This review provides a critical analysis of the study entitled “Smoking prevalence and attributable disease burden in 195 countries and territories, 1990–2015: a systematic analysis from the Global Burden of Disease Study 2015”. It will examine the study’s methodology, Design, Data collection methods, and Data Analysis. (PMC, NCBI, 2017)
Research methodology is the process by which researchers describe, explain and predict phenomena, in other words how the research is carried out. The methodology of the current research is explained as, spatial and temporal Generalized linear inference was used to produce 2818 sources of data and provided reports of regular smoking prevalence by gender, year, and age group for 195 states and regions from 1990–2015. Thirty-eight risk-outcome combinations were analyzed to calculate measures of attributable smoking fatalities and illness burden in terms of disability-adjusted years of life (DALYs). To better explain smoking temporal age trends, a cohort study was conducted of smoking prevalence by community sample of the birth-year cohort. Finally, the Socio-demographic Indicator was used to examine findings by the degree of growth (SDI). (PubMed.gov, 2017)
We used relative risk assessments obtained from longitudinal cohort trials compared smokers to non-smokers for each result used in this study. Measurements of risk estimated hazards, and the potential optimal risk incidence scale for smoking was used to measure population attributable ratios (zero smoking). The degree of success in limiting smoking prevalence has been inconsistent over regions of the world, future outlook, and gender, and as recent developments have shown, upholding the historic rate of regression should never be implicit especially for women in low SDI to the medium SDI regions. Aside from the influence of the smoking market and social expectations, a key obstacle awaiting smoking prevention efforts is that forces that are likely to escalate the international burden of smoking unless an improvement in limiting addiction and encouraging prevention is greatly increased. Substantial smoking prevention change can happen, but it would necessitate successful, comprehensive, and properly adopted and executed strategies, which will necessitate national and global rate of governmental effort aside from which has been accomplished over the last 2.5 decades. (PubMed.gov, 2017)
DATA COLLECTION METHODS
The data collection methods include;
· The Estimation Of Smoking Exposure_Two consumption indicators was calculated: the rate of daily cigarette smoking as well as the smoking effects ratio. A day smoker is anyone who uses some kind of smoking tobacco product on a routine basis.
· Defining Risk-Outcome Pairs_A well-organized methodology taken from “Hill’s criterion for causation and the World Cancer Research Fund” proof scoring system was used to examine all present data that confirmed correlations between health outcomes and smoking. (Marissa B Reitsma, 2017)
· Estimation of Attribute Burden_We specified a 5-year duration because research has found that the bulk of risk-reduction happens during the first 5 years of stopping smoking.
· Uncertainty Analysis_1000 draws of access and associated burden calculations were done for each territory, year, gender, and age, and 95 percent uncertainty intervals (UIs) were calculated using the 25th and 975th percentiles of the distributions.
· Drastic Changes In DALYs_Risk-adjusted DALY values were measured by multiplying measured contributing factor DALY values by one and subtracting cause-specific demographic attributable ratios. (PCM, NCBI, 2017)
· Smoking And SDI Association_Outcomes are compiled by SDI level, which is a comprehensive index of growth projected for each territory depending on lag-distributed spending per person, maximum academic achievement among people over the age of 15, and mean fertility rate.
These methods were appropriate concerning the large dataset needed. Any other method for determining the results could not be used and was not yet discovered. (PubMed.gov, 2017)
Analysis was carried out by reviewing the collected GBD data & systematically analyzing the results. In this analysis, the prevalence of tobacco smoking and smoking-attributable burden of disease was measured for 195 states and provinces from 1990 to 2015, based on deaths and disability-adjusted life-years (DALYs) by gender and age group. The disparities in smoking rates and attributable burden were also analyzed based on the Socio-demographic Scale, which is an accuracy-indicator of per capita income, academic achievement, and overall progress rate. Also, age and gender differences were analyzed by the birth group across developmental stages. (Marissa B Reitsma, 2017)
The methodology and analysis used were appropriate for the study. However, some confounding factors such as environmental and occupational factors along with underreporting of smoking in areas without resources, are challenging sources of error. (PMC, NCBI, 2017)
Accordingly, considering these interventions can increase the quality of the collected data and hence the study results. In the GBD research prevalence, mortality, and incidence of smoking, by age, gender, year, and territory have been measured. (Nancy Fullman, 20217)
Enhanced policy development, implementation, and compliance, as well as routine surveillance of smoking behavior, are all desperately required to drive all countries toward better smoking prevention by 2030. Without credible and valid results, these attempts risk getting increasingly aspirational than evidence-based. (PubMed.gov, 2017)