Peter Baker’s research while affiliated with The University of Queensland and other places

Objectives and importance Maternal and family patterns are changing, and these changes can influence birthweight. Past research and organisational reports focus on short temporal timelines or broad trends, but trends across a longer temporal period are important. The aim of this study is to assess the trends in birthweight and maternal characteristics across a 19-year period using descriptive statistics. Study type and methods Birth records (n = 1,166,055) were obtained for a 19-year period (2000–2019) and a descriptive secular trend analysis was performed. Results and conclusions Mean birthweight trended down across the study period, while rates of large for gestational age births increased. This appears to be driven by a decrease in gestational age across the period. Maternal factors, such as smoking, BMI and Indigenous status, were found to be linked with changes in mean birthweight and the proportion of small for gestational age or large for gestational age. More babies were born to older women by the end of the study period. There was a sharp rise in gestational diabetes, and more large for gestational age births to these women. Over time, the large for gestational age births started to decline, suggesting better care practices for women with gestational diabetes.

People living with HIV (PLHIV) have high rates of tobacco smoking. Nicotine vaping products (NVPs) may promote tobacco smoking cessation and/or harm reduction. This study aimed to trial the feasibility of NVPs for promoting tobacco smoking cessation among PLHIV. The Tobacco Harm Reduction with Vaporised Nicotine (THRiVe) study was a mixed-methods trial among 29 PLHIV who used tobacco daily. Participants trialled a 12-week intervention of NVPs. This study reports descriptive analyses of quantitative data on tobacco abstinence and associated adverse events. Short-term abstinence (7-day point prevalence; i.e., no tobacco use for 7 days) was achieved by 35% of participants at Week 12 and 31% reported short-term abstinence at Week 24. Sustained medium-term abstinence (8 weeks’ abstinence) was achieved by 15% of participants at Week 12 and 31% at Week 24. Most adverse events were mild. NVPs may represent a feasible and potentially effective short-to-medium term tobacco smoking cessation aid and/or harm reduction strategy among PLHIV.

Objectives To identify the maternal and social characteristics associated with age standardised birthweight in a modern developed setting.Methods Birth records (n = 414,478) were obtained for live, singleton births in the period 2007–2015 in Queensland, Australia. Age-standardised birth weights were calculated and a multinomial logistic regression was performed to obtain odds ratios and 95% confidence intervals for a range of maternal and social characteristics.ResultsMothers who smoke (OR 2.82, 95% CI 2.72–2.93), and mothers from Southern and Central Asia (OR 3.30, 95% CI 3.08–3.53) had the highest odds of delivering small for gestational age babies. Smoking alone accounted for 21% of low birthweight. Pre-existing diabetes (OR 5.98, 95% CI 5.12–6.99) had the highest odds ratio for large for gestational age births; however, maternal overweight and obesity accounted for 24% of all cases due to its greater prevalence in the population.Conclusion for practiceSmoking continues to be an important modifiable predictor of low birthweight. The predictors associated with large for gestational age are modifiable, with maternal overweight and obesity the largest contributor to high birthweight.SignificanceMaternal characteristics are changing alongside broader population change, with mothers often older and heavier than in previous decades. This study provides an update to the role of maternal and social characteristics in optimal birthweight within a large developed population. The present study finds a range of traditional and emerging risk factors remain important. Population attributable risk fractions show that maternal overweight and smoking are the most important modifiable risk factors for birthweight extremes (foetal macrosomia and small for gestational age, respectively). Public health efforts to address these risk factors could reduce up to 20% of birth weight extremes.

Introduction: Extremely preterm births (EPT), require complex decision making and significant medical interventions. While environmental factors such as ambient temperature extremes have been associated with preterm births, little is known of the environmental associations with EPT births. The objective of this study is to explore whether ambient temperature is associated with increased risk of EPT birth. Methods: Birth records for 315,226 infants born in Queensland Australia (2007-2015) were matched to average maximum and minimum temperature for the last month of pregnancy. Odds ratios and 95% confidence intervals were calculated using a generalised linear model. Population attributable risk was calculated for a 5% reduction in maximum temperature. Results: Each one degree increase in maximum [aOR 1.03 (95% CI 1.01, 1.05)] and minimum temperature [aOR 1.02 (95% CI 1.01, 1.04)] was associated with an increase in odds for EPT birth. Increased odds for EPT births was found for maternal smoking [aOR 1.46 (95% CI 1.23, 1.72)], increasing plurality [OR 6.38 (95% CI 5.48, 7.42)] and stillbirth [aOR 342.99 (95% CI 295.53, 398.06)]. When stratified by birth status, the association was only found for live births. Discussion: Higher temperatures are associated with small increases in the odds of delivering an infant in the EPT period. The risk may be enhanced for women who smoke during pregnancy. Women at an increased risk of preterm births should be counselled around methods to reduce their exposure to excessive heat.

