Detection of COVID-19 case clusters in Québec, May–October 2020

Objectives The Quebec Public Health Institute (INSPQ) was mandated to develop an automated tool for detecting space-time COVID-19 case clusters to assist regional public health authorities in identifying situations that require public health interventions. This article aims to describe the methodology used and to document the main outcomes achieved. Methods New COVID-19 cases are supplied by the “Trajectoire de santé publique” information system, geolocated to civic addresses and then aggregated by day and dissemination area. To target community-level clusters, cases identified as residents of congregate living settings are excluded from the cluster detection analysis. Detection is performed using the space-time scan statistic and Poisson statistical model, and implemented in the SaTScan software. Information on detected clusters is disseminated daily via an online interactive mapping interface. Results The number of clusters detected tracked with the number of new cases. Slightly more than 4900 statistically significant (p ≤ 0.01) space-time clusters were detected over 14 health regions from May to October 2020. The Montréal region was the most affected. Conclusion Considering the objective of timely cluster detection, the use of near-real-time health surveillance data of varying quality over time and by region constitutes an acceptable compromise between timeliness and data quality. This tool serves to supplement the epidemiologic investigations carried out by regional public health authorities for purposes of COVID-19 management and prevention.

A National Framework to Improve Mortality, Morbidity, and Disparities Data for COVID-19 and Other Large-Scale Disasters

Timely and accurate data on COVID-19 cases and COVID-19‒related deaths are essential for making decisions with significant health, economic, and policy implications. A new report from the National Academies of Sciences, Engineering, and Medicine proposes a uniform national framework for data collection to more accurately quantify disaster-related deaths, injuries, and illnesses. This article describes how following the report’s recommendations could help improve the quality and timeliness of public health surveillance data during pandemics, with special attention to addressing gaps in the data necessary to understand pandemic-related health disparities.

Still ‘dairy farm fever’? A Bayesian model for leptospirosis notification data in New Zealand

Routinely collected public health surveillance data are often partially complete, yet remain a useful source by which to monitor incidence and track progress during disease intervention. In the 1970s, leptospirosis in New Zealand (NZ) was known as ‘dairy farm fever’ and the disease was frequently associated with serovars Hardjo and Pomona. To reduce infection, interventions such as vaccination of dairy cattle with these two serovars was implemented. These interventions have been associated with significant reduction in leptospirosis incidence, however, livestock-based occupations continue to predominate notifications. In recent years, diagnosis is increasingly made by nucleic acid detection which currently does not provide serovar information. Serovar information can assist in linking the recognized maintenance host, such as livestock and wildlife, to infecting serovars in human cases which can feed back into the design of intervention strategies. In this study, confirmed and probable leptospirosis notification data from 1 January 1999 to 31 December 2016 were used to build a model to impute the number of cases from different occupational groups based on serovar and month of occurrence. We imputed missing occupation and serovar data within a Bayesian framework assuming a Poisson process for the occurrence of notified cases. The dataset contained 1430 notified cases, of which 927 had a specific occupation (181 dairy farmers, 45 dry stock farmers, 454 meatworkers, 247 other) while the remaining 503 had non-specified occupations. Of the 1430 cases, 1036 had specified serovars (231 Ballum, 460 Hardjo, 249 Pomona, 96 Tarassovi) while the remaining 394 had an unknown serovar. Thus, 47% (674/1430) of observations had both a serovar and a specific occupation. The results show that although all occupations have some degree of under-reporting, dry stock farmers were most strongly affected and were inferred to contribute as many cases as dairy farmers to the burden of disease, despite dairy farmer being recorded much more frequently. Rather than discard records with some missingness, we have illustrated how mathematical modelling can be used to leverage information from these partially complete cases. Our finding provides important evidence for reassessing the current minimal use of animal vaccinations in dry stock. Improving the capture of specific farming type in case report forms is an important next step.

Nonlinear Relationship between Sleep Duration and Non-suicidal Self-injurious Behaviour among Chinese Adolescents

Background Previous researches showed a positive association between short sleep duration and non-suicidal self-injury (NSSI) among adolescents, but few studies have described the effects of oversleeping and weekend catch-up sleep duration on NSSI. The present study aims to explore the nonlinear relationship between sleep duration and NSSI among Chinese adolescents. Methods China’s National Adolescent Health Surveillance data from 2014 to 2015 were collected from 15,713 students in four provinces within China. A self-report questionnaire was used to assess sleep duration and 12-month NSSI. Binomial logistic regression models were used to examine the associations of NSSI with sleep duration. The locally estimated scatter plot smoothing (LOESS) method was used to help explore the associations of total NSSI number with sleep duration, and binomial regression analysis was used to help test this relationship. Results About 68.5% of adolescents reported sleeping less than 8 h on weeknights while 37.8% slept more than 10 h per night during weekends. The 12-month prevalence rate of NSSI was 29.4%. Compared to the weekend catch-up sleeping for 0–1 hours, those who slept < 0 hours (adjusted Odd Ratio (aOR) = 1.38, 95% Confidence Interval (95% CI): 1.16–1.64) had higher risk of NSSI. Males who reported ≥ 3 hours of weekend catch-up sleep were significantly increased odds of NSSI (aOR = 1.20, 95%CI: 1.01–1.42). Notably, the positive U-shape association was observed between the entire sleep duration and total NSSI number. Conclusions The findings reveals that the nonlinear relationship between sleep duration and non-suicidal self-injurious behaviour among Chinese adolescents. Therefore, it is necessary to be vigilant and screen for sleep duration among adolescents in NSSI treatment or prevention.

