Risk adjustment and observation time: comparison between cross-sectional and 2-year panel data from the Medical Expenditure Panel Survey (MEPS)

Mental Illness Diagnoses May Not Cause All Mental Symptoms: A Simulation Study for Major Depressive Episodes, Dysthymic Disorder, and Manic Episodes

Objectives This study aims to understand the statistical significance of the associations between diagnoses and symptoms based on simulations that have been used to understand the interpretability of mental illness diagnoses. Methods The symptoms for the diagnosis of major depressive episodes, dysthymic disorder, and manic episodes were extracted from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR, American Psychiatric Association, Philadelphia, Pennsylvania). Without real-world symptom data, we simulated populations using various combinations of symptom prevalence and correlations. Assuming symptoms occurred with similar prevalence and correlations, for each combination of symptom prevalence (0.05, 0.1, 0.3, 0.5, and 0.7) and correlation (0, 0.1, 0.4, 0.7, and 0.9), 100 cohorts with 10,000 individuals were randomly created. Diagnoses were made according to the DSM-IV-TR criteria. The associations between the diagnoses and their input symptoms were quantified with odds ratios and correlation coefficients. P-values from 100 cohorts for each combination of symptom prevalence and correlation were summarized. Results Three mental illness diagnoses were not significantly correlated with their own symptoms in all simulations, particularly when symptoms were not correlated, except for the symptom in the major criteria of major depressive episodes or dysthymic disorder. The symptoms for the diagnosis of major depressive episodes and dysthymic disorder were significantly correlated with these two diagnoses in some simulations, assuming 0.1, 0.4, 0.7, or 0.9 symptom correlations, except for one symptom. The overlap in the input symptoms for the diagnosis of major depressive episodes and dysthymic disorder also leads to significant correlations between these two diagnoses, assuming 0.1, 0.4, 0.7, and 0.9 correlations between input symptoms. Manic episodes are not significantly associated with the input symptoms of major depressive episodes and dysthymic disorder. Conclusion There are challenges to establish the causation between psychiatric symptoms and mental illness diagnoses. There is insufficient prevalence and incidence data to show all psychiatric symptoms exist or can be observed in patients. The diagnostic accuracy of symptoms to detect a disease cause is far from perfect. Assuming the symptoms of three mood disorders may present in patients, three diagnoses are not significantly associated with all psychiatric symptoms used to diagnose them. The diagnostic criteria of the three diagnoses have not been designed to guarantee significant associations between symptoms and diagnoses. Because statistical associations are important for making causal inferences, there may be a lack of causation between diagnoses and symptoms. Previous research has identified factors that lead to insignificant associations between diagnoses and symptoms, including biases due to data processing and a lack of epidemiological evidence to support the design of mental illness diagnostic criteria.

Critical Hierarchical Appraisal and repOrting tool for composite measureS (CHAOS)

Background Composite measures are often used to represent certain concepts that cannot be measured with single variables and can be used as diagnoses, prognostic factors, or outcomes in clinical or health research. For example, frailty is a diagnosis confirmed based on the number of age-related symptoms and has been used to predict major health outcomes. However, undeclared assumptions and problems are prevalent among composite measures. Thus, we aim to propose a reporting guide and an appraisal tool for identifying these assumptions and problems. Methods We developed this reporting and assessment tool based on evidence and the consensus of experts pioneering research on index mining and syndrome mining. We designed a development framework for composite measures and then tested and revised it based on several composite measures commonly used in medical research, such as frailty, body mass index (BMI), mental illness diagnoses, and innovative indices mined for mortality prediction. We extracted review questions and reporting items from various issues identified by the development framework. This panel reviewed the identified issues, considered other aspects that might have been neglected in previous studies, and reached a consensus on the questions to be used by the reporting and assessment tool. Results We selected 19 questions in seven domains for reporting or critical assessment. Each domain contains review questions for authors and readers to critically evaluate the interpretability and validity of composite measures, which include candidate variable selection, variable inclusion and assumption declaration, data processing, weighting scheme, methods to aggregate information, composite measure interpretation and justification, and recommendations on the use. Conclusions For all seven domains, interpretability is central with respect to composite measures. Variable inclusion and assumptions are important clues to show the connection between composite measures and their theories. This tool can help researchers and readers understand the appropriateness of composite measures by exploring various issues. We recommend using this Critical Hierarchical Appraisal and repOrting tool for composite measureS (CHAOS) along with other critical appraisal tools to evaluate study design or risk of bias.