Background The infection of Corona Virus Disease (Covid-19) is challenging health problems worldwide. COVID-19 pandemic is spreading all over the world with the number of infected cases increased to 54.4 million with 1.32 million deaths. Different types of statistical models have been developed to predict viral infection and multiple studies have compared the performance of these predictive models, but results were not consistent. This study aimed to develop and provide easy to use model to predict the Covid-19 infection severity in the patients and to help understanding the patient’s condition. Methods This study analyzed simulated data obtained from the large database for 340 patients with an active Covid-19 infection. The study identified predictors of Covid-19 outcomes that may be measured in two different ways: the total T-cell levels in the blood with T-cell subsets and number of cells in the blood infected with virus. All measures are relatively unobtrusive as they only require a blood sample, however there is a significant laboratory cost implications for measuring the number of cells infected with virus. This study used methodological approach using two different methods showing how multiple regression and logistic regression can be used in the context of Covid-19 longitudinal data to develop the prediction models. Results This study has identified the predictors of Covid-19 infection outcomes and developed prediction models. In the regression model of Total_T Cell, the predictors BMI, comorbidity and Total_Tcell were all associated with increased levels of infection severity (p < 0.001). For BMI, the mean % of unhealthy cells increased by 0.42 (95% CI 0.24 to 0.60) and comorbidity predictor has on average 8.3% more unhealthy liver cells than without comorbidity (95% CI – 2.9% - 1.29%). The results of multivariate logistic regression model predicting the Covid-19 Infection severity were promising. The significant predictors were observed such as Age (OR 0.95, p = 0.02, 95% CI: 0.91 – 0.99), Helper T_cells (OR O.93, p = 0.03, 95% CI: 0.87 – 0.99), Basic_Tcell (OR 1.11, p = 0.001, 95% CI: 1.06 -1.71) and Comorbidity (OR 0.41, p = 0.05, 95% CI: 0.16 – 1.07). Conclusions In this study recommendation has been provided to clinical researchers on the best way to use the various Covid-19 infections measures along with identifying other possible predictors of Covid-19 infection. It is imperative to monitor closely the T-cell subsets using prediction models that might provide valuable information about the patient’s condition during the treatment process.

Introduction Environmental exposures can contribute both benefits and risks to human health. Maternal exposure to green space has been associated with improvements in birthweight, among other birth outcomes. Newer measures of green space have been developed, which allows for an exploration of the effect of different ground covers (green, dry and bare earth), as well as measures of biodiversity. This study explores the association of these novel green space measures with birthweight in a large birth cohort in Queensland, Australia. Methods Birthweight was acquired from the routine health records. Records were allocated green space values for fractional cover, biodiversity and foliage projective cover. Directed acyclic graphs were developed to guide variable selection. Mixed-effects linear regression and generalised linear mixed-effects models were developed, with random intercepts for maternal residential locality and year of birth. Results are presented as standardised beta coefficients or odds ratios, with 95% confidence intervals. Results An IQR increase of green cover (29.6 g, 95% CI 13.8–45.5) and foliage projective cover (26.0 g, 95% CI 10.8–41.3) are associated with birthweight in urban areas. An IQR increase in dry cover −34.4 g, 95% CI -60.4 to −8.4) and bare earth (−17.7 g, 95% CI -32.8 to −2.6) are associated with lower birthweight. Mothers living in rural areas had similar results, with an IQR increase in green cover (17.8 g, 95% CI 2.9–32.7) associated with higher birthweight, and bare earth (−27.7 g, 95% CI -45.7 to −9.7) was associated with lower birthweight. The biodiversity measure used in this study was not associated with any birthweight outcomes. Conclusion This study finds that the types of ground cover within the maternal residential locality are associated with small, but significant, changes in estimated birthweight, and these effects are not limited to urban areas.

Data analysis projects invariably involve a series of steps such as reading, cleaning, summarizing and plotting data, statistical analysis and reporting. To facilitate reproducible research, rather than employing a relatively ad-hoc point-and-click cut-and-paste approach, we typically break down these tasks into manageable chunks by employing separate files of statistical, programming or text processing syntax for each step including the final report. Real world data analysis often requires an iterative process because many of these steps may need to be repeated any number of times. Manually repeating these steps is problematic in that some necessary steps may be left out or some reported results may not be for the most recent data set or syntax. GNU Make may be used to automate the mundane task of regenerating output given dependencies between syntax and data files. In addition to facilitating the management of and documenting the workflow of a complex data analysis project, such automation can help minimize errors and make the project more reproducible. It is relatively simple to construct Makefiles for small data analysis projects. As projects increase in size, difficulties arise because GNU Make does not have inbuilt rules for statistical and related software. Without such rules, Makefiles can become unwieldy and error-prone. This article addresses these issues by providing GNU Make pattern rules for R, Sweave, rmarkdown, SAS, Stata, Perl and Python to streamline management of data analysis and reporting projects. Rules are used by adding a single line to project Makefiles. Additional flexibility is incorporated for modifying standard program options. An overall strategy is outlined for Makefile construction and illustrated via simple and complex examples.

