scholarly journals Towards NCD surveillance in Germany – diabetes as a paradigm

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
L Reitzle ◽  
R Paprott ◽  
C Heidemann ◽  
C Schmidt ◽  
J Baumert ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Surveillance System ◽  
Disease Incidence ◽  
Temporal Trends ◽  
Health Data ◽  
Data Sources ◽  
Extensive Literature ◽  
Effective Prevention ◽  
Shared Risk

Abstract Background Non-communicable diseases (NCD) are a major public health challenge in Germany and throughout the world. The epidemiology of diabetes, in particular type 2 diabetes, exemplifies the need for effective prevention and control. To support policy making with actionable evidence, the Robert Koch Institute (RKI) is developing a diabetes surveillance system for Germany serving as model for NCD surveillance. Methods First, we consented a conceptual framework and key indicators among experts and stakeholders based on an extensive literature review including national and international NCD surveillance systems. After prioritization of indicators applying a two-step Delphi method, we identified data sources for sustainable surveillance including data from nationwide RKI health surveys as well as external data such as claims or registry data. Lastly, in cooperation with stakeholders we developed first dissemination products. Results During concept phase, we identified 40 indicators ranging from risk factors, disease incidence and prevalence to quality of care, mortality and burden of disease. During implementation, suitable data sources and results on temporal trends have been obtained for a large part of the indicators. For dissemination we developed a website with interactive visualization of results supported by an explainer video on YouTube. Additionally, we prepared a printed diabetes report summarizing and interpreting key findings for a broader audience, including health politicians and public health researchers. Conclusions We demonstrated the feasibility of the systematic collection and analysis of health data to describe the disease and care situation of diabetes in Germany. The methodology and data sources of indicators can be transferred to further NCDs and shared risk factors are already depicted. Next steps are to close remaining data gaps and to advance dissemination products in collaboration with our stakeholder network tailored to their information needs. Key messages Considering available health data, we showed the feasibility of implementing a diabetes surveillance system for Germany providing reliable information on disease dynamics for various stakeholders. The knowledge on methodology and data sources gained establishing a diabetes surveillance system can be extended to other noncommunicable diseases.

scholarly journals Impacto da dengue no Brasil no período de 2007 a 2012

2019 ◽  
Vol 9 (27) ◽  
pp. 79
Author(s):  
Antonio Cláudio do Rego Coelho ◽  
Anna Klara Paim dos Anjos ◽  
Clerislene De Sousa Oliveira ◽  
Fábio Lucas da Cruz Viana ◽  
Maria Da Conceição Lisboa Dutra ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Health Problem ◽  
Surveillance System ◽  
Research Method ◽  
Public Health Problem ◽  
Exploratory Analysis ◽  
El Sistema ◽  
Factores De Riesgo ◽  
Epidemiological Profile

