Quantifying alcohol-attributable disability-adjusted life years to others than the drinker in Aotearoa/New Zealand: A modelling study based on administrative data.

BACKGROUND AND AIMS: Quantifying the health burden of alcohol has largely focused upon harm to drinkers, which is an underestimate. There is a growing literature on alcohol's harm to others (HTO), but it lacks the systematic transfer of HTO into a comparative risk assessment framework. This study calculated disability-adjusted life years (DALYs) for fetal alcohol spectrum disorder (FASD), interpersonal violence and traffic injury due to another's drinking. DESIGN: This study is a disease burden analysis, using modelling of DALYs for New Zealand in 2018. SETTING AND PARTICIPANTS: The study took place among the Aotearoa/New Zealand population in 2018. MEASUREMENTS: The involvement of others' drinking was obtained from prevalence, alcohol-attributable fraction studies and administrative data. Disability weights (DW) for FASD were adapted from fetal alcohol syndrome (FAS) weights using a Beta-Pert probability distribution; for interpersonal injury, DWs used hospital events linked with injury compensation; for traffic injury, DWs used hospital events. Populations were stratified by ethnicity, age group and gender. A descriptive comparison was made with a previous estimate of DALYs for drinkers. FINDINGS: In 2018, 78 277 healthy life years were lost in Aotearoa/New Zealand due to alcohol's HTO. The main contributor (90.3%) was FASD, then traffic crashes (6.3%) and interpersonal violence (3.4%). The indigenous population, Maori, was impacted at a higher rate (DALYs among Maori were 25 per 1000 population; among non-Maori 15 per 1000 population). The burden of HTO was greater than that to drinkers (DALYs HTO = 78 277; DALYs drinkers = 60 174). CONCLUSIONS: Disability from fetal alcohol spectrum disorder (FASD) appears to be a major contributor to alcohol's harm to others in Aotearoa/New Zealand. Taking FASD into account, the health burden of harm to others is larger than harm to the drinker in Aotearoa/New Zealand, and ethnicity differences show inequity in harm to others. Quantification of the burden of harm informs the value of implementing effective alcohol policies and should include the full range of harms.

Exploring Differences in Daily Vaping of Nicotine and Cannabis among People Who Use Drugs in New Zealand.

Background: Little is known about daily vaping of different substances, particularly cannabis. Aim: To explore daily vaping of cannabis and nicotine products in a sample of people who use drugs in New Zealand. Method: The online New Zealand Drug Trends convenience survey (N = 23,500) was promoted to those aged 16+ via a targeted Facebook™ campaign, with 9,042 reporting vaping in the past six months. Multivariate logistic regression models were fitted to identify predictors of daily vaping of: (i) nicotine e-liquids, (ii) no-nicotine e-liquids, (iii) cannabis e-liquids/oils, (iv)cannabis herb. Results: Forty-two percent of past 6-month vapers used a vaporizing device "daily or near daily" (n = 3,508). Nicotine was most common substance used by daily vapers (96%), followed by dry herb cannabis (12%), no-nicotine e-liquids (10%) and cannabis e-liquid (6%). Daily vaping of no-nicotine e-liquids was associated with abstinence from tobacco use. Frequency of cannabis use was negatively correlated with daily vaping of nicotine liquids and positively correlated with daily vaping of no-nicotine and herbal cannabis. Younger age strongly predicted daily vaping of nicotine and no-nicotine liquids, but the reverse association was observed for daily vaping of herbal cannabis. Maori were less likely to daily vape cannabis herb than NZ Europeans. Daily vaping of both cannabis e-liquid and cannabis herb was associated with medicinal cannabis use. Conclusion: Daily vapers of nicotine and cannabis differed by several characteristics. Younger age group is at risk of daily vaping nicotine and non-nicotine, while herbal cannabis vaping is associated with older and medicinal use, suggesting a need for a nuanced vape policy response.

Foetal alcohol spectrum disorder in Aotearoa, New Zealand: Estimates of prevalence and indications of inequity.

