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.
Background
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.
Methods
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.
Findings
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.
Interpretation
Both patient risk factors and opioid dose are associated with problematic opioid use behaviours.
Background: 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.
Methods: 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.
Results: 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.
Conclusion and implications for public health: 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.
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