Effects of Purchase Restrictions on Smoking During Pregnancy: An Analysis of U.S. Birth Records.

INTRODUCTION: In December 2019, the minimum legal sales age (MLSA) for tobacco products in the United States was raised from 18 to 21 years as a measure of preventing smoking initiation. Smoking during pregnancy, which causes maternal, fetal, and infant morbidity and mortality, is a major public health concern. We first study the effects of purchase restrictions on smoking during different stages of pregnancy covering the entire United States and using the records of all live births during 2014-2018 by birthing people aged 18-21 years. AIMS AND METHODS: Our identification strategy utilizes the variation in the MLSA of tobacco products over time and across counties in the United States during 2014-2018 using a two-way fixed effects model, modified to accommodate a partially observable treatment indicator. RESULTS: The results show that 37.4% of potential smokers in our study population would have been prevented from smoking before pregnancy, 50.5% during the first trimester, 53.4% during the second trimester, and 14.8% during the third trimester if the law prevented selling tobacco products to them in the locality they live. A purchase restriction is estimated to cause the smoking intensity of those who smoke, the intensive margin, to drop by 5.4% before pregnancy, 10.4% during the first trimester, 14.8% during the second trimester, and 5.2% during the third trimester. CONCLUSIONS: Our results show that purchase restrictions are highly effective in preventing smoking among birthing people in the 18-21 years age group. The effect on smoking prevalence during the first and second trimesters is more pronounced. IMPLICATIONS: Many countries around the world have recently tightened age-based purchase restriction policies as a tobacco control measure. We first study the effects of purchase restrictions on smoking during different stages of pregnancy using the birth records of all pregnancies in the United States by birthing people aged 18-21 years during 2014-2018. The study utilizes the variation in the MLSA of tobacco products over time and across counties during that period. Our results show that purchase restrictions are highly effective in preventing smoking among birthing people in the 18-21 years age group.

Adversity in Infancy and Childhood Cognitive Development: Evidence From Four Developing Countries.

Objectives: We investigated whether adverse experiences at age 1 (AE-1) affect the level of and change in cognition during childhood using harmonized data from four developing countries. Methods: Data included children born in 2001/2002 and were followed longitudinally in 2006/2007 and in 2009/2010 by Young Lives study in Ethiopia, India, Peru, and Vietnam. Childhood cognition was measured using the Peabody Picture Vocabulary Test (PPVT) at ages 5 (PPVT-5) and 8 (PPVT-8). We also examined the effect on a change in cognition between age 5-8 (PPVT-Change). The AE-1 scores were constructed using survey responses at age 1. The ordinary least squares regression was used for estimation. Results: We found that children with higher adversities as infants had lower cognition scores at ages 5 and 8. The change in cognition between the two ages was also generally smaller for those with severe adversities at infancy. The negative association between adversities and childhood cognition was strongest for India. Conclusion: The results provide policy relevant information for mitigation of undesirable consequences of early life adversities through timely interventions.

The impact of IOM recommendations on gestational weight gain among US women: An analysis of birth records during 2011-2019.

The prevailing guidelines of the Institute of Medicine (IOM) of United States on gestational weight gain (GWG) are based on women's prepregnancy body mass index (BMI) categories. Previous research has shown that the guidelines issued in 1990 and revised in 2009 had no effect. We investigate the effectiveness of new guidelines issued in 2009 analyzing the records of all singleton births in the U.S. during 2011-2019 (34.0 million observations). We use the discontinuity in recommended guidelines at the threshold values of BMI categories in a regression discontinuity (RD) research design to investigate the effect of IOM guidelines on GWG. We also use an RD analysis in a difference in difference (DID) framework where we compare the effect on women who had any prenatal care to others who did not receive prenatal care. The naïve RD estimator predicts an effect in the expected direction at the threshold BMI values of 18.5 and 25.0 but not at 30.0. After the DID based correction, the RD analyses show that the GWG, measured in kg, drop at the BMI values of 18.5, 25.0 and 30.0 by 0.189 [CI: 0.341, 0.037], 0.085 [CI: 0.179, -0.009] and 0.200 [CI: 0.328, 0.072] respectively when the midpoint of the recommended range in kg drops by 1.5, 4.5 and 2.25. This implies a responsiveness of 12.6%, 1.9% and 8.9% respectively to changes in guidelines at these BMI values. The findings show that the national guidelines have induced some behavioral changes among US women during their pregnancy resulting in a change in GWG in the expected direction. However, the magnitude of the change has not been large compared to the expectations, implying that the existing mechanisms to implement these guidelines have not been sufficiently strong.

