The present study describes the energy content of primary-school children’s lunchboxes and the proportion of lunchbox foods considered discretionary. Subgroup analyses by sex, socio-economic status, age and weight status were undertaken.

A cross-sectional study was conducted. Mean kilojoule content, number of items and categorisation of foods and drinks in lunchboxes as ‘everyday’ (healthy) or discretionary (sometimes) foods were assessed via a valid and reliable lunchbox observational audit.

Twelve Catholic primary schools (Kindergarten–Grade 6) located in the Hunter region of New South Wales, Australia.

Kindergarten to Grade 6 primary-school students.

In total, 2143 children (57 %) had parental consent to have their lunchboxes observed. School lunchboxes contained a mean of 2748 kJ, of which 61·2 % of energy was from foods consistent with the Australian Dietary Guidelines and 38·8 % of energy was discretionary foods. The proportion of lunchboxes containing only healthy foods was 12 %. Children in Kindergarten–Grade 2 packed more servings of ‘everyday’ foods (3·32 v. 2·98, P < 0·01) compared with children in Grades 3–6. Children in Grades 3–6 had a higher percentage of energy from discretionary foods (39·1 v. 33·8 %, P < 0·01) compared with children in Kindergarten–Grade 2 and children from the most socio-economically disadvantaged areas had significantly higher total kilojoules in the school lunchbox compared with the least disadvantaged students (2842 v. 2544 kJ, P = 0·03).

Foods packed within school lunchboxes may contribute to energy imbalance. The development of school policies and population-based strategies to support parents overcome barriers to packing healthy lunchboxes are warranted.

Hospital-acquired infections (HAIs) develop rapidly after brief and transient exposures, and ecological exposures are central to their etiology. However, many studies of HAIs risk do not correctly account for the timing of outcomes relative to exposures, and they ignore ecological factors. We aimed to describe statistical practice in the most cited HAI literature as it relates to these issues, and to demonstrate how to implement models that can be used to account for them.

We conducted a literature search to identify 8 frequently cited articles having primary outcomes that were incident HAIs, were based on individual-level data, and used multivariate statistical methods. Next, using an inpatient cohort of incident Clostridium difficile infection (CDI), we compared 3 valid strategies for assessing risk factors for incident infection: a cohort study with time-fixed exposures, a cohort study with time-varying exposures, and a case-control study with time-varying exposures.

Of the 8 studies identified in the literature scan, 3 did not adjust for time-at-risk, 6 did not assess the timing of exposures in a time-window prior to outcome ascertainment, 6 did not include ecological covariates, and 6 did not account for the clustering of outcomes in time and space. Our 3 modeling strategies yielded similar risk-factor estimates for CDI risk.

Several common statistical methods can be used to augment standard regression methods to improve the identification of HAI risk factors.

Infect. Control Hosp. Epidemiol. 2016;37(4):411–419

Standard estimates of the impact of Clostridium difficile infections (CDI) on inpatient lengths of stay (LOS) may overstate inpatient care costs attributable to CDI. In this study, we used multistate modeling (MSM) of CDI timing to reduce bias in estimates of excess LOS.

A retrospective cohort study of all hospitalizations at any of 120 acute care facilities within the US Department of Veterans Affairs (VA) between 2005 and 2012 was conducted. We estimated the excess LOS attributable to CDI using an MSM to address time-dependent bias. Bootstrapping was used to generate 95% confidence intervals (CI). These estimates were compared to unadjusted differences in mean LOS for hospitalizations with and without CDI.

During the study period, there were 3.96 million hospitalizations and 43,540 CDIs. A comparison of unadjusted means suggested an excess LOS of 14.0 days (19.4 vs 5.4 days). In contrast, the MSM estimated an attributable LOS of only 2.27 days (95% CI, 2.14–2.40). The excess LOS for mild-to-moderate CDI was 0.75 days (95% CI, 0.59–0.89), and for severe CDI, it was 4.11 days (95% CI, 3.90–4.32). Substantial variation across the Veteran Integrated Services Networks (VISN) was observed.

CDI significantly contributes to LOS, but the magnitude of its estimated impact is smaller when methods are used that account for the time-varying nature of infection. The greatest impact on LOS occurred among patients with severe CDI. Significant geographic variability was observed. MSM is a useful tool for obtaining more accurate estimates of the inpatient care costs of CDI.

Infect. Control Hosp. Epidemiol. 2015;36(9):1024–1030

Bloodstream infections due to methicillin-resistant Staphylococcus aureus (MRSA) have been associated with significant risk of in-hospital mortality. The acute physiology and chronic health evaluation (APACHE) II score was developed and validated for use among intensive care unit (ICU) patients, but its utility among non-ICU patients is unknown. The aim of this study was to determine the ability of APACHE II to predict death at multiple time points among ICU and non-ICU patients with MRSA bacteremia.

Retrospective cohort study.

Secondary analysis of data from 200 patients with MRSA bacteremia at 2 hospitals.

Logistic regression models were constructed to predict overall in-hospital mortality and mortality at 48 hours, 7 days, 14 days, and 30 days using APACHE II scores separately in ICU and non-ICU patients. The performance of APACHE II scores was compared with age adjustment alone among all patients. Discriminatory ability was assessed using the c-statistic and was compared at each time point using X2 tests. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test.

APACHE II was a significant predictor of death at all time points in both ICU and non-ICU patients. Discrimination was high in all models, with c-statistics ranging from 0.72 to 0.84, and was similar between ICU and non-ICU patients at all time points. APACHE II scores significantly improved the prediction of overall and 48-hour mortality compared with age adjustment alone.

The APACHE II score may be a valid tool to control for confounding or for the prediction of death among ICU and non-ICU patients with MRSA bacteremia.