Socioeconomic status and lifestyle choices: evidence from latent class analysis

Øvrum, Arnstein

doi:10.1002/hec.1662

Cited by 27 publications

(18 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This study adopted LCA to identify latent classes of manifest variables in elderly health behaviors in China. The principle of LCA is to classify latent characteristics based on individual manifest behaviors and calculate the proportion of different classes with the goal of adopting different intervention strategies for each subclass, which has been widely used in research concerning health behaviors . Compared with manifest variables, which are measured by dint of measuring instruments or methods such as scales or experiments, latent variables are often traits or abstract concepts formed from manifest variables.…”

Section: Methodsmentioning

confidence: 99%

“…The principle of LCA is to classify latent characteristics based on individual manifest behaviors and calculate the proportion of different classes with the goal of adopting different intervention strategies for each subclass, which has been widely used in research concerning health behaviors. 31,32 Compared with manifest variables, which are measured by dint of measuring instruments or methods such as scales or experiments, latent variables are often traits or abstract concepts formed from manifest variables. These traits or abstract concepts may not be directly measured but need be reflected or classified in accordance with data or characteristics obtained from manifest variables.…”

Section: Latent Class Analysismentioning

confidence: 99%

See 1 more Smart Citation

Differences in the health behaviors of elderly individuals and influencing factors: Evidence from the ChineseLongitudinalHealthyLongevitySurvey

Liu

Yan

Yue

et al. 2019

Health Planning & Management

View full text Add to dashboard Cite

Summary Introduction Health behaviors play an important role in determining individual health status; thus, understanding differences in the health behaviors of elderly individuals and their influencing factors is a prerequisite for the formulation and implementation of health behavior promotion policies for elderly individuals. The objectives of this study were to explore differences in health behaviors among Chinese elderly people and their influencing factors. Methods Based on data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2014, this paper applied latent class analysis (LCA) to explore differences in the health behaviors of elderly individuals in China and employed a multinomial logit model to identify the influencing factors that cause these differences. Results Results showed that there are five classes of health behaviors among elderly individuals in China: passive, relatively passive, general, relatively positive, and positive, the proportions of which were 31.07%, 15.86%, 24.06%, 17.24%, and 11.76%, respectively. Community medical and health services, pension, living arrangements, and family income were the primary factors explaining differences in the health behaviors of elderly individuals. In addition, there were significant demographic differences in the health behaviors of elderly individuals in China, including gender, age, education, marital status, census register, region, and others. Conclusion There are significant differences in the behaviors of elderly individuals in China, and the behaviors of the majority of elderly people are not healthy. China is expected to invest more medical and health resources to tackle health prevention and management and to provide targeted education, guidance, and intervention in elderly health behaviors, urging them to control and correct risky health behaviors with a focus on elderly individuals that are the oldest, are females, have low education levels, and live in the countryside and in towns.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Latent Class Analysismentioning

confidence: 99%

Differences in the health behaviors of elderly individuals and influencing factors: Evidence from the ChineseLongitudinalHealthyLongevitySurvey

Liu

Yan

Yue

et al. 2019

Health Planning & Management

View full text Add to dashboard Cite

show abstract

“…In addition to some normal variation, offspring birthweight is a result of the mother's nutritional status and lifestyle [12], [13], which are influenced by the mother's cultural and socioeconomic circumstances [14]. Smoking during pregnancy (SDP) is considered the single most important determinant of decreased birthweight [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Revisiting the Effect of Maternal Smoking during Pregnancy on Offspring Birthweight: A Quasi-Experimental Sibling Analysis in Sweden

