Dataset Information

Multicomorbidity of chronic diseases and substance use disorders and their association with hospitalization: Results from electronic health records data.

ABSTRACT: BACKGROUND:Chronic diseases are prevalent and the leading causes of mortality. Comorbidity of substance use disorders (SUDs) and chronic diseases is understudied to inform behavioral healthcare integration. OBJECTIVES:This study leveraged electronic health record (EHR) data of 211,880 adults from a large health system to examine prevalence and correlates of comorbidity of SUDs and nine chronic disease groups and to determine their association with hospitalization. METHODS:Logistic regression analyses were conducted to estimate associations between chronic diseases and SUDs. To control for severity of diagnosis, analyses of associations between SUD and hospitalization were stratified by the number of chronic conditions. RESULTS:In the sample, 48.3% had ?1 chronic condition (hypertension 33.7%, arthritis 16.2%, diabetes 13.7%, chronic kidney disease 9.9%, asthma 9.1%, chronic obstructive pulmonary disease 8.9%, ischemic heart disease 8.3%, cancer 4.6%, and hepatitis 1.3%). Prevalence of SUD (overall 13.3%) among patients increased with multiple chronic conditions (14.3% having SUD among patients with one condition; 21.2% having SUD among patients with two to three conditions; and 32.5% having SUD among patients with 4-9 conditions). Chronic conditions were associated with increased odds of SUDs. For all SUD groups, hospitalization was more prevalent among patients with SUD than those without it; prevalence of hospitalization increased with the number of comorbid chronic conditions. CONCLUSIONS:Findings reveal a striking pattern of multicomorbidity of SUD and chronic diseases and its positive association with hospitalization. Behavioral healthcare integration should consider efforts to assess and treat comorbid SUD and chronic diseases, especially among adults with multiple chronic conditions.

SUBMITTER: Wu LT

PROVIDER: S-EPMC6358206 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multicomorbidity of chronic diseases and substance use disorders and their association with hospitalization: Results from electronic health records data.

Wu Li-Tzy LT Zhu He H Ghitza Udi E UE

Drug and alcohol dependence 20181002

<h4>Background</h4>Chronic diseases are prevalent and the leading causes of mortality. Comorbidity of substance use disorders (SUDs) and chronic diseases is understudied to inform behavioral healthcare integration.<h4>Objectives</h4>This study leveraged electronic health record (EHR) data of 211,880 adults from a large health system to examine prevalence and correlates of comorbidity of SUDs and nine chronic disease groups and to determine their association with hospitalization.<h4>Methods</h4>L ...[more]

PMID: 30312893

Similar Datasets

Project description:ObjectiveSubstance use screening in adolescence is unstandardized and often documented in clinical notes, rather than in structured electronic health records (EHRs). The objective of this study was to integrate logic rules with state-of-the-art natural language processing (NLP) and machine learning technologies to detect substance use information from both structured and unstructured EHR data.Materials and methodsPediatric patients (10-20 years of age) with any encounter between July 1, 2012, and October 31, 2017, were included (n = 3890 patients; 19 478 encounters). EHR data were extracted at each encounter, manually reviewed for substance use (alcohol, tobacco, marijuana, opiate, any use), and coded as lifetime use, current use, or family use. Logic rules mapped structured EHR indicators to screening results. A knowledge-based NLP system and a deep learning model detected substance use information from unstructured clinical narratives. System performance was evaluated using positive predictive value, sensitivity, negative predictive value, specificity, and area under the receiver-operating characteristic curve (AUC).ResultsThe dataset included 17 235 structured indicators and 27 141 clinical narratives. Manual review of clinical narratives captured 94.0% of positive screening results, while structured EHR data captured 22.0%. Logic rules detected screening results from structured data with 1.0 and 0.99 for sensitivity and specificity, respectively. The knowledge-based system detected substance use information from clinical narratives with 0.86, 0.79, and 0.88 for AUC, sensitivity, and specificity, respectively. The deep learning model further improved detection capacity, achieving 0.88, 0.81, and 0.85 for AUC, sensitivity, and specificity, respectively. Finally, integrating predictions from structured and unstructured data achieved high detection capacity across all cases (0.96, 0.85, and 0.87 for AUC, sensitivity, and specificity, respectively).ConclusionsIt is feasible to detect substance use screening and results among pediatric patients using logic rules, NLP, and machine learning technologies.

