Identifying and Validating Pediatric Hospitalizations for MIS-C Through Administrative Data.

BACKGROUND: Individual children's hospitals care for a small number of patients with multisystem inflammatory syndrome in children (MIS-C). Administrative databases offer an opportunity to conduct generalizable research; however, identifying patients with MIS-C is challenging. METHODS: We developed and validated algorithms to identify MIS-C hospitalizations in administrative databases. We developed 10 approaches using diagnostic codes and medication billing data and applied them to the Pediatric Health Information System from January 2020 to August 2021. We reviewed medical records at 7 geographically diverse hospitals to compare potential cases of MIS-C identified by algorithms to each participating hospital's list of patients with MIS-C (used for public health reporting). RESULTS: The sites had 245 hospitalizations for MIS-C in 2020 and 358 additional MIS-C hospitalizations through August 2021. One algorithm for the identification of cases in 2020 had a sensitivity of 82%, a low false positive rate of 22%, and a positive predictive value (PPV) of 78%. For hospitalizations in 2021, the sensitivity of the MIS-C diagnosis code was 98% with 84% PPV. CONCLUSION: We developed high-sensitivity algorithms to use for epidemiologic research and high-PPV algorithms for comparative effectiveness research. Accurate algorithms to identify MIS-C hospitalizations can facilitate important research for understanding this novel entity as it evolves during new waves.

ICD2Vec: Mathematical representation of diseases.

BACKGROUND: The International Classification of Diseases (ICD) codes represent the global standard for reporting disease conditions. The current ICD codes connote direct human-defined relationships among diseases in a hierarchical tree structure. Representing the ICD codes as mathematical vectors helps to capture nonlinear relationships in medical ontologies across diseases. METHODS: We propose a universally applicable framework called "ICD2Vec" designed to provide mathematical representations of diseases by encoding corresponding information. First, we present the arithmetical and semantic relationships between diseases by mapping composite vectors for symptoms or diseases to the most similar ICD codes. Second, we investigated the validity of ICD2Vec by comparing the biological relationships and cosine similarities among the vectorized ICD codes. Third, we propose a new risk score called IRIS, derived from ICD2Vec, and demonstrate its clinical utility with large cohorts from the UK and South Korea. RESULTS: Semantic compositionality was qualitatively confirmed between descriptions of symptoms and ICD2Vec. For example, the diseases most similar to COVID-19 were found to be the common cold (ICD-10: J00), unspecified viral hemorrhagic fever (ICD-10: A99), and smallpox (ICD-10: B03). We show the significant associations between the cosine similarities derived from ICD2Vec and the biological relationships using disease-to-disease pairs. Furthermore, we observed significant adjusted hazard ratios (HR) and area under the receiver operating characteristics (AUROC) between IRIS and risks for eight diseases. For instance, the higher IRIS for coronary artery disease (CAD) can be the higher probability for the incidence of CAD (HR: 2.15 [95% CI 2.02-2.28] and AUROC: 0.587 [95% CI 0.583-0.591]). We identified individuals at substantially increased risk of CAD using IRIS and 10-year atherosclerotic cardiovascular disease risk (adjusted HR: 4.26 [95% CI 3.59-5.05]). CONCLUSIONS: ICD2Vec, a proposed universal framework for converting qualitatively measured ICD codes into quantitative vectors containing semantic relationships between diseases, exhibited a significant correlation with actual biological significance. In addition, the IRIS was a significant predictor of major diseases in a prospective study using two large-scale datasets. Based on this clinical validity and utility evidence, we suggest that publicly available ICD2Vec can be used in diverse research and clinical practices and has important clinical implications.

ICD-11 adjustment disorder following diagnostic procedures of prostate cancer: A 12-month follow-up study.