Q-VAX® is a vaccine used to prevent Q fever. Administration of the vaccine is complicated by the need to ensure, using intradermal and serological tests, that individuals have no prior immunity. Previous studies suggest that the vaccine is highly efficacious and long-lasting in adults. However, there has been no systematic follow-up of vaccine efficacy and the longevity of immunity using population-level data. We aimed to investigate the vaccine failure rate and duration of immunity in previously vaccinated individuals. We formulated a retrospective cohort study design within a linked data. We used a Q fever vaccination registry linked to Q fever notifications and hospital admissions (1991–2016) in the state of Queensland, which has Australia’s highest incidence of Q fever. Q-VAX® failure was defined as occurrence of Q fever > 14 days’ after vaccination. The incidence of Q fever in vaccinated and unvaccinated individuals was 5.40 (95% CI: 3.65, 7.72) and 89.50 (95% CI: 70.50, 112.00]) per 100,000 person-years of follow-up, respectively. The hazard ratio (HR) for Q fever was 0.07 (95% CI: 0.04, 0.10) in non-immune vaccinated compared with immune unvaccinated individuals. The overall vaccine effectiveness was found to be 94.37% suggesting that Q-VAX® is highly effective at preventing Q fever. However, the greater incidence observed in unvaccinated individuals considered immune during the pre-vaccination screening may suggest that pre-vaccination screening is sub-optimal among this study population.

Understanding contributors to smoking and quitting cigarettes is important to developing effective cessation programs and addressing smoking related morbidity and mortality among people living with HIV (PLHIV). Using data from a large cross-sectional study of Australian PLHIV we provide a smoking prevalence estimate and explore the relationship between socio-demographic variables and smoking status. We also explore the relationship between HIV diagnosis and antiretroviral therapy (ART) initiation and quitting smoking. Of the 1011 respondents included in the analysis, 30.6% were current smokers. The strongest predictor of smoking was regular cannabis use (AOR 6.2, 95% CI 3.6–10.8) while the strongest predictor of being a past smoker was receiving ART (AOR 2.4, 95% CI 1.2–4.7). Quitting also increased around the time of diagnosis and ART initiation, highlighting the potential for these events to be optimal times to address smoking among PLHIV.

Q fever caused by the gram negative bacteria, Coxiella burnetii, is an occupational hazard for those who live and work in rural settings and those who are in contact with animals, especially abattoir and slaughterhouse workers. Australia is the only country to register a vaccine to prevent Q fever (Q-vax®, Seqirus, Australia) that is used in high risk populations. Seroprevalence studies conducted to determine the burden of Q fever (C. burnetii infection) in different settings have demonstrated high levels of heterogeneity with estimates of the percent positive ranging from 30% to 70%. There is a need for a more systematic evaluation of the findings of these studies in order to provide summary estimates of the seroprevalence in different settings. We searched for published articles using PubMed, MEDLINE-EMBASE, and Scopus databases using search terms obtained from an initial review of published reports of recent Q fever outbreaks. Data on the seroprevalence of C. burnetii infection (Q fever) was extracted from the selected studies and a random effects meta-analysis was performed with stratification by outbreak status, year, country and serological techniques used. Results were visualised with a forest plot with 95% CI and measures of heterogeneity (I2) for the random effects model. A total of 19 articles that met the search criteria were included. The reported seroprevalence rate ranged from 4.7% to 91.7% among abattoir and slaughterhouse workers. No inter-group heterogeneity was observed (p = 0.956), supporting the pooling of all studies into one pooled measure. The pooled estimate of seropositivity for C. burnetii infection in people working in abattoirs and slaughterhouses was 26% (95% CI: 18-35%) regardless of the evidence of an "outbreak", the time of year or country. Seropositivity for C burnetii was independent of a person's age and years of occupational experience. Within abattoirs and slaughterhouses, slaughtering of cattle, sheep and goats are the most important risk factors associated with seropositivity and for those who showed over symptoms upon infection. We recommend that vaccination programmes are directed towards people employed in the meat processing industry to mitigate the significant health and economic impacts of Q fever.