Who is asked about alcohol consumption? A retrospective cohort study using a national repository of Electronic Medical Records

Identifying Intersecting Factors Associated With Suicidal Thoughts and Behaviors Among Transgender and Gender Diverse Adults: Preliminary Conditional Inference Tree Analysis

BACKGROUND

Transgender and gender diverse (TGD) individuals are disproportionately impacted by suicidal thoughts and behaviors (STBs), and intersecting demographic and psychosocial factors may contribute to STB disparities.

OBJECTIVE

We aimed to identify intersecting factors associated with increased risk for suicidal ideation, intent, plan, and attempts in the US transgender population health survey (N=274), and determine age of onset for each outcome using conditional inference trees (CITs), which iteratively partitions subgroups of greater homogeneity with respect to a specific outcome.

METHODS

In separate analyses, we restricted variables to those typically available within electronic medical records (EMRs) and then included variables not typically within EMRs. We also compared the results of the CIT analyses with logistic regressions and Cox proportional hazards models.

RESULTS

In restricted analyses, younger adults endorsed more frequent ideation and planning. Adults aged ≤26 years who identified as Black or with another race not listed had the highest risk for ideation, followed by White, Latine, or multiracial adults aged ≤39 years who identified as sexual minority individuals. Adults aged ≤39 years who identified as sexual minority individuals had the highest risk for suicide planning. Increased risk for suicidal intent was observed among those who identified as multiracial, whereas no variables were associated with previous suicide attempts. In EMR-specific analyses, age of onset for ideation and attempts were associated with gender identity, such that transgender women were older compared to transgender men and nonbinary adults when they first experienced ideation; for attempts, transgender women and nonbinary adults were older than transgender men. In expanded analyses, including additional psychosocial variables, psychiatric distress was associated with increased risk for ideation, intent, and planning. High distress combined with high health care stereotype threat was linked to increased risk for intent and for suicide planning. Only high everyday discrimination was associated with increased risk for lifetime attempts. Ages of onset were associated with gender identity for ideation, the intersection of psychiatric distress and drug use for suicide planning, and gender identity alone for suicide attempts. No factors were associated with age of onset for suicide intent in the expanded variable set. The results of the CIT analysis and the traditional regressions were comparable for ordinal outcomes, but CITs substantially outperformed the regressions for the age of onset outcomes.

CONCLUSIONS

In this preliminary test of the CIT approach to identify subgroups of TGD adults with increased STB risk, the risk was primarily influenced by age, racial identity, and sexual minority identity, as well as psychiatric distress, health care stereotype threat, and discrimination. Identifying intersecting factors linked to STBs is vital for early risk detection among TGD individuals. This approach should be tested on a larger scale using EMR data to facilitate service provision to TGD individuals at increased risk for STBs.

Alcohol and cause-specific mortality in Russia: the Know Your Heart Study 2015-23

BACKGROUND

Alcohol-related mortality in Russia exceeds the world average and presents a critical public health concern. This study assesses the impact of alcohol consumption levels on mortality and investigates mortality predictors among Russians, including people treated for alcohol-related diagnoses (narcology patients).

METHODS

We examined 2629 men and women aged 35-69 years who participated in the Know Your Heart study (2015-17), Arkhangelsk, Russia. The participants were categorized into five drinking levels (non-drinking, low-risk, hazardous, harmful, narcology patients) and followed up using a regional mortality database. We used Cox proportional hazards regressions to analyze sociodemographic and cardiovascular biomarkers as mortality predictors among narcology patients and general population and to compare mortality risks across the five drinking levels.

RESULTS

During a median follow-up of 6.3 years, 223 (8.5%) participants died. Age- and sex-standardized all-cause mortality rates per 100,000 person-years were 1229 (95% CI: 691-1767) in non-drinking participants, 890 (95%CI: 684-1096) and 877 (95%CI: 428-1325) in low-risk and hazardous drinking participants, 2170 (95%CI: 276-4064) in those with harmful drinking, and 4757 (95%CI: 3384-6131) in narcology patients. The largest proportions of deaths were caused by cardiovascular diseases (37.2%), neoplasms (20.2%), and external causes (13.9%). Compared with low-risk drinkers, narcology patients had higher risks of death with hazard ratios of 3.23 (95%CI: 2.02-5.16) for all-cause mortality, 3.25 (95%CI: 1.52-6.92) for cardiovascular diseases, 9.36 (95%CI: 2.63-33.3) for external causes, and 7.79 (95%CI: 3.34-18.1) for other causes. Neoplasm-related mortality did not differ between groups. All-cause mortality in the general population had positive associations with smoking, waist-to-hip ratio, resting heart rate, systolic blood pressure, high-sensitivity C-reactive protein, and negative associations with left ventricular ejection fraction (LVEF) and higher education. These associations were substantially weaker and non-significant in narcology patients. Cardiovascular mortality in narcology patients was increased with higher education, while male sex, LVEF and N-terminal prohormone of brain natriuretic peptide had less impact compared to the general population sample.