Juárez

Merlo

2013

PLoS ONE

View full text Add to dashboard Cite

Maternal smoking during pregnancy (SDP) seems associated with reduced birthweight in the offspring. This observation, however, is based on conventional epidemiological analyses, and it might be confounded by unobserved maternal characteristics related to both smoking habits and offspring birth weight. Therefore, we apply a quasi-experimental sibling analysis to revisit previous findings. Using the Swedish Medical Birth Register, we identified 677,922 singletons born between 2002 and 2010 from native Swedish mothers. From this population, we isolated 62,941 siblings from 28,768 mothers with discrepant habits of SDP. We applied conventional and mother-specific multilevel linear regression models to investigate the association between maternal SDP and offspring birthweight. Depending on the mother was light or heavy smoker and the timing of exposition during pregnancy (i.e., first or third trimester), the effect of smoking on birthweight reduction was between 6 and 78 g less marked in the sibling analysis than in the conventional analysis. Sibling analysis showed that continuous smoking reduces birthweight by 162 grams for mothers who were light smokers (1 to 9 cigarettes per day) and 226 g on average for those who were heavy smokers throughout the pregnancy in comparison to non-smoker mothers. Quitting smoking during pregnancy partly counteracted the smoking-related birthweight reduction by 1 to 29 g, and a subsequent smoking relapse during pregnancy reduced birthweight by 77 to 83 g. The sibling analysis provides strong evidence that maternal SDP reduces offspring birthweight, though this reduction was not as great as that observed in the conventional analysis. Our findings support public health interventions aimed to prevent SDP and to persuade those who already smoke to quit and not relapse throughout the pregnancy. Besides, further analyses are needed in order to explain the mechanisms through which smoking reduces birthweight and to identify other maternal characteristics that are common causes of both birthweight reduction and maternal smoking.

show abstract

“…To explore it properly and carefully one would need to learn more about the determinants of abdominal obesity, as opposed to obesity defined based on BMI, and factors determining where the body stores body fat. One speculative and potential explanation is that various lifestyle behaviors, like physical activity and diets, differ by socioeconomic status [ 34 ] and that these differences could result in different productions of abdominal fat, resulting in a slimmer waist line but not necessarily in lower weights to the same extent. However, again, more needs to be learnt about which factors affect fat storage, and no strong conclusions about the reasons should be drawn from the results of this study.…”

Section: Discussionmentioning

confidence: 99%

Misreporting and misclassification: implications for socioeconomic disparities in body-mass index and obesity

2013

View full text Add to dashboard Cite

Body-mass index (BMI) has become the standard proxy for obesity in social science research. This study deals with the potential problems related to, first, relying on self-reported weight and height to calculate BMI (misreporting), and, second, the concern that BMI is a deficient measure of body fat (misclassification). Using a regional Swedish sample, we analyze whether socioeconomic disparities in BMI are biased because of misreporting, and whether socioeconomic disparities in the risk of obesity are sensitive to whether BMI or waist circumference is used to define obesity. Education and income are used as socioeconomic indicators. The overall conclusion is that misreporting and misclassification may indeed matter for estimated educational and income disparities in BMI and obesity. In the misreporting part we find that women with higher education misreport less than those with lower education, leading to underestimation of the education disparity when using self-reported information. In the misclassification part we find that the probability of being misclassified decreases with income, for both men and women. Among women, the consequence is a steeper income gradient when obesity is defined using waist circumference instead of BMI. Among men the income gradient is statistically insignificant irrespective of how obesity is defined, but when estimating the probability of obesity defined by waist circumference, an educational gradient, which is not present when classifying men using BMI, arises.

show abstract

Socioeconomic status and lifestyle choices: evidence from latent class analysis

Cited by 27 publications

References 39 publications

Differences in the health behaviors of elderly individuals and influencing factors: Evidence from the ChineseLongitudinalHealthyLongevitySurvey

Differences in the health behaviors of elderly individuals and influencing factors: Evidence from the ChineseLongitudinalHealthyLongevitySurvey

Revisiting the Effect of Maternal Smoking during Pregnancy on Offspring Birthweight: A Quasi-Experimental Sibling Analysis in Sweden

Misreporting and misclassification: implications for socioeconomic disparities in body-mass index and obesity

Contact Info

Product

Resources

About