Simulation study to demonstrate biases created by diagnostic criteria of mental illnesses: major depressive episodes, dysthymia, and manic episodes

OBJECTIVES

Composite diagnostic criteria alone are likely to create and introduce biases into diagnoses that subsequently have poor relationships with input symptoms. This study aims to understand the relationships between the diagnoses and the input symptoms, as well as the magnitudes of biases created by diagnostic criteria and introduced into the diagnoses of mental illnesses with large disease burdens (major depressive episodes, dysthymic disorder, and manic episodes).

SETTINGS

General psychiatric care.

PARTICIPANTS

Without real-world data available to the public, 100 000 subjects were simulated and the input symptoms were assigned based on the assumed prevalence rates (0.05, 0.1, 0.3, 0.5 and 0.7) and correlations between symptoms (0, 0.1, 0.4, 0.7 and 0.9). The input symptoms were extracted from the diagnostic criteria. The diagnostic criteria were transformed into mathematical equations to demonstrate the sources of biases and convert the input symptoms into diagnoses.

PRIMARY AND SECONDARY OUTCOMES

The relationships between the input symptoms and diagnoses were interpreted using forward stepwise linear regressions. Biases due to data censoring or categorisation introduced into the intermediate variables, and the three diagnoses were measured.

RESULTS

The prevalence rates of the diagnoses were lower than those of the input symptoms and proportional to the assumed prevalence rates and the correlations between the input symptoms. Certain input or bias variables consistently explained the diagnoses better than the others. Except for 0 correlations and 0.7 prevalence rates of the input symptoms for the diagnosis of dysthymic disorder, the input symptoms could not fully explain the diagnoses.

CONCLUSIONS

There are biases created due to composite diagnostic criteria and introduced into the diagnoses. The design of the diagnostic criteria determines the prevalence of the diagnoses and the relationships between the input symptoms, the diagnoses, and the biases. The importance of the input symptoms has been distorted largely by the diagnostic criteria.

Drug trends among non-institutionalized Canadians and the impact of data collection changes in the Canadian Health Measures Survey 2007 to 2015

BACKGROUND

There is a global trend of increasing use in prescription and over-the-counter (OTC) drugs. This hasn't been verified in Canada. In addition, there are changes made to the collection method of medication information after the Canadian Health Measures Survey (CHMS) cycle 2. This study aims to review the potential impact of the changes in medication data collection and the trends in medication use if data quality remains similar throughout the CHMS cycles 1 to 4. This is fundamental for the analysis of this biomonitoring database.

METHODS

The CHMS cycle 1 to 4 medication and household data were used to study the trends of medication use between 2007 and 2015. The use of prescription or OTC drugs was grouped based on the first levels of the Anatomical Therapeutic Chemical (ATC) Classification system. The total numbers of medications were asked in all cycles. However, only a maximum of 15 and 5 drugs could be respectively reported for existing and new prescription or OTC drugs in cycles 1 and 2. There were no restrictions on drug reporting after cycle 2. The trends of medication use were described as ratios, compared to cycle 1.

RESULTS

The total numbers of the types of medication ever identified decreased from 739 to 603 between cycles 1 and 4. The proportions of using any drugs were from 0.90 to 0.88 between cycles 1 and 4 (ratio = 1.08 in cycle 4, 95% CI = 0.89 to 1.26). The numbers of drugs in use were from 3.9 to 3.8 (ratio = 1.05 in cycle 4, 95% CI = 0.86 to 1.24). The proportions of prescription drug use were from 0.53 to 0.55 (ratio = 1.13 in cycle 4, 95% CI = 0.89 to 1.37), while the numbers of prescription were from 1.51 to 1.68 (ratio = 1.20 in cycle 4, 95% CI = 0.92 to 1.48). The use of diabetes and thyroid medication had trends similar to the respective disease prevalence. The use and the numbers of drugs for blood and blood forming organs significantly increased between cycles 1 and 4 (ratio = 1.56 in cycle 4, 95% CI = 1.03 to 2.10).