CONCLUSION

Narcology patients face markedly higher mortality risks-threefold from all causes and cardiovascular diseases, ninefold from external causes, and sevenfold from other causes. Compared with the general population, conventional mortality risk factors were less predictive of deaths in narcology patients.

Characterizing primary care patients with posttraumatic stress disorder using electronic medical records: a retrospective cross-sectional study

BACKGROUND

Posttraumatic stress disorder (PTSD) has significant morbidity and economic costs. This study describes the prevalence and characteristics of patients with PTSD using primary care electronic medical record (EMR) data.

METHODS

This retrospective cross-sectional study used EMR data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). This study included 1,574 primary care providers located in 7 Canadian provinces. There were 689,301 patients that visited a CPCSSN provider between 1 January 2017 and 31 December 2019. We describe associations between PTSD and patient characteristics using descriptive statistics, chi-square, and multiple logistic regression models.

RESULTS

Among the 689,301 patients included, 8,817 (1.3%, 95% CI 1.2-1.3) had a diagnosis of PTSD. On multiple logistic regression analysis, patients with depression (OR 4.4, 95% CI 4.2-4.7, P < 0.001), alcohol abuse/dependence (OR 1.7, 95% CI 1.6-1.9, P < 0.001), and/or drug abuse/dependence (OR 2.6, 95% CI 2.5-2.8, P < 0.001) had significantly higher odds of PTSD compared with patients without those conditions. Patients residing in community areas considered the most material deprived (OR 2.1, 95% CI 1.5-2.1, P < 0.001) or the most socially deprived (OR 2.8, 95% CI 2.7-5.3, P < 0.001) had higher odds of being diagnosed with PTSD compared with patients in the least deprived areas.

CONCLUSIONS

The prevalence of PTSD in Canadian primary care is 1.3% (95% CI 1.25-1.31). Using EMR records we confirmed the co-occurrence of PTSD with other mental health conditions within primary care settings suggesting benefit for improved screening and evidence-based resources to manage PTSD.

Development and validation of a case definition to estimate the prevalence and incidence of cirrhosis in pan-Canadian primary care databases

Aims

To develop and validate case definitions to identify patients with cirrhosis and alcohol-related cirrhosis using primary care electronic medical records (EMRs) and to estimate cirrhosis prevalence and incidence in pan-Canadian primary care databases, between 2011 and 2019.

Methods

A total of 689,301 adult patients were included with ≥1 visit to a primary care provider within the Canadian Primary Care Sentinel Study Network between January 1, 2017, and December 31, 2018. A subsample of 17,440 patients was used to validate the case definitions. Sensitivity, specificity, predictive values were calculated with their 95% CIs and then determined the population-level prevalence and incidence trends with the most accurate case definition.

Results

The most accurate case definition included: ≥1 health condition, billing, or encounter diagnosis for International Classification of Diseases, Ninth Revision codes 571.2, 571.5, 789.59, or 571. Sensitivity (84.6; 95% CI 83.1%-86.%), specificity (99.3; 95% CI 99.1%-99.4%), positive predictive values (94.8; 95% CI 93.9%-95.7%), and negative predictive values (97.5; 95% CI 97.3%-97.7%). Application of this definition to the overall population resulted in a crude prevalence estimate of (0.46%; 95% CI 0.45%-0.48%). Annual incidence of patients with a clinical diagnosis of cirrhosis nearly doubled between 2011 (0.05%; 95% CI 0.04%-0.06%) and 2019 to (0.09%; 95% CI 0.08%-0.09%).

Conclusions

The EMR-based case definition accurately captured patients diagnosed with cirrhosis in primary care. Future work to characterize patients with cirrhosis and their primary care experiences can support improvements in identification and management in primary care settings.

Documenting cannabis use in primary care: a descriptive cross-sectional study using electronic medical record data in Alberta, Canada

OBJECTIVE

Documenting cannabis use is important for patient care, but no formal requirements for consistent reporting exist in primary care. The objective of this study was to understand how cannabis use is documented in primary care electronic medical record (EMR) data.

RESULTS

This was a cross-sectional study using de-identified EMR data from over 398,000 patients and 333 primary care providers in Alberta, Canada. An automated pattern-matching algorithm was developed to identify text and ICD-9 diagnostic codes indicating cannabis use in the EMR. There was a total of 11,724 records indicating cannabis use from 4652 patients, representing approximately 1.2% of the patient sample. Commonly used terms and ICD-9 codes included cannabis, marijuana/marihuana, THC, 304.3 and 305.2. Nabilone was the most frequently prescribed cannabinoid medication. Slightly more males and those with a chronic condition had cannabis use recorded more often. Overall, very few patients have cannabis use recorded in primary care EMR data and this is not captured in a systematic way. We propose several strategies to improve the documentation of cannabis use to facilitate more effective clinical care, research, and surveillance.

