Big data and predictive modelling for the opioid crisis: existing research and future potential

Bharat, Chrianna; Hickman, Matthew; Barbieri, Sebastiano; Degenhardt, Louisa

doi:10.1016/s2589-7500(21)00058-3

Cited by 27 publications

(18 citation statements)

References 86 publications

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“…This indicates an urgent need for improved public health surveillance to ensure that interventions are more targeted and that federal, state, tribal, and local governments have sufficient data and evidence to appropriately invest in harm reduction resources [ 39 ]. Insights into which populations and communities have been most affected are crucial [ 40 ], particularly in the context of those people disproportionately impacted by both COVID-19 and SUD. Hence, interdisciplinary approaches such as those used in this study warrant further exploration and validation to assess their utility in generating multimodal data-driven predictions of SUD risk and burden [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

“…Insights into which populations and communities have been most affected are crucial [ 40 ], particularly in the context of those people disproportionately impacted by both COVID-19 and SUD. Hence, interdisciplinary approaches such as those used in this study warrant further exploration and validation to assess their utility in generating multimodal data-driven predictions of SUD risk and burden [ 40 ]. Similarly, public health practitioners may benefit from these techniques to advance the prediction of opioid-related outcomes to inform data-driven prevention and treatment decisions targeted for specific communities that may help with SUD prevention and treatment funding.…”

Section: Discussionmentioning

confidence: 99%

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Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study

Cuomo¹,

Purushothaman²,

Calac³

et al. 2023

JMIR Form Res

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Background There were an estimated 100,306 drug overdose deaths between April 2020 and April 2021, a three-quarter increase from the prior 12-month period. There is an approximate 6-month reporting lag for provisional counts of drug overdose deaths from the National Vital Statistics System, and the highest level of geospatial resolution is at the state level. By contrast, public social media data are available close to real-time and are often accessible with precise coordinates. Objective The purpose of this study is to assess whether county-level overdose mortality burden could be estimated using opioid-related Twitter data. Methods International Classification of Diseases (ICD) codes for poisoning or exposure to overdose at the county level were obtained from CDC WONDER. Demographics were collected from the American Community Survey. The Twitter Application Programming Interface was used to obtain tweets that contained any of the 36 terms with drug names. An unsupervised classification approach was used for clustering tweets. Population-normalized variables and polynomial population-normalized variables were produced. Furthermore, z scores of the Getis Ord Gi clustering statistic were produced, and both these scores and their polynomial counterparts were explored in regression modeling of county-level overdose mortality burden. A series of linear regression models were used for predictive modeling to explore the interpretability of the analytical output. Results Modeling overdose mortality with normalized demographic variables alone explained only 7.4% of the variability in county-level overdose mortality, whereas this was approximately doubled by the use of specific demographic and Twitter data covariates based on a backward selection approach. The highest adjusted R2 and lowest AIC (Akaike Info Criterion) were obtained for the model with normalized demographic variables, normalized z scores from geospatial analyses, and normalized topic counts (adjusted R2=0.133, AIC=8546.8). The z scores of the Getis Ord Gi statistic appeared to have improved utility over population-normalization alone. In this model, median age, female population, and tweets about web-based drug sales were positively associated with opioid mortality. Asian race and Hispanic ethnicity were significantly negatively associated with county-level burdens of overdose mortality. Conclusions Social media data, when transformed using certain statistical approaches, may add utility to the goal of producing closer to real-time county-level estimates of overdose mortality. Prediction of opioid-related outcomes can be advanced to inform prevention and treatment decisions. This interdisciplinary approach can facilitate evidence-based funding decisions for various substance use disorder prevention and treatment programs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study

Cuomo¹,

Purushothaman²,

Calac³

et al. 2023

JMIR Form Res

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“…Predictive models can be subject to biases stemming from the original data or through model development which, when applied, may not provide equal benefit to every population subgroup [24].…”