OBJECTIVE: The medical procedures in diagnosing or treating prostate cancer may impair adjustment and quality of life. The current prospective study aimed to evaluate the trajectories of symptoms of ICD-11 adjustment disorder in patients diagnosed vs. non-diagnosed with prostate cancer before (T1), after diagnostic procedures (T2), and at 12-month follow-up (3). METHODS: In total, 96 male patients were recruited before prostate cancer diagnostic procedures. The mean age of the study participants at baseline was 63.5 (SD = 8.4), ranging from 47 to 80 years; 64% were diagnosed with prostate cancer. Adjustment disorder symptoms were measured using the Brief Adjustment Disorder Measure (ADNM-8). RESULTS: The prevalence of ICD-11 adjustment disorder was 15% at T1, 13% at T2, and 3% at T3. The effect of cancer diagnosis was not significant on adjustment disorder. A medium main effect for time was detected on adjustment symptom severity, F(2, 134) = 19.26, p < .001, partial η2 = 0.223, with symptoms significantly lower at 12-month follow-up, compared to T1 and T2, p < .001. CONCLUSIONS: The study's findings reveal the increased levels of adjustment difficulties in males undergoing the diagnostic process of prostate cancer.

Coding long COVID: characterizing a new disease through an ICD-10 lens.

BACKGROUND: Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." METHODS: We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. RESULTS: We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. CONCLUSIONS: This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation.

Direct Versus Indirect Query Performance of ICD-9/-10 Coding to Identify Anaphylaxis.

BACKGROUND: Anaphylaxis is an often under =diagnosed, severe allergic event for which epidemiological data are sporadic. Researchers have leveraged administrative and claims data algorithms to study large databases of anaphylactic events; however, little longitudinal data analysis is available after transition to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM). OBJECTIVE: Study longitudinal trends in anaphylaxis incidence using direct and indirect query methods. METHODS: Emergency department (ED) and inpatient data were analyzed from a large state health care administration database from 2011 to 2020. Incidence was calculated using direct queries of anaphylaxis ICD-9-CM and ICD-10-CM codes and indirect queries using a symptom-based ICD-9-CM algorithm and forward mapped ICD-10-CM version to identify undiagnosed anaphylaxis episodes and to assess algorithm performance at the population level. RESULTS: An average of 2.4 million inpatient and 7.5 million ED observations/y were analyzed. Using the direct query method, annual ED anaphylaxis cases increased steadily from 1,454 (2011) to 4,029 (2019) then declined to 3,341 in 2020 during the coronavirus disease 2019 (COVID-19) pandemic. In contrast, inpatient cases remained relatively steady, with a slight decline after 2015 during the ICD version transition, until a significant drop occurred in 2020. Using the indirect queries, anaphylaxis cases increased markedly after the ICD transition year, especially involving drug-related anaphylaxis. CONCLUSIONS: Nontypical drug associations with anaphylaxis episodes using the ICD-10-CM version of the algorithm suggest poor performance with drug-related codes. Further, the increased granularity of ICD-10-CM identified potential limitations of a previously validated symptom-based ICD-9-CM algorithm used to detect undiagnosed cases.

Trends in mortality from heart failure in Brazil: 1998 to 2019. / Tendência da mortalidade por insuficiência cardíaca no Brasil: 1998 a 2019.

OBJECTIVE: To analyze the trend of mortality from heart failure in Brazilians aged 50 years and over, within 21 years. METHODS: Ecological study with time series analysis of mortality from heart failure in Brazil, according to regions and Federation Units, in individuals aged 50 years or older in the period from 1998 to 2019. Deaths that had heart failure as the underlying cause (coded as I50 according to the International Classification of Diseases) that occurred during the study period were included in the study. Data were obtained from the Mortality Information System of the Brazilian Ministry of Health. Statistical analyses were performed using the Stata 11.1 program, by estimating the mortality rate due to heart failure per 100 thousand inhabitants. In the trend analysis, the Prais-Winsten regression was used. RESULTS: Between 1998 and 2019, 567,789 deaths from heart failure were recorded in adults aged over 50 years, which corresponds to an average rate of 75.5 per 100 thousand inhabitants. There was a downward trend per sex, regions, and in 23 Federation Units. The highest mortality rates were observed for older ages in all regions of the country. CONCLUSION: The trend in mortality rates from heart failure among Federation Units and Brazilian regions was downward over 21 years. There was an upward trend in mortality from heart failure in the northern region and in the category "other health facilities."

