Conservative estimates showed that care in the first year of life for a PTB infant costs $47,100 per infant, while an alternative algorithm estimated that costs could be up to $78,000 per preterm infant . We used the most conservative lower bounds of the published cost estimates in all analyses.Two logistic regressions analogous to those published were fit with policy indicators as the exposure and odds of LBW and odds of PTB as outcomes. Each policy indicator was coded as 0 if it was not in effect for the mother’s state of residence during the month/year of conception and 1 if it was in effect during the month/year of conception; this method of linking the policy indicators to the month and year of conception improves the accuracy of exposure timing. Both regressions adjusted for all policies simultaneously, as well as individual- and state-level covariates, fixed effects for state and year, and state-specific time trends; previously published analyses found no differences when controlling for all policies together simultaneously vs. each policy individually. Individual-level covariates were maternal age, race, marital status, education, nativity, parity, and version of birth certificate. State-level covariates were state- and year- specific poverty, unemployment, per capita cigarette consumption, and per capita total ethanol consumption,microgreen rack for sale as well as indicators for whether government control of wine sales and government control of spirit sales were in effect for that state in that year. Predicted margins for each significant policy were calculated and used to compute the excess proportion of low birthweight and preterm births under each significant policy. These proportions and their 95% confidence intervals were then applied to the number of births for 2015 in all U.S. states to estimate the number of excess LBW and PTBs in 2015.
Finally, using marginal effects and the most conservative lower bounds of published cost estimates obtained from, costs associated with excess LBW and PTB under each policy were estimated.Multiple state alcohol/pregnancy policies–specifically MWS, PTPREG, LCP, and CACN–lead to thousands of babies born low birthweight or preterm each year. These increased rates of adverse birth outcomes cost hundreds of millions of dollars in health care and related costs annually. The actual prevalence and associated costs indicate that the harms related to alcohol/ pregnancy policies are not only statistically significant, but also significant from a public health and public policy perspective. As most alcohol/pregnancy policies also apply to drugs, findings from this study for all policies other than MWS can be interpreted as applying to alcohol+drug/pregnancy policies rather than specific to alcohol/ pregnancy policies. The MWS finding in particular, though, suggests that as states continue to legalize recreational cannabis, public health policy makers may want to exercise caution before expanding MWS to apply to cannabis. Findings of adverse outcomes associated with MWS and CACN are plausible given previous literature showing that the fear of being reported to CPS and fear of having already irreversibly harmed ones baby are reasons women avoid prenatal care. However, explanations for findings related to both LCP and PTPREG are not as intuitive. One possible explanation comes from the historical context in which these particular policies emerged. Neither LCP nor PTPREG emerged as public health policies developed through a public health policy-making process, but rather emerged as advocacy arguments in response to the War on Drugs-related criminalization and punishment of pregnant women who used crack cocaine. It is also worth noting that that limits on criminal prosecution focuses primarily on limiting use of medical test results in criminal prosecutions related to alcohol/drug use during pregnancy– thus, states that adopt this policy could have more criminal prosecutions related to use during pregnancy.
Priority treatment policies could be a marker of a state that does not have sufficient treatment slots either in general or for pregnant women, and thus the findings could be due to the lack of treatment availability for pregnant women, for women prior to becoming pregnant, or for women’s partners. Future research should examine these mechanisms. In the meantime, our findings strongly suggest that new policy approaches to alcohol/drug use during pregnancy are needed. Strengths of this study include rigorously coded policy data and an outcome dataset that does not rely on self-report and that encompasses the entire population of singleton births for more than 40 years. Another strength is that analyses were able to incorporate state-specific time trends in addition to multiple individual and state-level controls and fixed effects for state and year. Adjusting for state-specific time trends helps address endogeneity and provides more confidence that the results we observe are due to the policies rather than the reverse. Limitations include that Vital Statistics data are not collected for research purposes, that they may not include key individual-level control variables, and that the measurement of some key variables have changed over time. However, our adjustment for birth certificate version and inclusion of fixed effects for state and year alleviates concerns regarding changes in birth certificate data collection over time.College-going individuals in the United States may have unique attitudes toward substance use behavior and tobacco use, including shifts in attitudes and behaviors that are associated with the constantly changing product landscape of alternative tobacco products , such as electronic-cigarettes . Psychosocial behaviors and campus culture, including class attendance, peer socializing, campus policies, and residential environments, may also facilitate these unique attitudes toward favorability of smoking among college subgroups, while also introducing a unique risk environments for tobacco initiation, uptake, transition, and use .
