Integrating Human Services and Criminal Justice Data With Claims Data to Predict Risk of Opioid Overdose Among Medicaid Beneficiaries: A Machine‐Learning Approach
Open accessAbstract
Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877–0.892 vs. C-statistic = 0.871; 95%CI = 0.863–0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.
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Methods included gradient boosting, random forest, ensemble models, XGBoost, and logistic regression-based ML pipelines with fairness optimization.
“Methods included gradient boosting [32], random forest [26, 34], ensemble models [26], XGBoost [27], and logistic regression‐based ML pipelines with fairness optimization [21].”
From Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review (2026) · cites this paper as [32]
The Materials and methods section says the authors developed and tested gradient boosting machine models, not a logistic regression-based pipeline. The Discussion says bias assessment and approaches to improve fairness were needed before implementation; it does not describe fairness optimization as a method used in the study. (LLM, conf=0.88)
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Experimental — accuracy not yet measuredCross-domain data linkage substantially improved overdose risk prediction versus claims alone.
“Cross‐domain data linkage substantially improved overdose risk prediction versus claims alone; incarceration history and social service utilization captured key SDOH signals”
From Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review (2026) · cites this paper as [32]
The Discussion says integrating human services and criminal justice data produced only “slight improvements” over Medicaid claims alone and explicitly says the improvement “was not large.” This contradicts the claim that data linkage substantially improved prediction. (LLM, conf=0.92)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredIntegrating human services and criminal justice data with Medicaid claims substantially improves opioid overdose risk prediction over claims data alone.
“showed that cross‐sector data linkage substantially improves predictive performance over claims data alone”
From Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review (2026) · cites this paper as [32]
The Discussion says the integrated models improved prediction only slightly over Medicaid claims alone and explicitly states that the improvement “was not large.” The Conclusions likewise describe small improvements, contrary to the claim of a substantial gain. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredIncarceration history and social service utilization captured key SDOH signals.
“Cross‐domain data linkage substantially improved overdose risk prediction versus claims alone; incarceration history and social service utilization captured key SDOH signals”
Lo-Ciganic et al. [32] linked Medicaid claims with human services records and criminal justice data to predict opioid overdose risk.
“Lo‐Ciganic et al. [32] achieved the most complex data integration, linking Medicaid claims with human services records and criminal justice data to predict opioid overdose risk”
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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,904 · see citing works
Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309 · see citing works
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147 · see citing works
STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., et al. (2015) · BMJ · doi:10.1136/bmj.h5527 · Cited by 3,660 · see citing works
Drug and Opioid-Involved Overdose Deaths — United States, 2013–2017
Scholl, L., Seth, P., Kariisa, M., Wilson, N. O., & Baldwin, G. T. (2018) · MMWR Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm675152e1 · Cited by 1,734 · see citing works
Association Between Opioid Prescribing Patterns and Opioid Overdose-Related Deaths
Bohnert, A. S. B. (2011) · JAMA · doi:10.1001/jama.2011.370 · Cited by 1,509 · see citing works
Opioid Prescriptions for Chronic Pain and Overdose
Dunn, K. M., Saunders, K. W., Rutter, C. M., Banta‐Green, C. J., Merrill, J. O., Sullivan, M. D., Weisner, C. M., Silverberg, M. J., Campbell, C. I., Psaty, B. M., et al. (2010) · Annals of Internal Medicine · doi:10.7326/0003-4819-152-2-201001190-00006 · Cited by 1,265 · see citing works
The economic burden of prescription opioid overdose, abuse and dependence in the United States, 2013