OBJETIVO: Analisar a tendência da mortalidade por insuficiência cardíaca (IC) em brasileiros com 50 anos ou mais, em um período de 21 anos. MÉTODOS: Estudo ecológico com análise de série temporal da mortalidade por IC no Brasil, segundo regiões e Unidades Federativas (UF), em indivíduos com 50 anos ou mais, no período de 1998 a 2019. Foram incluídos todos os óbitos registrados que tinham por causa básica a IC, codificada na Classificação Internacional de Doenças como I50, no período de 1998 a 2019. Os dados foram obtidos no Sistema de Informação sobre Mortalidade do Ministério da Saúde. As análises estatísticas foram realizadas no programa Stata 11.1, por meio do cálculo do coeficiente de mortalidade por IC por 100 mil habitantes. Na análise de tendência, foi utilizada a regressão de Prais-Winsten. RESULTADOS: Entre os anos de 1998 a 2019, foram registrados 567.789 óbitos por IC em adultos com idade acima de 50 anos, o que corresponde à taxa média de 75,5 a cada 100 mil habitantes. A tendência foi decrescente por sexo, regiões e em 23 UF. As maiores taxas de mortalidade observadas ocorreram nas idades mais avançadas em todas as regiões do país. CONCLUSÃO: A tendência das taxas de mortalidade por IC entre as UF e regiões brasileiras foi decrescente ao longo de 21 anos. Houve tendência crescente da mortalidade por IC na região Norte e na categoria outros estabelecimentos de saúde.

Evaluation of ICD-10-CM-based case definitions of ambulatory encounters for COVID-19 among Department of Defense health care beneficiaries.

SARS-CoV-2 ICD-10-CM-based case definitions are lacking in the literature. This analysis was conducted to evaluate the performance metrics of 3 COVID-19 case definitions among Department of Defense (DoD) beneficiaries. SARS-CoV-2 tested specimens collected from 1 March 2020 to 28 February 2021 were matched to ambulatory medical encounters (68% match). The COVID-19 case definition (ICD-10-CM: U07.1) had high specificity (99%) and positive predictive value (PPV) (94%) but low to moderate (29%-66%) sensitivity. The COVID-specific case definition (10 additional codes added), had moderate to high specificity (82-93%), moderate sensitivity (65-75%), and low to moderate PPV (23%-77%). The COVID-like illness case definition (19 additional codes added to the COVID-specific definition), had moderate specificity (65%-86%), moderate sensitivity (76%-79%), and low to moderate PPV (15%-62%). Regardless of the case definition, all metrics improved over the surveillance period. The COVID-19 case definition is ideal for studies that need to ensure all cases are true positives. However, for broad surveillance efforts, the COVID-specific case definition may be the best to maximize specificity without a large decrease in sensitivity and PPV.

Concordance of ICD-10 Codes and the Clinical Diagnosis of Alcoholic Hepatitis.

INTRODUCTION: Many studies on alcoholic hepatitis (AH) use the International Classification of Disease (ICD) coding to identify patients. Data regarding the diagnostic accuracy of ICD codes for AH are limited. METHODS: A total of 151 patients with ICD-10 codes for AH were reviewed for the presence or absence of AH using standardized diagnostic criteria. RESULTS: Sixty-eight of the 151 patients met AH criteria, corresponding to a positive predictive value of 45%. Patients with AH experienced higher model for end-stage liver disease and mortality than those who did not ( P < 0.05). DISCUSSION: Our results suggest ICD-10 codes are not reliable for identifying AH. Studies using the ICD codes should be interpreted cautiously.

Assessment of performance characteristics of COVID-19 ICD-10-CM diagnosis code U07.1 using SARS-CoV-2 nucleic acid amplification test results.

