There is growing evidence in the literature that patients' age is associated with increased risk of death in acute pesticide poisoning. However, few studies have investigated whether the age effect ...differs between males and females. We aimed to examine the association between age-sex and risk of death in acute pesticide self-poisoning.
A prospective cohort of deliberate pesticide-poisoned patients admitted to ten rural Sri Lankan hospitals between March 2002 and December 2019. The pesticide ingested was identified based on identification of container or history. A mixed effects logistic regression was fitted to investigate the effect of age-sex on death in acute pesticide self-poisoning adjusting for clinical symptoms on admission, measured by Glasgow Coma Scale and Poison Severity Score, and controlling for clustering among hospital sites.
In total, 201 different pesticides were ingested by patients. 6,643 patients ingested an unknown pesticide. A single pesticide was co-ingested with alcohol by 4,603 patients. Of the 28,303 patients enrolled, 2,028 patients died, resulting in case fatality of 7.2% (95% CI 6.9-7.5). The effect of age on case fatality was stronger for males after 21 years of age. The odds of dying for each 5 years increase in age was 1.26 (95% CI 1.23-1.28) times higher for males versus 1.14 (95% CI 1.10-1.19) times higher for females. Missing data were handled by multiple imputation.
Patient's age-sex are important risk factors for death in acute pesticide self-poisoning even after controlling for clinical effects. The age effect on the odds of dying was significantly different for males and females, with this effect being stronger for males. Given that patient's age and sex are very easy to collect on admission, our study highlights the need for incorporating these risk factors in policy and clinical decisions.
Alcohol‐related suicide across Australia: a geospatial analysis Hurzeler, Tristan; Buckley, Nicholas A.; Noghrehchi, Firouzeh ...
Australian and New Zealand journal of public health,
August 2021, 2021-08-00, 20210801, 2021-08-01, Volume:
45, Issue:
4
Journal Article
Peer reviewed
Open access
The acute effects of alcohol consumption are a major risk factor for suicide. Positive blood alcohol concentrations are present in almost one‐third of all suicides at time of death. These suicides ...are defined as alcohol‐related suicides. This cross‐sectional study examines the geospatial distribution/clustering of high proportions of alcohol‐related suicides and reports on socioeconomic and demographic risk factors.
National Coronial Information System (NCIS) data were used to calculate proportions of suicides with alcohol present at the time of death for each level 3 statistical areas (SA3) in Australia. A density analysis and hotspot cluster analysis were used to visualise and establish statistically significant clustering of areas with higher (hotspots) and lower (coldspots) proportions. Subsequently, socioeconomic and demographic risk factors for alcohol use and suicide were reported on for hot and cold spots.
Significant clustering of areas with higher proportions of alcohol‐related suicide occurred in northern Western Australia, the Northern Territory and Queensland, as well as inland New South Wales and inland Queensland. Clustering of SA3s with significantly lower proportions occurred in major city and inner regional Sydney and Melbourne.
Results from this study identify areas in which prevention strategies should target alcohol use and can be used to inform prevention strategy design. Additionally, hotspots and coldspots identified in this study can be used for further analysis to better understand contextual risk factors for alcohol‐related suicide.
The literature suggests patient characteristics and higher opioid doses and long-term duration are associated with problematic opioid behaviours but no one study has examined the role of all these ...factors simultaneously in a long-term prospective cohort study.
Five-year, community-based, prospective cohort of people prescribed opioids for chronic non-cancer pain (CNCP). Logistic mixed effect models with multiple imputation were used to address missing data. Oral morphine equivalent (OME) mg per day was categorised as: 0 mg OME/day, 1–49 mg OME/day (reference), 50–89 mg OME/day, 90–199 mg OME/day and 200mg+ OME/day. Patient risk factors included: age, gender, substance use, mental health history and pain-related factors. Main outcomes included: Prescribed Opioids Difficulties Scale (PODS), Opioid-Related Behaviours In Treatment (ORBIT) scale, and ICD-10 opioid dependence. Multiple confounders for problematic opioid behaviours were assessed.
Of 1,514 participants 44.4% were male (95%CI 41.9–46.9) and their mean age was 58 years (IQR 48–67). Participants had a mean duration of pain of 10 years (IQR 4.5–20.0) and had been taking strong opioids for a median of four years (IQR 1.0–10.0). At baseline, median OME/day was 73 (IQR 35–148). At 5-years, 85% were still taking strong opioids. PODS moderate-high scores reduced from 59.9% (95%CI 58.8–61.0) at baseline to 51.5% (95%CI 50.0–53.0) at 5-years. Around 9% met criteria for ICD-10 opioid dependence at each wave. In adjusted mixed effect models, the risk factors most consistently associated with problematic opioid use were: younger age, substance dependence, mental health histories and higher opioid doses.
Both patient risk factors and opioid dose are associated with problematic opioid use behaviours.
Paracetamol is one of the most used medicines worldwide and is the most common important poisoning in high-income countries. In overdose, paracetamol causes dose-dependent hepatotoxicity. ...Acetylcysteine is an effective antidote, however despite its use hepatotoxicity and many deaths still occur.
This review summarizes paracetamol overdose and toxicity (including mechanisms, risk factors, risk assessment, and treatment). In addition, we summarize the epidemiology of paracetamol overdose worldwide. A literature search on PubMed for poisoning epidemiology and mortality from 1 January 2017 to 26 October 2022 was performed to estimate rates of paracetamol overdose, liver injury, and deaths worldwide.