Florence, C. S., Zhou, C., Luo, F., & Xu, L. (2016) · Medical Care · doi:10.1097/mlr.0000000000000625 · Cited by 1,152 · see citing works
Webster, L. R., & Webster, R. M. (2005) · Pain Medicine · doi:10.1111/j.1526-4637.2005.00072.x · Cited by 1,096 · see citing works
Opioid crisis: no easy fix to its social and economic determinants
Dasgupta, N., Beletsky, L., & Ciccarone, D. (2018) · American Journal of Public Health · doi:10.2105/ajph.2017.304187 · Cited by 923 · see citing works
Patterns of abuse among unintentional pharmaceutical overdose fatalities
Hall, A. J. (2008) · JAMA · doi:10.1001/jama.2008.802 · Cited by 815 · see citing works
Characteristics of Opioid Prescriptions in 2009
Volkow, N. D. (2011) · JAMA · doi:10.1001/jama.2011.401 · Cited by 813 · see citing works
Predictors of opioid misuse in patients with chronic pain: a prospective cohort study
Ives, T. J., Chelminski, P. R., Hammett‐Stabler, C. A., Malone, R. M., Perhac, J. S., Potisek, N. M., Shilliday, B. B., DeWalt, D. A., & Pignone, M. P. (2006) · BMC Health Services Research · doi:10.1186/1472-6963-6-46 · Cited by 512 · see citing works
Becker, W. C., Sullivan, L. E., Tetrault, J. M., Desai, R. A., & Fiellin, D. A. (2007) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2007.09.018 · Cited by 398 · see citing works
Data Mining and Statistics for Decision Making
Tufféry, S. (2011) · Wiley eBooks · doi:10.1002/9780470979174 · Cited by 384 · see citing works
An analysis of the root causes for opioid-related overdose deaths in the United States
Webster, L. R., Cochella, S., Dasgupta, N., Fakata, K. L., Fine, P. G., Fishman, S. M., Grey, T., Johnson, E. M., Lee, L. K., Passik, S. D., et al. (2011) · Pain Medicine · doi:10.1111/j.1526-4637.2011.01134.x · Cited by 295 · see citing works
Altekruse, S. F., Cosgrove, C. M., Altekruse, W. C., Jenkins, R. A., & Blanco, C. (2020) · PLoS ONE · doi:10.1371/journal.pone.0227966 · Cited by 293 · see citing works
Sullivan, M. D., Edlund, M. J., Fan, M.-Y., DEVRIES, A. R., Braden, J. B., & Martin, B. C. (2010) · Pain · doi:10.1016/j.pain.2010.05.020 · Cited by 289 · see citing works
Edlund, M., Martin, B., DeVries, A., Fan, M.-Y., Braden, J., & Sullivan, M. (2010) · The Clinical Journal of Pain · doi:10.1097/ajp.0b013e3181b99f35 · Cited by 267 · see citing works
Haffajee, R. L., Lin, L. A., Bohnert, A. S. B., & Goldstick, J. E. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.6373 · Cited by 266 · see citing works
Smith, S. M., Dart, R. C., Katz, N. P., Paillard, F., Adams, E. H., Comer, S. D., Degroot, A., Edwards, R. R., Haddox, J. D., Jaffe, J. H., et al. (2013) · Pain · doi:10.1016/j.pain.2013.05.053 · Cited by 265 · see citing works
High-Risk Use by Patients Prescribed Opioids for Pain and Its Role in Overdose Deaths
Baumblatt, J. A. G., Wiedeman, C., Dunn, J. R., Schaffner, W., Paulozzi, L. J., & Jones, T. F. (2014) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2013.12711 · Cited by 252 · see citing works
Cotto, J. H., Davis, E., Dowling, G. J., Elcano, J. C., Staton, A. B., & Weiss, S. R. B. (2010) · Gender Medicine · doi:10.1016/j.genm.2010.09.004 · Cited by 247 · see citing works
A Public Health Strategy for the Opioid Crisis
Saloner, B. K., McGinty, E. E., Beletsky, L. A., Bluthenthal, R. N., Beyrer, C., Botticelli, M. P., & Sherman, S. G. (2018) · Public Health Reports · doi:10.1177/0033354918793627 · Cited by 240 · see citing works
Lo-Ciganic, W.-H., Huang, J. L., Zhang, H. H., Weiss, J. C., Wu, Y., Kwoh, C. K., Donohue, J. M., Cochran, G., Gordon, A. J., Malone, D. C., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.0968 · Cited by 227 · see citing works
Self-citationDrug-poisoning deaths involving heroin: United States, 2000-2013.
Hedegaard, H. B., Chen, L.-H., & Warner, M. (2015) · PubMed · Cited by 202 · see citing works
Oliva, E. M., Bowe, T. R., Tavakoli, S., Martins, S. B., Lewis, E. T., Paik, M. C., Wiechers, I. R., Henderson, P., Harvey, M., Avoundjian, T., et al. (2017) · Psychological Services · doi:10.1037/ser0000099 · Cited by 198 · see citing works
Thottakkara, P., Ozrazgat‐Baslanti, T., Hupf, B., Rashidi, P., Pardalos, P. M., Momčilović, P., & Bihorac, A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0155705 · Cited by 197 · see citing works
Data Mining and Statistics for Decision Making
Tuffry, S. (2011) · Medical Entomology and Zoology · Cited by 187 · see citing works
Analytic models to identify patients at risk for prescription opioid abuse.
