| Literature DB >> 30681714 |
Noriaki Kurita1,2.
Abstract
Importance: The association of the Great East Japan Earthquake and the subsequent Fukushima Daiichi Nuclear Power Plant disaster of March 11 and 12, 2011, in Fukushima, Japan, with birth rates has not been examined appropriately in the existing literature. Objective: To assess the midterm and long-term associations of the Great East Japan Earthquake and the Fukushima Daiichi Nuclear Power Plant disaster with birth rates. Design, Setting, and Participants: Cohort study in which interrupted time series analyses were used to assess monthly changes in birth rates among residents of Fukushima City, Japan, from March 1, 2011, to December 31, 2017, relative to projected birth rates without the disaster based on predisaster trends. Birth rates from January 1, 2007, to December 31, 2017, in Fukushima City were determined using information from the Fukushima City government office. Exposure: The Great East Japan Earthquake and the Fukushima Daiichi Nuclear Power Plant disaster, expressed via 5 potential models of the association with birth rate: level change, level and slope changes, temporal level change, and temporal level change with 1 or 2 slope change(s). Main Outcomes and Measures: Birth rate, calculated from monthly data on the number of births and total population.Entities:
Mesh:
Year: 2019 PMID: 30681714 PMCID: PMC6484541 DOI: 10.1001/jamanetworkopen.2018.7455
Source DB: PubMed Journal: JAMA Netw Open ISSN: 2574-3805
Figure. Trend in Birth Rates Estimated From Interrupted Time Series Analysis
The trend in birth rates was estimated using the Poisson regression model, including the temporal level change (gap) and the indicator variable of the calendar month. The rate ratio during the 2 years after the Fukushima Daiichi Nuclear Power Plant disaster was 0.90 (95% CI, 0.86-0.93). The dots indicate observed birth rates. The vertical line indicates the time of the Fukushima Daiichi Nuclear Power Plant disaster between February and March 2011.
Values of the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) Calculated From Models for Interrupted Time Series Analysis
| Model | Parameters, No. | AIC | BIC |
|---|---|---|---|
| Change in level | 3 | 1240.3 | 1249.0 |
| Change in level plus seasonality adjustment (indicator) | 14 | 1164.8 | 1205.2 |
| Change in level plus seasonality adjustment (sine and cosine pairs) | 7 | 1191.5 | 1211.7 |
| Change in level and trend | 4 | 1237.5 | 1249.0 |
| Change in level and trend plus seasonality adjustment (indicator) | 15 | 1161.8 | 1205.0 |
| Change in level and trend plus seasonality adjustment (sine and cosine pairs) | 8 | 1188.1 | 1211.1 |
| Temporal level change (gap) | 3 | 1210.9 | 1219.6 |
| Temporal level change (gap) plus seasonality adjustment (indicator) | 14 | 1135.5 | 1175.9 |
| Temporal level change (gap) plus seasonality adjustment (sine and cosine pairs) | 7 | 1161.1 | 1181.3 |
| Gap plus change in trend at 2 y after the disaster | 4 | 1212.5 | 1224.0 |
| Gap plus change in trend at 2 y after the disaster plus seasonality adjustment (indicator) | 15 | 1137.0 | 1180.3 |
| Gap plus change in trend at 2 y after the disaster plus seasonality adjustment (sine and cosine pairs) | 8 | 1162.6 | 1185.7 |
| Gap plus changes in trends at the disaster and 2 y after the disaster | 5 | 1214.5 | 1228.9 |
| Gap plus changes in trends at the disaster and 2 y after the disaster plus seasonality adjustment (indicator) | 16 | 1138.9 | 1185.0 |
| Gap plus changes in trends at the disaster and 2 y after the disaster plus seasonality adjustment (sine and cosine pairs) | 9 | 1164.5 | 1190.4 |
Poisson regression models with adjustment of scale parameter were used to estimate birth rate. Change in level modeled intercept change after March 2011. Change in level and trend modeled intercept and slope changes after March 2011. Temporal level change (ie, gap) modeled intercept change for the 2 years after March 2011. Gap plus change in trend modeled gap plus slope change 2 years after March 2011. Gap plus changes in trends modeled gap plus slope changes after March 2011 and 2 years after March 2011. Seasonality adjustment was applied by including indicator variables of the calendar month (“indicator”) or sine and cosine pairs (sin [2π × t/12], cos [2π × t/12], sin [4π × t/12], and cos [4π × t/12], where t indicates secular 12 months). None of the changes in the trend component added to the gap component were statistically significant. Taken together with this table, models including temporal level changes with seasonality adjustment are optimal and parsimonious.