The Food and Drug Administration's Biologics Effectiveness and Safety Initiative conducts active surveillance to protect public health during the coronavirus disease 2019 (COVID-19) pandemic. This study evaluated performance of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code U07.1 in identifying COVID-19 cases in claims compared with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid amplification test results in linked electronic health records (EHRs). Care episodes in three populations were defined using COVID-19-related diagnoses (population 1), SARS-CoV-2 nucleic acid amplification test procedures (population 2), and all-cause hospitalizations (population 3) in two linked claims-EHR databases: IBM® MarketScan® Explorys® Claims-EMR Data Set (commercial) and OneFlorida Data Trust linked Medicaid-EHR. Positive and negative predictive values were calculated. Respectively, populations 1, 2, and 3 included 26,686, 26,095, and 2,564 episodes (commercial) and 29,117, 23,412, and 9,629 episodes (Florida Medicaid). The positive predictive value was >80% and the negative predictive value was >95% in each population, with the highest positive predictive value in population 3 (commercial: 91.9%; Medicaid: 93.1%). Findings did not vary substantially by patient age. Positive predictive values in populations 1 and 2 fluctuated during April-June 2020. They then stabilized in the commercial but not the Medicaid population. Negative predictive values were consistent over time in all populations and databases. Our findings indicate that U07.1 has high performance in identifying COVID-19 cases and noncases in claims databases. Performance may vary across populations and periods.

The network structure of ICD-11 adjustment disorder: A comparison of clinical and nonclinical samples.

BACKGROUND: International Classification of Diseases, 11th revision (ICD-11) adjustment disorder (AjD) is characterized by two main symptom clusters: preoccupation with the stressor and failure to adapt to the stressor. The network analytic approach provides important information on the structural validity of a disorder and reveals which symptoms are most prominent. To date, no study compared the network structure of AjD symptoms in clinical and nonclinical samples, which could potentially inform our understanding of psychopathological mechanisms that underlie AjD and identify core targets for therapy. METHODS: A network analysis was conducted on AjD symptoms as assessed by the Adjustment Disorder-New Module (ADNM-8) using data from 330 clinical participants from the UK and a nonclinical sample of 699 participants from Switzerland. RESULTS: Comparisons of network structure invariance revealed differences between the network structure of the clinical and the nonclinical samples. Results highlight that in terms of both edges strength and centrality, failure to adapt symptoms was more prominent in the clinical sample, while the preoccupation symptoms were more prominent in the nonclinical sample. Importantly, global strength was similar across networks. CONCLUSIONS: Results provide evidence of the coherence of AjD in the ICD-11 as assessed by the ADNM questionnaire. They tentatively suggest that subclinical AjD may be characterized by emerging preoccupation symptoms that may result in failure to adapt and functional impairment in clinical manifestation of AjD. However, there is a need for replication and longitudinal research to further validate this hypothesis.

Applying ICD-11 criteria of Gaming Disorder to identify problematic video streaming in adolescents: Conceptualization of a new clinical phenomenon.

Background and aims: Internet video streaming (VS) has become a popular leisure activity among the majority of adolescents, especially under the COVID-19 pandemic. Research on binge watching patterns in adults suggests an addictive potential of VS. To date, no unified conceptualization on problematic VS and no standardized assessment tools for adolescents exist even though they might be especially vulnerable. Methods: STREDIS-A is based on the ICD-11 criteria of gaming disorder. It was validated in a representative sample of 959 dyads of 10- to 17-year old adolescents with frequent VS and a respective parent using standardized questionnaires on Internet addiction, depressive and anxiety symptoms, insomnia, loneliness, and academic performance in an online survey. Item structure was investigated by factorial analyses. Cutoffs were estimated and latent profile analysis was performed. Results: The two-factorial structure of STREDIS-A describes cognitive-behavioral symptoms and negative consequences of VS. Internal consistency and criterion validity were good to excellent. It could excellently discriminate between affected and non-affected adolescents. Discussion and conclusions: The present study makes a significant contribution to the conceptualization of a new phenomenon. It provides the very first tool to assess streaming disorder in adolescents for clinical and research settings. Clinical validation is highly warranted.

