19 to the logistic growth model to estimate the reproduction number. Using a Weibull distribution, we estimated the mean SI to be 5.9 days (95% CI: 3.9–9.6) with a standard deviation (SD) of 4.8 days (95% CI: 3.1–10.1). Using a logistic growth model, we estimated the basic reproduction number in Shenzhen to be 2.6 (95% CI: 2.4–2.8). The SI of COVID‐19 is relatively shorter than that of SARS and MERS, the other two betacoronavirus diseases, which suggests the iteration of the transmission may be rapid. Thus, it is crucial to isolate close contacts promptly to effectively control the spread of COVID‐19.

Market Segmentation as a Tool to Inform Health and Social Services Policy and Planning: The Case of Autism Spectrum Disorder and Intellectual Disability.

In the absence of complete public surveillance data, estimating the prevalence and needs of sub-groups of heterogeneous populations is nonetheless critical to properly inform health and social services planning and policy. We thus introduce market segmentation as an innovative tool for health and social services planners and policy makers. Segmentation is a process whereby broad populations are categorized into sub-groups with common characteristics and needs. Using the example of autism spectrum disorder and/or intellectual disability in Quebec, Canada, we triangulate available data and mathematical problem solving to present a portrait of the varying support needs of this under-served, yet growing, population.

School Obesity Prevention Policies and Practices in Minnesota and Student Outcomes: A Longitudinal Cohort Study.

The School Obesity-related Policy Evaluation (ScOPE) Study uses existing public surveillance data and applies a rigorous study design to evaluate effectiveness of school policies and practices impacting student behavioral and weight outcomes. The ScOPE Study used a cohort of 50 combined junior-senior and high schools in Minnesota to evaluate the change in weight-related policy environments in 2006 and 2012 and test the effect of policy change on students attending those schools in 2007 and 2013. Exposure variables included school practices about foods and beverages available in school vending machines and school stores, physical education requirements, and intramural opportunities. Primary study outcomes were average school-level ninth grade student BMI percentile, obesity prevalence, daily servings of fruits/vegetables, and daily glasses of soda. Availability of fruits/vegetables in schools was associated with a significant increase in total daily intake among ninth grade students by 0.4 servings. Availability of soda in schools was associated with a significant increase in total daily intake among ninth grade boys by 0.5 servings. Less-healthy snack and drink availability in schools was associated with a small, significant increase (1%) in student BMI percentile at the school level. Use of a school-level longitudinal cohort study design over a 6-year period uniquely adds to the methodologic rigor of school policy and practice evaluation studies. The ScOPE Study provides marginal evidence that school policies and practices, especially those that restrict vending and school store offerings, may have small effects on weight status among ninth grade students.

Assessing the Scale-Up of a Weight Loss Program: Narrowing the Gap Between Research and Practice

Assessing the Scale-Up of a Weight Loss Program: Narrowing the Gap Between Research and Practice

The development and evaluation of a PDA-based method for public health surveillance data collection in developing countries

Background and purpose EpiData and Epi Info are often used together by public health agencies around the world, particularly in developing countries, to meet their needs of low-cost public health data management; however, the current open source data management technology lacks a mobile component to meet the needs of mobile public health data collectors. The goal of this project is to explore the opportunity of filling this gap through developing and trial of a personal digital assistant (PDA) based data collection/entry system. It evaluated whether such a system could increase efficiency and reduce data transcription errors for public surveillance data collection in developing countries represented by Fiji. Methods A generic PDA-based data collection software eSTEPS was developed. The software and the data collected using it directly interfaces with EpiData. A field trial was conducted to test the viability of public health surveillance data collection using eSTEPS. The design was a randomised, controlled trial with cross-over design. 120 participants recruited from the Fiji School of Medicine were randomly assigned to be interviewed by one of six interviewers in one of the two ways: (1) paper-based survey followed by PDA survey and (2) PDA survey followed by paper-based survey. Data quality was measured by error rates (logical range errors/inconsistencies, skip errors, missing values, date or time field errors and incorrect data type). Work flow and cost were evaluated in three stages of the survey process: (1) preparation of data collection instrument, (2) data collection and (3) data entry, validation and cleaning. User acceptance was also evaluated in the two groups of participants: (1) data collectors and (2) survey participants. Results None of the errors presented in 20.8% of the paper questionnaires was found in the data set collected using PDA. Sixty-two percent of the participants perceived that the PDA-based questionnaire took less time to complete. Data entry, validation and cleaning for the PDA-based data collection from 120 participants took a total of 1.5 h, a 93.26% reduction of time from 20.5 h required using paper and pen. The cost is also significantly reduced with PDA-based protocol. Both data collectors and participants prefer to use PDA instead of paper for data collection. The trial results prove that eSTEPS is a feasible solution for public health surveillance data collection in the field. Several deficiencies of the software were also identified and would be addressed in the next version. Conclusion eSTEPS offers the potential to meet the need for an effective mobile public health data collection tool for use in the field. The eSTEPS field trial proves that PDA was more efficient than paper for public health survey data collection. It also significantly reduced errors in data entry. The later benefit was derived from the software providing its users with the flexibility of building their own constraints to control the data type, range and logic of data entry.