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Cook, Benjamin

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Cook

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Benjamin

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Cook, Benjamin
Author's Publications: search.filters.isAuthorOfPublication.13bddd25-9807-4972-8eb8-d6eacec10393

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Now showing 1 - 4 of 4
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    Identifying and reducing disparities in successful addiction treatment completion: testing the role of Medicaid payment acceptance
    (BioMed Central, 2017) Guerrero, Erick G.; Garner, Bryan R.; Cook, Benjamin; Kong, Yinfei; Vega, William A.; Gelberg, Lillian
    Background: Medicaid has become the largest payer of substance use disorder treatment and may enhance access to quality care and reduce disparities. We tested whether treatment programs’ acceptance of Medicaid payments was associated with reduced disparities between Mexican Americans and non-Latino Whites. Methods: We analyzed client and program data from 122 publicly funded treatment programs in 2010 and 112 programs in 2013. These data were merged with information regarding 15,412 adult clients from both periods, of whom we selected only Mexican Americans (n = 7130, 46.3%) and non-Latino Whites (n = 8282, 53.7%). We used multilevel logistic regression and variance decomposition to examine associations and underlying factors associated with Mexican American and White differences in treatment completion. Variables of interest included client demographics; drug use severity and mental health issues; and program license, accreditation, and acceptance of Medicaid payments. Results: Mexican Americans had lower odds of treatment completion (OR = 0.677; 95% CI = 0.534, 0.859) compared to non-Latino Whites. This disparity was explained in part by primary drug used, greater drug use severity, history of mental health disorders, and program acceptance of Medicaid payments. The interaction between Mexican Americans and acceptance of Medicaid was statistically significant (OR = 1.284; 95% CI = 1.008, 1.637). Conclusions: Findings highlighted key program and client drivers of this disparity and the promising role of program acceptance of Medicaid payment to eliminate disparities in treatment completion among Mexican Americans. Implications for health policy during the Trump Administration are discussed.
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    Organizational capacity to eliminate outcome disparities in addiction health services
    (BioMed Central, 2015) Guerrero, Erick G; Aarons, Gregory; Grella, Christine; Garner, Bryan R; Cook, Benjamin; Vega, William A
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    Novel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid
    (Hindawi Publishing Corporation, 2016) Cook, Benjamin; Progovac, Ana; Chen, Pei; Mullin, Brian; Hou, Sherry; Baca-Garcia, Enrique
    Natural language processing (NLP) and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recently discharged from psychiatric inpatient or emergency room settings in Madrid, Spain. Participants responded to structured mental and physical health instruments at multiple follow-up points. Outcome variables of interest were suicidal ideation and psychiatric symptoms (GHQ-12). Predictor variables included structured items (e.g., relating to sleep and well-being) and responses to one unstructured question, “how do you feel today?” We compared NLP-based models using the unstructured question with logistic regression prediction models using structured data. The PPV, sensitivity, and specificity for NLP-based models of suicidal ideation were 0.61, 0.56, and 0.57, respectively, compared to 0.73, 0.76, and 0.62 of structured data-based models. The PPV, sensitivity, and specificity for NLP-based models of heightened psychiatric symptoms (GHQ-12 ≥ 4) were 0.56, 0.59, and 0.60, respectively, compared to 0.79, 0.79, and 0.85 in structured models. NLP-based models were able to generate relatively high predictive values based solely on responses to a simple general mood question. These models have promise for rapidly identifying persons at risk of suicide or psychological distress and could provide a low-cost screening alternative in settings where lengthy structured item surveys are not feasible.
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    Does foreign aid crowd out government investments? Evidence from rural health centres in Rwanda
    (BMJ Publishing Group, 2017) Lu, Chunling; Cook, Benjamin; Desmond, Chris
    Background: Rural healthcare facilities in low-income countries play a major role in providing primary care to rural populations. We examined the link of foreign aid with government investments and medical service provision in rural health centres in Rwanda. Methods: Using the District Health System Strengthening Tool, a web-based database built by the Ministry of Health in Rwanda, we constructed two composite indices representing provision of (1) child and maternal care and (2) HIV, tuberculosis (TB) and malaria services in 330 rural health centres between 2009 and 2011. Financing variables in a healthcare centre included received funds from various sources, including foreign donors and government. We used multilevel random-effects model in regression analyses and examined the robustness of results to a range of alternative specification, including scale of dependent variables, estimation methods and timing of aid effects. Findings: Both government and foreign donors increased their direct investments in the 330 rural healthcare centres during the period. Foreign aid was positively associated with government investments (0.13, 95% CI 0.06 to 0.19) in rural health centres. Aid in the previous year was positively associated with service provision for child and maternal health (0.008, 95% CI 0.002 to 0.014) and service provision for HIV, TB and malaria (0.014, 95% CI 0.004 to 0.022) in the current year. The results are robust when using fixed-effects models. Conclusions: These findings suggest that foreign aid did not crowd out government investments in the rural healthcare centres. Foreign aid programmes, conducted in addition to government investments, could benefit rural residents in low-income countries through increased service provision in rural healthcare facilities.