Tobacco is one of the greatest public health issues of modern times. It consumes a considerable amount of resources of the healthcare system in Pakistan for both treatment and prevention  . Currently, about 5.4 million deaths occur every year due to tobacco use  . More than 8 million tobacco deaths are expected by 2030 if urgent action is not taken. The dilemma is that 80% of tobacco-related deaths within a few decades will occur in the developing countries  . This devastating shift to the developing countries is due to the fact that the global tobacco industry is targeting young adults  . Currently, 150 million adolescents are tobacco users worldwide. According to recent studies, school environment has an impact on outcomes of adolescent behaviour that include substance abuse and committing crime  . A study from Karachi, Pakistan, reported prevalence of tobacco use among school-going children at 16.1%   .
Studies from Pakistan have documented the association of betel quid, areca and tobacco use with head and neck cancers   . Although tobacco kills far more people than human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), unfortunately funding for tobacco control is less than that is required for its prevention  . About 27 countries have imposed tax rates of more than 75% of the retail price of tobacco to control its use  . There is no data available to measure the expenditure for treating diseases caused by tobacco use in Pakistan.
Mostly, survey-based studies have been designed to discuss health and smoking issues which are carried out either in communities, clinics, hospitals or schools. To consider environmental/school-level factors affecting adolescent smoking, surveys often employ multi-stage cluster sampling that would incur a hierarchical structure of the population.
If hierarchical data is analysed using analytical techniques that will not consider clustering of data, this will produce misleading inferences and conclusions regarding the association of smoking with other predictors. A researcher may need to consider intra-class correlation (ICC) in the sample size calculation, if accounting for clustering in the structure of the study. About 55% studies have revealed that a multi-level modelling approach shows that the environmental factors have considerable contribution in variation of smoking prevalence between schools besides individual-level factors  . A study suggested that there was more similarity in children within school compared to between schools  . The current review study was planned to determine the methodological approaches in recent surveys, and to identify the common pitfalls in the methodology, especially design and analysis, in school-based observational studies for teenage smoking.
2. Material and Methods
The systematic review was conducted in 2009 and comprised of observational studies in school settings published between January 2005 and January 2009 with smoking status as the main outcome of interest. Following the Cochrane methodology  , five steps were followed: setting selection criteria for studies and conducting a literature search; review of the abstracts; review of the complete articles; data extraction and quality assessment of included studies; and, finally, synthesis of studies. The focus was not on pooling estimates.
In the first step, the eligibility criteria for the inclusion of the studies and the search strategy to be used for selection of the studies for literature review were defined.
The search was limited to peer review published studies in English language. All the studies selected for this systematic review were observational; cohort retrospective, prospective, case-control, longitudinal and cross-sectional with population aged 11 - 18 years in school settings from both the developed and the developing world with smoking status as the main outcome of interest.
We excluded non-peer reviewed articles, letters to editors, conference proceedings and articles published in a language other than English. All studies done on adolescents smoking in hospitals or clinics or communities were also excluded from the review. If the main outcome of interest of the study was something other than smoking then the study was not included in the systematic review.
Search Strategy for Identification of Studies
A comprehensive literature search was performed in both general databases, such as PubMed free access database, Embase, and subject-specific databases such as: PSYCINFO. Moreover, to retrieve publications reporting observational studies for smoking among teenage children attending school, we performed a combined search strategy that included the following terms as both medical subject heading (MeSH) terms and text words: Adolescent in MeSH; Adolescences; Adolescents; Adolescents, Female; Adolescents, Male; Teenagers; Teens; Youth; 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8; Schools in MeSH; Primary Schools; Schools, Secondary; Secondary Schools; 10 or 11 or 12 or 13; Smoking in MeSH; Cigarette Smoking; Smoking Tobacco; Tobacco Smoking; Tobacco Smoking Pollution; 15 or 16 or 17 or 18 or 19; and 9 and 14 and 20.
Filters were also used for observational studies during the literature search defined by the SIGN group and University of York  . This filtered the extracts of the observational studies from the retrieved lists after the POL search which is the population (adolescents), outcome (smoking) and location (secondary schools). Moreover, in the second step articles that were fulfilling our eligibility criteria were extracted (Figure 1).
Figure 1. Search and selection process.
This resulted in selection of articles for second-level review of the eligibility of the retrieved full papers. One investigator carried out the second level review but uncertainty about inclusion of studies was resolved with the other reviewer. To validate the inclusion of studies, two reviewers independently reviewed the inclusion process on a 10% sample selected randomly by the principal investigator. Finally, 100% agreement was achieved on inclusion status.
In the 3rd step one investigator extracted data for the included studies on a pre-designed data-abstraction form. Most questions on the form were pre-coded. Validation of data-abstraction was performed by selecting a random sample of the included studies for the second reviewer to check (5 papers). In case of a disagreement, the relevant field was checked on all abstraction forms and a mutually agreed description was achieved. Data were primarily extracted to assess design and analysis, including whether there was a reported sample size calculation or if analysis had been conducted appropriately or not. We also assessed whether the investigators had provided details of sample size calculation and analysis considering clustering of data and intra-class correlation or not. Place of publication, date of publication, population characteristics, detailed description of outcomes, study design, sampling strategy and detail of individual and school-level factors relating to adolescents smoking were also extracted. The research team discussed any discrepancies.