INTRODUCTION: Foetal alcohol spectrum disorder (FASD) is 100% caused by alcohol. The lifelong disability caused by prenatal alcohol exposure cannot be reversed. Lack of reliable national prevalence estimates of FASD is common internationally and true of Aotearoa, New Zealand. This study modelled the national prevalence of FASD and differences by ethnicity. METHODS: FASD prevalence was estimated from self-reported data on any alcohol use during pregnancy for 2012/2013 and 2018/2019, combined with risk estimates for FASD from a meta-analysis of case-ascertainment or clinic-based studies in seven other countries. A sensitivity analysis using four more recent active case ascertainment studies was performed to account for the possibility of underestimation. RESULTS: We estimated FASD prevalence in the general population to be 1.7% (95% confidence interval [CI] 1.0%; 2.7%) in the 2012/2013 year. For Maori, the prevalence was significantly higher than for Pasifika and Asian populations. In the 2018/2019 year, FASD prevalence was 1.3% (95% CI 0.9%; 1.9%). For Maori, the prevalence was significantly higher than for Pasifika and Asian populations. The sensitivity analysis estimated the prevalence of FASD in the 2018/2019 year to range between 1.1% and 3.9% and for Maori, from 1.7% to 6.3%. DISCUSSION AND CONCLUSIONS: This study used methodology from comparative risk assessments, using the best available national data. These findings are probably underestimates but indicate a disproportionate experience of FASD by Maori compared with some ethnicities. The findings support the need for policy and prevention initiatives to support alcohol-free pregnancies to reduce lifelong disability caused by prenatal alcohol exposure.

Estimating child maltreatment cases that could be alcohol-attributable in New Zealand.

AIMS: Children are an important group harmed by others' alcohol consumption. This study (1) compared the risk of occurrence of child maltreatment among children exposed versus not exposed to parents with an alcohol-attributable hospitalization or service use for mental health/addiction and (2) conducted sensitivity analyses to estimate the cases of child maltreatment that could be attributable to alcohol under two different conditions in New Zealand. DESIGN: A cohort study conducted among children 0-17 years and their parents (years 2000-2017) using the Statistics New Zealand Integrated Data Infrastructure. The New Zealand Health Survey 2017 was also used. SETTING: New Zealand. PARTICIPANTS: 58 359 children aged 0-17 years and their parents. MEASUREMENTS: Survival analysis based on a Bayesian piecewise exponential model was used to estimate the risk of time to first substantiated child maltreatment event (identified from social service, hospital, mortality and police data) related to exposure to parents with an alcohol-attributable hospitalization or who used a mental health/addiction service (versus no exposure). Potential confounders were included for parents and children. The sensitivity analyses (i) estimated an alcohol-attributable admissions/service use fraction for maltreatment in 2017 and (ii) calculated a population-attributable fraction using the relative risk from the cohort and prevalence of hazardous drinking (AUDIT 8+) among parents in 2017. FINDINGS: There was a 65.1% [1.65; 95% confidence interval (CI) = 1.46-1.86] increased risk of child maltreatment if a child was exposed to parents who had an alcohol-attributable hospitalized or mental health/addictions service use. The sensitivity analyses estimated that in 2017 14.6% (CI = 14.0-15.3%) and 11.4% (95% CI = 8.4-14.3%) of the documented cases of child maltreatment in New Zealand could be attributable to parents with severe or hazardous consumption. CONCLUSIONS: In New Zealand, exposure to parents with an alcohol-attributable hospitalization or service use is a risk factor for substantiated child maltreatment.

Predictors of using social media to purchase drugs in New Zealand: Findings from a large-scale online survey.

BACKGROUND: There are increasing reports of social media being used to buy and sell illegal drugs internationally. Studies of social media drug markets to date have largely involved general explorations of social media drug transactions in select countries. Social media drug markets may operate differently for different drug types and in different country contexts. AIMS: To identify predictors of the use of social media to purchase cannabis, methamphetamine, LSD and ecstasy/MDMA in New Zealand using a large-scale online survey sample. METHODS: The annual New Zealand Drug Trends Survey (NZDTS), an online convenience survey promoted via targeted Facebook™ campaign, was completed by 23,500 respondents aged 16+. Logistic regression models were fitted to identify demographic, drug use and drug market predictors of reporting the use of social media to purchase cannabis, methamphetamine, LSD and ecstasy/MDMA. RESULTS: Fifty-one percent of the sample had purchased cannabis, methamphetamine, ecstasy/MDMA or LSD in the past six months, of which 22% had done so via social media (n = 2,650). Cannabis was the drug type most purchased from social media among drug purchasers (24%), followed by ecstasy/MDMA (13%). Sixty percent of social media purchasers had used Facebook™, 48% Snapchat™, and 20% Instagram™. The leading advantages of social media purchasing were "high convenience" (74%), and "fast transaction speed" (43%). Younger age was a significant predictor of social media purchasing for all drug types (particularly among 16-17 year olds). Purchasing from someone identified as a "drug dealer" was a predictor of social media purchasing among respondents for all drug types. CONCLUSION: Social media drug markets may have significant implications for drug purchasing by youth through providing greater access to supply and breaking down age-related barriers between social supply and commercial drug markets. Our findings highlight the importance of incorporating social media platforms into youth drug prevention and harm minimisation strategies.