CONCLUSIONS

There is an increasing trend in the use of blood and blood forming agents through cycles 2 to 4 and cardiovascular drugs in cycle 3. For diabetes and thyroid medication, the proportions of medication use increase proportionally with disease prevalence. The changes in the medication information collection method may not have important impact on the reporting of the use of prescription or OTC drugs.

Trend analysis for national surveys: Application to all variables from the Canadian Health Measures Survey cycle 1 to 4

BACKGROUND

Trend analysis summarizes patterns over time in the data to show the direction of change and can be used to investigate uncertainties in different time points and associations with other factors. However, this approach is not widely applied to national surveys and only selected outcomes are investigated. This study demonstrates a research framework to conduct trend analysis for all variables in a national survey, the Canadian Health Measures Survey (CHMS).

DATA AND METHODS

The CHMS cycle 1 to 4 was implemented between 2007 and 2015. The characteristics of all variables were screened and associated to the weight variables. Missing values were identified and cleaned according to the User Guide. The characteristics of all variables were extracted and used to guide data cleaning. Trend analysis examined the statistical significance of candidate predictors: the cycles, age, sex, education, household income and body mass index (BMI). R (v3.2) and RStudio (v0.98.113) were used to develop the framework.

RESULTS

There were 26557 variables in 79 data files from four cycles. There were 1055 variables significantly associated with the CHMS cycles and 2154 associated with the BMI after controlling for other predictors. The trend of blood pressure was similar to those published.

CONCLUSION

Trend analysis for all variables in the CHMS is feasible and is a systematic approach to understand the data. Because of trend analysis, we have detected data errors and identified several environmental biomarkers with extreme rates of change across cycles. The impact of these biomarkers has not been well studied by Statistics Canada or others. This framework can be extended to other surveys, especially the Canadian Community Health Survey.

Stages of Biological Development across Age: An Analysis of Canadian Health Measure Survey 2007-2011

Introduction

The stages of biological development are not clearly defined despite the fact that they have been used to refer to concepts such as adolescence and aging. This study aimed to (1) propose and test a framework to search for stages of representative components and determine stages of stability and transition, (2) identify stages of biological development based on health questionnaire and biomarker data, and (3) interpret the major trajectories in a health and biomarker database.

Methods

This study analyzed the data on the Canadian Health Measures Survey (CHMS) interviewees from cycle 1 to 3 (2007–2013) in Canada. We selected 282 variables containing information from questionnaire and on biomarkers after removing redundant variables based on high correlation. Fifty-nine nominal variables were replaced by 122 binominal variables, leaving 345 variables for analysis. Principal component (PC) analysis was conducted to summarize the data and the loadings were used to interpret the PCs. A stable stage was assumed to be the age groups without significantly different values of PCs.

Results

The CHMS interviewed 16,340 Canadians. Of all, 51.25% were female. The age ranged from 6 to 79 years (mean = 34.41 years, 95% CI = 34.74–34.08). The proportions of total variance explained by the first three PCs were 12.14, 4.03, and 3.19%, respectively. The differences of the first PC were not significant, especially between age 22 and 33, 34 and 40, 41 and 45, 46 and 71, and 72 and 79 years (adjusted p > 0.05 for all). The leading variable, in terms of the variance contributed to PC1, was time spent in physical activities, followed by variables related to alcohol consumption, and smoking. The 13 leading contributors to PC2 variances were all lung function measures.

Discussion and conclusion

There are stages of stability and transition across all age groups based on the first PCs. The first and second PCs are related to physical development and lung function. The identification of stable stages is the first step to understanding how human biology develops in a population perspective and will be important for research that relies on a research population with similar characteristics to draw samples for observation or intervention.