Assessing the social and environmental determinants of pertussis epidemics in Queensland, Australia: a Bayesian spatio-temporal analysis

Abstract

Pertussis epidemics have displayed substantial spatial heterogeneity in countries with high socioeconomic conditions and high vaccine coverage. This study aims to investigate the relationship between pertussis risk and socio-environmental factors on the spatio-temporal variation underlying pertussis infection. We obtained daily case numbers of pertussis notifications from Queensland Health, Australia by postal area, for the period January 2006 to December 2012. A Bayesian spatio-temporal model was used to quantify the relationship between monthly pertussis incidence and socio-environmental factors. The socio-environmental factors included monthly mean minimum temperature (MIT), monthly mean vapour pressure (VAP), Queensland school calendar pattern (SCP), and socioeconomic index for area (SEIFA). An increase in pertussis incidence was observed from 2006 to 2010 and a slight decrease from 2011 to 2012. Spatial analyses showed pertussis incidence across Queensland postal area to be low and more spatially homogeneous during 2006-2008; incidence was higher and more spatially heterogeneous after 2009. The results also showed that the average decrease in monthly pertussis incidence was 3·1% [95% credible interval (CrI) 1·3-4·8] for each 1 °C increase in monthly MIT, while average increase in monthly pertussis incidences were 6·2% (95% CrI 0·4-12·4) and 2% (95% CrI 1-3) for SCP periods and for each 10-unit increase in SEIFA, respectively. This study demonstrated that pertussis transmission is significantly associated with MIT, SEIFA, and SCP. Mapping derived from this work highlights the potential for future investigation and areas for focusing future control strategies.

The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004–2018

“…Sixth, a recent research found that the proportion of the reported pertussis cases aged 1–5 and over 5 years have increased about 4 folds and the proportion aged 3–12 months reduced from 70.59 to 58.39% from 2013 to 2018, respectively, in Chongqing, which can be ascribed to the unqualified vaccine quality 44 , 52 . Finally, weather parameters may also play an important role in the manifest rebound of pertussis infections, as evidenced by the findings of our study and others 14 , 24 – 26 , and it can also be seen from the data in Table S4 that the monthly aggregate precipitation and average wind velocity showed a higher level during the period 2016–2018 as compared to that during the period 2004–2015, while the monthly average air pressure displayed a lower level during the period 2016–2018 relative to the level during the period 2004–2015.…”

“…Prior studies also reported the relationship between weather parameters and pertussis. For example, Huang et al found that a 1 °C increment in the monthly mean minimum temperature was correlated with a 3.1% (95% CI 1.3-4.8%) decrease in monthly pertussis morbidity 24 . Blackwood et al reported that the rainy season may play an important role in driving the seasonal pattern of pertussis transmission 25 .…”

Monitoring Pertussis Infections Using Internet Search Queries

“…Pertussis is recognised as a disease of increasing concern in Australia; incidence was observed to increase in all states except in Tasmania. Although Australia has run a pertussis vaccination programme for decades, with high national coverage 17 . The increased incidence may partly result from the currently used acellular vaccine which is transited from whole-cell pertussis vaccines.…”

“…A study reported that the level of protection of acellular vaccine is lower than that of the previously used whole-cell vaccine 18 . Additionally, the shift to PCR testing, which is more sensitive than early diagnostic tests and evolution of B. pertussis may also contribute to the increase in the outbreaks of pertussis in Australia 17 , 19 . Other factors that contribute to this epidemic will require further studies, especially for Tasmania.…”

Spatial analysis of reported new cases and local risk of leprosy in hyper‐endemic situation in Northeastern Brazil

“…This method smoothes raw rates by applying weighted averages, thus creating a third corrected rate. It is useful for controlling random fluctuations in small areas and enabling estimation of the true spatial distribution of the disease studied .…”