In addition, part of the variation explaining these health behaviors may be influenced by the specific demographic and socioeconomic characteristics of a college campus population and community. Data from social media platforms are often used to self-report and publicly communicate health-related attitudes and behaviors . Young adults [ages 18–25 ] in the United States are much more likely than older populations to actively use social media, including popular platforms Twitter, Snapchat, and Instagram . Infoveillance research, which uses online information sources to detect trends about the distribution and determinants of disease, including health knowledge and behaviors, has been used to develop insights on numerous public health issues including infectious diseases, vaccination sentiment, opioid use disorder, mental health issues, and, relevant to the exploratory aims of this study, tobacco and alternative tobacco use attitudes and behavior . However, smoking-related discussions on social media tied to specific colleges with geographic specificity has not been widely investigated. Existing studies using social media to examine tobacco-related attitudes and behaviors in college aged populations have primarily focused on evaluating the impact of social media health promotion anti-tobacco campaigns, recruiting hard-to-reach college populations using social media platforms, and examining the influence of exposure to tobacco-related social media content and marketing on current and future behavior and use . Other research on college-aged populations has focused on assessing tobacco initiation and transition of use patterns,cannabis grow facility layout particularly as new alternative and emerging tobacco products become available . Accelerating research using social media to assess tobacco-related attitudes/inflfluences among youth has also been supported by U.S. Federal initiatives, including projects funded by the National Cancer Institute and U.S. Food and Drug Administration Tobacco Centers of Regulatory Science, which for have identified and characterized e-cigarette advertisements on image-focused social media sites and tobacco user experiences with little cigars and e-cigarettes as discussed on Twitter . Changes in local, state, and national health policy related to tobacco and other products smoked or used concurrently with tobacco and electronic cigarettes can also have an impact on attitudes and behaviors of these populations. For example, recent debate in the United States regarding the legalization of marijuana/cannabis may positively influence marijuana-related attitudes for college populations, who tend to skew toward more liberal policies regarding decriminalization, legalization, and increased access . Similarly, the 2019 outbreak of e-cigarette and vaping-related lung injury associated with products containing tetrahydrocannabinol may dissuade tobacco or THC use in certain young adult populations, particularly since they were most heavily impacted by the disease . Examining the changing public attitudes and behaviors of college-aged smokers is particularly salient for the State of California, USA. As of January 2014, all campuses in the statewide University of California system became tobacco free , and the California State University system followed suit in 2017 . In addition, voters in California approved Proposition 56 in late 2016, which added a $2.00 increase to the cigarette tax effective April 2017, with an equivalent increase on other tobacco products and electronic cigarettes . Voters in 2016 also approved Proposition 64, which legalized the use of recreational cannabis in November 2016 . During this time, the popularity of e-cigarettes in the United States was increasing . These changes in policy and preferences underscore the interconnected nature of the Triangulum of tobacco products , including potential for dual-use, transition between products, and challenges associated with conducting surveillance and implementing cessation programs .
This changing policy landscape supporting tobacco control measures, as reflected in the shift of California’s public university systems to become smoke-free, is a key impetus for this study. The ability of these colleges to eliminate on-campus smoking relies in large part on understanding past and existing knowledge and attitudes held by the campus smoking populations, along with their perceptions and behaviors that may be associated with compliance or non-compliance to smoke free campus policies. In response, this study conducted exploratory research on the popular micro-blogging platform Twitter. specifically, we used big data, data mining, and geospatial approaches to identify and characterize tweets originating from Twitter users specifically geolocated at California 4-year university campuses. Our primary objective was to assess types of tobacco and ATP products mentioned by users, the distribution of user sentiment toward tobacco and smoking behavior, and to assess the feasibility of detecting self-reported smoking behavior that may represent a violation of campus smoke free policies. Secondarily, we also sought to conduct a cross-campus assessment to determine how these factors vary across different university and college communities and over time. The objective of the study’s data collection approach was to obtain a highly refined subset of tweets, which were both posted from college campus’ geolocated coordinates in California and also included user discussions about smoking, in preparation for manual review to more purposefully identify tweets that specifically discussed different types of tobacco and smoking products, sentiment of users toward smoking behavior, and self reported smoking behavior on campus. Data were collected from the Twitter public streaming Application Programming Interface using the cloud-computing service Amazon Web Services . The public streaming API was set with filters to collect all tweets that included metadata containing latitude and longitude coordinates, initially with no filter for keywords. Tweets were collected continuously from 2015 to 2019. All tweets collected included the text of the tweet and associated metadata, including the date and time of tweets. The use of the public Twitter streaming API to collect data pre-filtered only for tweets including latitude and longitude coordinates represent a subset of all tweets posted during the time frame of the study. There exists the potential for sampling bias associated with different Twitter APIs that are not representative of all Twitter data , and data filtered only for geocoded data may omit many conversations from college aged populations about topics, such as smoking, which may be linked to college-related user groups . Though resulting in a much smaller volume of data, our approach nevertheless allows for detection of tweets in specific geospatial bounds at the high resolution of latitude and longitude coordinates in the state of California. Therefore, by using this data collection approach, we were able to isolate tweets originating from geospatial coordinates within the formal spatial boundaries of all 4-year universities in California. To enable this geolocation, a base map of California 4-year universities from the Stanford Prevention Research Center was obtained and cross-referenced. Tweet geolocated points were spatially joined to campus polygons using ArcGIS software.College areas were comprised of multiple polygons for different campuses and associated properties, though aggregation was conducted at the overall college level to enable comparison across different colleges. Tweets were then filtered for 37 keywords which were broadly related to tobacco-related topics, including the names and brands of different tobacco and ATPs and descriptive terms associated with smoking and vaping as expanded upon from those used in prior studies . specifically, the following keywords were used: bidis, cigarette, cigarettes, cigarillos, cigars, cigie, class, dip, e-cig, hookah, huqqa, joint, JUUl, kereteks, Marlboro, Newport, njoy, pipe, roll-up, shag, smoke, smoking, snuff, snus, tobacco, vape, vaped, vapejuice, vaper, vapes, vaping, vapor, water pipe, waxpen, and weed.