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

Diagnostic accuracy of symptoms for an underlying disease: a simulation study

Symptoms have been used to diagnose conditions such as frailty and mental illnesses. However, the diagnostic accuracy of the numbers of symptoms has not been well studied. This study aims to use equations and simulations to demonstrate how the factors that determine symptom incidence influence symptoms' diagnostic accuracy for disease diagnosis. Assuming a disease causing symptoms and correlated with the other disease in 10,000 simulated subjects, 40 symptoms occurred based on 3 epidemiological measures: proportions diseased, baseline symptom incidence (among those not diseased), and risk ratios. Symptoms occurred with similar correlation coefficients. The sensitivities and specificities of single symptoms for disease diagnosis were exhibited as equations using the three epidemiological measures and approximated using linear regression in simulated populations. The areas under curves (AUCs) of the receiver operating characteristic (ROC) curves was the measure to determine the diagnostic accuracy of multiple symptoms, derived by using 2 to 40 symptoms for disease diagnosis. With respect to each AUC, the best set of sensitivity and specificity, whose difference with 1 in the absolute value was maximal, was chosen. The results showed sensitivities and specificities of single symptoms for disease diagnosis were fully explained with the three epidemiological measures in simulated subjects. The AUCs increased or decreased with more symptoms used for disease diagnosis, when the risk ratios were greater or less than 1, respectively. Based on the AUCs, with risk ratios were similar to 1, symptoms did not provide diagnostic values. When risk ratios were greater or less than 1, maximal or minimal AUCs usually could be reached with less than 30 symptoms. The maximal AUCs and their best sets of sensitivities and specificities could be well approximated with the three epidemiological and interaction terms, adjusted R-squared ≥ 0.69. However, the observed overall symptom correlations, overall symptom incidence, and numbers of symptoms explained a small fraction of the AUC variances, adjusted R-squared ≤ 0.03. In conclusion, the sensitivities and specificities of single symptoms for disease diagnosis can be explained fully by the at-risk incidence and the 1 minus baseline incidence, respectively. The epidemiological measures and baseline symptom correlations can explain large fractions of the variances of the maximal AUCs and the best sets of sensitivities and specificities. These findings are important for researchers who want to assess the diagnostic accuracy of composite diagnostic criteria.

Interpretable Trials: Is Interpretability a Reason Why Clinical Trials Fail?

Background: There are clinical trials using composite measures, indices, or scales as proxy for independent variables or outcomes. Interpretability of derived measures may not be satisfying. Adopting indices of poor interpretability in clinical trials may lead to trial failure. This study aims to understand the impact of using indices of different interpretability in clinical trials.

Methods: The interpretability of indices was categorized as: fair-to-poor, good, and unknown. In the literature, frailty indices were considered fair to poor interpretability. Body mass index (BMI) was highly interpretable. The other indices were of unknown interpretability. The trials were searched at clinicaltrials.gov on October 2, 2018. The use of indices as conditions/diseases or other terms was searched. The trials were grouped as completed, terminated, active, and other status. We tabulated the frequencies of frailty, BMI, and other indices.

Results: There were 263,928 clinical trials found and 155,606 were completed or terminated. Among 2,115 trials adopting indices or composite measures as condition or disease, 244 adopted frailty and 487 used BMI without frailty indices. Significantly higher proportions of trials of unknown status used indices as conditions/diseases or other terms, compared to completed and terminated trials. The proportions of active trials using frailty indices were significantly higher than those of completed or terminated trials.

Discussion: Clinical trial databases can be used to understand why trials may fail. Based on the findings, we suspect that using indices of poor interpretability may be associated with trial failure. Interpretability has not been conceived as an essential criterion for outcomes or proxy measures in trials. We will continue verifying the findings in other databases or data sources and apply this research method to improve clinical trial design. To prevent patients from experiencing trials likely to fail, we suggest further examining the interpretability of the indices in trials.

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.

Using syndrome mining with the Health and Retirement Study to identify the deadliest and least deadly frailty syndromes

Syndromes are defined with signs or symptoms that occur together and represent conditions. We use a data-driven approach to identify the deadliest and most death-averse frailty syndromes based on frailty symptoms. A list of 72 frailty symptoms was retrieved based on three frailty indices. We used data from the Health and Retirement Study (HRS), a longitudinal study following Americans aged 50 years and over. Principal component (PC)-based syndromes were derived based on a principal component analysis of the symptoms. Equal-weight 4-item syndromes were the sum of any four symptoms. Discrete-time survival analysis was conducted to compare the predictive power of derived syndromes on mortality. Deadly syndromes were those that significantly predicted mortality with positive regression coefficients and death-averse ones with negative coefficients. There were 2,797 of 5,041 PC-based and 964,774 of 971,635 equal-weight 4-item syndromes significantly associated with mortality. The input symptoms with the largest regression coefficients could be summed with three other input variables with small regression coefficients to constitute the leading deadliest and the most death-averse 4-item equal-weight syndromes. In addition to chance alone, input symptoms’ variances and the regression coefficients or p values regarding mortality prediction are associated with the identification of significant syndromes.

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.