Online alcohol delivery is associated with heavier drinking during the first New Zealand COVID-19 pandemic restrictions.

INTRODUCTION: This paper aimed to assess purchasing and drinking behaviour during the first COVID-19 pandemic restrictions in New Zealand. METHOD: A convenience sample was collected via Facebook from 2173 New Zealanders 18+ years during pandemic restrictions April/May 2020. Measures included: the quantity typically consumed during a drinking occasion and heavier drinking (6+ drinks on a typical occasion) in the past week; place of purchase including online alcohol delivery. Descriptive statistics were generated, logistic and linear regression models predicted heavier drinking and typical occasion quantity, respectively. Weighting was not applied. RESULTS: During pandemic restrictions, around 75% of respondents purchased from supermarkets, 40% used online alcohol delivery services (18% for the first time during COVID-19). Purchasing online alcohol delivery during pandemic restrictions was associated with heavier drinking (75% higher odds) in the past week, while purchasing from supermarkets was not. About 58% of online purchasers under 25 reported no age checks. Sixteen percent of those purchasing online repeat ordered online to keep drinking after running out. Of respondents who had tried to buy alcohol and food online, 56% reported that alcohol was easier to get delivered than fresh food. Advertising for online alcohol delivery was seen by around 75% of the sample. Half of the sample reported drinking more alcohol during the restrictions. DISCUSSION AND CONCLUSIONS: Online alcohol delivery during the COVID-19 pandemic restrictions was associated with heavier drinking in the past week. The rapid expansion of online alcohol delivery coupled with a lack of regulatory control requires public health policy attention.

Determinants of the retail price of illegal drugs in New Zealand.

INTRODUCTION: New Zealand has a number of unusual geographical features that may influence the price of illegal drugs including international isolation, numerous rural communities, and two separate islands (North and South Islands). AIMS: (1) Collect data on the prices of different drug types from all regions of New Zealand; (2) Identify independent predictors of the reported prices. METHOD: An online drug survey was promoted via a targeted Facebook™ campaign. A total of 6331 respondents completed the survey. Normal regression models were constructed to identify predictors of the reported price of an ounce of cannabis, gram of methamphetamine, pill of ecstasy and tab of LSD using demographics, drug use frequency, drug market indicators, region, community size, type of seller, and location of purchase as predictor variables. RESULTS: Higher availability was a predicator of lower prices for cannabis (-3% per higher availability category), methamphetamine (-4%) and ecstasy (-5%). Those living in the North Island (-26%) and buying from gangs (-5%) paid lower prices for methamphetamine. Those living in rural communities reported higher prices for methamphetamine (+5%) but lower prices for cannabis (-3%). Daily cannabis users (+4%) and those on social welfare (+2%) paid higher prices for cannabis. CONCLUSION: Lower prices for methamphetamine in the North Island may reflect the concentration of methamphetamine manufacture there. The fact that gangs offer lower prices for methamphetamine suggests they do not have monopoly control of this market. Gangs may be able to offer lower prices for methamphetamine due to scale of production/trafficking and lower risk of victimisation. Higher prices for methamphetamine in rural areas may reflect less competitive markets. Lower prices for cannabis in rural communities may be due to proximity to growing locations. Daily cannabis users and those on social welfare may be less able to delay their cannabis purchases to low price harvest months.

Determinants of high availability of methamphetamine, cannabis, LSD and ecstasy in New Zealand: Are drug dealers promoting methamphetamine rather than cannabis?

BACKGROUND: Small towns in New Zealand have reported high availability of methamphetamine, and conversely a shortage of cannabis. Stakeholders have suggested drug dealers are purposely promoting methamphetamine rather than cannabis. AIMS: (1) To compare the availability of methamphetamine and cannabis in different size communities; (2) Identify determinants of the high availability of methamphetamine, including low availability of cannabis. METHOD: An online drug survey was promoted via a broadly targeted Facebook™ campaign. Participants were asked if they lived in a "city", "small town" or "rural area", their drug use patterns, and local drug market characteristics, including current availability. A total of 6311 people completed the survey. Logistic regression models were constructed to identify independent predictors of reporting high availability of methamphetamine, cannabis, ecstasy and LSD respectively, with low availability of cannabis included as a predictor in the non-cannabis markets. RESULTS: Methamphetamine was reported to be more available than cannabis in all regions. Methamphetamine was more available in towns/rural areas than in cities. Significant predictors of high availability of methamphetamine were living in a town/rural area (OR = 1.38), purchasing from a gang member (OR = 1.88), daily methamphetamine use (OR = 2.41), Maori ethnicity (OR = 1.36) and reporting low availability of cannabis (OR = 1.89). Low availability of cannabis was not a predictor of high availability of ecstasy or LSD. Living in a town/rural area was not a predictor of high availability of cannabis, LSD or ecstasy. Purchasing from a gang member was a predictor of high availability of cannabis (OR = 1.80) and LSD (OR = 4.61). CONCLUSIONS: Further research is required to identify what causal relationships, if any, there are between the statistical associations of high methamphetamine availability, living in a small town, purchasing from a gang, and low cannabis availability. It may be the case that small towns offer an environment where a gang can control the local drugs market.

