Clinical and healthcare utilization outcomes during the 6 months following COVID infection in children.

BACKGROUND: We aimed to identify the impact of COVID infection in children in the US prior to vaccine availability on clinical and healthcare utilization outcomes within 6 months of infection. METHODS: Using claims data from a large national insurer, we identified 223,842 children with a COVID diagnosis in May 2020-March 2021 and matched them to 223,842 children with a COVID test and no diagnosis. We compared the two cohorts' outcomes during the 6 months after infection/test. RESULTS: Uncommon acute adverse events occurring in <0.5% of cases, including MIS-C (relative risk (RR) = 45.2), myocarditis (RR = 10.3), acute heart failure (RR = 2.14), sepsis (RR = 2.02), and viral pneumonia (RR = 2.43) were more frequent in the COVID cohort (all p < 0.001). Development of arrhythmias (RR = 1.24, p < 0.001) and atherosclerotic cardiovascular disease (RR = 1.41, p = 0.007) were more common in the COVID group, while behavioral health disorders were less common (RR = 0.94, p < 0.001). Lab testing and imaging were slightly higher in the COVID group (RR ranging 1.05-1.11 depending on the service and timeframe), though medical costs did not increase. CONCLUSION: Severe disease and diagnoses of new conditions are rare in children following COVID infection. We observed an increase in cardiac complications, though they may not last long term. IMPACT: Few studies have analyzed the association between COVID infection and medium-term outcomes in children. Our study of >447,000 geographically and socioeconomically diverse children in the US found that uncommon acute adverse events, including myocarditis, MIS-C, and acute heart failure, were more frequent in children with COVID than matched controls, and development of arrhythmias and cardiovascular disease were 1.2 and 1.4 times more common, respectively. Six-month healthcare utilization was similar between cohorts. We provide data on the risks of COVID in children, particularly with respect to cardiac complications, that decision makers may find useful when weighing the benefits and harms of preventive measures.

One-Year Adverse Outcomes Among US Adults With Post-COVID-19 Condition vs Those Without COVID-19 in a Large Commercial Insurance Database.

Importance: Many individuals experience ongoing symptoms following the onset of COVID-19, characterized as postacute sequelae of SARS-CoV-2 or post-COVID-19 condition (PCC). Less is known about the long-term outcomes for these individuals. Objective: To quantify 1-year outcomes among individuals meeting a PCC definition compared with a control group of individuals without COVID-19. Design, Setting, and Participants: This case-control study with a propensity score-matched control group included members of commercial health plans and used national insurance claims data enhanced with laboratory results and mortality data from the Social Security Administration's Death Master File and Datavant Flatiron data. The study sample consisted of adults meeting a claims-based definition for PCC with a 2:1 matched control cohort of individuals with no evidence of COVID-19 during the time period of April 1, 2020, to July 31, 2021. Exposures: Individuals experiencing postacute sequelae of SARS-CoV-2 using a Centers for Disease Control and Prevention-based definition. Main Outcomes and Measures: Adverse outcomes, including cardiovascular and respiratory outcomes and mortality, for individuals with PCC and controls assessed over a 12-month period. Results: The study population included 13 435 individuals with PCC and 26 870 individuals with no evidence of COVID-19 (mean [SD] age, 51 [15.1] years; 58.4% female). During follow-up, the PCC cohort experienced increased health care utilization for a wide range of adverse outcomes: cardiac arrhythmias (relative risk [RR], 2.35; 95% CI, 2.26-2.45), pulmonary embolism (RR, 3.64; 95% CI, 3.23-3.92), ischemic stroke (RR, 2.17; 95% CI, 1.98-2.52), coronary artery disease (RR, 1.78; 95% CI, 1.70-1.88), heart failure (RR, 1.97; 95% CI, 1.84-2.10), chronic obstructive pulmonary disease (RR, 1.94; 95% CI, 1.88-2.00), and asthma (RR, 1.95; 95% CI, 1.86-2.03). The PCC cohort also experienced increased mortality, as 2.8% of individuals with PCC vs 1.2% of controls died, implying an excess death rate of 16.4 per 1000 individuals. Conclusions and Relevance: This case-control study leveraged a large commercial insurance database and found increased rates of adverse outcomes over a 1-year period for a PCC cohort surviving the acute phase of illness. The results indicate a need for continued monitoring for at-risk individuals, particularly in the area of cardiovascular and pulmonary management.

Medication non-adherence patterns and profiles for patients with incident myocardial infarction: Observations from a large multi-morbid US population.

