The impact of multimorbidity on adult physical and mental health in low- and middle-income countries: what does the study on global ageing and adult health (SAGE) reveal?

BACKGROUND: Chronic diseases contribute a large share of disease burden in low- and middle-income countries (LMICs). Chronic diseases have a tendency to occur simultaneously and where there are two or more such conditions, this is termed as 'multimorbidity'. Multimorbidity is associated with adverse health outcomes, but limited research has been undertaken in LMICs. Therefore, this study examines the prevalence and correlates of multimorbidity as well as the associations between multimorbidity and self-rated health, activities of daily living (ADLs), quality of life, and depression across six LMICs. METHODS: Data was obtained from the WHO's Study on global AGEing and adult health (SAGE) Wave-1 (2007/10). This was a cross-sectional population based survey performed in LMICs, namely China, Ghana, India, Mexico, Russia, and South Africa, including 42,236 adults aged 18 years and older. Multimorbidity was measured as the simultaneous presence of two or more of eight chronic conditions including angina pectoris, arthritis, asthma, chronic lung disease, diabetes mellitus, hypertension, stroke, and vision impairment. Associations with four health outcomes were examined, namely ADL limitation, self-rated health, depression, and a quality of life index. Random-intercept multilevel regression models were used on pooled data from the six countries. RESULTS: The prevalence of morbidity and multimorbidity was 54.2 % and 21.9 %, respectively, in the pooled sample of six countries. Russia had the highest prevalence of multimorbidity (34.7 %) whereas China had the lowest (20.3 %). The likelihood of multimorbidity was higher in older age groups and was lower in those with higher socioeconomic status. In the pooled sample, the prevalence of 1+ ADL limitation was 14 %, depression 5.7 %, self-rated poor health 11.6 %, and mean quality of life score was 54.4. Substantial cross-country variations were seen in the four health outcome measures. The prevalence of 1+ ADL limitation, poor self-rated health, and depression increased whereas quality of life declined markedly with an increase in number of diseases. CONCLUSIONS: Findings highlight the challenge of multimorbidity in LMICs, particularly among the lower socioeconomic groups, and the pressing need for reorientation of health care resources considering the distribution of multimorbidity and its adverse effect on health outcomes

Injured patients with very high blood alcohol concentrations

Objective—Most data regarding high blood alcohol concentrations (BAC) ≥400 mg/dL have been from alcohol poisoning deaths. Few studies have described this group and reported their alcohol consumption patterns or outcomes compared to other trauma patients. We hypothesized trauma patients with very high BACs arrived to the trauma center with less severe injuries than their sober counterparts. Method—Historical cohort of 46,222 patients admitted to a major trauma center between January 1, 2002 and October 31, 2011. BAC was categorized into ordinal groups by 100 mg/dL intervals. Alcohol questionnaire data on frequency and quantity was captured in the BAC ≥400 mg/dL group. The primary analysis was for BAC ≥400 mg/dL. Results—BAC was recorded in 44,502 (96.3%) patients. Those with a BAC ≥400 mg/dL accounted for 1.1% (147) of BAC positive cases. These patients had the lowest proportion of severe trauma and in-hospital death in comparison with the other alcohol groups (p\u3c0.001), and the group comprised mainly of falls. Admission Glasgow Coma Scale was a poor predictor for traumatic brain injury in the high BAC group. Readmission occurred in 22.4% (33) of patients the BAC ≥400 group. The majority of these patients reported drinking alcohol four or more days per week (81, 67.5%) and five or more drinks per day (79, 65.8%), evident of risky alcohol use. Conclusions—Most traumas admitted with BAC ≥400 mg/dL survived and their injuries were less severe than their less intoxicated and sober counterparts. They also had evidence for risky alcohol use and nearly one-quarter returned to the trauma center with another injury over the study period. Recognition of t

Subtypes in patients with opioid misuse: A prognostic enrichment strategy using electronic health record data in hospitalized patients.