Malaysia and COVID-19: In Data We Trust

The recent spike of transmissibility of COVID-19 was evident by a large number of COVID-19 cases and apparent quick spread of SARS-CoV-2 in the state of Sabah, Selangor and Negeri Sembilan in Malaysia. The question remains as to what are the main contributory factors for the impending COVID-19 second wave in Malaysia and why the current surveillance system fails to show signs of the impending second — or the third — COVID-19 wave. In public health surveillance, data are the ultimate indicator, and in the era of big data and the Industrial Revolution 4.0, data has become a valuable commodity. The COVID-19 data keeper must fulfil some criteria to ensure COVID-19 data are useful. Researchers are obligated to share their COVID-19 data responsibly. The surveillance for COVID-19 is paramount, and the guidelines such as the one published by the World Health Organization ‘Public health surveillance for COVID-19: interim guidance’ must be referred to. Data must be taken seriously and shared to enable scientists, clinicians, epidemiologists and public health experts fight COVID-19.

Case study of Argus in Togo: An SMS and web-based application to support public health surveillance, results from 2016 to 2019

Introduction Argus is an open source electronic solution to facilitate the reporting and management of public health surveillance data. Its components include an Android-phone application, used by healthcare facilities to report results via SMS; and a central server located at the Ministry of Health, displaying aggregated results on a web platform for intermediate and central levels. This study describes the results of the use of Argus in two regions of Togo. Methods Argus was used in 148 healthcare facilities from May 2016 to July 2018, expanding to 185 healthcare facilities from July 2018. Data from week 21 of 2016 to week 12 of 2019 was extracted from the Argus database and analysed. An assessment mission took place in August 2016 to collect users’ satisfaction, to estimate the concordance of the received data with the collected data, and to estimate the time required to report data with Argus. Results Overall completeness of data reporting was 76%, with 80% of reports from a given week being received before Tuesday 9PM. Concordance of data received from Argus and standard paper forms was 99.7%. Median time needed to send a report using Argus was 4 minutes. Overall completeness of data review at district, regional, and central levels were 89%, 68%, and 35% respectively. Implementation cost of Argus was 23 760 USD for 148 facilities. Conclusions The use of Argus in Togo enabled healthcare facilities to send weekly reports and alerts through SMS in a user-friendly, reliable and timely manner. Reengagement of surveillance officers at all levels, especially at the central level, enabled a dramatic increase in completeness and timeliness of data report and data review.

Health Surveillance and Response to SARS-CoV-2 Mass Testing in Health Workers of a Large Italian Hospital in Verona, Veneto

Italy presented the first largest COVID-19 outbreak outside of China. Veneto currently ranks fourth among the Italian regions for COVID-19 confirmed cases (~19,000). This study presents health surveillance data for SARS-CoV-2 in 6100 health workers (HW) employed in a large public hospital. Workers underwent oropharyngeal and nasopharyngeal swabs, with a total of 5942 participants (97.5% of the population). A total of 11,890 specimens were tested for SARS-CoV-2 infection using PCR, identifying the viral genes E, RdRP, and N. Positive tests were returned for 238 workers (cumulative incidence of 4.0%, similar in both COVID and nonCOVID units). SARS-CoV-2 risk was not affected by gender, age, or job type, whereas work setting and occupation were both predictors of infection. The risk was higher in medical wards (OR 2.7, 95% CI 1.9–3.9) and health services (OR 4.3, 95% CI 2.4–7.6), and lower in surgical wards and administration areas. To our knowledge, this study represents the largest available HW case list swab-tested for SARS-CoV-2, covering almost the total workforce. Mass screening enabled the isolation of HW, improved risk assessment, allowed for close contacts of and infected HW to return to work, provided evidence of SARS-CoV-2 diffusion, and presented solid ground to prevent nosocomial SARS-CoV-2 infections. The ongoing concurrent sero-epidemiological study aims to enable the improvement of health surveillance to maintain the safety of HWs and the communities they serve.

Citrobacter freundii Foodborne Disease Outbreaks Related to Environmental Conditions in Yazd Province, Iran

Background: The aim of this study was to assess associations of Citrobacter freundii foodborne outbreaks with environmental factors in various regions of Yazd Province, Iran, 2012–2016. Methods: The public health surveillance data were used for one period of five years reported foodborne disease outbreaks in various regions of the Province. Multilevel regression statistical method was used to analyze associations of climatic and demographic variables with outbreaks. Significant associations were tested using likelihood ratio tests. Results: Results showed a significant association between C. freundii outbreaks and air dust conditions, age groups and various regional cities. Conclusion: The current study revealed necessity of etiologic agent identification for use in foodborne disease outbreak guidance in future outbreaks. Systemic surveillance schemes can help prevent and control similar scenarios using reports of environmental effects on foodborne disease outbreaks.

Consultative meeting that examined alignment and discrepancies between health facility and household survey data in Malawi

Malawi is midway through its current Malaria Strategic Plan 2017–2022, which aims to reduce malaria incidence and deaths by at least 50% by 2022. Malariometric data are available with health surveillance data housed in District Health Information Software 2 (DHIS2) and household survey data from two recent Malaria Indicator Surveys (MIS) and a Demographic and Health Survey (DHS). Strengths and weaknesses of the data were discussed during a consultative meeting in Lilongwe, Malawi in July 2019. The first 3 days included in-depth exploration and analysis of surveillance and survey data by 13 participants from the National Malaria Control Programme, district health offices, and partner organizations. Key indicators derived from both DHIS2 and MIS/DHS sources were analysed with three case studies, and presented to stakeholders on the fourth day of the meeting. Applications of the findings to programmatic decision-making and strategic plan evaluation were critiqued and discussed.