White, A. G., Birnbaum, H. G., Schiller, M., Tang, J., & Katz, N. P. (2009) · PubMed · Cited by 171 · see citing works
Doctor and Pharmacy Shopping for Controlled Substances
Peirce, G. L., Smith, M. J., Abate, M. A., & Halverson, J. A. (2012) · Medical Care · doi:10.1097/mlr.0b013e31824ebd81 · Cited by 152 · see citing works
A Model to Identify Patients at Risk for Prescription Opioid Abuse, Dependence, and Misuse
Rice, J. B., White, A. G., Birnbaum, H. G., Schiller, M., Brown, D. A., & Roland, C. L. (2012) · Pain Medicine · doi:10.1111/j.1526-4637.2012.01450.x · Cited by 141 · see citing works
Why the C-statistic is not informative to evaluate early warning scores and what metrics to use
Brufau, S. R., Huddleston, J. M., Escobar, G. J., & Liebow, M. (2015) · Critical Care · doi:10.1186/s13054-015-0999-1 · Cited by 141 · see citing works
Zedler, B., Xie, L., Wang, L., Joyce, A., Vick, C., Brigham, J., Kariburyo, F., Baser, O., & Murrelle, L. (2015) · Pain Medicine · doi:10.1111/pme.12777 · Cited by 138 · see citing works
Yang, Z., Wilsey, B. L., Bohm, M. K., Weyrich, M. S., Roy, K., Ritley, D., Jones, C., & Melnikow, J. A. (2015) · Journal of Pain · doi:10.1016/j.jpain.2015.01.475 · Cited by 124 · see citing works
Green, C. A., Perrin, N., Janoff, S. L., Campbell, C. I., Chilcoat, H. D., & COPLAN, P. M. (2017) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4157 · Cited by 119 · see citing works
Zedler, B. K., Saunders, W. B., Joyce, A. R., Vick, C. C., & Murrelle, E. L. (2018) · Pain Medicine · doi:10.1093/pm/pnx009 · Cited by 119 · see citing works
Overdose Deaths Involving Prescription Opioids Among Medicaid Enrollees--Washington, 2004-2007
(2009) · PsycEXTRA Dataset · doi:10.1037/e552162010-002 · Cited by 117 · see citing works
Glanz, J. M., Narwaney, K. J., Mueller, S. R., Gardner, E. M., Calcaterra, S. L., Xu, S., Breslin, K. D., & Binswanger, I. A. (2018) · Journal of General Internal Medicine · doi:10.1007/s11606-017-4288-3 · Cited by 98 · see citing works
Assessing Opioid Shopping Behaviour
Cepeda, M. S., Fife, D., Chow, W. S., Mastrogiovanni, G., & Henderson, S. C. (2012) · Drug Safety · doi:10.2165/11596600-000000000-00000 · Cited by 87 · see citing works
Automated Prediction of Risk for Problem Opioid Use in a Primary Care Setting
Hylan, T. R., Korff, M. V., Saunders, K. W., Masters, E. T., Palmer, R. E., Carrell, D., Cronkite, D., Mardekian, J., & Gross, D. E. (2015) · Journal of Pain · doi:10.1016/j.jpain.2015.01.011 · Cited by 78 · see citing works
An Examination of Claims-based Predictors of Overdose from a Large Medicaid Program
Cochran, G., Gordon, A. J., Lo-Ciganic, W.-H., Gellad, W. F., Frazier, W. T., Lobo, C. P., Chang, C.-C. H., Zheng, P., & Donohue, J. M. (2016) · Medical Care · doi:10.1097/mlr.0000000000000676 · Cited by 70 · see citing works
Self-citationPotentially Inappropriate Opioid Prescribing, Overdose, and Mortality in Massachusetts, 2011–2015
Rose, A. J., Bernson, D., Chui, K. K. H., Land, T. G., Walley, A. Y., Larochelle, M. R., Stein, B. D., & Stopka, T. J. (2018) · Journal of General Internal Medicine · doi:10.1007/s11606-018-4532-5 · Cited by 66 · see citing works
Patterns of Potential Opioid Misuse and Subsequent Adverse Outcomes in Medicare, 2008 to 2012
Carey, C. M., Jena, A. B., & Barnett, M. L. (2018) · Annals of Internal Medicine · doi:10.7326/m17-3065 · Cited by 60 · see citing works
Amalakuhan, B., Kiljanek, L., Parvathaneni, A., Hester, M., Cheriyath, P., & Fischman, D. (2012) · Journal of Community Hospital Internal Medicine Perspectives · doi:10.3402/jchimp.v2i1.9915 · Cited by 59 · see citing works
Stopka, T. J., Amaravadi, H., Kaplan, A. R., Hoh, R., Bernson, D., Chui, K. K. H., Land, T. G., Walley, A. Y., Larochelle, M. R., & Rose, A. J. (2019) · International Journal of Drug Policy · doi:10.1016/j.drugpo.2019.03.024 · Cited by 51 · see citing works