BACKGROUND: Consistent adherence levels to multiple long-term medications for patients with cardiovascular conditions are typically advocated in the range of 50% or higher, although very likely to be much lower in some populations. We investigated this issue in a large cohort covering a broad age and geographical spectrum, with a wide range of socio-economic disability status. METHODS: The patients were drawn from three different health plans with a varied mix of socio-economic/disability levels. Adherence patterns were examined on a monthly basis for up to 12 months past the index date for myocardial infarction (MI) using longitudinal analyses of group-based trajectory modelling. Each of the non-adherent patterns was profiled from comorbid history, demographic and health plan factors using main effect logistic regression modelling. Four medication classes were examined for MI: betablockers, statin, ACE inhibitors and anti-platelets. RESULTS: The participant population for the MI/non-MI cohorts was 1,987,605 (MI cohort: mean age 62 years, 45.9% female; non-MI cohort: mean age 45 years, 55.3% females). Cohorts characterized by medication non-adherence dominated the majority of MI population with values ranging from 74% to 82%. There were four types of consistent non-adherence patterns as a function of time for each medication class: fast decline, slow decline, occasional users and early gap followed by increased adherence. The characteristics of non-adherence profiles eligible for improvement included patients with a prior history of hypertension, diabetes mellitus and stroke as co-morbidities, and Medicare plan. CONCLUSIONS: We found consistent patterns of intermediate non-adherence for each of four drug classes for MI cohorts in the order of 56% who are eligible for interventions aimed at improving cardiovascular medication adherence levels. These insights may help improve cardiovascular medication adherence using large medication non-adherence improvement programs.

Effects of multimorbidity on incident COVID-19 events and its interplay with COVID-19 event status on subsequent incident myocardial infarction (MI).

BACKGROUND: With the spread of COVID-19 pandemic, there have been reports on its impact on incident myocardial infarction (MI) emanating from studies with small to modest sample sizes. We therefore examined the incidence of MI in a very large population health cohort with COVID-19 using a methodology which integrates the dynamicity of prior comorbid history. We used two approaches, i.e. main effect modelling and a machine learning (ML) methodology, accounting for the complex dynamic relationships among comorbidity and other variables. METHODS: We studied a very large prospective 18-90-year US population, including 4,289,481 patients from medical databases in a 12-month investigation of those with/without newly incident COVID-19 cases together with a 2-year comorbid profile in the baseline period. Incident MI outcomes were examined in relationship to diverse multimorbid conditions, COVID-19 status and demographic variables-with ML accounting for the dynamic nature of changing multimorbidity risk factors. RESULTS: Multimorbidity, defined as a composite of cardiometabolic/noncardiometabolic comorbid profile, significantly contributed to the onset of confirmed COVID-19 cases. Furthermore, a main effect model (C-index value 0.932; 95%CI 0.930-0.934) had medium to large effect sizes with incident MI outcomes in a COVID-19 cohort for the classic multimorbid conditions in medical history profile which includes prior coronary artery disease (OR 4.61 95%CI 4.49-4.73); hypertension (OR 3.55 95%CI 3.55-3.83); congestive heart failure (2.31 95%CI 2.24-2.37); valvular disease (1.43 95%CI 1.39-1.47); stroke (1.30 95%CI 1.26-1.34); and diabetes (1.26 95%CI 1.23-1.34). COVID-19 status (1.86 95%CI 1.79-1.93) contributed an independent large size risk effect for incident MI. The ML algorithm demonstrated better discriminatory validity than the main effect model (training: C-index 0.949, 95%CI 0.948-0.95; validation: C-index 0.949, 95%CI 0.948-0.95). Calibration of the ML-based formulation was satisfactory and better than the main effect model. Decision curve analysis demonstrated that the ML clinical utility was better than the 'treat all' strategy and the main effect model. The ML logistic regression model was better than the neural network algorithm. CONCLUSION: The very large investigation conducted herein confirmed the importance of cardiometabolic and noncardiometabolic multimorbidity in increasing vulnerabilities to a higher risk of COVID-19 infections. Furthermore, the presence of COVID-19 infections increased incident MI complications both in terms of independent effects and interactions with the multimorbid profile and age.

Improving Stroke Risk Prediction in the General Population: A Comparative Assessment of Common Clinical Rules, a New Multimorbid Index, and Machine-Learning-Based Algorithms.