Project description:Background: As hospitals in the US face pressures to reduce lengths of stay, healthcare systems are increasingly utilizing skilled nursing facilities (SNFs) to continue treating patients stable enough to leave the hospital, but not to return home. Substance use disorder (SUD) can complicate care of patients transferred to SNFs. The objective of this paper is to understand SNF experiences for this population of patients with comorbid SUD transferred to SNFs and examine care experiences in these facilities. Methods: This secondary mixed-methods analysis focuses on SNF experiences from a clinical trial of patient navigation services for medically-hospitalized adults with comorbid opioid, cocaine, and/or alcohol use disorder. This study compared baseline assessments and medical record review for participants (N = 400) with vs. without SNF transfer, and analyzed semi-structured qualitative interviews with a subsample of 15 participants purposively selected based on their transfer to a SNF. Results: Over 1 in 4 participants had a planned discharged to a SNF (26.8% sub-acute, 3.3% acute). Compared to participants with other types of discharge, participants discharged to a SNF had longer initial hospitalizations (4.9 vs. 11.8 days, p < 0.001), and were more likely to be White (38.6 vs. 50.8%; p = 0.02), female (38.9 vs. 52.5%; p = 0.01), have opioid use disorder (75.7 vs. 85.0%, p = 0.03), and be hospitalized for infection (43.6 vs. 58.3%; p = 0.007), and less likely to have worked prior to hospitalization (24.3 vs. 12.5%; p = 0.006). Qualitative narratives identified several themes from the SNF experience, including opioid analgesic dosing issues, challenges to the use of opioid agonist treatment of OUD, illicit opioid dealing/use, and limited access to addiction recovery support services during and following the SNF stay. Conclusions: SNFs are a common disposition for patients in need of subacute services following hospitalization but may be ill-equipped to properly manage patients in need of new or continuing SUD treatment.

Project description:BackgroundPatterns of disease incidence can identify new risk factors for the disease or provide insight into the etiology. For example, allergies and infectious diseases have been shown to follow periodic temporal patterns due to seasonal changes in environmental or infectious agents. Previous work searching for seasonal or other temporal patterns in disease diagnosis rates has been limited both in the scope of the diseases examined and in the ability to distinguish unexpected seasonal patterns. Electronic Health Records (EHR) compile extensive longitudinal clinical information, constituting a unique source for discovery of trends in occurrence of disease. However, the data suffer from inherent biases that preclude an identification of temporal trends.MethodsMotivated by observation of the biases in this data source, we developed a method (Lomb-Scargle periodograms in detrended data, LSP-detrend) to find periodic patterns by adjusting the temporal information for broad trends in incidence, as well as seasonal changes in total hospitalizations. LSP-detrend can sensitively uncover periodic temporal patterns in the corrected data and identify the significance of the trend. We apply LSP-detrend to a compilation of records from 1.5 million patients encoded by ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), including 2,805 disorders with more than 500 occurrences across a 12 year period, recorded from 1.5 million patients.Results and conclusionsAlthough EHR data, and ICD-9 coded records in particular, were not created with the intention of aggregated use for research, these data can in fact be mined for periodic patterns in incidence of disease, if confounders are properly removed. Of all diagnoses, around 10% are identified as seasonal by LSP-detrend, including many known phenomena. We robustly reproduce previous findings, even for relatively rare diseases. For instance, Kawasaki disease, a rare childhood disease that has been associated with weather patterns, is detected as strongly linked with winter months. Among the novel results, we find a bi-annual increase in exacerbations of myasthenia gravis, a potentially life threatening complication of an autoimmune disease. We dissect the causes of this seasonal incidence and propose that factors predisposing patients to this event vary through the year.

Project description:AimsEating disorders (EDs) and substance use disorders (SUDs) often co-occur, and both involve somatic diseases. So far, no study has considered whether comorbid SUDs may impact somatic disease risk in patients with EDs. Therefore, this study aimed to examine the impact of comorbid SUDs on the risk of 11 somatic disease categories in patients with anorexia nervosa (AN), bulimia nervosa (BN) and unspecified eating disorder (USED) compared to matched controls.MethodsA retrospective cohort study was conducted using Danish nationwide registries. The study population included 20 759 patients with EDs and 83 036 controls matched on month and year of birth, sex and ethnicity. Hazard ratios (HRs) were calculated to compare the risk of being diagnosed with a somatic disease (within 11 categories defined by the ICD-10) following first ED diagnosis (index date) between ED patients and controls both with and without SUDs (alcohol, cannabis or hard drugs).ResultsThe ED cohort and matched controls were followed for 227 538 and 939 628 person-years, respectively. For ED patients with SUDs, the risk pattern for being diagnosed with different somatic diseases (relative to controls without SUDs) varied according to type of ED and SUD [adjusted HRs ranged from 0.95 (99% CI = 0.57; 1.59) to 4.17 (2.68, 6.47)]. The risk estimates observed among ED patients with SUDs were generally higher than those observed among ED patients without SUDs [adjusted HRs ranged from 1.08 (99% CI = 0.95, 1.22) to 2.56 (2.31, 2.84)]. Abuse of alcohol only had a non-synergistic effect on six disease categories in AN patients and five in BN and USED patients. Abuse of cannabis (with/without alcohol) had a non-synergistic effect on five disease categories in AN and BN patients and two in USED patients. Abuse of hard drugs (with/without alcohol or cannabis) had a non-synergistic effect on nine disease categories in AN patients, eight in BN patients and seven in USED patients.ConclusionsThe present study documents non-synergistic but not synergistic harmful somatic consequences of SUDs among patients with different EDs, with AN and hard drugs being the most predominant factors. Hence, EDs and SUDs did not interact and result in greater somatic disease risk than that caused by the independent effects. Since EDs and SUDs have independent effects on many somatic diseases, it is important to monitor and treat ED patients for SUD comorbidity to prevent exacerbated physical damage in this vulnerable population.

Dataset Information

Multicomorbidity of chronic diseases and substance use disorders and their association with hospitalization: Results from electronic health records data.

Publications

Multicomorbidity of chronic diseases and substance use disorders and their association with hospitalization: Results from electronic health records data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets