Depression and all-cause mortality risk in HIV-infected and HIV-uninfected US veterans: a cohort study.

OBJECTIVES: The contribution of depression to mortality in adults with and without HIV infection is unclear. We hypothesized that depression increases mortality risk and that this association is stronger among those with HIV infection. METHODS: Veterans Aging Cohort Study (VACS) data were analysed from the first clinic visit on or after 1 April 2003 (baseline) to 30 September 2015. Depression definitions were: (1) major depressive disorder defined using International Classification of Diseases, Ninth Revision (ICD-9) codes; (2) depressive symptoms defined as Patient Health Questionnaire (PHQ)-9 scores ≥ 10. The outcome was all-cause mortality. Covariates were demographics, comorbid conditions and health behaviours. RESULTS: Among 129 140 eligible participants, 30% had HIV infection, 16% had a major depressive disorder diagnosis, and 24% died over a median follow-up time of 11 years. The death rate was 25.3 [95% confidence interval (CI) 25.0-25.6] deaths per 1000 person-years. Major depressive disorder was associated with mortality [hazard ratio (HR) 1.04; 95% CI 1.01, 1.07]. This association was modified by HIV status (interaction P-value = 0.02). In HIV-stratified analyses, depression was significantly associated with mortality among HIV-uninfected veterans but not among those with HIV infection. Among those with PHQ-9 data (n = 7372), 50% had HIV infection, 22% had PHQ-9 scores ≥ 10, and 28% died over a median follow-up time of 12 years. The death rate was 27.3 (95% CI 26.1-28.5) per 1000 person-years. Depressive symptoms were associated with mortality (HR 1.16; 95% CI 1.04, 1.28). This association was modified by HIV status (interaction P-value = 0.05). In HIV-stratified analyses, depressive symptoms were significantly associated with mortality among veterans with HIV infection but not among those without HIV infection. CONCLUSIONS: Depression was associated with all-cause mortality. This association was modified by HIV status and method of depression ascertainment.

Relationships between misreported energy intake and pregnancy in the pregnancy, infection and nutrition study: new insights from a dynamic energy balance model.

OBJECTIVE: Providing effective dietary counselling so that pregnancy weight gain remains within the 2009 Institute of Medicine (IOM) guidelines requires accurate maternal energy intake measures. Current practice is based on self-reported intake that has been demonstrated unreliable. This study applies an objective calculation of energy intake from a validated mathematical model to identify characteristics of individuals more likely to misreport during pregnancy. METHODS: A validated maternal energy balance equation was used to calculate energy intake from gestational weight gain in 1,368 subjects. The difference between self-reported and model-predicted energy intake was tested for demographics, economic status, education level and maternal health status. RESULTS: A weight gain of 15.2 kg resulted in model-predicted intake during pregnancy of 2,882.97 ± 135.71 kcal day-1, which differed from self-reported intake of 2,180.5 ± 856.0 kcal day-1. The achieved weight gain exceeded the IOM guidelines; however, the model predicted weight gain from self-reported energy intake was below IOM guidelines. Higher income (p = 0.004), education (p = 0.003), birth weight (p = 0.017), gestational diabetes (p = 0.008) and pre-existing diabetes (p < 0.001) were associated with under-reported energy intake. More children living at home (p = 0.001) were associated with more accurate self-reported intake. CONCLUSIONS: When assessing self-reported energy intake in pregnancy studies, birth weight, gestational diabetes status, pre-existing diabetes, higher income and education predict higher under-reporting. Clinicians providing dietary treatment recommendations during pregnancy should be aware that individuals with pre-existing diabetes and gestational diabetes mellitus are more likely to misreport their intake. Additionally, the systems model approach can be applied early in intervention to objectively monitor dietary compliance to treatment recommendations.