BACKGROUND: There are few large studies examining and predicting the diversified cardiovascular/noncardiovascular comorbidity relationships with stroke. We investigated stroke risks in a very large prospective cohort of patients with multimorbidity, using two common clinical rules, a clinical multimorbid index and a machine-learning (ML) approach, accounting for the complex relationships among variables, including the dynamic nature of changing risk factors. METHODS: We studied a prospective U.S. cohort of 3,435,224 patients from medical databases in a 2-year investigation. Stroke outcomes were examined in relationship to diverse multimorbid conditions, demographic variables, and other inputs, with ML accounting for the dynamic nature of changing multimorbidity risk factors, two clinical risk scores, and a clinical multimorbid index. RESULTS: Common clinical risk scores had moderate and comparable c indices with stroke outcomes in the training and external validation samples (validation-CHADS2: c index 0.812, 95% confidence interval [CI] 0.808-0.815; CHA2DS2-VASc: c index 0.809, 95% CI 0.805-0.812). A clinical multimorbid index had higher discriminant validity values for both the training/external validation samples (validation: c index 0.850, 95% CI 0.847-0.853). The ML-based algorithms yielded the highest discriminant validity values for the gradient boosting/neural network logistic regression formulations with no significant differences among the ML approaches (validation for logistic regression: c index 0.866, 95% CI 0.856-0.876). Calibration of the ML-based formulation was satisfactory across a wide range of predicted probabilities. Decision curve analysis demonstrated that clinical utility for the ML-based formulation was better than that for the two current clinical rules and the newly developed multimorbid tool. Also, ML models and clinical stroke risk scores were more clinically useful than the "treat all" strategy. CONCLUSION: Complex relationships of various comorbidities uncovered using a ML approach for diverse (and dynamic) multimorbidity changes have major consequences for stroke risk prediction. This approach may facilitate automated approaches for dynamic risk stratification in the significant presence of multimorbidity, helping in the decision-making process for risk assessment and integrated/holistic management.

Acute chemical incidents surveillance­Hazardous Substances Emergency Events Surveillance, nine states, 1999-2008.

PROBLEM/CONDITION: Although they are infrequent, acute chemical incidents (i.e., uncontrolled or illegal release or threatened release of hazardous substances lasting <72 hours) with mass casualties or extraordinary levels of damage or disruption severely affecting the population, infrastructure, environment, and economy occur, and thousands of less damaging chemical incidents occur annually. Surveillance data enable public health and safety professionals to better understand the patterns and causes of these incidents, which can improve prevention efforts and preparation for future incidents. REPORTING PERIOD: 1999-2008. DESCRIPTION OF SYSTEM: The Hazardous Substances Emergency Events Surveillance (HSEES) system was operated by the Agency for Toxic Substances and Disease Registry (ATSDR) during January 1991-September 2009 to describe the public health consequences of chemical releases and to develop activities aimed at reducing the harm. This report provides a historical overview of HSEES and summarizes incidents from the nine states (Colorado, Iowa, Minnesota, New York, North Carolina, Oregon, Texas, Washington, and Wisconsin) that participated in HSEES during its last 10 full years of data collection (1999-2008). RESULTS: During 1999-2008, a total of 57,975 chemical incidents occurred: 41,993 (72%) occurred at fixed facilities, and 15,981 (28%) were transportation related. Chemical manufacturing (NAICS 325) (23%) was the industry with the most incidents; however, the number of chemical incidents in chemical manufacturing decreased substantially over time (R² = 0.78), whereas the educational services category (R² = 0.65) and crop production category (R² = 0.61) had a consistently increasing trend. The most common contributing factors for an incident were equipment failure (n = 22,535, 48% of incidents) and human error (n = 16,534, 36%). The most frequently released chemical was ammonia 3,366 (6%). Almost 60% of all incidents occurred in two states, Texas and New York. A decreasing trend occurred in the number of incidents in Texas, Wisconsin, and Colorado, and an increasing trend occurred in Minnesota. INTERPRETATION: Although chemical manufacturing accounted for the largest percentage of incidents in HSEES, the number of chemical incidents over time decreased substantially for this industry while heightened awareness and prevention measures were being implemented. However, incidents in educational services and crop production settings increased. Trends in incidents and number of incidents varied by state. Only a certain few chemicals, sectors, and areas were found to be related to the majority of incidents and injured persons. Equipment failure and human error, both common casual factors, are preventable. PUBLIC HEALTH IMPLICATIONS: The findings in this collection of surveillance summaries underscore the need for educational institutions and the general public to receive more focused outreach. In addition, the select few chemicals and industries that result in numerous incidents can be the focus of prevention activities. The data in these surveillance summaries show that equipment maintenance, as well as training to prevent human error, could alleviate many of the incidents; NTSIP has begun work in these areas. State surveillance allows a state to identify its problem areas and industries and chemicals for prevention and preparedness. Beginning in 2010, ATSDR replaced HSEES with the National Toxic Substance Incidents Program (NTSIP) to expand on the work of HSEES. NTSIP helps states to collect surveillance data and to promote cost-effective, proactive measures such as converting to an inherently safer design, developing geographic mapping of chemically vulnerable areas, and adopting the principles of green chemistry (design of chemical products and processes that reduce or eliminate the generation of hazardous substances). Because the more populous states such as New York and Texas had the most incidents, areas with high population density should be carefully assessed for preparedness and prevention measures. NTSIP develops estimated incident numbers for states that do not collect data to help with state and national planning. NTSIP also collects more detailed data on chemical incidents with mass casualties. HSEES and NTSIP data can be used by public and environmental health and safety practitioners, worker representatives, emergency planners, preparedness coordinators, industries, emergency responders, and others to prepare for and prevent chemical incidents and injuries.