Risk of persistent and new clinical sequelae among adults aged 65 years and older during the post-acute phase of SARS-CoV-2 infection: retrospective cohort study.

OBJECTIVE: To characterize the risk of persistent and new clinical sequelae in adults aged ≥65 years after the acute phase of SARS-CoV-2 infection. DESIGN: Retrospective cohort study. SETTING: UnitedHealth Group Clinical Research Database: deidentified administrative claims and outpatient laboratory test results. PARTICIPANTS: Individuals aged ≥65 years who were continuously enrolled in a Medicare Advantage plan with coverage of prescription drugs from January 2019 to the date of diagnosis of SARS-CoV-2 infection, matched by propensity score to three comparison groups that did not have covid-19: 2020 comparison group (n=87 337), historical 2019 comparison group (n=88 070), and historical comparison group with viral lower respiratory tract illness (n=73 490). MAIN OUTCOME MEASURES: The presence of persistent and new sequelae at 21 or more days after a diagnosis of covid-19 was determined with ICD-10 (international classification of diseases, 10th revision) codes. Excess risk for sequelae caused by infection with SARS-CoV-2 was estimated for the 120 days after the acute phase of the illness with risk difference and hazard ratios, calculated with 95% Bonferroni corrected confidence intervals. The incidence of sequelae after the acute infection was analyzed by age, race, sex, and whether patients were admitted to hospital for covid-19. RESULTS: Among individuals who were diagnosed with SARS-CoV-2, 32% (27 698 of 87 337) sought medical attention in the post-acute period for one or more new or persistent clinical sequelae, which was 11% higher than the 2020 comparison group. Respiratory failure (risk difference 7.55, 95% confidence interval 7.18 to 8.01), fatigue (5.66, 5.03 to 6.27), hypertension (4.43, 2.27 to 6.37), memory difficulties (2.63, 2.23 to 3.13), kidney injury (2.59, 2.03 to 3.12), mental health diagnoses (2.50, 2.04 to 3.04), hypercoagulability 1.47 (1.2 to 1.73), and cardiac rhythm disorders (2.19, 1.76 to 2.57) had the greatest risk differences compared with the 2020 comparison group, with similar findings to the 2019 comparison group. Compared with the group with viral lower respiratory tract illness, however, only respiratory failure, dementia, and post-viral fatigue had increased risk differences of 2.39 (95% confidence interval 1.79 to 2.94), 0.71 (0.3 to 1.08), and 0.18 (0.11 to 0.26) per 100 patients, respectively. Individuals with severe covid-19 disease requiring admission to hospital had a markedly increased risk for most but not all clinical sequelae. CONCLUSIONS: The results confirm an excess risk for persistent and new sequelae in adults aged ≥65 years after acute infection with SARS-CoV-2. Other than respiratory failure, dementia, and post-viral fatigue, the sequelae resembled those of viral lower respiratory tract illness in older adults. These findings further highlight the wide range of important sequelae after acute infection with the SARS-CoV-2 virus.

Psychosocial hazards, posttraumatic stress disorder, complex posttraumatic stress disorder, depression, and anxiety in the U.K. rail industry: A cross-sectional study.

This study examined posttraumatic stress disorder (PTSD), complex PTSD, depression, and anxiety among U.K. rail workers. A cross-sectional survey examining exposure to seven psychosocial hazards (bullying/harassment; verbal abuse; physical and sexual assault; and hearing about, seeing the aftermath of, or witnessing a fatality), working conditions, physical health, and the impact of COVID-19 was administered to 3,912 participants. Outcome measures were the ITQ, PHQ-9, and GAD-7. Among trauma-exposed participants, 24.3% met the criteria for PTSD or CPTSD; 38.6% and 29.2% of all participants scored in the moderate-to-severe range on the PHQ-9 and GAD-7, respectively. Data were analyzed using logistic and linear regression. Bullying/harassment was positively associated with GAD-7 scores, f2 = .001, and PTSD and CPTSD, ORs = 1.83-2.02. Hearing about and witnessing a fatality were associated with PTSD and CPTSD, ORs = 1.77-2.10. Poorer ergonomics at work were positively associated with PHQ-9 and GAD-7 scores, f2 = .001. Higher job satisfaction was associated with lower odds of PTSD and CPTSD, ORs = 0.87-0.91, and negatively associated with PHQ-9 and GAD-7 scores, f2 = .008-.01. Work intensity was associated with PTSD and CPTSD, ORs = 1.79-1.83, and positively associated with PHQ-9 and GAD-7 scores, f2 = .02-.03. Reporting more physical health problems was associated with PTSD, OR = 1.07, and positively associated with GAD-7 and PHQ-9 scores, f2 = .008-.01. The results suggest bullying/harassment and work intensity are important variables in employee mental health and could drive future research and industry initiatives.