Krawczyk, N., Schneider, K. E., Eisenberg, M. D., Richards, T. M., Ferris, L. M., Mojtabai, R., Stuart, E. A., Lyons, B. C., Jackson, K., Weiner, J. P., et al. (2020) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2020.107997 · Cited by 48 · see citing works
Predicting Opioid Overdose Deaths Using Prescription Drug Monitoring Program Data
Ferris, L. M., Saloner, B. K., Krawczyk, N., Schneider, K. E., Jarman, M. P., Jackson, K., Lyons, B. C., Eisenberg, M. D., Richards, T. M., Lemke, K., et al. (2019) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2019.07.026 · Cited by 44 · see citing works
High-risk Use by Patients Prescribed Opioids for Pain and its Role in Overdose Deaths
Quinlan, M. (2014) · Journal of Emergency Medicine · doi:10.1016/j.jemermed.2014.06.008 · Cited by 42 · see citing works
Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review
Canan, C. E., Polinski, J. M., Alexander, G. C., Kowal, M. K., Brennan, T. A., & Shrank, W. H. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx066 · Cited by 42 · see citing works
Defining Nonmedical Use of Prescription Opioids within Health Care Claims: A Systematic Review
Cochran, G., Woo, B., Lo‐Ciganic, W., Gordon, A. J., Donohue, J. M., & Gellad, W. F. (2015) · Substance Abuse · doi:10.1080/08897077.2014.993491 · Cited by 41 · see citing works
Self-citationDrug Overdose: Differing Risk Models for Women and Men among Opioid Users with Non-Cancer Pain
Liang, Y., Goros, M. W., & Turner, B. J. (2016) · Pain Medicine · doi:10.1093/pm/pnw071 · Cited by 36 · see citing works
Pain Management With Opioids in 2019-2020
Wood, E., Simel, D. L., & Klimas, J. (2019) · JAMA · doi:10.1001/jama.2019.15802 · Cited by 33 · see citing works
Hacker, K. A., Jones, L. D., Brink, L. L., Wilson, A., Cherna, M., Dalton, E., & Hulsey, E. G. (2018) · Public Health Reports · doi:10.1177/0033354918803938 · Cited by 20 · see citing works
Self-citationRough, K., Huybrechts, K. F., Hernández–Dı́az, S., Desai, R. J., Patorno, E., & Bateman, B. T. (2018) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4443 · Cited by 19 · see citing works
Fernandes, J. C., Campana, D., Harwell, T. S., & Helgerson, S. D. (2015) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2015.05.032 · Cited by 19 · see citing works
Chirikov, V., Shaya, F. T., Onukwugha, E., Mullins, C. D., dosReis, S., & Howell, C. D. (2015) · Medical Care · doi:10.1097/mlr.0000000000000405 · Cited by 18 · see citing works
Harle, C. A., Apathy, N. C., Cook, R. L., Danielson, E. C., Diiulio, J., Downs, S. M., Hurley, R. W., Mamlin, B. W., Militello, L. G., & Anders, S. H. (2018) · Europe PMC (PubMed Central) · Cited by 14 · see citing works
Wei, Y. J., Chen, C., Sarayani, A., & Winterstein, A. G. (2019) · JAMA · doi:10.1001/jama.2018.20404 · Cited by 12 · see citing works
Chirikov, V., Shaya, F. T., Onukwugha, E., Mullins, C. D., dosReis, S., & Howell, C. D. (2015) · Value in Health · doi:10.1016/j.jval.2015.03.122 · Cited by 4 · see citing works
Identification of Opioid Abuse or Dependence: No Tool Is Perfect
Goyal, H., Singla, U., & Grimsley, E. W. (2017) · The American Journal of Medicine · doi:10.1016/j.amjmed.2016.09.022 · Cited by 4 · see citing works
Flagged first, then by citations
1 of the 63 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Lo‐Ciganic, W., Donohue, J. M., Yang, Q., Huang, J. L., Chang, C., Weiss, J. C., Guo, J. S., Zhang, H. H., Cochran, G., Gordon, A. J., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00062-0 · Cited by 60 · see citing works
Self-citationIdentifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning
Schell, R. C., Allen, B., Goedel, W. C., Hallowell, B. D., Scagos, R. P., Li, Y., Krieger, M. S., Neill, D. B., Marshall, B. D. L., Cerdá, M., et al. (2021) · American Journal of Epidemiology · doi:10.1093/aje/kwab279 · Cited by 50 · see citing works
Self-citationJohnson, M., Albizri, A., Harfouche, A., & Tutun, S. (2021) · Industrial Management & Data Systems · doi:10.1108/imds-04-2021-0248 · Cited by 50 · see citing works
Machine-learning-based adverse drug event prediction from observational health data: A review
Denck, J., Özkırımlı, E., & Wang, K. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103715 · Cited by 31 · see citing works
Using machine learning to study the effect of medication adherence in Opioid Use Disorder
Warren, D., Marashi, A. H., Siddiqui, A., Eijaz, A. A., Pradhan, P., LIM, D. J., Call, G., & Dras, M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0278988 · Cited by 29 · see citing works
Li, S., Li, M., Wu, J., Li, Y., Han, J., Song, Y., Cao, W., & Zhou, X. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05131-9 · Cited by 24 · see citing works
Self-citationA systematic review of machine learning applications in predicting opioid associated adverse events
Medina, C. R. R., Benitez-Aurioles, J., Jenkins, D. A., & Jani, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01312-4 · Cited by 17 · see citing works
Allen, B., Schell, R. C., Jent, V. A., Krieger, M. S., Pratty, C., Hallowell, B. D., Goedel, W. C., Basta, M., Yedinak, J. L., Li, Y., et al. (2024) · Epidemiology · doi:10.1097/ede.0000000000001695 · Cited by 14 · see citing works
Self-citationNguyen, K. A., Wilson, D. L., Diiulio, J., Hall, B., Militello, L. G., Gellad, W. F., Harle, C. A., Lewis, M. O., Schmidt, S. O., Rosenberg, E. I., et al. (2024) · Bioelectronic Medicine · doi:10.1186/s42234-024-00156-3 · Cited by 13 · see citing works
Self-citationUtilizing Machine Learning for Early Intervention and Risk Management in the Opioid Overdose Crisis
Tai, A. M. Y., Kazemi, A., Kim, J. J., Schmeckenbecher, J., Kitchin, V., Suen, J., Moro, R., & Krausz, M. R. (2025) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.70008 · Cited by 10 · see citing works
Gellad, W. F., Yang, Q., Adamson, K. M., Kuza, C. C., Buchanich, J. M., Bolton, A., Murzynski, S. M., Goetz, C. T., Washington, T. L., Lann, M. F., et al. (2023) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2023.109856 · Cited by 9 · see citing works
Self-citationAccelerating Opioid Use Disorders Research by Integrating Multiple Data Modalities
Bianchi, S. B., Jeffery, A. D., Samuels, D. C., Schirle, L. M., Palmer, A. A., & Sanchez‐Roige, S. (2022) · Complex Psychiatry · doi:10.1159/000525079 · Cited by 5 · see citing works
Heuton, K., Kapoor, J., Shrestha, S., Stopka, T. J., & Hughes, M. C. (2024) · American Journal of Epidemiology · doi:10.1093/aje/kwae343 · Cited by 4 · see citing works
Hong, J.-W. J., Wilson, D. L., Nguyen, K. A., Gellad, W. F., Diiulio, J., Militello, L. G., Yan, S., Harle, C. A., Nelson, D., Rosenberg, E. I., et al. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14238522 · Cited by 3 · see citing works
Self-citationBiasi, A. D., McGarrell, E. F., & Wolfe, S. E. (2025) · Journal of Crime and Justice · doi:10.1080/0735648x.2025.2461515 · Cited by 2 · see citing works
Parton, J., Wang, Q., Wang, E. C., Hanson, C., & Higginbotham, J. A. (2026) · Scientific Reports · doi:10.1038/s41598-026-36047-7 · Cited by 1 · see citing works
Black, T. (2026) · Psychiatric Annals · doi:10.3928/00485713-20260331-02 · Cited by 1 · see citing works
Okwei, A. N. N. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1785700 · Cited by 1 · see citing works
Hall, L., Donohue, J. M., Larochelle, M. R., Rossom, R. C., Wang, X., Afshar, M., Gellad, W. F., Groenwold, R. H. H., Han, B., Jones, C. M., et al. (2026) · Journal of Substance Use and Addiction Treatment · doi:10.1016/j.josat.2026.210065 · Cited by 1 · see citing works