Socio-economic disadvantage is associated with heavier drinking in high but not middle-income countries participating in the International Alcohol Control Study.

INTRODUCTION AND AIMS: To investigate if socio-economic disadvantage, at the individual- and country-level, is associated with heavier drinking in some middle- and high-income countries. DESIGN AND METHODS: Surveys of drinkers were undertaken in some high- and middle-income countries. Participating countries were Australia, England, New Zealand, Scotland (high-income) and Peru, Thailand and Vietnam (middle-income). Disadvantage at the country-level was defined as per World Bank (categorised as middle-or high-income); individual-level measures were (i) years of education and (ii) whether and individual was under or over the poverty line in each country. Measures of heavier drinking were (i) proportion of drinkers that consumed 8+ drinks and (ii) three drinking risk groups (lower, increasing and higher). Multi-level logistic regression models were used. RESULTS: Individual-level measures of disadvantage, lower education and living in poverty, were associated with heavier drinking, consuming 8+ drinks on a typical occasion or drinking at the higher risk level, when all countries were considered together. Drinkers in the middle-income countries had a higher probability of consuming 8+ drinks on a typical occasion relative to drinkers in the high-income countries. Interactions between country-level income and individual-level disadvantage were undertaken: disadvantaged drinkers in the middle-income countries were less likely to be heavier drinkers relative to those with less disadvantage in the high-income countries. DISCUSSION AND CONCLUSIONS: Associations between socio-economic disadvantage and heavier drinking vary depending on country-level income. These findings highlight the value of exploring cross-country differences in heavier drinking and disadvantage and the importance of including country-level measurements to better elucidate relationships.

Bayesian bivariate survival analysis using the power variance function copula.

Copula models have become increasingly popular for modelling the dependence structure in multivariate survival data. The two-parameter Archimedean family of Power Variance Function (PVF) copulas includes the Clayton, Positive Stable (Gumbel) and Inverse Gaussian copulas as special or limiting cases, thus offers a unified approach to fitting these important copulas. Two-stage frequentist procedures for estimating the marginal distributions and the PVF copula have been suggested by Andersen (Lifetime Data Anal 11:333-350, 2005), Massonnet et al. (J Stat Plann Inference 139(11):3865-3877, 2009) and Prenen et al. (J R Stat Soc Ser B 79(2):483-505, 2017) which first estimate the marginal distributions and conditional on these in a second step to estimate the PVF copula parameters. Here we explore an one-stage Bayesian approach that simultaneously estimates the marginal and the PVF copula parameters. For the marginal distributions, we consider both parametric as well as semiparametric models. We propose a new method to simulate uniform pairs with PVF dependence structure based on conditional sampling for copulas and on numerical approximation to solve a target equation. In a simulation study, small sample properties of the Bayesian estimators are explored. We illustrate the usefulness of the methodology using data on times to appendectomy for adult twins in the Australian NH&MRC Twin registry. Parameters of the marginal distributions and the PVF copula are simultaneously estimated in a parametric as well as a semiparametric approach where the marginal distributions are modelled using Weibull and piecewise exponential distributions, respectively.

Bayesian semiparametric analysis of recurrent failure time data using copulas.

The analysis of recurrent event data is of particular importance in medical statistics where patients suffering from chronic diseases often present with multiple recurring relapses or cancer patients experience several tumor recurrences. Whereas individual subjects can be assumed to be independent, the times between events of one subject are neither independent nor identically distributed. Apart from the marginal approach by Wei et al. (1989), the shared frailty model, see for example Duchateau and Janssen (2008), has been used extensively to analyze recurrent event data, where the correlation between sequential times is implicitly taken into account via a random effect. Oakes (1989) and Romeo et al. (2006) showed and exemplified the equivalence of frailty models for bivariate survival data to Archimedean copulas. Despite the fact that copula-based models have been used to model parallel survival data, their application to recurrent failure time data has only recently been suggested by Lawless and Yilmaz (2011) for the bivariate case. Here, we extend this to more than two recurrent events and model the joint distribution of recurrent events explicitly using parametric copulas within a Bayesian framework. This framework allows for parametric as well as a nonparametric modeling of the marginal baseline hazards and models the influence of covariates on the marginals via a proportional hazards assumption. Furthermore, the parameters of the copula may also depend on the covariates. We illustrate the flexibility of this approach using data from an asthma prevention trial in young children.

