Literature Collection

Opioid use and misuse are a widespread problem across the United States. Identifying and targeting social determinants of opioid use may help to identify predictive factors to influence intervention and policy. The purpose of this study was to identify social determinants of opioid use frequency among patients seeking primary care in rural Alabama healthcare facilities. This survey-based study focused on a patient population located in rural west Alabama surveyed for a screening, brief intervention, and referral to treatment program. The screening tool contained demographic information and questions regarding the social determinants of health and opioid use, among others. Adjusted incidence rate ratios (IRRs) for the relationship between social determinants of health and opioid use frequency (in days/month) were estimated in Poisson regression models. Eleven percent of the population self-reported opioid use in the past 30 days. Three social determinants of health measured (level of education, housing stability, and employment status) were identified as having a significant association with the frequency of opioid use. Targeting certain social determinants of health may allow for further predictive interventions to mitigate opioid misuse and potential fatality or mortality.

BACKGROUND: Social functioning (SF), the ability to engage with life and fulfill roles may be a salient "patient important outcome" in addiction treatment. It is not known if medication-assisted treatment (MAT) impacts SF in opioid use disorder (OUD). There is a growing evidence to suggest that men and women are impacted differently by OUD. This study is the largest to date to study sex differences in OUD and explore associations between MAT and SF. METHODS: Data were collected from 2736 participants with OUD, enrolled in MAT for varying lengths of time, in outpatient clinics across Ontario. SF was defined according to the Maudsley Addiction Profile's domains of (1) employment, (2) criminal activity, and (3) interpersonal conflict. Using logistic regression analysis, we examined sociodemographic and clinical factors associated with domains of SF. RESULTS: There were 1544 men (56%) and 1192 women (44%) in this study, and ages varied from 17 to 76 years for men and 18 to 69 years for women. At study entry, participants had been on MAT for a median of 2 years. Compared to men, women reported more psychological (mean MAP score 14/40, SD = 9.55, versus 11/40, SD = 8.64; p < 0.001) and physical symptoms (mean MAP score 17/40, SD = 7.70 versus 14/40, SD = 7.74; p < 0.001). More women reported unemployment(74% versus 58%; p < 0.0001) and interpersonal conflict (46% versus 35%; p < 0.0001). Men were more likely than women to report criminal activity (11%, versus 8%; p = 0.001). Psychological symptoms increased the risk of worse SF, across domains, for men and for women. Every year on MAT was associated with a 7% increase in the odds of women engaging with criminal activity (OR = 1.07, 95% CI 1.02, 1.12, p = 0.006). CONCLUSIONS: Men and women had different SF profiles and psychological symptoms scores while on MAT. The length of time on MAT increased the risk of criminal activity in women, and overall, duration of MAT was not associated with improvement in SF. This may suggest that MAT alone may not support continual improvements in SF in OUD.

IMPORTANCE: Health-related social risks are increasingly recognized as important contributors to health. Compared with individual screening, neighborhood measures are potentially a lower cost, scalable strategy for identifying social risk. OBJECTIVE: To inform health resource planning and social risk screening strategies by comparing self-reported vs neighborhood-level social risk with inpatient, emergency department (ED), and outpatient care. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional analysis of self-reported social risks measured during Medicaid enrollment and neighborhood-level social risk in relation to health care use was conducted. Members of Kaiser Permanente Northern California, a large integrated health care delivery system, who completed the Medicaid Integrated Outcomes Questionnaire from January 1, 2018, to February 29, 2020, were included. Analysis took place from January 8 to November 29, 2024. EXPOSURE: Neighborhood-level social risk (living in the least-resourced Neighborhood Deprivation Index quartile) and self-reported social risk (indicating a need or wanting help with finances, food, housing, or transportation domains). MAIN OUTCOMES AND MEASURES: Hospital and ED admissions, primary care, specialty care, mental health, and social work visits in the year prior to questionnaire completion. Multivariable negative binomial regression models were analyzed for each type of health care use, controlling for demographic characteristics and several health conditions (eg, asthma, hypertension, and chronic pain). RESULTS: Among 13 527 respondents (8631 [63.8%] female; 5289 [39.1%] aged 25-44 years; 2846 [21.0%] Asian, 1986 [14.7%] Black or African American, 3040 [22.5%] Hispanic, 4602 [34.0%] White, and 1053 [7.8%] other race or ethnicity), 33.8% in the most-resourced neighborhood reported at least 1 social risk vs 40.1% in the least-resourced quartile (P < .001). Individual- and neighborhood-level measures were each associated with ED visits (marginal effect estimate for both measures: 0.23; 95% CI, 0.17-0.29). Neither measure was associated with hospital admissions. Individual risk was associated with greater use of all outpatient services (ranging from primary care visit marginal effect estimate: 0.22; 95% CI, 0.13-0.31 to mental health visit marginal effect estimate: 1.21; 95% CI, 0.67-1.75). Neighborhood-level risk was not associated with most outpatient visits and was negatively associated with mental health visits. CONCLUSIONS AND RELEVANCE: In this cross-sectional study, associations were found for hospital and ED use but not outpatient visits, especially mental health visits. These findings suggest that individual social risk screening appears to provide distinct information compared with neighborhood social risk.

In this paper, we present findings from a qualitative study that explored Indigenous people's experiences of mental health and addictions care in the context of an inner-city area in Western Canada. Using an ethnographic design, a total of 39 clients accessing 5 community-based mental health care agencies were interviewed, including 18 in-depth individual interviews and 4 focus groups. Health care providers also were interviewed (n = 24). Data analysis identified four intersecting themes: normalization of social suffering; re-creation of trauma; the challenge of reconciling constrained lives with harm reduction; and mitigating suffering through relational practice. The results highlight the complexities of experiences of accessing systems of care for Indigenous people marginalized by poverty and other forms of social inequity, and the potential harms that arise from inattention to the intersecting social context(s) of peoples' lives. Service delivery that aims to address the mental health concerns of Indigenous people must be designed with awareness of, and responsiveness to, the impact of structural violence and social suffering on peoples' lived realities. A relational policy and policy lens is key to alleviate patterns of social suffering and counter the harms that are unwittingly created when social suffering is normalized.

The social support network is a health protective factor involving physical, mental and psychological aspects, providing a better quality of life, favoring better adaptation to adverse conditions, promoting resilience and mobilizing resources for a more effective coping with negative life events that can lead to illness. We aimed to analyze the association between physical diseases, common mental disorders and the social support network of patients serviced at primary care facilities in the cities of Rio de Janeiro and Sao Paulo through a cross-sectional study with 1,466 patients in the 18-65 years age group. We used the Social Network Index (SNI) to assess the support network through the categories of isolation and integration. The doctor/nurse completed the questionnaire to evaluate the physical disease diagnosis, while the Hospital Anxiety and Depression Scale was used to detect mental disorders. We found that the pattern of social support was different depending on the presence of physical diseases or mental disorders. Negative associations were found between diabetes and isolation; integration and anxiety; integration and depression. Positive associations were identified between isolation and anxiety and isolation and depression.