A dengue é um problema de saúde pública, acometendo especialmente os países tropicas e subtropicais. No Brasil, até meados de dezembro de 2012, o sistema nacional de vigilância da dengue havia registrado mais de 1,4 milhões de casos suspeitos. O artigo tem como objetivo, investigar a incidência dos casos de dengue no Brasil no período de 2007 a 2012, analisando, conforme o que consta na literatura, os fatores de risco que levam a um grande número de casos. O método de pesquisa consistiu na análise documental e exploratória do número de notificações de casos da dengue no período de 2007 a 2012 em todo território nacional através do banco de dados do SINAN. As informações foram apresentadas segundo Unidade da Federação e ano dos primeiros sintomas, considerando todas as notificações. Através do resultado obtido conclui-se que a maioria das notificações de casos ocorreu na região Sudeste, Nordeste, Centro-Oeste.Descritores: Dengue, Epidemiologia, Perfil Epidemiológico. Dengue in Brazil impact of the period 2007 to 2012Abstract: Dengue is a public health problem, especially affecting tropical and subtropical countries. In Brazil, by mid-December 2012, the national dengue surveillance system had recorded more than 1.4 million suspected cases. The objective of this article is to investigate the incidence of dengue cases in Brazil from 2007 to 2012, analyzing, according to the literature, the risk factors that lead to a large number of cases. The research method consisted of documental and exploratory analysis of the number of reports of dengue cases in the period from 2007 to 2012 throughout the national territory through the SINAN database. The information was presented according to the Federation Unit and year of the first symptoms, considering all the notifications. Through the obtained results it is concluded that the majority of cases reports occurred in the Southeast, Northeast, Midwest.Descriptors: Dengue, Epidemiology, Epidemiological Profile. Impacto del dengue en Brasil en período 2007 a 2012Resumen: El dengue es un problema de salud pública, afectando especialmente a los países tropicales y subtropicales. En Brasil, hasta mediados de diciembre de 2012, el sistema nacional de vigilancia del dengue había registrado más de 1,4 millones de casos sospechosos. El artículo tiene como objetivo, investigar la incidencia de los casos de dengue en Brasil en el período de 2007 a 2012, analizando, según lo que consta en la literatura, los factores de riesgo que llevan a un gran número de casos. El método de investigación consistió en el análisis documental y exploratorio del número de notificaciones de casos del dengue en el período de 2007 a 2012 en todo el territorio nacional a través de la base de datos del SINAN. Las informaciones fueron presentadas según Unidad de la Federación y año de los primeros síntomas, considerando todas las notificaciones. A través del resultado obtenido se concluye que la mayoría de las notificaciones de casos ocurrió en la región Sudeste, Nordeste, Centro-Oeste.Descriptores: Dengue, Epidemiología, Perfil Epidemiológico.

scholarly journals Spatial distribution and temporal trends of leprosy in Uganda, 2012–2016: a retrospective analysis of public health surveillance data

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Freda Loy Aceng ◽  
Herman-Joseph Kawuma ◽  
Robert Majwala ◽  
Maureen Lamunu ◽  
Alex Riolexus Ario ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Census Data ◽  
Temporal Trends ◽  
Northern Region ◽  
Control Measures ◽  
Population Estimates ◽  
Case Detection ◽  
Detection Rates ◽  
Leprosy Case

Abstract Background Leprosy is a neglected disease that poses a significant challenge to public health in Uganda. The disease is endemic in Uganda, with 40% of the districts in the country affected in 2016, when 42 out of 112 districts notified the National Tuberculosis and Leprosy Program (NTLP) of at least one case of leprosy. We determined the spatial and temporal trends of leprosy in Uganda during 2012–2016 to inform control measures. Methods We analyzed quarterly leprosy case-finding data, reported from districts to the Uganda National Leprosy Surveillance system (managed by NTLP) during 2012–2016. We calculated new case detection by reporting district and administrative regions of treatment during this period. New case detection was defined as new leprosy cases diagnosed by the Uganda health services divided by regional population; population estimates were based on 2014 census data. We used logistic regression analysis in Epi-Info version 7.2.0 to determine temporal trends. Population estimates were based on 2014 census data. We used QGIS software to draw choropleth maps showing leprosy case detection rates, assumed to approximate the new case detection rates, per 100,000 population. Results During 2012–2016, there was 7% annual decrease in reported leprosy cases in Uganda each year (p = 0.0001), largely driven by declines in the eastern (14%/year, p = 0.0008) and central (11%/year, p = 0.03) regions. Declines in reported cases in the western (9%/year, p = 0.12) and northern (4%/year, p = 0.16) regions were not significant. The combined new case detection rates from 2012 to 2016 for the ten most-affected districts showed that 70% were from the northern region, 20% from the eastern, 10% from the western and 10% from the central regions. Conclusion There was a decreasing trend in leprosy new case detection in Uganda during 2012–2016; however, the declining trends were not consistent in all regions. The Northern region consistently identified more leprosy cases compared to the other regions. We recommend evaluation of the leprosy surveillance system to ascertain the leprosy situation.

