Association of longitudinal pet ownership with wheezing in 3-year-old children using the distributed lag model: the Japan Environment and Children's Study

Abstract

Background: Time-varying exposures like pet ownership pose challenges for identifying critical windows due to multicollinearity when modeled simultaneously. The Distributed Lag Model (DLM) estimates critical windows for time-varying exposures, which are mainly continuous variables. However, applying complex functions such as high-order splines and nonlinear functions within DLMs may not be suitable for situations with limited time points or binary exposure, such as in questionnaire surveys.

Objectives: (1) We examined the estimation performance of a simple DLM with fractional polynomial function for time-varying binary exposures through simulation experiments. (2) We evaluated the impact of pet ownership on childhood wheezing onset and estimate critical windows.

Methods: (1) We compared logistic regression including time-varying exposure in separate models, in one model simultaneously, and using DLM. For evaluation, we employed bias, empirical standard error (EmpSE), and mean squared error (MSE). (2) The Japan Environment and Children's Study (JECS) is a prospective birth cohort study of approximately 100,000 parent-child pairs, registered across Japan from 2011 to 2014. We applied DLM to the JECS data up to age 3. The estimated odds ratios (OR) were considered to be within critical windows when they were significant at the 5% level.

Results: (1) DLM and the separate model exhibited lower bias compared to the simultaneously model. Additionally, both DLM and the simultaneously model demonstrated lower EmpSEs than the separate model. In all scenarios, DLM had lower MSEs than the other methods. Specifically, where critical windows is clearly present and exposure correlation is high, DLM showed MSEs about 1/2 to 1/200 of those of other models. (2) Application of DLM to the JECS data showed that, unlike other models, a significant exposure effect was observed only between the ages of 0 and 6 months. During that periods, the highest ORs were 1.07 (95% confidence interval, 1.01 to 1.14) , observed between the ages of 2 and 5 months.

Conclusions: (1) A simple DLM improves the accuracy of exposure effect and critical windows estimation. (2) 0-6 months may be the critical windows for the effect of pet ownership on the wheezing onset at 3 years.

Keywords: Critical window; Distributed lag model; JECS; Japan; Pets; Time-varying exposure; Wheezing.

Fig. 1

Subject selection flowchart. The numbers correspond to the number of people

Fig. 2

True value scenario for β(t) used in the simulation experiment. Scenario 1: Exposure effects are constant regardless of time point. Scenario 2: Exposure effects’ magnitude is an upward convex quadratic curve. Scenario 3: Exposure effects exist only from $t=36\text{to}47$

Fig. 3

Change pattern of time-varying exposures of pet keeping. The numbers correspond to the number of people

Fig. 4

Odds ratio of pet-keeping exposure and onset of wheezing at age 3 years by DLM. For comparison, the point estimates and 95% confidence intervals of the odds ratios estimated by the Multi Model are shown for the time point of the mode of each questionnaire response. The 95% confidence interval for the DLM is narrower than that for the Multi Model. The 95% confidence intervals were widened at the two ends of the figure (from approximately 15 to 18 months and from approximately 39 to 42 months) due to a lack of available data