Variation of the prevalence of pediatric polypharmacy: A scoping review.

PURPOSE: To examine the range of prevalence of pediatric polypharmacy in literature through a scoping review, focusing on factors that contribute to its heterogeneity in order to improve the design and reporting of quality improvement, pharmacovigilance, and research studies. METHODS: We searched Ovid Medline, PubMed, EMBASE, CINAHL, Ovid PsycINFO, Cochrane CENTRAL, and Web of Science Core Collection databases for studies with concepts of children and polypharmacy, along with a hand search of the bibliographies of six reviews and 30 included studies. We extracted information regarding study design, disease conditions, and prevalence of polypharmacy. RESULTS: Two hundred eighty-four studies reported prevalence of polypharmacy. They were more likely to be conducted in North America (37.7%), published after 2010 (44.4%), cross-sectional (67.3%), in outpatient settings (59.5%). Prevalence ranged from 0.9% to 98.4%, median 39.7% (interquartile range [IQR] 22.0%-54.0%). Studies from Asia reported the highest median prevalence of 45.4% (IQR 27.3%-61.0%) while studies from North America reported the lowest median prevalence of 30.4% (IQR 14.7%-50.2%). Prevalence decreased over time: median 45.6% before 2001, 38.1% during 2001 to 2010, and 34% during 2011 to 2017. Studies involving children under 12 years had a higher median prevalence (46.9%) than adolescent studies (33.7%). Inpatient setting studies had a higher median prevalence (50.3%) than studies in outpatient settings (38.8%). Community level samples, higher number and duration of medications defining polypharmacy, and psychotropic medications were associated with lower prevalence. CONCLUSIONS: The prevalence of pediatric polypharmacy is high and variable. Studies reporting pediatric polypharmacy should account for context, design, polypharmacy definition, and medications evaluated.

Functional health status of adults with serious mental illness and diabetes mellitus: A latent profile analysis.

OBJECTIVE: Adults with serious mental illness are at increased risk for diabetes mellitus and diabetes-related complications. This article classifies subgroups among people with serious mental illness and comorbid diabetes with respect to functional status and examines differences among those groups. METHODS: This analysis used a baseline sample of 157 adults with serious mental illness and diabetes mellitus enrolled in a National Institute of Health-funded research study. Latent profile analysis was used to distinguish health status profiles and investigate how these subgroups differed across assessment domains. RESULTS: Participants with depression, schizophrenia, and bipolar disorder (n = 157) were included in the study. Mean age was 52.9 years (standard deviation = 9.8), and 62 (40%) were African American. From the latent profile analysis, a three-class model appeared to provide the best fit. Class 1 (34.9%) had a very low functional health status approximately two standard deviations below the general population mean. Class 2 (43.7%) had a low functional status approximately one standard deviation below the general mean. Class 3 (21.4%) had moderate functional status with scores near population mean. Groups differed on measures of personal characteristics, clinical status and symptom severity, self-care behaviors, and environmental characteristics. CONCLUSIONS: Although individuals with schizophrenia generally have poor prognosis once they develop diabetes, latent profile analysis identified distinct health status subgroups. Although all three groups demonstrated illness burden, the pattern of differences between these groups across measures may suggest the need for different interventions for highly diverse adults who received care within safety-net primary care.

Accuracy of Cardiovascular Risk Prediction Varies by Neighborhood Socioeconomic Position: A Retrospective Cohort Study.