Survey Research in Disaster Public Health

2000 ◽  
Vol 15 (1) ◽  
pp. 65-71 ◽  
Author(s):  
Kimberley I. Shoaf ◽  
Corrinne Peek-Asa
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Health Outcomes ◽  
Data Sources ◽  
Health Impacts ◽  
Northridge Earthquake ◽  
Injury Rates ◽  
Telephone Interviews ◽  
The Past ◽  
Human Losses

AbstractIntroduction:While much has been learned during the past three decades of research in the disaster field, there still are some major gaps in knowledge. The need for more and better research on the health aspects of disasters is especially noted. Often, research into the health aspects has been anecdotal in nature and suffers from poor documentation of human losses. However, there are valid research methodologies that can be adapted to better document losses, evaluate interventions, and set priorities for investments to reduce the burden on the health of the population caused by disasters.Methods:A number of data sources are used to demonstrate the potential uses of surveys in disaster health. The majority of the examples reflect data collected by telephone interviews following earthquakes in California.Results:By using comparable instruments, it is possible to track the changes in preparedness levels across time. Similarly, it is possible to compare injury rates or other health impacts across time, place, and disaster type. In addition, risk factors can be identified for health outcomes. For example, in the Northridge earthquake, those over age 60 years were three times more likely to be hospitalized or die as a result of injuries than were those aged 20–59 years. Interventions can be evaluated. Slightlyless than half of the respondents of the El Niño study had heard messages about preparing for the on-coming weather and their preparedness levels were not significantly different from those who had not heard about preparing for the weather.Conclusion:Surveys are useful tools for identifying and evaluating the health impacts of disasters.

Administrative health databases for addressing emerging issues in adults with CHD: a systematic review

2018 ◽  
Vol 28 (6) ◽  
pp. 844-853 ◽  
Author(s):  
Sarah Cohen ◽  
Harel Gilutz ◽  
Ariane J. Marelli ◽  
Laurence Iserin ◽  
Arriel Benis ◽  
...  
Keyword(s):  
Temporal Trends ◽  
The United States ◽  
Health Data ◽  
Data Sources ◽  
Resource Utilisation ◽  
Cross Sectional ◽  
Administrative Health Data ◽  
Patient Level ◽  
Administrative Health Databases ◽  
The Impact

AbstractThe need for population-based studies of adults with CHD has motivated the growing use of secondary analyses of administrative health data in a variety of jurisdictions worldwide. We aimed at systematically reviewing all studies using administrative health data sources for adult CHD research from 2006 to 2016. Using PubMed and Embase (1 January, 2006 to 1 January, 2016), we identified 2217 abstracts, from which 59 studies were included in this review. These comprised 12 different data sources from six countries. Of these, 55% originated in the United States of America, 28% in Canada, and 17% in Europe and Asia. No study was published before 2007, after which the number of publications grew exponentially. In all, 41% of the studies were cross-sectional and 25% were retrospective cohort studies with a wide variation in the availability of patient-level compared with hospitalisation-level episodes of care; 58% of studies from eight different data sources linked administrative data at a patient level; and 37% of studies reported validation procedures. Assessing resource utilisation and temporal trends of relevant epidemiological and outcome end points were the most reported objectives. The median impact factor of publication journals was 4.04, with an interquartile range of 3.15, 7.44. Although not designed for research purposes, administrative health databases have become powerful data sources for studying adult CHD populations because of their large sample sizes, comprehensive records, and long observation periods, providing a useful tool to further develop quality of care improvement programmes. Data linkage with electronic records will become important in obtaining more granular life-long adult CHD data. The health services nature of the data optimises the impact on policy and public health.

scholarly journals Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering (Preprint)

2020 ◽  
Author(s):  
Mehnaz Adnan ◽  
Xiaoying Gao ◽  
Xiaohan Bai ◽  
Elizabeth Newbern ◽  
Jill Sherwood ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Case Reports ◽  
Surveillance Data ◽  
Data Sources ◽  
Google Trends ◽  
School Absenteeism ◽  
Disease Surveillance System ◽  
Spatiotemporal Clustering

BACKGROUND Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. OBJECTIVE The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. METHODS We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the affected area. RESULTS Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. CONCLUSIONS Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.

scholarly journals Evaluating a cardiovascular disease risk management care continuum within a learning healthcare system: a prospective cohort study

BJGP Open ◽  
2020 ◽  
Vol 4 (5) ◽  
pp. bjgpopen20X101109
Author(s):  
T Katrien J Groenhof ◽  
A Titia Lely ◽  
Saskia Haitjema ◽  
Hendrik M Nathoe ◽  
Marlous F Kortekaas ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Risk Management ◽  
Risk Factor ◽  
Cohort Study ◽  
Cardiovascular Risk ◽  
Health Data ◽  
Data Sources ◽  
Learning Healthcare System