Both internal and external validity were assessed during methodological quality assessment. The interpretation of the findings of a study depended on design, conduct and assessment (internal validity), as well as on populations and outcome measures (external validity). In the final step, the findings of the literature review were synthesized and the key points were summarised in tabular form.
Assessment of quality of study
The quality of each study was assessed independently. Any disagreements on study quality were resolved through discussion with the other reviewer to confirm whether or not the study had achieved the quality dimension   . The quality of reviewed studies based on internal and external validity was assessed which aimed at ascertaining whether the methods used in the design and conduct of the studies were likely to prevent systematic error. For this purpose, study design, method of analysis, sample size calculation, whether the researchers had discussed the limitations of their study or not, selection bias, consideration of potential confounders of the study (age, gender, socio-economic status), the quality of reporting, the generalizability of the results, conclusion, and recommendations based on study results were the factors assessed.
Table 1 presents the methodological characteristics of the 45 studies, including: Setting, school type and grades, sample size, study design, and the sampling strategy. Of the 292 abstracts retrieved, 45 (15.4%) articles were selected for final review. About 32.4% of the published studies were conducted in the United States, followed by Canada (10.8%), China (8.6%), the United Kingdom (8.6%), and India (8.6%), while the remaining 32.8% were in other countries including Malaysia, Norway, Scotland, Lebanon, Taiwan, Australia, Sudan, Iceland, Brazil, New Zealand, Belgium, Greece, Netherland and Germany. The countries involved in joint projects were the USA, the UK, Greece, Romania, Denmark, Israel and other European countries.
The studies included were conducted in public and private schools, vocational schools, and technical education schools. However, most of the reviewed studies did not clearly state the proportion of public and private schools that they had included in their study. The studies under review included 77.7% cross-sectional and 22.3% prospective cohort or longitudinal follow-up studies. However, in some longitudinal studies (8.8%), the length of follow-up was not reported.
Twenty percent of studies comprised two-stage or multi-stage cluster sampling, 11.1% studies employed random sampling with stratification, 13.3% studies reported simple random sampling, 15.5% reported convenience sampling and 15.5% derived data from other projects for secondary analysis and did not clearly state their sampling strategy. However, 11.3% studies reported other sampling techniques such as systematic sampling, and single-stage cluster sampling, while the rest of the studies (13.3%) did not discuss sampling strategy.
The reviewed studies determined samples from schools in specific regional areas, whereas few studies obtained samples at national level. Some schools were selected using systematic random sampling. Mostly school-based studies conducted surveys on a particular school day. Those adolescents who were absent on the day of the interview or had dropped out of school were excluded, which may have biased the results and generalisability of the findings.
Sample sizes ranged from 384 to 91,778 school-children. About 20% studies employed single-stage or multi-stage cluster sampling. However, clustering was
Table 1. Summary of methodological aspects and analytical issues of studies reviewed.
SEM: Structural equation modeling, EFA: Exploratory factor analysis, CFA: Confirmatory factor analysis; ETS: Environmental tobacco smoke; TAR; Tobacco advertisement receptivity, DFT: Drive for thinness; GEE: Generalized estimating equations; ICC: Intra-class correlation, ASO: General education, TSO: Technical education, BSO: Vocational training.
not taken into account during sample size estimation by all the 45 studies. Such studies that did not take clustering into account in sample size calculation suffered from a considerable loss of power since the design effect was multiplied by the sample size calculated under simple random sampling to account for clustering.
However, we made no judgment regarding sample size calculations for studies which did not mention clustering pattern.
The definitions of adolescents’ tobacco use varied from study to study and, hence, it was not possible to aggregate them together. Smoking use among adolescents was categorised into five levels or stages 1) experimentation, 2) regular use, 3) occasional use, 4) frequent use, and 5) heavy use across studies. About 75% of reviewed studies did not discuss level or stage of tobacco use but they explained whether or not adolescents had ever smoked and/or were currently smoking.
While most of the studies applied simple definitions of smoking behaviour, it is imperative to recognise that some research studies also reported progression across stages of tobacco use.
Few studies (4.4%) were purely descriptive (i.e. prevalence, rates or means). Most of the studies reviewed employed traditional methods of analysis e.g., 8.8% performed a t-test, 28.8% multivariable logistic regression and linear regression, 11.1% analysis of variance and 17.7% a chi-squared test. Also, 55% studies correctly accounted for clustering and performed hierarchical linear modelling, random effects modelling, and generalized estimating equation (GEE). Although in the 55% of studies appropriate statistical techniques were used to answer the research questions, some studies employed longitudinal samples, but this was not reflected in their analysis, reflecting lack of competence in advanced statistical methods.