SYSTEMATICALLY MISCLASSIFIED BINARY DEPENDENT VARIABLES.

When a binary dependent variable is misclassified, that is, recorded in the category other than where it really belongs, probit and logit estimates are biased and inconsistent. In some cases the probability of misclassification may vary systematically with covariates, and thus be endogenous. In this paper we develop an estimation approach that corrects for endogenous misclassification, validate our approach using a simulation study, and apply it to the analysis of a treatment program designed to improve family dynamics. Our results show that endogenous misclassification could lead to potentially incorrect conclusions unless corrected using an appropriate technique.

The Pot Calling the Kettle Black? A Comparison of Measures of Current Tobacco Use.

Researchers often use the discrepancy between self-reported and biochemically assessed active smoking status to argue that self-reported smoking status is not reliable, ignoring the limitations of biochemically assessed measures and treating it as the gold standard in their comparisons. Here, we employ econometric techniques to compare the accuracy of self-reported and biochemically assessed current tobacco use, taking into account measurement errors with both methods. Our approach allows estimating and comparing the sensitivity and specificity of each measure without directly observing true smoking status. The results, robust to several alternative specifications, suggest that there is no clear reason to think that one measure dominates the other in accuracy.

'Behold, a virgin is with HIV!' misreporting sexual behavior among infected adolescents.

In four Southern African countries where the HIV prevalence rate is among the highest in the world, 46.4% of a sample of female adolescents infected with HIV report having never engaged in sex. This would indicate either the dominance of non-sexual modes of HIV transmission or rampant misreporting of sexual behavior in the sample. We propose a method to estimate the extent of misreporting and calculate that the true percentages of virgins among the sample of HIV-infected adolescent women is 32.1%. After accounting for misreporting, the contribution of sexual modes of HIV transmission is projected as 50.4%, compared with an estimate of 35.5% if we assume no misreporting.

Selection effects and prevention program outcomes.

A primary goal of the paper is to provide an example of an evaluation design and analytic method that can be used to strengthen causal inference in nonexperimental prevention research. We used this method in a nonexperimental multisite study to evaluate short-term outcomes of a preventive intervention, and we accounted for effects of two types of selection bias: self-selection into the program and differential dropout. To provide context for our analytic approach, we present an overview of the counterfactual model (also known as Rubin's causal model or the potential outcomes model) and several methods derived from that model, including propensity score matching, the Heckman two-step approach, and full information maximum likelihood based on a bivariate probit model and its trivariate generalization. We provide an example using evaluation data from a community-based family intervention and a nonexperimental control group constructed from the Washington State biennial Healthy Youth Survey (HYS) risk behavior data (HYS n = 68,846; intervention n = 1,502). We identified significant effects of participant, program, and community attributes in self-selection into the program and program completion. Identification of specific selection effects is useful for developing recruitment and retention strategies, and failure to identify selection may lead to inaccurate estimation of outcomes and their public health impact. Counterfactual models allow us to evaluate interventions in uncontrolled settings and still maintain some confidence in the internal validity of our inferences; their application holds great promise for the field of prevention science as we scale up to community dissemination of preventive interventions.

Measuring bias in self-reported data.

Response bias shows up in many fields of behavioural and healthcare research where self-reported data are used. We demonstrate how to use stochastic frontier estimation (SFE) to identify response bias and its covariates. In our application to a family intervention, we examine the effects of participant demographics on response bias before and after participation; gender and race/ethnicity are related to magnitude of bias and to changes in bias across time, and bias is lower at post-test than at pre-test. We discuss how SFE may be used to address the problem of 'response shift bias' - that is, a shift in metric from before to after an intervention which is caused by the intervention itself and may lead to underestimates of programme effects.