Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database

<p>Abstract</p> <p>Background</p> <p>The generalized estimating equations (GEE) technique is often used in longitudinal data modeling, where investigators are interested in population-averaged effects of covariates on responses of interest. GEE involves specifying a model relating covariates to outcomes and a plausible correlation structure between responses at different time periods. While GEE parameter estimates are consistent irrespective of the true underlying correlation structure, the method has some limitations that include challenges with model selection due to lack of absolute goodness-of-fit tests to aid comparisons among several plausible models. The quadratic inference functions (QIF) method extends the capabilities of GEE, while also addressing some GEE limitations.</p> <p>Methods</p> <p>We conducted a comparative study between GEE and QIF via an illustrative example, using data from the "National Longitudinal Survey of Children and Youth (NLSCY)" database. The NLSCY dataset consists of long-term, population based survey data collected since 1994, and is designed to evaluate the determinants of developmental outcomes in Canadian children. We modeled the relationship between hyperactivity-inattention and gender, age, family functioning, maternal depression symptoms, household income adequacy, maternal immigration status and maternal educational level using GEE and QIF. Basis for comparison include: (1) ease of model selection; (2) sensitivity of results to different working correlation matrices; and (3) efficiency of parameter estimates.</p> <p>Results</p> <p>The sample included 795, 858 respondents (50.3% male; 12% immigrant; 6% from dysfunctional families). QIF analysis reveals that gender (male) (odds ratio [OR] = 1.73; 95% confidence interval [CI] = 1.10 to 2.71), family dysfunctional (OR = 2.84, 95% CI of 1.58 to 5.11), and maternal depression (OR = 2.49, 95% CI of 1.60 to 2.60) are significantly associated with higher odds of hyperactivity-inattention. The results remained robust under GEE modeling. Model selection was facilitated in QIF using a goodness-of-fit statistic. Overall, estimates from QIF were more efficient than those from GEE using AR (1) and Exchangeable working correlation matrices (Relative efficiency = 1.1117; 1.3082 respectively).</p> <p>Conclusion</p> <p>QIF is useful for model selection and provides more efficient parameter estimates than GEE. QIF can help investigators obtain more reliable results when used in conjunction with GEE.</p

Sequence variants of interleukin 6 (IL-6) are significantly associated with a decreased risk of late-onset Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>Interleukin 6 (IL-6) has been related to beta-amyloid aggregation and the appearance of hyperphosphorylated tau in Alzheimer's disease (AD) brain. However, previous studies relating <it>IL-6 </it>genetic polymorphisms to AD included few and unrepresentative single nucleotide polymorphisms (SNPs) and the results were inconsistent.</p> <p>Methods</p> <p>This is a case-control study. A total of 266 patients with AD, aged≧65, were recruited from three hospitals in Taiwan (2007-2010). Controls (n = 444) were recruited from routine health checkups and volunteers of the hospital during the same period of time. Three common <it>IL-6 </it>haplotype-tagging SNPs were selected to assess the association between <it>IL-6 </it>polymorphisms and the risk of late-onset AD (LOAD).</p> <p>Results</p> <p>Variant carriers of <it>IL-6 </it>rs1800796 and rs1524107 were significantly associated with a reduced risk of LOAD [(GG + GC vs. CC): adjusted odds ratio (AOR) = 0.64 and (CC + CT vs. TT): AOR = 0.60, respectively]. Haplotype CAT was associated with a decreased risk of LOAD (0 and 1 copy vs. 2 copies: AOR = 0.65, 95% CI = 0.44-0.95). These associations remained significant in <it>ApoE e4 </it>non-carriers only. Hypertension significantly modified the association between rs2069837 polymorphisms and the risk of LOAD (<it>p</it>_interaction= 0.03).</p> <p>Conclusions</p> <p><it>IL-6 </it>polymorphisms are associated with reduced risk of LOAD, especially in <it>ApoE e4 </it>non-carriers. This study identified genetic markers for predicting LOAD in <it>ApoE e4 </it>non-carriers.</p

Challenges of self-reported medical conditions and electronic medical records among members of a large military cohort

<p>Abstract</p> <p>Background</p> <p>Self-reported medical history data are frequently used in epidemiological studies. Self-reported diagnoses may differ from medical record diagnoses due to poor patient-clinician communication, self-diagnosis in the absence of a satisfactory explanation for symptoms, or the "health literacy" of the patient.</p> <p>Methods</p> <p>The US Department of Defense military health system offers a unique opportunity to evaluate electronic medical records with near complete ascertainment while on active duty. This study compared 38 self-reported medical conditions to electronic medical record data in a large population-based US military cohort. The objective of this study was to better understand challenges and strengths in self-reporting of medical conditions.</p> <p>Results</p> <p>Using positive and negative agreement statistics for less-prevalent conditions, near-perfect negative agreement and moderate positive agreement were found for the 38 diagnoses.</p> <p>Conclusion</p> <p>This report highlights the challenges of using self-reported medical data and electronic medical records data, but illustrates that agreement between the two data sources increases with increased surveillance period of medical records. Self-reported medical data may be sufficient for ruling out history of a particular condition whereas prevalence studies may be best served by using an objective measure of medical conditions found in electronic healthcare records. Defining medical conditions from multiple sources in large, long-term prospective cohorts will reinforce the value of the study, particularly during the initial years when prevalence for many conditions may still be low.</p