BackgroundApproaches are needed to better delineate the continuum of opioid misuse that occurs in hospitalized patients. A prognostic enrichment strategy with latent class analysis (LCA) may facilitate treatment strategies in subtypes of opioid misuse. We aim to identify subtypes of patients with opioid misuse and examine the distinctions between the subtypes by examining patient characteristics, topic models from clinical notes, and clinical outcomes.MethodsThis was an observational study of inpatient hospitalizations at a tertiary care center between 2007 and 2017. Patients with opioid misuse were identified using an operational definition applied to all inpatient encounters. LCA with eight class-defining variables from the electronic health record (EHR) was applied to identify subtypes in the cohort of patients with opioid misuse. Comparisons between subtypes were made using the following approaches: (1) descriptive statistics on patient characteristics and healthcare utilization using EHR data and census-level data; (2) topic models with natural language processing (NLP) from clinical notes; (3) association with hospital outcomes.FindingsThe analysis cohort was 6,224 (2.7% of all hospitalizations) patient encounters with opioid misuse with a data corpus of 422,147 clinical notes. LCA identified four subtypes with differing patient characteristics, topics from the clinical notes, and hospital outcomes. Class 1 was categorized by high hospital utilization with known opioid-related conditions (36.5%); Class 2 included patients with illicit use, low socioeconomic status, and psychoses (12.8%); Class 3 contained patients with alcohol use disorders with complications (39.2%); and class 4 consisted of those with low hospital utilization and incidental opioid misuse (11.5%). The following hospital outcomes were the highest for each subtype when compared against the other subtypes: readmission for class 1 (13.9% vs. 10.5%, pConclusionsA 4-class latent model was the most parsimonious model that defined clinically interpretable and relevant subtypes for opioid misuse. Distinct subtypes were delineated after examining multiple domains of EHR data and applying methods in artificial intelligence. The approach with LCA and readily available class-defining substance use variables from the EHR may be applied as a prognostic enrichment strategy for targeted interventions

Patterns of opioid use behaviors among patients seen in the emergency department: Latent class analysis of baseline data from the POINT pragmatic trial

Introduction: The nation's overdose epidemic has been characterized by increasingly potent opioids resulting in more emergency department (ED) encounters over time. ED-based opioid use interventions are growing in popularity; however, they tend to treat people who use opioids as a homogenous population. The current study sought to understand heterogeneity among people who use opioids who encounter the ED by identifying qualitatively different subgroups among participants in an opioid use intervention clinical trial at baseline and examining associations between subgroup membership and multiple correlates. Methods: Participants were from a larger pragmatic clinical trial of the Planned Outreach, Intervention, Naloxone, and Treatment (POINT) intervention (n = 212; 59.2 % male, 85.3 % Non-Hispanic White, mean age = 36.6 years). The study employed latent class analysis (LCA) using five indicators of opioid use behavior: preference for opioids, preference for stimulants, usually use drugs alone, injection drug use, and opioid-related problem at ED encounter. Correlates of interest included participants' demographics, prescription histories, health care contact histories, and recovery capital (e.g., social support, naloxone knowledge). Results: The study identified three classes: (1) noninjecting opioid preferers, (2) injecting opioid and stimulant preferers, and (3) social nonopioid preferers. We identified limited significant differences in correlates across the classes: differences existed for select demographics, prescription histories, and recovery capital but not for health care contact histories. For example, members of Class 1 were the most likely to be a race/ethnicity other than non-Hispanic White, oldest on average, and most likely to have received a benzodiazepine prescription, whereas members of Class 2 had the highest average barriers to treatment and members of Class 3 were the least likely to have been diagnosed with a major mental health illness and had the lowest average barriers to treatment. Conclusions: LCA identified distinct subgroups among POINT trial participants. Knowledge of such subgroups assists with the development of better-targeted interventions and can help staff to identify the most appropriate treatment and recovery pathways for patients

Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients

Background: Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in text, but they remain inadequate at ensuring perfect PHI removal. As an alternative to relying on de-identification systems, we propose the following solutions: (1) Mapping the corpus of documents to standardized medical vocabulary (concept unique identifier [CUI] codes mapped from the Unified Medical Language System) thus eliminating PHI as inputs to a machine learning model; and (2) training character-based machine learning models that obviate the need for a dictionary containing input words/n-grams. We aim to test the performance of models with and without PHI in a use-case for an opioid misuse classifier. Methods: An observational cohort sampled from adult hospital inpatient encounters at a health system between 2007 and 2017. A case-control stratified sampling (n = 1000) was performed to build an annotated dataset for a reference standard of cases and non-cases of opioid misuse. Models for training and testing included CUI codes, character-based, and n-gram features. Models applied were machine learning with neural network and logistic regression as well as expert consensus with a rule-based model for opioid misuse. The area under the receiver operating characteristic curves (AUROC) were compared between models for discrimination. The Hosmer-Lemeshow test and visual plots measured model fit and calibration. Results: Machine learning models with CUI codes performed similarly to n-gram models with PHI. The top performing models with AUROCs > 0.90 included CUI codes as inputs to a convolutional neural network, max pooling network, and logistic regression model. The top calibrated models with the best model fit were the CUI-based convolutional neural network and max pooling network. The top weighted CUI codes in logistic regression has the related terms 'Heroin' and 'Victim of abuse'. Conclusions: We demonstrate good test characteristics for an opioid misuse computable phenotype that is void of any PHI and performs similarly to models that use PHI. Herein we share a PHI-free, trained opioid misuse classifier for other researchers and health systems to use and benchmark to overcome privacy and security concerns

Attitudes, Beliefs, Barriers, and Facilitators of Emergency Department Nurses Toward Patients with Opioid Use Disorder and Naloxone Distribution

Introduction: As opioid overdose deaths continue to rise, the emergency department (ED) remains an important point of contact for many at risk for overdose. In this study our purpose was to better understand the attitudes, beliefs, and knowledge of ED nurses in caring for patients with opioid use disorder (OUD). We hypothesized a difference in training received and attitudes toward caring for patients with OUD between nurses with <5 years and ≥6 years of clinical experience. Methods: We conducted a survey among ED nurses in a large academic medical center from May–July 2022. All ED staff nurses were surveyed. Data entry instruments for the nursing surveys were programmed in Qualtrics, and we analyzed results R using a chi-square test or Fisher exact test tocompare nurses with <5 years and ≥6 years of clinical experience. A P-value of < 0.05 was considered statistically significant. Results: We distributed 74 surveys, and 69 were completed (93%). Attitudes toward naloxone distribution from the ED were positive, with 72% of respondents reporting they were “very” or “extremely” supportive of distributing naloxone kits to individuals at risk of overdose. While attitudes were positive, barriers included limited time, lack of system support, and cost. Level of comfort in caring for patients with OUD was high, with 78% of respondents “very” or “extremely” comfortable. More education is needed on overdose education and naloxone distribution (OEND) with respondents 38% and 45% “a little” or“somewhat” comfortable, respectively. Nurses with <5 years of experience reported receiving more training on OEND in nursing school compared to those with ≥6 years of experience (P = 0.03). There were no significant differences in reported attitudes, knowledge, or comfort in caring for patients with OUD. Conclusion: In this single-center survey, we found ED nurses were supportive of overdose education and naloxone distribution. There are opportunities for targeted education and addressing systemic barriers to OEND. All interventions should be evaluated to gauge impact on knowledge, attitudes, and behaviors

Family Planning in Substance Use Disorder Treatment Centers: Opportunities and Challenges

<p><i>Background</i>: Alcohol, tobacco, and drug use during pregnancy can cause a range of adverse birth outcomes. Promoting family planning among women with substance use disorders (SUD) can help reduce substance exposed pregnancies. <i>Objectives</i>: We conducted qualitative research to determine the acceptability and feasibility of offering family planning education and services SUD treatment centers. <i>Methods</i>: Focus groups and in-depth interviews were conducted with clients, staff and medical providers at three treatment centers. Interviews were transcribed and data was analyzed using a flexible coding scheme. <i>Results</i>: Clients reported being interested in family planning services while they were in treatment. Most preferred to receive these services onsite. Providers also felt that services should be received onsite, though cited several barriers to implementation, including time constraints and staff levels of comfort with the subject. <i>Conclusions/Importance</i>: Women in SUD treatment are open to the integration of family planning services into treatment. Treatment centers have the opportunity to serve as models of client-centered health homes that offer a variety of educational, preventive, and medical services for women in both treatment and recovery.</p

Accessibility of Naloxone in Pharmacies Registered Under the Illinois Standing Order

Introduction: To expand access to naloxone, the state of Illinois implemented a standing order allowing registered pharmacies to dispense the drug without an individual prescription. To participate under the standing order, pharmacies were required to opt in through a formal registration process. In our study we aimed to evaluate the availability and price of naloxone at registered pharmacies. Methods: This was a prospective, de-identified, cross-sectional telephone survey. Trained interviewers posed as potential customers and used a standardized script to determine the availability of naloxone between February–December, 2019. The primary outcome was defined as a pharmacy indicating it carried naloxone, currently had naloxone in stock, and was able to dispense it without anindividual prescription. Results: Of 948 registered pharmacies, 886 (93.5%) were successfully contacted. Of those, 792 (83.4%) carried naloxone, 659 (74.4%) had naloxone in stock, and 472 (53.3%) allowed purchase without a prescription. Naloxone nasal spray (86.4%) was the formulation most commonly stocked. Chain pharmacies were more likely to carry naloxone (adjusted odds ratio [aOR] 3.16, 95% confidence interval [CI] 1.97–5.01, P &lt; 0.01) and have naloxone in stock (aOR 2.72, 95% CI 1.76–4.20, P &lt; 0.01), but no more likely to dispense it without a prescription. Pharmacies in higher population areas (aOR 0.99, 95% CI 0.99–0.99, P &lt; 0.05) and rural areas adjacent to metropolitan areas (aOR 0.5, 95% CI 025–0.98, P &lt; 0.05) were less likely to have naloxone available without a prescription. Associations of naloxone availability based on other urbanicity designations, overdose count, and overdose rate were not significant. Conclusion: Among pharmacies in Illinois that formally registered to dispense naloxone without a prescription, the availability of naloxone remains limited. Additional interventions may be needed to maximize the potential impact of a statewide standing order