BackgroundMany patients now present with multimorbidity and chronicity of disease. This means that multidisciplinary management in a care continuum, integrating primary care and hospital care services, is needed to ensure high quality care.AimTo evaluate cardiovascular risk management (CVRM) via linkage of health data sources, as an example of a multidisciplinary continuum within a learning healthcare system (LHS).Design & settingIn this prospective cohort study, data were linked from the Utrecht Cardiovascular Cohort (UCC) to the Julius General Practitioners' Network (JGPN) database. UCC offers structured CVRM at referral to the University Medical Centre (UMC) Utrecht. JGPN consists of electronic health record (EHR) data from referring GPs.MethodThe cardiovascular risk factors were extracted for each patient 13 months before referral (JGPN), at UCC inclusion, and during 12 months follow-up (JGPN). The following areas were assessed: registration of risk factors; detection of risk factor(s) requiring treatment at UCC; communication of risk factors and actionable suggestions from the specialist to the GP; and change of management during follow-up.ResultsIn 52% of patients, ≥1 risk factors were registered (that is, extractable from structured fields within routine care health records) before UCC. In 12%–72% of patients, risk factor(s) existed that required (change or start of) treatment at UCC inclusion. Specialist communication included the complete risk profile in 67% of letters, but lacked actionable suggestions in 86%. In 29% of patients, at least one risk factor was registered after UCC. Change in management in GP records was seen in 21%–58% of them.ConclusionEvaluation of a multidisciplinary LHS is possible via linkage of health data sources. Efforts have to be made to improve registration in primary care, as well as communication on findings and actionable suggestions for follow-up to bridge the gap in the CVRM continuum.

scholarly journals Assessing the Emergent Public Health Concern of All-Terrain Vehicle Injuries in Rural and Agricultural Environments: Initial Review of Available National Datasets in the United States

10.2196/15477 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e15477
Author(s):  
Bryan Weichelt ◽  
Serap Gorucu ◽  
Charles Jennissen ◽  
Gerene Denning ◽  
Stephen Oesch
Keyword(s):  
Public Health ◽  
United States ◽  
Surveillance System ◽  
Reporting System ◽  
The United States ◽  
Data Sources ◽  
Health Concern ◽  
Federally Funded ◽  
Public Health Concern ◽  
Injury Surveillance System

Background Injuries related to the operation of off-road vehicles (ORVs), including all-terrain vehicles (ATVs), continue to be a significant public health concern, especially in rural and agricultural environments. In the United States alone, ATVs have played a role in thousands of fatalities and millions of injuries in the recent decades. However, no known centralized federal surveillance system consistently captures these data. Traditional injury data sources include surveys, police reports, trauma registries, emergency department data, newspaper and online media reports, and state and federal agency databases. Objective The objectives of this study paper were to (1) identify published articles on ORV-related injuries and deaths that used large databases and determine the types of datasets that were used, (2) examine and describe several national US-based surveillance systems that capture ORV-related injuries and fatalities, and (3) promote and provide support for the establishment of a federally-funded agricultural injury surveillance system. Methods In this study, we examined several national United States–based injury datasets, including the web-based AgInjuryNews, the Fatality Analysis Reporting System, databases compiled by the US Consumer Product Safety Commission, and the National Fatality Review Case Reporting System. Results Our review found that these data sources cannot provide a complete picture of the incidents or the circumstantial details needed to effectively inform ORV injury prevention efforts. This is particularly true with regard to ORV-related injuries in agricultural production. Conclusions We encourage the establishment of a federally funded national agricultural injury surveillance system. However, in lieu of this, use of multiple data sources will be necessary to provide a more complete picture of ORV- and other agriculture-related injuries and fatalities.

scholarly journals User-friendly Rshiny web applications for supporting syndromic surveillance analysis

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Anne Fouillet ◽  
Marc Ruello ◽  
Lucie Leon ◽  
Cecile Sommen ◽  
Laurent Marie ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Web Applications ◽  
Age Groups ◽  
Data Sources ◽  
Mortality Data ◽  
Syndromic Surveillance System ◽  
R Shiny ◽  
User Friendly