Adult attention deficit hyperactivity disorder symptom profiles and concurrent problems with alcohol and cannabis: Sex differences in a representative, population survey

Background: Adult attention deficit hyperactivity disorder (ADHD) shows a robust association with alcohol and cannabis misuse, and these relationships are expressed differently in males and females. Manifestation of specific ADHD symptom profiles, even in the absence of the full disorder, may also be related to problems with alcohol and cannabis, although these relationships have not been investigated in epidemiological studies. To address this question, we studied the sex-specific associations of ADHD symptomatology with problematic alcohol and cannabis use in a representative sample of adults aged 18 years and older residing in Ontario, Canada. Methods: Data were obtained from the Centre for Addiction and Mental Health Monitor, an ongoing cross-sectional telephone survey, between January 2011 and December 2013. Respondents (n = 5080) reported on current ADHD symptomatology, measured using the Adult ADHD Self-Report Version 1.1 Screener (ASRS-V1.1) and four additional items, and alcohol and cannabis use, which were measured using the Alcohol Use Disorders Identification Test (AUDIT) and the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), respectively. Logistic regression analyses were conducted in men and women to test the association of each ADHD symptom cluster (hyperactivity, inattentiveness, impulsivity) with problematic alcohol and cannabis use. Results: After controlling for age, education, and comorbid internalizing and externalizing psychopathology, hyperactive symptoms were associated with problematic alcohol use in both men and women and with problematic cannabis use in men. Impulsive symptoms were independently associated with problematic cannabis use in men. By contrast, inattentive symptomatology predicted problems with alcohol and cannabis only in women. In all models, age was negatively associated with substance misuse and externalizing behavior was positively correlated and the strongest predictor of hazardous alcohol and cannabis use. Conclusions: ADHD symptom expression in adulthood is related to concurrent hazardous use of alcohol and cannabis. Distinctive ADHD symptom profiles may confer increased risk for substance misuse in a sex-specific manner

The contribution of risk factors to socioeconomic inequalities in multimorbidity across the lifecourse: a longitudinal analysis of the Twenty-07 cohort

Background: Multimorbidity is a major challenge to health systems globally and disproportionately affects socioeconomically disadvantaged populations. We examined socioeconomic inequalities in developing multimorbidity across the lifecourse and investigated the contribution of five behaviour-related risk factors. Methods: The Twenty-07 study recruited participants aged approximately 15, 35, and 55 years in 1987 and followed them up over 20 years. The primary outcome was development of multimorbidity (2+ health conditions). The relationship between five different risk factors (smoking, alcohol consumption, diet, body mass index (BMI), physical activity) and the development of multimorbidity was assessed. Social patterning in the development of multimorbidity based on two measures of socioeconomic status (area-based deprivation and household income) was then determined, followed by investigation of potential mediation by the five risk factors. Multilevel logistic regression models and predictive margins were used for statistical analyses. Socioeconomic inequalities in multimorbidity were quantified using relative indices of inequality and attenuation assessed through addition of risk factors. Results: Multimorbidity prevalence increased markedly in all cohorts over the 20 years. Socioeconomic disadvantage was associated with increased risk of developing multimorbidity (most vs least deprived areas: odds ratio (OR) 1.46, 95% confidence interval (CI) 1.26–1.68), and the risk was at least as great when assessed by income (OR 1.53, 95% CI 1.25–1.87) or when defining multimorbidity as 3+ conditions. Smoking (current vs never OR 1.56, 1.36–1.78), diet (no fruit/vegetable consumption in previous week vs consumption every day OR 1.57, 95% CI 1.33–1.84), and BMI (morbidly obese vs healthy weight OR 1.88, 95% CI 1.42–2.49) were strong independent predictors of developing multimorbidity. A dose–response relationship was observed with number of risk factors and subsequent multimorbidity (3+ risk factors vs none OR 1.91, 95% CI 1.57–2.33). However, the five risk factors combined explained only 40.8% of socioeconomic inequalities in multimorbidity development. Conclusions: Preventive measures addressing known risk factors, particularly obesity and smoking, could reduce the future multimorbidity burden. However, major socioeconomic inequalities in the development of multimorbidity exist even after taking account of known risk factors. Tackling social determinants of health, including holistic health and social care, is necessary if the rising burden of multimorbidity in disadvantaged populations is to be redressed