Don’t give up on me baby: Spousal correlation in smoking behaviour

Clark, Andrew E.; Étilé, Fabrice

doi:10.1016/j.jhealeco.2006.02.002

Cited by 95 publications

(114 citation statements)

References 29 publications

(30 reference statements)

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“…There is empirical evidence that these two conditions hold for smoking (6,(8)(9)(10), and many other risk factors of infectious disease such as age, race/ethnicity and SES. Hence, the assortativity bias is likely present in many epidemiological studies examining risk factors of STIs and other infectious diseases.…”

Section: Discussionmentioning

confidence: 99%

“…We term one particular form of confounding bias the "assortativity bias", because it stems from assortativity in sexual mixing (partner choice). That is, on average, people have partners with similar characteristics (e.g., age) and behavior (e.g., smoking status) as themselves (5)(6)(7)(8).…”

Section: Word Count=200mentioning

confidence: 99%

“…In this example, assortativity bias can occur because the two conditions above are met: sexual mixing is assortative according to smoking status (C1) (6,8), and the relation of smoking with the STI is confounded by sexual activity (smokers have more lifetime sexual partners than non-smokers and number of lifetime partners is a risk factor of STI (C2))(9, 10). Figure 1 illustrates the essential components of assortativity bias, assuming, for simplicity, that smoking is the only factor for which partner selection is assortative and that smoking itself has no biological effect on STI acquisition/transmission or duration.…”

Section: Word Count=200mentioning

confidence: 99%

“…Based on empirical evidence, we assume that the two conditions for the assortativity bias are met: C1) sexual mixing is assortative for smoking (6,8), and C2) smokers are more sexually active than non-smokers (9, 10).…”

Section: Mathematical Modelmentioning

confidence: 99%

“…We used biological parameter values estimated in prior modeling work (21)(22)(23), and estimated the proportion of smokers in each sexual activity class from an epidemiological study (24). Although studies suggest that sexual mixing by level of sexual activity and smoking status is assortative (6,8,25,26), no empirical estimates are available in the literature. In our base-case, we assumed assortativity parameters values for smoking status and for sexual activity to be 0.8 and 0.4, respectively (0.0=no assortativity, 1.0=complete assortativity), using parametric equations for mixing presented in the Web Appendix 2.…”

Section: Parameterizationmentioning

confidence: 99%

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Assortative mixing as a source of bias in epidemiological studies of sexually transmitted infections: the case of smoking and human papillomavirus

et al. 2015

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For studies examining risk factors of sexually transmitted infections (STIs), confounding can stem from the characteristics of the partners of study subjects, and persist after adjustment for the subjects' individual-level characteristics. Two conditions can produce confounding by the subjects' partners: C1) partner choice is assortative by the risk factor examined and, C2) sexual activity is associated with the risk factor. We term this bias the assortativity bias. The objective of this paper is to illustrate the potential impact of the assortativity bias in studies examining STI risk factors, using smoking and Human papillomavirus (HPV) as an example. We developed an HPV transmission-dynamic mathematical model in which we nested a crosssectional study assessing the smoking-HPV association. In our base-case, we assumed 1) no effect of smoking on HPV, and 2) conditions C1-2 hold for smoking (based on empirical data).The assortativity bias caused an overestimation of the odds ratio (OR) in the simulated study after perfect adjustment for the subjects' individual-level characteristics (adjusted OR=1.51 instead of 1.00). The bias was amplified by a lower basic reproductive number (R 0 ), greater mixing assortativity and greater sexual activity among smokers. To mitigate the assortativity bias studies should adjust for the characteristics of study partners. 2 Word count=200Infectious diseases are transmissible, which implies that the contact network of an individual is a crucial determinant of his risk of infection (1). Thus, an individual's risk of infection not only depends on individual-level risk factors, such as his age and gender, but on network-level risk factors. A classic example of this particularity of infectious diseases is herd immunity: vaccinating a portion of the population reduces the chance of non-vaccinated individuals being exposed to the infectious agent (2). Hence, an individual's risk of infection following the introduction of vaccination depends on his/her vaccine status (individual-level risk factor),

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Section: Discussionmentioning

confidence: 99%

Section: Word Count=200mentioning

confidence: 99%

Section: Word Count=200mentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

Section: Parameterizationmentioning

confidence: 99%

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Assortative mixing as a source of bias in epidemiological studies of sexually transmitted infections: the case of smoking and human papillomavirus

et al. 2015

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Latent class models for use of primary care: evidence from a British panel

d’Uva

2005

Health Economics

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This paper models access to and utilisation of primary care using data from the British Household Panel Survey for the period 1991-2001. A latent class panel data framework is adopted to model individual unobserved heterogeneity in a flexible way. Accounting for the panel structure of the data leads to a substantial improvement in fit, and permits the identification of latent classes of users of health care. Analysis by gender shows that men and women respond differently to some factors, in particular, to age and income. There is evidence of a positive impact of income on the probability of seeking primary care. This effect is especially significant in the case of women. For both genders, the marginal effect of income on the propensity to visit a GP is greater for individuals who are less likely to seek primary care. A latent class aggregated count data model for the number of GP visits classifies individuals in three latent classes and shows a positive income effect particularly amongst those with lower levels of utilisation.

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Spillovers of Health Education at School on Parents' Physical Activity

Berniell¹,

Mata

Valdés

2013

Health Economics

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To prevent modern diseases such as obesity, cancer, cardiovascular conditions and diabetes, which have reached epidemic-like proportions in the last decades, many health experts have called for students to receive Health Education (HED) at school. Although this type of education aims mainly to improve children's health profiles, it might affect other family members as well. This paper exploits state HED reforms as quasi-natural experiments to estimate the causal impact of HED received by children on their parents' physical activity. We use data from the Panel Study of Income Dynamics (PSID) for the period 1999-2005 merged with data on state HED reforms from the National Association of State Boards of Education (NASBE) Health Policy Database, and the 2000 and 2006 School Health Policies and Programs Study (SHPPS). To identify the spillover effects of HED requirements on parents' behavior we use a "differences-in-differencesin-differences" (DDD) methodology in which we allow for different types of treatments. We find a positive effect of HED reforms at elementary school on parents' probability of doing light physical activity. The implementation of HED for the first time increases fathers' probability of engaging in physical activity in 14 percentage points, although it does not seem to affect mothers' probability of being physically active. We find evidence of two channels that may drive these spillovers. We conclude that information sharing between children and parents as well as the specialization of parents in doing typically-male or female activities with their children may play a role in generating these indirect effects and in turn in shaping healthy lifestyles within the household.JEL Classification: I12, I18, I28, C21.

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Don’t give up on me baby: Spousal correlation in smoking behaviour

Cited by 95 publications

References 29 publications

Assortative mixing as a source of bias in epidemiological studies of sexually transmitted infections: the case of smoking and human papillomavirus

Assortative mixing as a source of bias in epidemiological studies of sexually transmitted infections: the case of smoking and human papillomavirus

Latent class models for use of primary care: evidence from a British panel

Spillovers of Health Education at School on Parents' Physical Activity

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