Predictors of pertussis outbreak in urban and rural municipalities of Saskatchewan, Canada

Medu, Olanrewaju; Anderson, Maureen; Enns, Andrea; Wright, J. C.; Dunlop, Terry G.; Kapaj, Simon; Opondo, Johnmark

doi:10.17269/s41997-018-0074-1

Cited by 5 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These contradictory findings may be the result of distortion effects of percent females in the model. However, our finding that rurality is a predictor of higher county-level pertussis risk is consistent with reports of a geospatial analysis of pertussis in Saskatchewan, Canada, that rural areas had higher pertussis risk than non-rural areas (Medu et al, 2018). There is a clear need to explore the relationship between population density, rurality, and risk of pertussis more deeply, as there is conflict not only in this study but also in the literature.…”

Section: Predictors Of Pertussis Risksupporting

confidence: 90%

Geographic disparities and socio-demographic predictors of pertussis risk in Florida

Tandy¹,

Odoi²

2021

PeerJ

View full text Add to dashboard Cite

Background Pertussis is a toxin-mediated respiratory illness caused by Bordetella pertussis that can result in severe complications and death, particularly in infants. Between 2008 and 2011, children less than 3 months old accounted for 83% of the pertussis deaths in the United States. Understanding the geographic disparities in the distribution of pertussis risk and identifying high risk geographic areas is necessary for guiding resource allocation and public health control strategies. Therefore, this study investigated geographic disparities and temporal changes in pertussis risk in Florida from 2010 to 2018. It also investigated socioeconomic and demographic predictors of the identified disparities. Methods Pertussis data covering the time period 2010–2018 were obtained from Florida HealthCHARTS web interface. Spatial patterns and temporal changes in geographic distribution of pertussis risk were assessed using county-level choropleth maps for the time periods 2010–2012, 2013–2015, 2016–2018 and 2010–2018. Tango’s flexible spatial scan statistics were used to identify high-risk spatial clusters which were displayed in maps. Ordinary least squares (OLS) regression was used to identify significant predictors of county-level risk. Residuals of the OLS model were assessed for model assumptions including spatial autocorrelation. Results County-level pertussis risk varied from 0 to 116.31 cases per 100,000 people during the study period. A total of 11 significant (p < 0.05) spatial clusters were identified with risk ratios ranging from 1.5 to 5.8. Geographic distribution remained relatively consistent over time with areas of high risk persisting in the western panhandle, northeastern coast, and along the western coast. Although county level pertussis risks generally increased from 2010–2012 to 2013–2015, risk tended to be lower during the 2016–2018 time period. Significant predictors of county-level pertussis risk were rurality, percentage of females, and median income. Counties with high pertussis risk tended to be rural (p = 0.021), those with high median incomes (p = 0.039), and those with high percentages of females (p < 0.001). Conclusion There is evidence that geographic disparities exist and have persisted over time in Florida. This study highlights the application and importance of Geographic Information Systems (GIS) technology and spatial statistical/epidemiological tools in identifying areas of highest disease risk so as to guide resource allocation to reduce health disparities and improve health for all.

show abstract

Section: Predictors Of Pertussis Risksupporting

confidence: 90%

Geographic disparities and socio-demographic predictors of pertussis risk in Florida

Tandy¹,

Odoi²

2021

PeerJ

View full text Add to dashboard Cite

show abstract

“…Pertussis or whooping cough is a vaccine-preventable respiratory disease that is mainly caused by Bordetella pertussis (1,2). This infection may present with mild symptoms to fatal complication especially in under 3-month infants (3) and it is one of the main causes of morbidity and mortality among neonates and infants (4).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal analysis of Pertussis using geographic information system among Iranian population during 2012-2018

Alimohamadi

Zahraei

Karami

et al. 2020

mjiri

View full text Add to dashboard Cite

show abstract

“…Epidemiology (or non-epidemic) models are based on different statistical mathematical models, such as (i) regression, mainly used to detect outbreaks [31], as well as (ii) the autoregressive integrated moving average model, ARIMA, and seasonal SARIMA, both used to predict outbreaks and risk factors [32,33]. Currently, more complex models are gainging popularity, including [34] (iii) models based on a time series, which are more useful in antimicrobial studies to unravel trends [35], (iv) methods including cumulative sum and an exponentially weighted moving average (for example, in the ARIBACA project, were this model was implemented to detect and forecast selected Caribbean diseases, such as dengue [36]), and (v) spatial models, which are more sophisticated, including place-specific models requiring multivariate techniques to model the spatial heterogeneity of all the infection´s covariates [37] (Table A1).…”

Section: Overview Of Mathematical Models To Predict Infectious Diseasesmentioning

confidence: 99%

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

2019

View full text Add to dashboard Cite

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

show abstract

Predictors of pertussis outbreak in urban and rural municipalities of Saskatchewan, Canada

Cited by 5 publications

References 23 publications

Geographic disparities and socio-demographic predictors of pertussis risk in Florida

Geographic disparities and socio-demographic predictors of pertussis risk in Florida

Spatio-temporal analysis of Pertussis using geographic information system among Iranian population during 2012-2018

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Contact Info

Product

Resources

About