Differential immune gene expression profiles in susceptible and resistant full-sibling families of Atlantic salmon (Salmo salar) challenged with infectious pancreatic necrosis virus (IPNV).

This study aims to identify at the expression level the immune-related genes associated with IPN-susceptible and resistant phenotypes in Atlantic salmon full-sibling families. We have analyzed thirty full-sibling families infected by immersion with IPNV and then classified as resistant or susceptible using a multivariate survival analysis based on a gamma-Cox frailty model and the Kaplan-Meier mortality curves. In four families within each group head kidneys were pooled for real-time PCR and one-color salmon-specific oligonucleotide microarray (21K) analysis at day 1 and 5 post-infection. Transcripts involved in innate response (IL-6, IFN-α), antigen presentation (HSP-70, HSP-90, MHC-I), TH1 response (IL-12, IFN-γ, CRFB6), immunosuppression (IL-10, TGF-ß1) and leukocyte activation and migration (CCL-19, CD18) showed a differential expression pattern between both phenotypes, except in IL-6. In susceptible families, except for IFN-γ, the expressions dropped to basal values at day 5 post-infection. In resistant families, unlike susceptible families, levels remained high or increased (except for IL-6) at day 5. Transcriptomic analysis showed that both families have a clear differential expression pattern, resulting in a marked down-regulation in immune related genes involved in innate response, complement system, antigen recognition and activation of immune response in IPN-resistant. Down-regulation of genes, mainly related to tissue differentiation and protein degradation metabolism, was also observed in resistant families. We have identified an immune-related gene patterns associated with susceptibility and resistance to IPNV infection of Atlantic salmon. This suggests that a limited immune response is associated with resistant fish phenotype to IPNV challenge while a highly inflammatory but short response is associated with susceptibility.

Data mining and influential analysis of gene expression data for plant resistance gene identification in tomato (Solanum lycopersicum)

Background Molecular mechanisms of plant-pathogen interactions have been studied thoroughly but much about them is still unknown. A better understanding of these mechanisms and the detection of new resistance genes can improve crop production and food supply. Extracting this knowledge from available genomic data is a challenging task. Results Here, we evaluate the usefulness of clustering, data-mining and regression to identify potential new resistance genes. Three types of analyses were conducted separately over two conditions, tomatoes inoculated with Phytophthora infestans and not inoculated tomatoes. Predictions for 10 new resistance genes obtained by all applied methods were selected as being the most reliable and are therefore reported as potential resistance genes. Conclusion Application of different statistical analyses to detect potential resistance genes reliably has shown to conduct interesting results that improve knowledge on molecular mechanisms of plant resistance to pathogens.

Detection of influent virulence and resistance genes in microarray data through quasi likelihood modeling.

Publicly available genomic data are a great source of biological knowledge that can be extracted when appropriate data analysis is used. Predicting the biological function of genes is of interest to understand molecular mechanisms of virulence and resistance in pathogens and hosts and is important for drug discovery and disease control. This is commonly done by searching for similar gene expression behavior. Here, we used publicly available Streptococcus pyogenes microarray data obtained during primate infection to identify genes that have a potential influence on virulence and Phytophtora infestance inoculated tomato microarray data to identify genes potentially implicated in resistance processes. This approach goes beyond co-expression analysis. We employed a quasi-likelihood model separated by primate gender/inoculation condition to model median gene expression of known virulence/resistance factors. Based on this model, an influence analysis considering time course measurement was performed to detect genes with atypical expression. This procedure allowed for the detection of genes potentially implicated in the infection process. Finally, we discuss the biological meaning of these results, showing that influence analysis is an efficient and useful alternative for functional gene prediction.

Bivariate survival modeling: a Bayesian approach based on Copulas.

Copula models have become increasingly popular for modeling multivariate survival data. In this paper we review some of the recent work that has been appeared for copula model for bivariate survival data and propose a Bayesian modeling. Our approach is very flexible with respect to the choice of marginal distributions and, depending on the copula model employed, it is possible to have a class of variation for the dependence parameter. We compare some of the copula models using a descriptive diagnostic method and three popular Bayesian model selection criteria. Our methodology is illustrated with the Diabetic Retinopathy Study (1976).