Different statistical software packages were used to analyse the data. Most studies used Statistical Package for the Social Science (SPSS) (28.8%) and Statistical Analysis System (SAS) (24.4%). Some studies reported using STATA (17.7%), Multilevel software (MLWIN) (8.8%), or Survey data analysis (SUDAAN) (4.4%) and 4.4% of studies reported the use of MPLUS for statistical analysis. However, 20% studies did not mention the type of software used by them for their analysis.
Approximately 73.3% of reviewed studies reported about missing information in their datasets. About 17.7% of studies imputed missing values or used a regression technique with imputation and about 28.8% of them excluded the missing information from their analysis. However, 33.2% studies did not discuss anything about the missing data in their studies. About 11.1% of studies had no missing information.
The median prevalence of smoking across studies was 13.1% interquartile range [IQR] = 11.4). The demographic variables included in the review studies were adolescent age and gender, family, socio-economic status and education, and family smoking. The risk factor most commonly associated with teenage smoking leading them to become habitual or regular smokers were senior students smoking in school, peer smoking, public schools, school suspension, retailer near the school, higher level of exposure to movies, pupil’s relation and attitude to school, school size, social environment of the school, and quality of school teachers. In schools where anti-smoking policies were well implemented, the ratio of adolescents smoking to those who did not was lower compared to those schools where no tobacco monitoring policies were present. Good grades, school connectedness, and knowledge about public policies were prominent school-level protective factors that restricted adolescents from smoking.
The assessment of potential confounders and risk factors is vital. In most of the studies, smoking status of family members was considered. Some studies reported the effect of individual (mother’s and father’s) smoking habits on adolescents’ smoking, while a few observed the combined effect of parents’ smoking on adolescent smoking, Furthermore, the effect of sibling smoking was also assessed in some studies. A few studies combined parent, siblings and other relatives smoking as family member smoking.
The association between parents or family members smoking and adolescent smoking suggested that parental smoking influenced the initiation of adolescent smoking. When family members of adolescent were using tobacco, they had easy access to use it and it made them believe that it was socially acceptable.
Adolescents whose friends were smokers were more likely to smoke compared to those with no peer smoking. The average age of smoking in reviewed studies was approximately 13.1 years. At this age usually peer pressure or peer relationships become stronger than family relationships, so adolescents were more likely to be influenced by the behaviour of friends.
It was found that only a few studies fulfilled the criteria of good quality study. We scored studies according to their assessed validity, thus, the more valid a study was the higher the score it received. The quality of the reviewed studies was below average in approximately one-third (37%) of studies, 48% were marked as average, and 15% as good according to the criteria of strobe  and consort guidelines for randomised controlled trials (RCTs)  . This reflects an inadequate understanding of the substantive issues underpinning hierarchical data in a survey research.
Discrepancies were observed in the definition of smoking in the reviewed studies and the tools that were used to measure adolescent smoking were not standardised, which may hinder generalisability. A number of studies were designed to assess the social influences of adolescent smoking along with individual-level covariates. Although we found different study designs, analytical approach and covariates, our results indicate that environmental level factors are as crucial as individual factors for studying the aetiology of smoking.
In all the reviewed studies, the questionnaire was self-administered by students who not only reported their own smoking habits but also the smoking habits of their parents, family and friends who currently smoke cigarettes. However, it was difficult to report accurately the frequency and number of cigarettes used by their family and friends. Studies reported that parents, siblings and peers were powerful influencers for adolescent smoking.
As in most of the studies, the interviews were conducted on one particular day, hence it was expected that a few students might be absent on that particular day thus not including them in the study may have produced biased results and may also have an effect on generalisability.
Causal inferences cannot be determined from cross-sectional studies. The predictors of smoking reported by cross-sectional studies were not markedly different from that reported by longitudinal studies.
Confounders and biases are major concerns in observational studies, which need to be addressed appropriately. However, most of the studies failed to measure important confounding factors. In most of the studies, clustering was not considered during sample size calculation which may have caused a reduction in the power. In conclusion, the results of this review highlight concerns about the analysis of complex survey data. Hierarchical linear modelling, random effects modelling and structural equation modelling were used in comparatively fewer studies. About 45% studies ignored the contextual/ environmental (e.g. retailer near the school, social environment of school and implementation of smoking policies at school) factors in their studies which may have produced misleading inferences.
The results of reviewed studies guided us in comprehending the effect of school-level factors and variances between schools. Moreover, it also provided us with more intuitive information on school-level covariates that can have an influence on the adolescents smoking behaviour.
One of the limitations of our study is that we only reviewed studies in English. This could have resulted in some studies being excluded from our review.
It is recommended that future studies should consider environmental/social settings from where the individuals are drawn at the design and analysis stage as well as taking clustering into account to come up with a leading inference to deal with public health issues.
There were different study designs, analytical approaches and covariates in the studies reviewed, but results indicate that environmental level factors are as crucial as individual factors for studying the aetiology of smoking.
Source of Funding
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