Diagnoses per Encounter by Telephone, Televideo, and In-Office Visits.

INTRODUCTION: We sought to determine if there are differences between number of International Classification of Disease-10 (ICD-10) codes per visit before and after COVID-19 when comparing in-office visits and between telemedicine vs in-office visits, toward the goal of determining value of telemedicine visits relative to in-office visits. METHODS: We did a chart review study assessing the number of ICD-10 codes noted by providers at a large academic medical institution in 2019 and 2020. Only in-office visits were reviewed in 2019. The focus of analysis was on individual patient visits per visit type; however, a subset of patients who had visits in both 2019 and 2020 were also analyzed. We compared mean number of diagnoses for encounter types using encounter, billing and coding data. RESULTS: We analyzed 211,829 patient encounters. For 2020, 73% were in office. Mean number of diagnoses per encounter for 2019 was 2.65 (in office only), compared with 3.04 in office, 2.76 telephone, and 2.48 televideo for 2020. DISCUSSION: We found an increase in the number of diagnoses addressed during in-office visits from 2019 to 2020. When looking at diagnoses managed per visit, all 3 types of visits had similar complexity. These results may guide future reimbursement policy for telemedicine visits.

Mental health emergency department visits: An exploration of case definitions in North Carolina.

BACKGROUND: Mental health (MH) disorders comprise a high disease burden and have long-lasting impacts. To improve MH, it is important to define public health MH surveillance. METHODS: We compared MH related definitions using ICD-10-CM codes: The Council of State and Territorial Epidemiologists' (CSTE) surveillance indicators for all MH, mood or depressive, schizophrenic, and drug/alcohol-induced disorders; and North Carolina's (NC) syndromic surveillance system's definition for anxiety/mood/psychotic disorders, and suicide/self-harm. We compared code definitions and frequent codes in 2019 emergency department (ED) data for those age ≥ 10 years. RESULTS: CSTE's definition resulted in over one million MH-related visits (23% of all ED visits) and NC's definitions in 451,807 MH-related visits (9% of all ED visits). Using CSTE's broadest definition, nicotine use was the most common visit type; using NC's definitions, it was major depressive disorder. CONCLUSIONS: Standardizing population-level MH indicators benefits surveillance efforts. Given its prevalence, efforts should focus on documenting MH to improve treatment and prevention.

Examining Social Determinants of Health During a Pandemic: Clinical Application of Z Codes Before and During COVID-19.

Recognition of the impact of social determinants of health (SDoH) on healthcare outcomes, healthcare service utilization, and population health has prompted a global shift in focus to patient social needs and lived experiences in assessment and treatment. The International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) provides a list of non-billable "Z codes" specific to SDoH for use in electronic health records. Using population-level analysis, this study aims to examine clinical application of Z codes in South Carolina before and during the COVID-19 pandemic. The study population consists of South Carolina residents who had a healthcare visit and had their COVID-19 test result reported to the state's Department of Health and Environmental Control before January 14, 2021. Of the 1,190,531 individuals in the overall sample, Z codes were used only for 14,665 (1.23%) of the patients, including 2,536 (0.97%) COVID-positive patients and 12,129 (1.30%) COVID-negative patients. Compared with hospitals that did not use Z codes, those that did were significantly more likely to have higher bed capacity (p = 0.017) and to be teaching hospitals (p = 0.03), although this was significant only among COVID-19 positive individuals. Those at inpatient visits were most likely to receive Z codes (OR: 5.26; 95% CI: 5.14, 5.38; p < 0.0001) compared to those at outpatient visits (OR: 0.07; 95%CI: 0.06, 0.07; p < 0.0001). There was a slight increase of Z code use from 2019 to 2020 (OR: 1.33, 95% CI: 1.30, 1.36; p < 0.0001), which was still significant when stratified by facility type across time. As one of the first studies to examine Z code use among a large patient population, findings clearly indicate underutilization by providers. Additional study is needed to understand the potentially long-lasting health effects related to SDoH among underserved populations.

