Validation of Administrative Measures of Social and Behavioral Risk in Veterans Affairs Medical Records

Characterising methamphetamine use to inform health and social policies in Manitoba, Canada: a protocol for a retrospective cohort study using linked administrative data

INTRODUCTION

Rising use of methamphetamine is causing significant public health concern in Canada. The biological and behavioural effects of methamphetamine range from wakefulness, vigour and euphoria to adverse physical health outcomes like myocardial infarction, haemorrhagic stroke, arrhythmia and seizure. It can also cause severe psychological complications such as psychosis. National survey data point to increasing rates of methamphetamine use, as well as increasing ease of access and serious methamphetamine-related harms. There is an urgent need for evidence to address knowledge gaps, provide direction to harm reduction and treatment efforts and inform health and social policies for people using methamphetamine. This protocol describes a study that aims to address this need for evidence.

METHODS

The study will use linked, whole population, de-identified administrative data from the Manitoba Population Research Data Repository. The cohort will include individuals in the city of Winnipeg, Manitoba, who came into contact with the health system for reasons related to methamphetamine use from 2013 to 2021 and a comparison group matched on age, sex and geography. We will describe the cohort's sociodemographic characteristics, calculate incidence and prevalence of mental disorders associated with methamphetamine use and examine rates of health and social service use. We will evaluate the use of olanzapine pharmacotherapy in reducing adverse emergency department outcomes. In partnership with Indigenous co-investigators, outcomes will be stratified by First Nations and Métis identity.

ETHICS AND DISSEMINATION

The study was approved by the University of Manitoba Health Research Ethics Board, and access datasets have been granted by all data providers. We also received approval from the First Nations Health and Social Secretariat of Manitoba's Health Information Research Governance Committee and the Manitoba Métis Federation. Dissemination will be guided by an 'Evidence 2 Action' group of public rightsholders, service providers and knowledge users who will ensure that the analyses address the critical issues.

Examining the association of social risk with heart failure readmission in the Veterans Health Administration

BACKGROUND

Previous research has found that social risk factors are associated with an increased risk of 30-day readmission. We aimed to assess the association of 5 social risk factors (living alone, lack of social support, marginal housing, substance abuse, and low income) with 30-day Heart Failure (HF) hospital readmissions within the Veterans Health Affairs (VA) and the impact of their inclusion on hospital readmission model performance.

METHODS

We performed a retrospective cohort study using chart review and VA and Centers for Medicare and Medicaid Services (CMS) administrative data from a random sample of 1,500 elderly (≥ 65 years) Veterans hospitalized for HF in 2012. Using logistic regression, we examined whether any of the social risk factors were associated with 30-day readmission after adjusting for age alone and clinical variables used by CMS in its 30-day risk stratified readmission model. The impact of these five social risk factors on readmission model performance was assessed by comparing c-statistics, likelihood ratio tests, and the Hosmer-Lemeshow goodness-of-fit statistic.

RESULTS

The prevalence varied among the 5 risk factors; low income (47 % vs. 47 %), lives alone (18 % vs. 19 %), substance abuse (14 % vs. 16 %), lacks social support (2 % vs. <1 %), and marginal housing (< 1 % vs. 3 %) among readmitted and non-readmitted patients, respectively. Controlling for clinical factors contained in CMS readmission models, a lack of social support was found to be associated with an increased risk of 30-day readmission (OR 4.8, 95 %CI 1.35-17.88), while marginal housing was noted to decrease readmission risk (OR 0.21, 95 %CI 0.03-0.87). Living alone (OR: 0.9, 95 %CI 0.64-1.26), substance abuse (OR 0.91, 95 %CI 0.67-1.22), and having low income (OR 1.01, 95 %CI 0.77-1.31) had no association with HF readmissions. Adding the five social risk factors to a CMS-based model (age and comorbid conditions; c-statistic 0.62) did not improve model performance (c-statistic: 0.62).

CONCLUSIONS

While a lack of social support was associated with 30-day readmission in the VA, its prevalence was low. Moreover, the inclusion of some social risk factors did not improve readmission model performance. In an integrated healthcare system like the VA, social risk factors may have a limited effect on 30-day readmission outcomes.

Characterizing trends in methamphetamine-related health care use when there is no ICD code for "methamphetamine use disorder"

BACKGROUND AND AIMS

The recent surge in methamphetamine use highlights the need for timely data on its health effects and healthcare service use impact. However, there is no ICD code for methamphetamine use. This study quantifies the positive predictive value of ICD-9-CM and ICD-10-CM psychostimulant codes for methamphetamine use.

METHODS

A retrospective chart review of 220 adults aged 18 and older who had an inpatient admission with a psychostimulant-associated billing diagnosis at an urban safety-net hospital. Diagnoses were categorized as either methamphetamine-related or involving another specific psychostimulant. The positive predictive value of both ICD-9-CM or ICD-10-CM psychostimulant diagnosis codes for methamphetamine use was calculated.

RESULTS

ICD-9-CM and ICD-10-CM psychostimulant codes had high positive predictive values of 78.2% (95% CI 70.3%-86.0%) and 85.5% (95% CI 78.8%-92.1%), respectively, for methamphetamine use. The most common non-methamphetamine psychostimulant in our cohort was khat, a cathinone-containing plant native to East Africa, accounting for psychostimulant-related diagnosis in 16 of the 220 hospitalizations.

CONCLUSIONS

The high predictive values of psychostimulant codes for methamphetamine use support the application of administrative data in measuring methamphetamine-related healthcare use, as well as co-morbid health conditions and treatment patterns.