Paracetamol is widely available and yet is substantially more toxic than other analgesics available without prescription. Where data were available, we estimate that paracetamol is involved in 6% of poisonings, 56% of severe acute liver injury and acute liver failure, and 7% of drug-induced liver injury. These estimates are limited by lack of available data from many countries, particularly in Asia, South America, and Africa. Harm reduction from paracetamol is possible through better identification of high-risk overdoses, and better treatment regimens. Large overdoses and those involving modified-release paracetamol are high-risk and can be targeted through legislative change.
•In Australia, codeine was rescheduled from over-the-counter to prescription only in 2018.•Codeine rescheduling appears to have reduced paracetamol poisoning-related hospital admissions.•This ...reduction was due to decrease in poisonings with likely paracetamol-codeine combinations.
Codeine was restricted to prescription only in Australia in 2018. This intervention aimed to reduce harms from codeine dependance and use, including toxicity from co-formulated paracetamol. We aimed to quantify the impact of this intervention on paracetamol poisoning hospital admissions in a national hospital admissions database.
We analyzed the number of paracetamol overdoses resulting in hospital admissions from the Australian Institute of Health and Welfare National Hospital Morbidity Database, January 2011 to June 2020. We used interrupted time series analysis to quantify the effect of codeine re-scheduling on the monthly number of paracetamol poisoning-related hospital admissions in Australia. We compared paracetamol poisonings with no opioid combinations, and poisonings with probable paracetamol-codeine combinations.
There was an immediate and sustained decrease (level shift) in the number of paracetamol poisoning-related hospital admissions following codeine re-scheduling (RR=0.85; 95% CI 0.80–0.89). This reduction was due to the decrease in poisonings with likely paracetamol-codeine combinations (RR=0.62; 95% CI 0.57–0.67) while there was no change in other paracetamol poisonings (RR=0.91; 95% CI 0.96–1.01).
Codeine re-scheduling in Australia appears to have reduced paracetamol poisoning-related hospital admissions.
Multiple imputation and maximum likelihood estimation (via the expectation‐maximization algorithm) are two well‐known methods readily used for analyzing data with missing values. While these two ...methods are often considered as being distinct from one another, multiple imputation (when using improper imputation) is actually equivalent to a stochastic expectation‐maximization approximation to the likelihood. In this article, we exploit this key result to show that familiar likelihood‐based approaches to model selection, such as Akaike's information criterion (AIC) and the Bayesian information criterion (BIC), can be used to choose the imputation model that best fits the observed data. Poor choice of imputation model is known to bias inference, and while sensitivity analysis has often been used to explore the implications of different imputation models, we show that the data can be used to choose an appropriate imputation model via conventional model selection tools. We show that BIC can be consistent for selecting the correct imputation model in the presence of missing data. We verify these results empirically through simulation studies, and demonstrate their practicality on two classical missing data examples. An interesting result we saw in simulations was that not only can parameter estimates be biased by misspecifying the imputation model, but also by overfitting the imputation model. This emphasizes the importance of using model selection not just to choose the appropriate type of imputation model, but also to decide on the appropriate level of imputation model complexity.
In many applications involving regression analysis, explanatory variables (or covariates) may be imprecisely measured or may contain missing values. Although there exists a vast literature on ...measurement error modeling to account for errors-in-variables, and on missing data methodology to handle missingness, very few methods have been developed to simultaneously address both. In this paper, we consider likelihood-based multiple imputation to handle missing data, and combine this with two well-known functional measurement error methods: simulation-extrapolation and corrected score. This unified approach has several appealing characteristics: the model fitting procedure is easy to understand and off-the-shelf software can be incorporated into the modeling framework; no calibration data or a validation subset is required in the model fitting procedure; and the missing data component of the proposed approach is likelihood-based which allows standard likelihood machinery. We demonstrate our methods on simulated datasets and apply them to daily ozone pollution measurements in Los Angeles where observed covariates consist of missing data and imprecise measurements. We conclude that the proposed methods substantially reduce bias and mean squared errors in regression coefficients, in comparison to methods that ignore either measurement error or missingness in covariates.
Paediatric poisoning is a major cause of childhood injury, and most poisonings are preventable. We aimed to describe hospitalisations resulting from poisoning and envenomation in Australian children, ...including demographics, cause of the exposure, hospital length of stay, rates of intensive care unit admission and in-hospital deaths. We also aimed to describe risk factors for increased length of stay and intensive care unit admission.
A retrospective analysis was performed of hospitalised poisoning and envenomation cases of children (<15 years) in Australia from 1 July 2009 to 30 June 2019. A nationwide hospital admissions database was used for this study.
During the 10-year study period 33,438 children were admitted to hospital due to a pharmaceutical or non-pharmaceutical poisoning/envenomation; an average of 74.8 cases per 100,000 population per year. Approximately 10 children were admitted to hospital each day for poisoning. Over 70% of these cases were due to pharmaceuticals (n = 23,628), most frequently non-opioid analgesics, anti-pyretics and anti-rheumatics (n = 8759, 37.1% of pharmaceutical exposures). The most common non-pharmaceutical exposure was contact with venomous animals and toxic plants (n = 4578, 46.7% of non-pharmaceuticals). Intentional self-harm occurred in 7833 (23.4%) of cases. Intensive care unit admission was required in 519 cases (2.5% of the 20,739 cases where this information was available), while 200 (0.96% of 20,739) needed ventilator support. Ten children (0.03%) died. Older age, female sex, poisoning with pharmaceuticals and metropolitan hospital location were associated with increased length of stay. Older age and poisoning with pharmaceuticals were also associated with intensive care unit admission.
Approximately 10 children were admitted to hospital with poisoning every day in Australia. Most poisonings were due to pharmaceuticals, particularly simple analgesics that are found in most Australian homes. Severe outcomes (intensive care unit admissions and deaths) were rare.
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