FIB-4 and incident severe liver outcomes in patients with undiagnosed chronic liver disease: A Fine-Gray competing risk analysis

BACKGROUND AND AIMS

The Fibrosis-4 index (FIB-4) can reliably assess fibrosis risk in patients with chronic liver disease, and advanced fibrosis is associated with severe liver disease (SLD) outcomes. However, CLD is underdiagnosed in primary care. We examined the association between FIB-4 risk strata and the incidence of SLD preceding a CLD diagnosis while considering incident CLD diagnoses as competing risks.

METHODS

Using primary care clinic data between 2007 and 2018, we identified patients with two FIB-4 scores and no liver disease diagnoses preceding the index FIB-4. Patients were followed from index FIB-4 until an incident SLD (a composite of cirrhosis, hepatocellular carcinoma or liver transplantation), CLD or were censored. Hazard ratios were computed using a Fine-Gray competing risk model.

RESULTS

Of 20 556 patients, there were 54.8% in the low, 34.8% in the indeterminate, 6.6% in the high and 3.8% in the persistently high-risk FIB-4 strata. During a mean 8.2 years of follow-up, 837 (4.1%) patients experienced an SLD outcome and 11.5% of the sample received a CLD diagnosis. Of patients with an SLD event, 49% received no preceding CLD diagnosis. In the adjusted Fine-Gray model, the indeterminate (HR 1.41, 95% CI 1.17-1.71), high (HR 4.65, 95% CI 3.76-5.76) and persistently high-risk (HR 7.60, 95% CI 6.04-9.57) FIB-4 risk strata were associated with a higher incidence of SLD compared to the low-risk stratum.

CONCLUSIONS

FIB-4 scores with indeterminate- and high-risk values are associated with an increased incidence of SLD in primary care patients without known CLD.

Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study

BACKGROUND

The availability of electronic medical record (EMR) free-text data for research varies. However, access to short diagnostic text fields is more widely available.

OBJECTIVE

This study assesses agreement between free-text and short diagnostic text data from primary care EMR for identification of posttraumatic stress disorder (PTSD).

METHODS

This retrospective cross-sectional study used EMR data from a pan-Canadian repository representing 1574 primary care providers at 265 clinics using 11 EMR vendors. Medical record review using free text and short diagnostic text fields of the EMR produced reference standards for PTSD. Agreement was assessed with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.

RESULTS

Our reference set contained 327 patients with free text and short diagnostic text. Among these patients, agreement between free text and short diagnostic text had an accuracy of 93.6% (CI 90.4%-96.0%). In a single Canadian province, case definitions 1 and 4 had a sensitivity of 82.6% (CI 74.4%-89.0%) and specificity of 99.5% (CI 97.4%-100%). However, when the reference set was expanded to a pan-Canada reference (n=12,104 patients), case definition 4 had the strongest agreement (sensitivity: 91.1%, CI 90.1%-91.9%; specificity: 99.1%, CI 98.9%-99.3%).

CONCLUSIONS

Inclusion of free-text encounter notes during medical record review did not lead to improved capture of PTSD cases, nor did it lead to significant changes in case definition agreement. Within this pan-Canadian database, jurisdictional differences in diagnostic codes and EMR structure suggested the need to supplement diagnostic codes with natural language processing to capture PTSD. When unavailable, short diagnostic text can supplement free-text data for reference set creation and case validation. Application of the PTSD case definition can inform PTSD prevalence and characteristics.

Data Quality of Automated Comorbidity Lists in Patients With Mental Health and Substance Use Disorders

EHRs provide an opportunity to conduct research on underrepresented oncology populations with mental health and substance use disorders. However, a lack of data quality may introduce unintended bias into EHR data. The objective of this article is describe our analysis of data quality within automated comorbidity lists commonly found in EHRs. Investigators conducted a retrospective chart review of 395 oncology patients from a safety-net integrated healthcare system. Statistical analysis included κ coefficients and a condition logistic regression. Subjects were racially and ethnically diverse and predominantly used Medicaid insurance. Weak κ coefficients ( κ = 0.2-0.39, P < .01) were noted for drug and alcohol use disorders indicating deficiencies in comorbidity documentation within the automated comorbidity list. Further, conditional logistic regression analyses revealed deficiencies in comorbidity documentation in patients with drug use disorders (odds ratio, 11.03; 95% confidence interval, 2.71-44.9; P = .01) and psychoses (odds ratio, 0.04; confidence interval, 0.02-0.10; P < .01). Findings suggest deficiencies in automatic comorbidity lists as compared with a review of provider narrative notes when identifying comorbidities. As healthcare systems increasingly use EHR data in clinical studies and decision making, the quality of healthcare delivery and clinical research may be affected by discrepancies in the documentation of comorbidities.