Associations between moral injury and ICD-11 post-traumatic stress disorder (PTSD) and complex PTSD among help-seeking nurses: a cross-sectional study.

OBJECTIVES: This study aimed to evaluate how moral injury (MI), traumatic experiences and daily stressors were related to the symptoms of post-traumatic stress disorder (PTSD) and International Classification of Diseases 11th revision specific complex PTSD (CPTSD) symptoms of disturbances in self-organisation (DSO) in a treatment-seeking sample of nurses. DESIGN: A cross-sectional study. SETTING: Nurses from all regions of Lithuania participated in the study. The data were collected between April and May 2021. PARTICIPANTS: A total of 206 nurses, mean age 42.34 years (SD=11.68), 97.1% women and with 65% >10 years of work experience. RESULTS: The prevalence of PTSD and CPTSD in the treatment-seeking sample of nurses was 9.2% and 10.2%, respectively. The results of structural equation modelling indicated an acceptable model fit for the model regarding the links between trauma exposure, daily stressors, MI, PTSD and DSO symptoms, (χ2 (df)=219.718 (123), p<0.001, Comparative Fit Index/Tucker-Lewis Index=0.937/0.922, root mean square error of approximation (90% CI)=0.062 (0.048 to 0.075), standardised root mean square residual=0.049). MI had a large effect on DSO symptoms, ß=0.667, p<0.001, and a medium effect on PTSD symptoms, ß=0.394, p<0.001. Daily stress but not trauma exposure was significantly related to MI, ß=0.618, p<0.001. CONCLUSIONS: The prevalence of PTSD and CPTSD in a treatment-seeking sample of nurses inform healthcare administrators, policymakers and medical staff about the demand for psychosocial interventions for healthcare workers focused on stress management to address their daily stressors and mitigate effects on MI or trauma-focused treatments for PTSD/CPTSD. TRIAL REGISTRATION NUMBER: NCT04817995; Pre-results.

COVID-19-related posttraumatic stress disorder in adults with lived experience of psychiatric disorder.

BACKGROUND: Prevalence estimates of COVID-19-related posttraumatic stress disorder (PTSD) have ranged from 1% to over 60% in the general population. Individuals with lived experience of a psychiatric disorder may be particularly vulnerable to COVID-19-related PTSD but this has received inadequate attention. METHODS: Participants were 1571 adults with lived experience of psychiatric disorder who took part in a longitudinal study of mental health during the COVID-19 pandemic. PTSD was assessed by the International Trauma Questionnaire (ITQ) anchored to the participant's most troubling COVID-19-related experiencevent. Factors hypothesised to be associated with traumatic stress symptoms were investigated by linear regression. RESULTS: 40.10% of participants perceived some aspect of the pandemic as traumatic. 5.28% reported an ICD-11 PTSD qualifying COVID-19 related traumatic exposure and 0.83% met criteria for probable ICD-11 COVID-19-related PTSD. Traumatic stress symptoms were associated with younger age, lower income, lower social support, and financial worries, and lived experience of PTSD/complex PTSD. Depression and anxiety measured in June 2020 predicted traumatic stress symptoms at follow-up approximately 20 weeks later in November 2020. CONCLUSIONS: We did not find evidence of widespread COVID-19-related PTSD among individuals with lived experience of a psychiatric disorder. There is a need for future research to derive valid prevalence estimates of COVID-19-related PTSD.