Risks of opioid abuse among uninsured primary care patients utilizing a free clinic

Kamimura, Akiko; Panahi, Samin; Rathi, Naveen; Pye, Mu; Sin, Kai; Ashby, Jeanie

doi:10.1080/15332640.2018.1456387

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…These risk factors are inextricably linked to personal and environmental factors and systems that hinder rather than support individuals [6]. For instance, among individuals receiving healthcare services at a free clinic, prescription opioid misuse was more likely among patients who were employed and less likely among those with post-high school education [7]. Uninsured individuals were significantly more likely than insured individuals to be high-risk drug users [8].…”

Section: Introductionmentioning

confidence: 99%

Exploring county-level spatio-temporal patterns in opioid overdose related emergency department visits

et al. 2022

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Opioid overdoses within the United States continue to rise and have been negatively impacting the social and economic status of the country. In order to effectively allocate resources and identify policy solutions to reduce the number of overdoses, it is important to understand the geographical differences in opioid overdose rates and their causes. In this study, we utilized data on emergency department opioid overdose (EDOOD) visits to explore the county-level spatio-temporal distribution of opioid overdose rates within the state of Virginia and their association with aggregate socio-ecological factors. The analyses were performed using a combination of techniques including Moran’s I and multilevel modeling. Using data from 2016–2021, we found that Virginia counties had notable differences in their EDOOD visit rates with significant neighborhood-level associations: many counties in the southwestern region were consistently identified as the hotspots (areas with a higher concentration of EDOOD visits) whereas many counties in the northern region were consistently identified as the coldspots (areas with a lower concentration of EDOOD visits). In most Virginia counties, EDOOD visit rates declined from 2017 to 2018. In more recent years (since 2019), the visit rates showed an increasing trend. The multilevel modeling revealed that the change in clinical care factors (i.e., access to care and quality of care) and socio-economic factors (i.e., levels of education, employment, income, family and social support, and community safety) were significantly associated with the change in the EDOOD visit rates. The findings from this study have the potential to assist policymakers in proper resource planning thereby improving health outcomes.

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Section: Introductionmentioning

confidence: 99%

Exploring county-level spatio-temporal patterns in opioid overdose related emergency department visits

et al. 2022

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“…Our study revealed higher adverse event rates in zip codes with only English-speaking individuals, consistent with increased suicide rates in rural English-speaking areas( 146 ). Concerns over opioid abuse and overdose have also risen, with U.S.-born English speakers displaying higher opioid risk levels compared to non-U.S. born language speakers( 147 ). Language, employment, and education status contribute to increased opioid risks.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Adverse Events Risk in Patients with Comorbid Post- Traumatic Stress Disorder and Alcohol Use Disorder Using Electronic Medical Records by Deep Learning Models

Miranda,

Fan,

et al. 2023

Preprint

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Background Prediction of high-risk events in mental disorder patients is crucial. In our previous study, we developed a deep learning model: DeepBiomarker by using electronic medical records (EMR) to predict suicide related event (SRE) risk in post-traumatic stress disorder (PTSD) patients. Methods We applied DeepBiomarker2 through data integration of multimodal information: lab test, medication, co-morbidities, and social determinants of health. We analyzed EMRs of 5,565 patients from University of Pittsburgh Medical Center with a diagnosis of PTSD and alcohol use disorder (AUD) on risk of developing an adverse event (opioid use disorder, SREs, depression and death). Results DeepBiomarker2 predicted whether a PTSD + AUD patient will have a diagnosis of any adverse events (SREs, opioid use disorder, depression, death) within 3 months with area under the receiver operator curve (AUROC) of 0.94. We found piroxicam, vilazodone, dronabinol, tenofovir, suvorexant, empagliflozin, famciclovir, veramyst, amantadine, sulfasalazine, and lamivudine to have potential to reduce risk. Conclusions DeepBiomarker2 can predict multiple adverse event risk with high accuracy and identify potential risk and beneficial factors. Our results offer suggestions for personalized interventions in a variety of clinical and diverse populations.

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“…Data were analyzed using IBM SPSS ver 22. The analyses were primarily based on the comparison of the following 3 groups: US born English speakers, non-US born English speakers, and Spanish speakers because previous studies at the free clinic suggest the 3 groups reported different levels of substance use and opioid risks (6). The 3 groups were compared by Pearson w 2 tests for categorical variables and analysis of variance (ANOVA) tests for continuous variables.…”

Section: Discussionmentioning

confidence: 99%

“…A previous study on opioid risks among uninsured individuals using a free clinic indicated several gaps in opioid-related research (6). First, opioid abuse among Hispanic free clinic patients needs to be further examined because there is little research regarding this population.…”

Section: Introductionmentioning

confidence: 99%

The Use of Complementary and Alternative Medicine Strategies, Opioids, and Nonsteroidal Anti-Inflammatory Drugs (NSAIDS) Among Patients Attending a Free Clinic

Kamimura

Howard

Panahi

et al. 2020

Journal of Patient Experience

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The purpose of this study was to describe the use of opioids and complementary and alternative medicine (CAM) among uninsured free clinic patients. A self-administered paper survey was collected to describe the use of opioids, complementary, and alternative medicine, and other pain relievers from 877 free clinic patients from January to April 2018. The US born English speakers are more likely to use CAM, nonprescription pain relievers, and prescription opioids, and to be more knowledgeable about opioids compared to non-US born English speakers and Spanish speakers. The main source of opioids for free clinic patients is a health care facility other than a free clinic as well as friends. Although nonprescription pain relievers are commonly used among free clinic patients, CAM is less common to use for pain control. More health education programs are needed to increase the knowledge of opioid risks among free clinic patients as well as other underserved population.

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Risks of opioid abuse among uninsured primary care patients utilizing a free clinic

Cited by 6 publications

References 26 publications

Exploring county-level spatio-temporal patterns in opioid overdose related emergency department visits

Exploring county-level spatio-temporal patterns in opioid overdose related emergency department visits

Prediction of Adverse Events Risk in Patients with Comorbid Post- Traumatic Stress Disorder and Alcohol Use Disorder Using Electronic Medical Records by Deep Learning Models

The Use of Complementary and Alternative Medicine Strategies, Opioids, and Nonsteroidal Anti-Inflammatory Drugs (NSAIDS) Among Patients Attending a Free Clinic

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