scholarly journals Updating syndromic surveillance baselines following public health interventions

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Andre Charlett ◽  
Sally Harcourt ◽  
Gillian Smith
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Regression Models ◽  
Step Change ◽  
Health Interventions ◽  
Model Fit ◽  
Surveillance Systems ◽  
Rotavirus Vaccine ◽  
Public Health Interventions ◽  
Post Intervention

ObjectiveTo adjust modelled baselines used for syndromic surveillance to account for public health interventions. Specifically to account for a change in the seasonality of diarrhoea and vomiting indicators following the introduction of a rotavirus vaccine in England.IntroductionPublic Health England's syndromic surveillance service monitor presentations for gastrointestinal illness to detect increases in health care seeking behaviour driven by infectious gastrointestinal disease. We use regression models to create baselines for expected activity and then identify any periods of signficant increases. The introduction of a rotavirus vaccine in England during July 2013 (Bawa, Z. et al. 2015) led to a reduction in incidence of the disease, requiring a readjustment of baselines.MethodsWe identified syndromes where rates had dropped significantly following the vaccine’s introduction. For these indicators, we introduced new variables into the regression models used to create baselines. Specifically we tested for a ‘step-change’ drop in rates and a change in the seasonality of baselines. Finally we checked the new models accuracy against actual syndromic data before and after the vaccine introduction.ResultsWe were able to improve model fit post-intervention, with the best-fitting models based on a change in seasonality. All post-intervention regression models had reduced average residual square error. Reductions in residual errors ranged from <1% to 60% when a ‘step-change’ variable was included and 4% to 75% when accounting for seasonality. Furthermore, every syndrome showed a better model fit when a change in seasonality was included.ConclusionsPrior to the vaccine’s introduction, rotavirus caused a spring-time peak in vomiting and diarrhoea recorded by syndromic surveillance systems. Failure to account for the reduction in this peak post-vaccine would have made surveillance systems less effective. In particular, any increased activity during spring may have been undetected. Moreover, models that did not account for changes in seasonality would increase the chances of false alarms during other seasons. By adjusting our baselines for the changes in seasonality due to the vaccine we were able to maintain effective surveillance systems.ReferencesBawa, Z., et al. Assessing the Likely Impact of a Rotavirus Vaccination Program in England: The Contribution of Syndromic Surveillance. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2015;61(1):77-85.

scholarly journals Adapting Syndromic Surveillance Baselines After Public Health Interventions

2020 ◽  
Vol 135 (6) ◽  
pp. 737-745
Author(s):  
Roger Antony Morbey ◽  
Alex James Elliot ◽  
Gillian Elizabeth Smith ◽  
Andre Charlett
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Public Health Intervention ◽  
Step Change ◽  
Emergency Department Visits ◽  
Health Interventions ◽  
False Alarms ◽  
Rotavirus Vaccine ◽  
Public Health Interventions ◽  
Before And After

Background Public health surveillance requires historical baselines to identify unusual activity. However, these baselines require adjustment after public health interventions. We describe an example of such an adjustment after the introduction of rotavirus vaccine in England in July 2013. Methods We retrospectively measured the magnitude of differences between baselines and observed counts (residuals) before and after the introduction of a public health intervention, the introduction of a rotavirus vaccine in July 2013. We considered gastroenteritis, diarrhea, and vomiting to be indicators for national syndromic surveillance, including telephone calls to a telehealth system, emergency department visits, and unscheduled consultations with general practitioners. The start of the preintervention period varied depending on the availability of surveillance data: June 2005 for telehealth, November 2009 for emergency departments, and July 2010 for general practitioner data. The postintervention period was July 2013 to the second quarter of 2016. We then determined whether baselines incorporating a step-change reduction or a change in seasonality resulted in more accurate models of activity. Results Residuals in the unadjusted baseline models increased by 42%-198% from preintervention to postintervention. Increases in residuals for vomiting indicators were 19%-44% higher than for diarrhea. Both step-change and seasonality adjustments improved the surveillance models; we found the greatest reduction in residuals in seasonally adjusted models (4%-75%). Conclusion Our results demonstrated the importance of adjusting surveillance baselines after public health interventions, particularly accounting for changes in seasonality. Adjusted baselines produced more representative expected values than did unadjusted baselines, resulting in fewer false alarms and a greater likelihood of detecting public health threats.

scholarly journals Correlates of Omani adults’ physical inactivity and sitting time

2012 ◽  
Vol 16 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Ruth M Mabry ◽  
Elisabeth AH Winkler ◽  
Marina M Reeves ◽  
Elizabeth G Eakin ◽  
Neville Owen
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Marital Status ◽  
Physical Inactivity ◽  
Regression Models ◽  
Health Interventions ◽  
Sitting Time ◽  
Work Status ◽  
Public Health Interventions ◽  
Education Work

