Risk of Severe Influenza Among Adults With Chronic Medical Conditions.

BACKGROUND: Severe influenza illness is presumed more common in adults with chronic medical conditions (CMCs), but evidence is sparse and often combined into broad CMC categories. METHODS: Residents (aged 18-80 years) of Central and South Auckland hospitalized for World Health Organization-defined severe acute respiratory illness (SARI) (2012-2015) underwent influenza virus polymerase chain reaction testing. The CMC statuses for Auckland residents were modeled using hospitalization International Classification of Diseases, Tenth Revision codes, pharmaceutical claims, and laboratory results. Population-level influenza rates in adults with congestive heart failure (CHF), coronary artery disease (CAD), cerebrovascular accidents (CVA), chronic obstructive pulmonary disease (COPD), asthma, diabetes mellitus (DM), and end-stage renal disease (ESRD) were calculated by Poisson regression stratified by age and adjusted for ethnicity. RESULTS: Among 891 276 adults, 2435 influenza-associated SARI hospitalizations occurred. Rates were significantly higher in those with CMCs compared with those without the respective CMC, except for older adults with DM or those aged <65 years with CVA. The largest effects occurred with CHF (incidence rate ratio [IRR] range, 4.84-13.4 across age strata), ESRD (IRR range, 3.30-9.02), CAD (IRR range, 2.77-10.7), and COPD (IRR range, 5.89-8.78) and tapered with age. CONCLUSIONS: Our findings support the increased risk of severe, laboratory-confirmed influenza disease among adults with specific CMCs compared with those without these conditions.

Risk Factors and Attack Rates of Seasonal Influenza Infection: Results of the Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) Seroepidemiologic Cohort Study.

Background: Understanding the attack rate of influenza infection and the proportion who become ill by risk group is key to implementing prevention measures. While population-based studies of antihemagglutinin antibody responses have been described previously, studies examining both antihemagglutinin and antineuraminidase antibodies are lacking. Methods: In 2015, we conducted a seroepidemiologic cohort study of individuals randomly selected from a population in New Zealand. We tested paired sera for hemagglutination inhibition (HAI) or neuraminidase inhibition (NAI) titers for seroconversion. We followed participants weekly and performed influenza polymerase chain reaction (PCR) for those reporting influenza-like illness (ILI). Results: Influenza infection (either HAI or NAI seroconversion) was found in 321 (35% [95% confidence interval, 32%-38%]) of 911 unvaccinated participants, of whom 100 (31%) seroconverted to NAI alone. Young children and Pacific peoples experienced the highest influenza infection attack rates, but overall only a quarter of all infected reported influenza PCR-confirmed ILI, and one-quarter of these sought medical attention. Seroconversion to NAI alone was higher among children aged <5 years vs those aged ≥5 years (14% vs 4%; P < .001) and among those with influenza B vs A(H3N2) virus infections (7% vs 0.3%; P < .001). Conclusions: Measurement of antineuraminidase antibodies in addition to antihemagglutinin antibodies may be important in capturing the true influenza infection rates.

Evaluating the effect of measurement error when using one or two 24 h dietary recalls to assess eating out: a study in the context of the HECTOR project.

Eating out is often recorded through short-term measurements and the large within-person variability in intakes may not be adequately captured. The present study aimed to understand the effect of measurement error when using eating-out data from one or two 24 h dietary recalls (24hDR), in order to describe intakes and assess associations between eating out and personal characteristics. In a sample of 366 adults from Potsdam, Germany, two 24hDR and a FFQ were collected. Out-of-home intakes were estimated based on either one 24hDR or two 24hDR or the Multiple Source Method (MSM) combining the two 24hDR and the questionnaire. The distribution of out-of-home intakes of energy, macronutrients and selected foods was described. Multiple linear regression and partial correlation coefficients were estimated to assess associations between out-of-home energy intake and participants' characteristics. The mean daily out-of-home intakes estimated from the two 24hDR were similar to the usual intakes estimated through the MSM. The out-of-home energy intake, estimated through either one or two 24hDR, was positively associated with total energy intake, inversely with age and associations were stronger when using the two 24hDR. A marginally significant inverse association between out-of-home energy intake and physical activity at work was observed only on the basis of the two 24hDR. After applying the MSM, all significant associations remained and were more precise. Data on eating out collected through one or two 24hDR may not adequately describe intake distributions, but significant associations between eating out and participants' characteristics are highly unlikely to appear when in reality these do not exist.

Estimating usual food intake distributions by using the multiple source method in the EPIC-Potsdam Calibration Study.

Estimating usual food intake distributions from short-term quantitative measurements is critical when occasionally or rarely eaten food groups are considered. To overcome this challenge by statistical modeling, the Multiple Source Method (MSM) was developed in 2006. The MSM provides usual food intake distributions from individual short-term estimates by combining the probability and the amount of consumption with incorporation of covariates into the modeling part. Habitual consumption frequency information may be used in 2 ways: first, to distinguish true nonconsumers from occasional nonconsumers in short-term measurements and second, as a covariate in the statistical model. The MSM is therefore able to calculate estimates for occasional nonconsumers. External information on the proportion of nonconsumers of a food can also be handled by the MSM. As a proof-of-concept, we applied the MSM to a data set from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Calibration Study (2004) comprising 393 participants who completed two 24-h dietary recalls and one FFQ. Usual intake distributions were estimated for 38 food groups with a proportion of nonconsumers > 70% in the 24-h dietary recalls. The intake estimates derived by the MSM corresponded with the observed values such as the group mean. This study shows that the MSM is a useful and applicable statistical technique to estimate usual food intake distributions, if at least 2 repeated measurements per participant are available, even for food groups with a sizeable percentage of nonconsumers.