Tobacco smoking and depressive symptoms in Chinese middle-aged and older adults: Handling missing values in panel data with multiple imputation

IntroductionThe high co-occurrence of tobacco smoking and depression is a major public health concern during the novel coronavirus disease-2019 pandemic. However, no studies have dealt with missing values when assessing depression. Therefore, the present study aimed to examine the effect of tobacco...

Full description

Saved in:
Bibliographic Details
Main Authors: Xiahua Du (Author), Rina Wu (Author), Lili Kang (Author), Longlong Zhao (Author), Changle Li (Author)
Format: Book
Published: Frontiers Media S.A., 2022-08-01T00:00:00Z.
Subjects:
Online Access:Connect to this object online.
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000 am a22000003u 4500
001 doaj_15172e90c9cc4937b906e9faa95d61d4
042 |a dc 
100 1 0 |a Xiahua Du  |e author 
700 1 0 |a Rina Wu  |e author 
700 1 0 |a Lili Kang  |e author 
700 1 0 |a Longlong Zhao  |e author 
700 1 0 |a Changle Li  |e author 
245 0 0 |a Tobacco smoking and depressive symptoms in Chinese middle-aged and older adults: Handling missing values in panel data with multiple imputation 
260 |b Frontiers Media S.A.,   |c 2022-08-01T00:00:00Z. 
500 |a 2296-2565 
500 |a 10.3389/fpubh.2022.913636 
520 |a IntroductionThe high co-occurrence of tobacco smoking and depression is a major public health concern during the novel coronavirus disease-2019 pandemic. However, no studies have dealt with missing values when assessing depression. Therefore, the present study aimed to examine the effect of tobacco smoking on depressive symptoms using a multiple imputation technique.MethodsThis research was a longitudinal study using data from four waves of the China Health and Retirement Longitudinal Study conducted between 2011 and 2018, and the final sample consisted of 74,381 observations across all four waves of data collection. The present study employed a multiple imputation technique to deal with missing values, and a fixed effects logistic regression model was used for the analysis.ResultsThe results of fixed effects logistic regression showed that heavy smokers had 20% higher odds of suffering from depressive symptoms than those who never smoked. Compared to those who never smoked, for short-term and moderate-term quitters, the odds of suffering from depressive symptoms increased by 30% and 22%, respectively. The magnitudes of the odds ratios for of the variables short-term quitters, moderate-term quitters, and long-term quitters decreased in absolute terms with increasing time-gaps since quitting. The sub-group analysis for men and women found that heavy male smokers, short-term and moderate-term male quitters had higher odds of suffering from depressive symptoms than those who never smoked. However, associations between smoking status and depressive symptoms were not significant for women.ConclusionsThe empirical findings suggested that among Chinese middle-aged and older adults, heavy smokers and short-term and moderate-term quitters have increased odds of suffering from depressive symptoms than those who never smoked. Moreover, former smokers reported that the probability of having depressive symptoms decreased with a longer duration since quitting. Nevertheless, the association between depressive symptoms and smoking among Chinese middle-aged and older adults is not straightforward and may vary according to gender. These results may have important implications that support the government in allocating more resources to smoking cessation programs to help middle-aged and older smokers, particularly in men. 
546 |a EN 
690 |a smoking 
690 |a depressive symptoms 
690 |a multiple imputation 
690 |a China 
690 |a missing value 
690 |a Public aspects of medicine 
690 |a RA1-1270 
655 7 |a article  |2 local 
786 0 |n Frontiers in Public Health, Vol 10 (2022) 
787 0 |n https://www.frontiersin.org/articles/10.3389/fpubh.2022.913636/full 
787 0 |n https://doaj.org/toc/2296-2565 
856 4 1 |u https://doaj.org/article/15172e90c9cc4937b906e9faa95d61d4  |z Connect to this object online.