AbstractObjectiveTo inform public health approaches for chronic disease prevention, the present study identified sociodemographic, anthropometric and behavioural correlates of work, transport and leisure physical inactivity and sitting time among adults in Oman.DesignCross-sectional study using the WHO STEPwise study methodology.SettingSur City, Oman.SubjectsMen and women aged 20 years and older (n 1335) in the Sur City Healthy Lifestyle Study who had complete data for demographic variables (gender, age, education, work status and marital status), BMI and behavioural risk factors – smoking and dietary habits plus physical inactivity and sitting time (the outcome variables).ResultsThe highest level of physical inactivity was in the leisure domain (55·4 %); median sitting time was about 2 h/d. Gender-stratified logistic regression models found that the statistically significant (P < 0·05) correlates of inactivity (in one or more domains) were age, work status and fruit and vegetable intake in women, and age, education, work status, marital status and BMI in men. Gender-stratified linear regression models found that the statistically significant correlates of sitting time were age, work status and BMI in women and education in men.ConclusionsFindings suggest that public health interventions need to be gender responsive and focus on domain-specific physical inactivity. In the Omani context, this might include gender-segregated exercise facilities to promote leisure physical activity among women and walking-friendly environmental initiatives to promote transport physical activity among men. Further evidence on barriers to physical activity and factors that influence prolonged sitting is required to develop relevant public health interventions.

scholarly journals “That was then, this is now” improving public health syndromic surveillance baselines

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Roger Morbey ◽  
Alex J. Elliot ◽  
Paul Loveridge ◽  
Helen Hughes ◽  
Sally Harcourt ◽  
...  
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Regression Models ◽  
Major Change ◽  
Surveillance Systems ◽  
Care Activity ◽  
Working Practices ◽  
Available Information ◽  
Traditional Surveillance

ObjectiveTo improve the ability of syndromic surveillance systems to detectunusual events.IntroductionSyndromic surveillance systems are used by Public Health England(PHE) to detect changes in health care activity that are indicative ofpotential threats to public health. By providing early warning andsituational awareness, these systems play a key role in supportinginfectious disease surveillance programmes, decision making andsupporting public health interventions.In order to improve the identification ofunusualactivity, wecreated new baselines to modelseasonally expectedactivity inthe absence of outbreaks or other incidents. Although historicaldata could be used to model seasonality, changes due to publichealth interventions or working practices affected comparability.Specific examples of these changes included a major change in theway telehealth services were provided in England and the rotavirusvaccination programme introduced in July 2013 that changed theseasonality of gastrointestinal consultations. Therefore, we needed toincorporate these temporal changes in our baselines.MethodsWe used negative binominal regression to model daily syndromicsurveillance, allowing for day of week and public holiday effects.To account for step changes in data caused by changes in healthcaresystem working practices or public health interventions we introducedspecific independent variables into the models. Finally, we smoothedthe regression models to provide short term forecasts of expectedtrends.The new baselines were applied to PHE’s four syndromicsurveillance systems for daily surveillance and public-facing weeklybulletins.ResultsWe replaced traditional surveillance baselines (based on simpleaverages of historical data) with the regression models for dailysurveillance of 53 syndromes across four syndromic surveillancesystems. The improved models captured current seasonal trends andmore closely reflected actual data outside of outbreaks.ConclusionsSyndromic surveillance baselines provide context forepidemiologists to make decisions about seasonal disease activity andemerging public health threats. The improved baselines developedhere showed whether current activity was consistent with expectedactivity, given all available information, and improved interpretationwhen trends diverged from expectations.

Novel conceptual framework and toolset for country-wide assessments of opportunities and challenges for public health interventions

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Scott McNabb ◽  
Joseph Wamala ◽  
Anila Naz ◽  
Anna Hartrampf ◽  
Dan Samoly ◽  
...  
Keyword(s):  
Public Health ◽  
Conceptual Framework ◽  
Health Interventions ◽  
Public Health Interventions

scholarly journals An analysis of COVID-19 clusters in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pooja Sengupta ◽  
Bhaswati Ganguli ◽  
Sugata SenRoy ◽  
Aditya Chatterjee
Keyword(s):  
Public Health ◽  
Cluster Analysis ◽  
Population Density ◽  
Case Studies ◽  
Compartment Model ◽  
Health Interventions ◽  
Disease Spread ◽  
Second Phase ◽  
Public Health Interventions ◽  
Homogeneous Groups

Abstract Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

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