Section: Fairnessmentioning

confidence: 99%

Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use

et al. 2023

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Aims Likelihood of alcohol dependence (AD) is increased among people who transition to greater levels of alcohol involvement at a younger age. Indicated interventions delivered early may be effective in reducing risk, but could be costly. One way to increase cost‐effectiveness would be to develop a prediction model that targeted interventions to the subset of youth with early alcohol use who are at highest risk of subsequent AD. Design A prediction model was developed for DSM‐IV AD onset by age 25 years using an ensemble machine‐learning algorithm known as ‘Super Learner’. Shapley additive explanations (SHAP) assessed variable importance. Setting and Participants Respondents reporting early onset of regular alcohol use (i.e. by 17 years of age) who were aged 25 years or older at interview from 14 representative community surveys conducted in 13 countries as part of WHO's World Mental Health Surveys. Measurements The primary outcome to be predicted was onset of life‐time DSM‐IV AD by age 25 as measured using the Composite International Diagnostic Interview, a fully structured diagnostic interview. Findings AD prevalence by age 25 was 5.1% among the 10 687 individuals who reported drinking alcohol regularly by age 17. The prediction model achieved an external area under the curve [0.78; 95% confidence interval (CI) = 0.74–0.81] higher than any individual candidate risk model (0.73–0.77) and an area under the precision‐recall curve of 0.22. Overall calibration was good [integrated calibration index (ICI) = 1.05%]; however, miscalibration was observed at the extreme ends of the distribution of predicted probabilities. Interventions provided to the 20% of people with highest risk would identify 49% of AD cases and require treating four people without AD to reach one with AD. Important predictors of increased risk included younger onset of alcohol use, males, higher cohort alcohol use and more mental disorders. Conclusions A risk algorithm can be created using data collected at the onset of regular alcohol use to target youth at highest risk of alcohol dependence by early adulthood. Important considerations remain for advancing the development and practical implementation of such models.

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“…Restrictive eligibility criteria are prevalent among services that lack sufficient resources to meet demand, a situation endemic in parts of the sector. To manage demand and efficiently allocate limited resources, programs often employ risk assessments to identify those deemed at “greatest risk” and then tie such conditionality to eligibility ( Bharat et al, 2021 ; Sandino, 2020 ). In so doing, harm reduction programs amass significant and sensitive personal information about PWUD.…”

Section: Potential Implications For Pwudmentioning

confidence: 99%

Between Care and Control: Examining Surveillance Practices in Harm Reduction

Michaud

Meulen

Guţă

2022

Contemporary Drug Problems

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As harm reduction programs and services proliferate, people who use drugs (PWUD) are increasingly subjected to surveillance through the collection of their personal information, systematic observation, and other means. The data generated from these practices are frequently repurposed across various institutional sites for clinical, evaluative, epidemiological, and administrative uses. Rationales provided for increased surveillance include the more effective provision of care, service optimization, risk stratification, and efficiency in resource allocation. With this in mind, our reflective essay draws on empirical analysis of work within harm reduction services and movements to reflect critically on the impacts and implications of surveillance expansion. While we argue that many surveillance practices are not inherently problematic or harmful, the unchecked expansion of surveillance under a banner of health and harm reduction may contribute to decreased uptake of services, rationing and conditionalities tied to service access, the potential deepening of health disparities amongst some PWUD, and an overlay of health and criminal-legal systems. In this context, surveillance relies on the enlistment of a range of therapeutic actors and reflects the permeable boundary between care and control. We thus call for a broader critical dialogue within harm reduction on the problems and potential impacts posed by surveillance in service settings, the end to data sharing of health information with law enforcement and other criminal legal actors, and deference to the stated need among PWUD for meaningful anonymity when accessing harm reduction and health services.

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Big data and predictive modelling for the opioid crisis: existing research and future potential

Cited by 27 publications

References 86 publications

Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study

Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study

Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use

Between Care and Control: Examining Surveillance Practices in Harm Reduction

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