BACKGROUND: Inequality in health outcomes in relation to Americans' socioeconomic position is rising. OBJECTIVE: First, to evaluate the spatial relationship between neighborhood disadvantage and major atherosclerotic cardiovascular disease (ASCVD)-related events; second, to evaluate the relative extent to which neighborhood disadvantage and physiologic risk account for neighborhood-level variation in ASCVD event rates. DESIGN: Observational cohort analysis of geocoded longitudinal electronic health records. SETTING: A single academic health center and surrounding neighborhoods in northeastern Ohio. PATIENTS: 109 793 patients from the Cleveland Clinic Health System (CCHS) who had an outpatient lipid panel drawn between 2007 and 2010. The date of the first qualifying lipid panel served as the study baseline. MEASUREMENTS: Time from baseline to the first occurrence of a major ASCVD event (myocardial infarction, stroke, or cardiovascular death) within 5 years, modeled as a function of a locally derived neighborhood disadvantage index (NDI) and the predicted 5-year ASCVD event rate from the Pooled Cohort Equations Risk Model (PCERM) of the American College of Cardiology and American Heart Association. Outcome data were censored if no CCHS encounters occurred for 2 consecutive years or when state death data were no longer available (that is, from 2014 onward). RESULTS: The PCERM systematically underpredicted ASCVD event risk among patients from disadvantaged communities. Model discrimination was poorer among these patients (concordance index [C], 0.70 [95% CI, 0.67 to 0.74]) than those from the most affluent communities (C, 0.80 [CI, 0.78 to 0.81]). The NDI alone accounted for 32.0% of census tract-level variation in ASCVD event rates, compared with 10.0% accounted for by the PCERM. LIMITATIONS: Patients from affluent communities were overrepresented. Outcomes of patients who received treatment for cardiovascular disease at Cleveland Clinic were assumed to be independent of whether the patients came from a disadvantaged or an affluent neighborhood. CONCLUSION: Neighborhood disadvantage may be a powerful regulator of ASCVD event risk. In addition to supplemental risk models and clinical screening criteria, population-based solutions are needed to ameliorate the deleterious effects of neighborhood disadvantage on health outcomes. PRIMARY FUNDING SOURCE: The Clinical and Translational Science Collaborative of Cleveland and National Institutes of Health.

Associations among comorbid anxiety, psychiatric symptomatology, and diabetic control in a population with serious mental illness and diabetes: Findings from an interventional randomized controlled trial.

Objective Serious mental illness and type II diabetes mellitus have a high comorbidity, and both have a higher prevalence of anxiety disorders compared to the general population. Targeted Training in Illness Management is a group-based self-management training approach which targets serious mental illness and type II diabetes mellitus concurrently. This analysis examines data from a randomized controlled trial of Targeted Training in Illness Management intervention to examine the impact of comorbid anxiety on baseline psychiatric symptomatology and diabetic control, and on longitudinal treatment outcomes. Methods We conducted secondary analyses on data from a prospective, 60-week, randomized controlled trial testing Targeted Training in Illness Management versus treatment as usual in 200 individuals with serious mental illness and diabetes. Primary outcomes included measures related to serious mental illness symptoms, functional status, general health status, and diabetes control. Measures were compared between those participants with anxiety disorders versus those without anxiety at baseline as well as over time using linear mixed effects analyses. Results Forty seven percent of the participants had one or more anxiety disorders. At baseline, those with an anxiety diagnosis had higher illness severity, depressive, and other psychiatric symptomatology and disability. Diabetic control (HbA1c) was not significantly different at baseline. In the longitudinal analyses, no significant mean slope differences over time (group-by-time interaction effect) between those with anxiety diagnoses and those without in treatment as usual group were found for primary outcomes. Within the Targeted Training in Illness Management arm, those with anxiety disorders had significantly greater improvement in mental health functioning. Those with anxiety comorbidity in the Targeted Training in Illness Management group demonstrated significantly lower HbA1c levels compared to no anxiety comorbidity and also demonstrated a greater improvement in HbA1c over the first 30 weeks compared to those without anxiety comorbidity. Conclusion Comorbid anxiety in serious mental illness and type II diabetes mellitus population is associated with increased psychiatric symptomatology and greater disability. Individuals from this population appear to experience greater improvement in functioning from baseline with the Targeted Training in Illness Management intervention. Anxiety comorbidity in the serious mental illness and type II diabetes mellitus population does not appear to have a negative impact on diabetic control. These complex relationships need further study. Clinical Trials Registration ClinicalTrials.gov: Improving outcomes for individuals with serious mental illness and diabetes (NCT01410357).

What Works in a Nurse Led Self-Management Program for Patients with Serious Mental Illness (SMI) and Diabetes (DM).

OBJECTIVE: To develop a deeper understanding of "how" a nurse led self-management intervention in a successful randomized controlled trial (RCT) for individuals with both serious mental illness (SMI) and diabetes (DM) influenced health outcomes and the lives of the participants. METHODS: In depth interviews, were conducted with a sample of 10 participants who received the self-management intervention in the RCT. Interviews were audiotaped, transcribed verbatim, and analyzed using content analysis, with an emphasis on dominant themes. RESULTS: The mean age of the respondents was 53.9years (SD=5.6); 6 (60%) were women and the mean level of education was 12.4years (SD=2.4). Transcript based analysis generated 3 major mechanisms of action that led to improved self-management of their SMI and DM: (Colton & Manderscheid, 2006) positive group experience, (Osborn et al., 2008) increased health knowledge, and (Newcomer, 2005) increased self- confidence. CONCLUSIONS: Developing complex interventions for testing in RCTs of individuals with SMI and other comorbid conditions is of increasing importance in healthcare planning for this vulnerable population. Using qualitative methods to explore mechanisms of action underlying quantitative outcomes, can enrich our understanding of processes relevant for individuals with SMI and comorbid conditions.

Practice Variation Among Hospitals in Revascularization Therapy and Its Association With Procedure-related Mortality.

BACKGROUND: While substantial practice variation in coronary revascularization has been described and deviation from clinical practice guidelines has been associated with worse outcomes, the degree to which this is driven by flawed decision making and/or appropriate deviation associated with comorbid conditions is unknown. We evaluated heterogeneity in procedure use, and the extent to which hospital-level practice variation is related to surgical mortality. METHODS: We analyzed data on 554,563 inpatients undergoing either percutaneous coronary intervention or coronary artery bypass grafting at 391 centers in 6 states. Procedure-specific risk models were developed based on demographics and comorbidities, allowing for differential effects of comorbidities for each sex. For each patient, the revascularization procedure that minimized predicted probability of inhospital mortality was designated as the model-preferred procedure.Hospital-level discordance rates-the proportion of cases in each hospital for which the opposite from the model-preferred procedure was performed-were calculated. Hierarchical linear models were used to analyze the relationship between HDRs and hospital-level risk-standardized mortality ratios (RSMRs). RESULTS: Comorbidities and demographics alone explained between 68% and 86% of overall variation in inhospital mortality (corresponding C-statistics of 0.84-0.93). The mean (SD) HDR was 26.3% (9.6%). There was a positive independent association between HDRs and inhospital mortality, with a 10% increase in HDR associated with an 11% increase in RSMR (P<0.001). CONCLUSIONS: Variance in procedure use according to model preference was strongly associated with worse outcomes. A systematic approach to incorporating comorbidity as part of the decision-making process for coronary revascularization is needed.

A Nursing Approach to Self-Management Education for Individuals With Mental Illness and Diabetes.

Patients with serious mental illness (SMI) and diabetes often seek care in primary care settings and have worse health outcomes than patients who have either illness alone. Individual, provider, and system-level barriers present challenges to addressing both psychiatric and medical comorbidities. This article describes the feasibility, acceptability, and implementation of Targeted Training and Illness Management (TTIM), a self-management intervention delivered by trained nurse educators and peer educators to groups of individuals with SMI and diabetes to improve self-management of both diseases. TTIM is intended to be delivered in a primary care setting. Findings are intended to support the future development of nurse-led programs within the primary care setting that teach self-management to individuals with concurrent SMI and diabetes. This approach supports both adaptability and flexibility in delivering the intervention. Interventions such as TTIM can provide self-management skills, accommodate people with both SMI and diabetes in primary care settings such as patient-centered medical homes, and address known barriers to access.

Playing the numbers: how hepatitis C patients create meaning and make healthcare decisions from medical test results.

In this article we describe how patients assign meanings to medical test results and use these meanings to justify their actions. Evidence is presented from lay interpretations of medical tests for monitoring hepatitis C viral infection (HCV) to show how numeracy becomes embodied in the absence of physical symptoms. Illness narratives from 307 individuals infected with HCV were collected from the internet and analysed qualitatively. As part of standard medical care, chronically infected HCV patients are required to have periodic blood tests for laboratory testing. The lab results are presented numerically and compared with established physiological standards. HCV patients' knowledge and interpretations of test results have important consequences for their health behaviour and their medical decisions. In their stories, the patients described their decisions to begin, delay or stop treatment and developed strategies to alter their diet, exercise and use alternative therapies according to changes in their test result. The perceived meanings of test results are powerful signifiers that are capable of altering the course of HCV patients' illness, lives and stories. An interpretive model of health numeracy has the advantage of promoting understanding between patients and healthcare providers over a model that views innumeracy as a skill deficit.

Racial, ethnic and neighborhood socioeconomic differences in incidence of dementia: A regional retrospective cohort study.

BACKGROUND: Evidence on the effects of neighborhood socioeconomic disadvantage on dementia risk in racially and ethically diverse populations is limited. Our objective was to evaluate the relative extent to which neighborhood disadvantage accounts for racial/ethnic variation in dementia incidence rates. Secondarily, we evaluated the spatial relationship between neighborhood disadvantage and dementia risk. METHODS: In this retrospective study using electronic health records (EHR) at two regional health systems in Northeast Ohio, participants included 253,421 patients aged >60 years who had an outpatient primary care visit between January 1, 2005 and December 31, 2015. The date of the first qualifying visit served as the study baseline. Cumulative incidence of composite dementia outcome, defined as EHR-documented dementia diagnosis or dementia-related death, stratified by neighborhood socioeconomic deprivation (as measured by Area Deprivation Index) was determined by competing-risk regression analysis, with non-dementia-related death as the competing risk. Fine-Gray sub-distribution hazard ratios were determined for neighborhood socioeconomic deprivation, race/ethnicity, and clinical risk factors. The degree to which neighborhood socioeconomic position accounted for racial/ethnic disparities in the incidence of composite dementia outcome was evaluated via mediation analysis with Poisson rate models. RESULTS: Increasing neighborhood disadvantage was associated with increased risk of EHR-documented dementia diagnosis or dementia-related death (most vs. least disadvantaged ADI quintile HR = 1.76, 95% confidence interval = 1.69-1.84) after adjusting for age and sex. The effect of neighborhood disadvantage on this composite dementia outcome remained after accounting for known medical risk factors of dementia. Mediation analysis indicated that neighborhood disadvantage accounted for 34% and 29% of the elevated risk for composite dementia outcome in Hispanic and Black patients compared to White patients, respectively. CONCLUSION: Neighborhood disadvantage is related to the risk of EHR-documented dementia diagnosis or dementia-related death and accounts for a portion of racial/ethnic differences in dementia burden, even after adjustment for clinically important confounders.

The oncology acuity tool: a reliable, valid method for measuring patient acuity for nurse assignment decisions.

BACKGROUND AND PURPOSE: Matching nurse assignments with patient acuity has critical implications for providing safe, effective, and efficient care. Despite this, we lack well-established methods for accurate assessment of acuity. This study aimed to evaluate the reliability and validity of the Oncology Acuity Tool (OAT), which is used for determining nurse assignments. METHODS: Inter-rater reliability and concurrent validity were assessed via surveys of current users of the tool. Content validity data were collected from expert oncology nurses. Predictive validity was assessed by tracking patients who sustained either of two acute events. RESULTS: Findings included high inter-rater reliability, moderately strong concurrent validity, and moderate content validity. Acuity significantly predicted rapid response team consults but not falls. CONCLUSIONS: The OAT demonstrated sufficient reliability and validity for measuring acuity prospectively in this population.

Evaluating and Modeling Neighborhood Diversity and Health Using Electronic Health Records.

BACKGROUND: Electronic health records (EHRs) provide researchers with abundant sample sizes, detailed clinical data, and other advantages for performing high-quality observational health research on diverse populations. We review and demonstrate strategies for the design and analysis of cohort studies on neighborhood diversity and health, including evaluation of the effects of race, ethnicity, and neighborhood socioeconomic position on disease prevalence and health outcomes, using localized EHR data. METHODS: Design strategies include integrating and harmonizing EHR data across multiple local health systems and defining the population(s) of interest and cohort extraction procedures for a given analysis based on the goal(s) of the study. Analysis strategies address inferential goals, including the mechanistic study of social risks, statistical adjustment for differences in distributions of social and neighborhood-level characteristics between available EHR data and the underlying local population, and inference on individual neighborhoods. We provide analyses of local variation in mortality rates within Cuyahoga County, Ohio. RESULTS: When the goal of the analysis is to adjust EHR samples to be more representative of local populations, sampling and weighting are effective. Causal mediation analysis can inform effects of racism (through racial residential segregation) on health outcomes. Spatial analysis is appealing for large-scale EHR data as a means for studying heterogeneity among neighborhoods even at a given level of overall neighborhood disadvantage. CONCLUSIONS: The methods described are a starting point for robust EHR-derived cohort analysis of diverse populations. The methods offer opportunities for researchers to pursue detailed analyses of current and historical underlying circumstances of social policy and inequality. Investigators can employ combinations of these methods to achieve greater robustness of results. HIGHLIGHTS: EHR data are an abundant resource for studying neighborhood diversity and health.When using EHR data for these studies, careful consideration of the goals of the study should be considered in determining cohort specifications and analytic approaches.Causal mediation analysis, stratification, and spatial analysis are effective methods for characterizing social mechanisms and heterogeneity across localized populations.

Opportunities, Pitfalls, and Alternatives in Adapting Electronic Health Records for Health Services Research.

Electronic health records (EHRs) offer the potential to study large numbers of patients but are designed for clinical practice, not research. Despite the increasing availability of EHR data, their use in research comes with its own set of challenges. In this article, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be nonrepresentative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building 2 large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets.

Mechanisms of socioeconomic differences in COVID-19 screening and hospitalizations.

BACKGROUND: Social and ecological differences in early SARS-CoV-2 pandemic screening and outcomes have been documented, but the means by which these differences have arisen are not well understood. OBJECTIVE: To characterize socioeconomic and chronic disease-related mechanisms underlying these differences. DESIGN: Observational cohort study. SETTING: Outpatient and emergency care. PATIENTS: 12900 Cleveland Clinic Health System patients referred for SARS-CoV-2 testing between March 17 and April 15, 2020. INTERVENTIONS: Nasopharyngeal PCR test for SARS-CoV-2 infection. MEASUREMENTS: Test location (emergency department, ED, vs. outpatient care), COVID-19 symptoms, test positivity and hospitalization among positive cases. RESULTS: We identified six classes of symptoms, ranging in test positivity from 3.4% to 23%. Non-Hispanic Black race/ethnicity was disproportionately represented in the group with highest positivity rates. Non-Hispanic Black patients ranged from 1.81 [95% confidence interval: 0.91-3.59] times (at age 20) to 2.37 [1.54-3.65] times (at age 80) more likely to test positive for the SARS-CoV-2 virus than non-Hispanic White patients, while test positivity was not significantly different across the neighborhood income spectrum. Testing in the emergency department (OR: 5.4 [3.9, 7.5]) and cardiovascular disease (OR: 2.5 [1.7, 3.8]) were related to increased risk of hospitalization among the 1247 patients who tested positive. LIMITATIONS: Constraints on availability of test kits forced providers to selectively test for SARS-Cov-2. CONCLUSION: Non-Hispanic Black patients and patients from low-income neighborhoods tended toward more severe and prolonged symptom profiles and increased comorbidity burden. These factors were associated with higher rates of testing in the ED. Non-Hispanic Black patients also had higher test positivity rates.

The emerging problem of diabetes in the seriously mentally ill.

We describe the increasing prevalence of chronic illnesses such as obesity and type 2 diabetes have markedly increased in both developed and developing countries. We describe the relationship between type 2 diabetes and mental illness. The extant literature suggests a critical need for innovative treatments targeted to individuals with comorbid diabetes and mental illness. Given the complexity and challenge of both of these disorders in tandem with the interactive challenges and burdens of psychiatric and medical comorbidity, it is essential that interventions address the issue of mental and medical health from the perspective of the individual with the disorder, engage individuals to actively participate in illness self-management, and include consideration of the multiple barriers to care.

Failure of Traditional Risk Factors to Adequately Predict Cardiovascular Events in Older Populations.

BACKGROUND: Accurate assessment of atherosclerotic cardiovascular disease (ASCVD) risk across heterogeneous populations is needed for effective primary prevention. Little is known about the performance of standard cardiovascular risk factors in older adults. OBJECTIVE: To evaluate the performance of the American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) risk model, as well as the underlying cardiovascular risk factors, among adults older than 65 years. DESIGN AND SETTING: Retrospective cohort derived from a regional referral system's electronic medical records. PARTICIPANTS: A total of 25 349 patients who were 65 years or older at study baseline (date of the first outpatient lipid panel taken between 2007 and 2010). MEASUREMENTS: Exposures of interest were traditional cardiovascular risk factors, as defined by inclusion in the PCE model. The primary outcome was major ASCVD events, defined as a composite of myocardial infarctions, stroke, and cardiovascular death. RESULTS: The PCE and internally estimated models produced similar risk distributions for white men aged 65 to 74 years. For all other groups, PCE predictions were generally lower than those of the internal models, particularly for African Americans. Discrimination of the PCE was poor for all age groups, with concordance index (95% confidence interval) estimates of 0.62 (0.60-0.64), 0.56 (0.54-0.57), and 0.52 (0.49-0.54) among patients aged 65 to 74, 75 to 84, and 85 years and older, respectively. Reestimating relationships within these age groups resulted in better calibration but negligible improvements in discrimination. Blood pressure, total cholesterol, and diabetes either were not associated at all or had inverse associations in the older age groups. CONCLUSION: Traditional clinical risk factors for cardiovascular disease failed to accurately characterize risk in a contemporary population of Medicare-aged patients. Among those aged 85 years and older, some traditional risk factors were not associated with ASCVD events. Better risk models are needed to appropriately inform treatment decision making for the growing population of older adults. J Am Geriatr Soc 68:754-761, 2020.

A Scoping Review of Medications Studied in Pediatric Polypharmacy Research.

PURPOSE: The purpose of this study is to describe medications most commonly studied in pediatric polypharmacy research by pharmacologic classes and disease using a scoping review methodology. METHODS: A search of electronic databases was conducted in July 2019 that included Ovid Medline, PubMed, Elsevier Embase, and EBSCO CINAHL. Primary observational studies were selected if they evaluated polypharmacy as an aim, outcome, predictor, or covariate in children 0-21 years of age. Studies not differentiating between adults and children or those not written in English were excluded. Study characteristics, pharmacologic categories, medication classes, and medications were extracted from the included studies. RESULTS: The search identified 8790 titles and after de-duplicating and full-text screening, 414 studies were extracted for the primary data. Regarding global pharmacologic categories, central nervous system (CNS) agents were most studied (n = 185, 44.9%). The most reported pharmacologic category was the anticonvulsants (n = 250, 60.4%), with valproic acid (n = 129), carbamazepine (n = 123), phenobarbital (n = 87), and phenytoin (n = 83) being the medications most commonly studied. In studies that reported medication classes (n = 105), serotonin reuptake inhibitors (n = 32, 30.5%), CNS stimulants (n = 30, 28.6%), and mood stabilizers (n = 27, 25.7%) were the most studied medication classes. CONCLUSION: While characterizing the literature on pediatric polypharmacy in terms of the types of medication studied, we further identified substantive gaps within this literature outside of epilepsy and psychiatric disorders. Medications frequently identified in use of polypharmacy for treatment of epilepsy and psychiatric disorders reveal opportunities for enhanced medication management in pediatric patients.

Lay management of chronic disease: a qualitative study of living with hepatitis C infection.

OBJECTIVES: To examine management strategies and goals reported by people diagnosed with chronic hepatitis C. METHODS: We analyzed data from semistructured interviews (N = 42) and from electronic sources [illness narratives (N = 79) and Internet threaded discussions (N = 264)]. Line-by-line coding, comparisons, and team discussions generated catalogs of lay management strategies and goals. We analyzed code-based files to identify informants' selection of specific strategies for each goal. RESULTS: We classified lay management strategies into 3 categories: medical self-care, behavior change, and coping. These strategies were used selectively in addressing multiple goals, categorized as fighting the virus, strengthening the body, and managing consequences. CONCLUSIONS: Results underscore the diversity of strategies for living with a disease characterized by uncertain prognosis and variable expression of symptoms.

Risk-period-cohort approach for averting identification problems in longitudinal models.

In epidemiology, gerontology, human development and the social sciences, age-period-cohort (APC) models are used to study the variability in trajectories of change over time. A well-known issue exists in simultaneously identifying age, period and birth cohort effects, namely that the three characteristics comprise a perfectly collinear system. That is, since age = period-cohort, only two of these effects are estimable at a time. In this paper, we introduce an alternative framework for considering effects relating to age, period and birth cohort. In particular, instead of directly modeling age in the presence of period and cohort effects, we propose a risk modeling approach to characterize age-related risk (i.e., a hybrid of multiple biological and sociological influences to evaluate phenomena associated with growing older). The properties of this approach, termed risk-period-cohort (RPC), are described in this paper and studied by simulations. We show that, except for pathological circumstances where risk is uniquely determined by age, using such risk indices obviates the problem of collinearity. We also show that the size of the chronological age effect in the risk prediction model associates with the correlation between a risk index and chronological age and that the RPC approach can satisfactorily recover cohort and period effects in most cases. We illustrate the advantages of RPC compared to traditional APC analysis on 27496 individuals from NHANES survey data (2005-2016) to study the longitudinal variability in depression screening over time. Our RPC method has broad implications for examining processes of change over time in longitudinal studies.