Self-citationEarly Detection of Opioid Over-Procurement: A Semisupervised Machine Learning Approach
Bertsimas, D., Fazel‐Zarandi, M. M., Ivanhoe, J., & Petridis, P. (2025) · Manufacturing & Service Operations Management · doi:10.1287/msom.2020.0369 · Cited by 1 · see citing works
Razzano, A. V. (2026) · Health Science Reports · doi:10.1002/hsr2.73083
Lee, H., Wilcox, P., & Chang, W. (2025) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2025.102474
Li, S., Li, M., Wu, J., Li, Y., Han, J., Song, Y., Cao, W., & Zhou, X. (2024) · Figshare · doi:10.6084/m9.figshare.26714464
Self-citationLi, S., Li, M., Wu, J., Li, Y., Han, J., Song, Y., Cao, W., & Zhou, X. (2024) · Figshare · doi:10.6084/m9.figshare.26714470
Self-citationLi, S., Li, M., Wu, J., Li, Y., Han, J., Song, Y., Cao, W., & Zhou, X. (2024) · Figshare · doi:10.6084/m9.figshare.26714476
Self-citationLi, S., Li, M., Wu, J., Li, Y., Han, J., Song, Y., Cao, W., & Zhou, X. (2024) · Figshare · doi:10.6084/m9.figshare.26714467
Self-citationLi, S., Li, M., Wu, J., Li, Y., Han, J., Song, Y., Cao, W., & Zhou, X. (2024) · Figshare · doi:10.6084/m9.figshare.26714473
Self-citationGellad, W. F., Chen, Y., Park, T. W., Yang, Q., Arnold, J. D., Kuza, C. C., Fedro-Byrom, S. N., Diiulio, J., Militello, L. G., Whitlock, M., et al. (2026) · JMIR Research Protocols · doi:10.2196/94007
Self-citationSchneider, J. E. (2026) · Global perspectives on wealth and distribution · doi:10.1007/978-3-032-20355-7_4
Stronach, O., Dietze, P., Livingston, M., Wilkinson, A. L., Moran, L. L., Nehme, E., & Roxburgh, A. (2026) · BMC Health Services Research · doi:10.1186/s12913-026-14952-x
Suh, K., Mayanil, T., Ko, P., Yang, J., Jhon, P., & Chengappa, K. N. R. (2026) · Early Intervention in Psychiatry · doi:10.1111/eip.70232
Initiatives to Prevent Fatal Drug Overdose Mortality After Incarceration
Brickner, S., Lopez, A. A., Boch, S. J., LeBel, T. P., & Polfuss, M. L. (2026) · Substance Use & Addiction Journal · doi:10.1177/29767342261492898
- Authors
- Lo-Ciganic, W.-H., Donohue, J. M., Hulsey, E. G., Barnes, S., Li, Y., Kuza, C. C., Yang, Q., Buchanich, J., Huang, J. L., Mair, C., Wilson, D. L., & Gellad, W. F.
- Affiliations
- University of Pittsburgh (US)
- Allegheny County Department of Human Services (US)
- University of Florida (US)
- Corresponding author
- Donohue, Julie M.
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021-03-18
- Volume
- 16
- Issue
- 3
- Pages
- e0248360
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7971495
- OpenAlex
- W3139034436
- Cited by
- 32
- References
- 63
- Citations by year
- 2026: 10 · 2025: 6 · 2024: 9 · 2023: 2 · 2022: 3 · 2021: 2
- FWCI
- 3.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- creativecommons.org/publicdomain/zero/1.0/ (vor)
- Locations
- PLoS ONE (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Drug Abuse (R01DA044985)
- Richard King Mellon Foundation (9111)
- Dean of the Graduate School of Public Health at the University of Pittsburgh
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Opioid Use Disorder Treatment
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- opioid overdose, Medicaid beneficiaries, criminal justice data, social determinants of health, data integration, gradient boosting, social risk factors, risk stratification, Allegheny County, medical examiner records, health insurance claims data, addiction treatment
- MeSH terms
- Machine Learning, Opiate Overdose, Adolescent, Adult, Aged, Analgesics, Opioid, Child, Criminal Law, Female, Humans, Male, Medicaid, Middle Aged, Predictive Value of Tests, Retrospective Studies, Risk Factors, Social Work, United States, Risk Assessment, Young Adult
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