ObjectiveThe presentation describes the design and the main functionalitiesof two user-friendly applications developed using R-shiny to supportthe statistical analysis of morbidity and mortality data from the Frenchsyndromic surveillance system SurSaUD.IntroductionThe French syndromic surveillance system SursaUD® has beenset up by Santé publique France, the national public health agency(formerly French institute for public health - InVS) in 2004. In 2016,the system is based on three main data sources: the attendancesin about 650 emergency departments (ED), the consultations to62 emergency general practitioners’ (GPs) associations SOSMédecins and the mortality data from 3,000 civil status offices [1].Daily, about 60,000 attendances in ED (88% of the nationalattendances), 8,000 visits in SOS Médecins associations (95% ofthe national visits) and 1,200 deaths (80% of the national mortality)are recorded all over the territory and transmitted to Santé publiqueFrance.About 100 syndromic groupings of interest are constructed fromthe reported diagnostic codes, and monitored daily or weekly, fordifferent age groups and geographical scales, to characterize trends,detect expected or unexpected events (outbreaks) and assess potentialimpact of both environmental and infectious events. All-causesmortality is also monitored in similar objectives.Two user-friendly interactive web applications have beendeveloped using the R shiny package [2] to provide a homogeneousframework for all the epidemiologists involved in the syndromicsurveillance at the national and the regional levels.MethodsThe first application, named MASS-SurSaUD, is dedicated to theanalysis of the two morbidity data sources in Sursaud, along with dataprovided by a network of Sentinel GPs [3]. Based on pre-aggregateddata availaible daily at 10:30 am, R programs create daily, weeklyand monthly time series of the proportion of each syndromic groupingamong all visits/attendances with a valid code at the national andregional levels. Twelve syndromic groupings (mainly infectious andrespiratory groups, like ILI, gastroenteritis, bronchiolitis, pulmonarydiseases) and 13 age groups have been chosen for this application.For ILI, 3 statistical methods (periodic regression, robust periodicregression and Hidden Markov model) have been implementedto identify outbreaks. The results of the 3 methods applied to the3 data sources are combined with a voting algorithm to compilethe influenza alarm level for each region each week: non-epidemic,pre/post epidemic or epidemic.The second application, named MASS-Euromomo, allowsconsulting results provided by the model developed by the Europeanproject EuroMomo for the common analysis of mortality in theEuropean countries (www.euromomo.eu). The Euromomo model,initially developed using Stata software, has been transcripted inR. The model has been adapted to run in France both at a national,regional and other geographical administrative levels, and for 7 agegroups.ResultsThe two applications, accessible on a web-portal, are similarlydesigned, with:- a dropdown menu and radio buttons on the left hand side to selectthe data to display (e.g. filter by data source, age group, geographicallevels, syndromic grouping and/or time period),- several tab panels allowing to consult data and statistical resultsthrough tables, static and dynamic charts, statistical alarm matrix,geographical maps,... (Figure 1),- a “help” tab panel, including documentations and guidelines,links, contact details.The MASS-SurSaUD application has been deployed in December2015 and used during the 2015-2016 influenza season. MASS-Euromomo application has been deployed in July 2016 for the heat-wave surveillance period. Positive feedbacks from several users havebeen reported.ConclusionsBusiness Intelligence tools are generally focused on datavisualisation and are not generally tailored for providing advancedstatistical analysis. Web applications built with the R-shiny packagecombining user-friendly visualisations and advanced statistics can berapidly built to support timely epidemiological analyses and outbreakdetection.Figure 1: screen-shots of a page of the two applications

scholarly journals Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering

10.2196/18281 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e18281
Author(s):  
Mehnaz Adnan ◽  
Xiaoying Gao ◽  
Xiaohan Bai ◽  
Elizabeth Newbern ◽  
Jill Sherwood ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Case Reports ◽  
Surveillance Data ◽  
Data Sources ◽  
Google Trends ◽  
School Absenteeism ◽  
Disease Surveillance System ◽  
Spatiotemporal Clustering

Background Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. Objective The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. Methods We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the affected area. Results Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. Conclusions Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.

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