Simultaneity and aggregation of risk factors for noncommunicable diseases among brazilian adolescents

Determinants of Inequalities in the Exposure to and Adoption of Multiple Health Risk Behaviors among Brazilian Adolescents, 2009-2019

The occurrence of multiple risk behaviors among adolescents imposes challenges in the context of public policies of health, particularly in low- and middle-income countries. Evidence on the conditions leading to the exposure to and adoption of multiple risk behaviors allows the identification of vulnerable groups of adolescents, and may support the proposition of targeted strategies directed to individuals at risk. Therefore, the aim of this study was to perform a quantitative analysis to identify recent trends in the exposure to and adoption of multiple health risk behaviors among Brazilian adolescents, highlighting individual-, household-, and school-level characteristics linked to inequalities among social groups. The analysis was based on cross-sectional data from the National Student Health Survey (PeNSE), conducted by the Brazilian Institute for Geography and Statistics in 2009, 2012, 2015, and 2019. The trends in the occurrence of multiple risk behaviors among adolescents were estimated according to social strata, allowing the calculation of concentration indexes and their disaggregation into major determinants of inequalities in the exposure and adoption of risk behaviors. The analyses were conducted using a complex survey design to allow representativeness at the population level. The results showed a rise in the incidence of multiple risk behaviors among youngsters in Brazil from 2009 to 2019. Factors influencing inequalities in the exposure to multiple risk behaviors were socioeconomic status and the characteristics of the household and school environments, whilst the adoption of multiple risk behaviors was also influenced by early exposure to multiple risk behaviors. Furthermore, trends in inequalities in the exposure to and adoption of multiple risk behaviors showed an intensification from 2009 to 2019, being initially concentrated among wealthier adolescents, followed by a transition to higher incidence in the lower socioeconomic strata in 2012 and 2015, respectively. The findings underscore the role of support systems for adolescents at risk within the familial and school contexts, whereas strategies of public policies of health based on the strengthening of community ties may require improvements to tackle socioeconomic inequalities in the occurrence of risk behaviors among youngsters.

Multiple behavioral risk factors for non-communicable diseases among the adolescent population in Brazil: the analysis derived from the Brazilian national survey of school health 2019

BACKGROUND

Health risk behaviors often emerge or intensify during adolescence and tend to co-occur, exposing individuals to an even greater risk for the development of non-communicable diseases (NCDs). The likelihood of exhibiting multiple health risk factors also increases throughout life and is associated with sociodemographic characteristics contributing to their escalation and severity. In this context, the objective of this study was to analyze the association between sociodemographic characteristics and multiple behavioral risk factors for non-communicable diseases among the adolescent population in Brazil.

METHODS

This cross-sectional study utilized data from the Brazilian National Survey of School Health 2019. The sample comprised 121,580 adolescents aged 13 to 17. The analyzed variables included irregular intake of fruits and vegetables, regular consumption of soft drinks and treats, insufficient physical activity, sedentary lifestyle, cigarette smoking, and alcohol consumption. To analyze MBRFs, a classification ranging from zero to five was created, and associations were estimated using Odds Ratio (OR) with the respective 95% confidence interval (CI). The Backward method was employed for the multivariate regression model, utilizing ordinal logistic regression.

RESULTS

Adolescents without behavioral risk factors for NCDs constituted only 3.9% (95% CI 3.7-4.1). The most prevalent categories were two and three MBRFs, accounting for 28.3% (95% CI: 27.7-28.8) and 27.0% (95% CI: 26.5-27.5), respectively. Adolescents aged 16 and 17 (ORadj: 1.39; 95% CI: 1.32-1.48), residing in the Brazilian Southeast (ORadj: 1.66; 95% CI: 1.52-1.81), and those reporting poor or very poor self-rated health (ORadj: 2.05; 95% CI: 1.87-2.25) were more likely to exhibit multiple behavioral risk factors. Conversely, male adolescents (adjusted OR: 0.65; 95% CI: 0.62-0.69), those of mixed race (adjusted OR: 0.92; 95% CI: 0.87-0.97), and residents of rural areas (adjusted OR: 0.76; 95% CI: 0.70-0.84) were less likely to manifest MBRFs for NCDs.

CONCLUSION

The majority of adolescents displayed MBRFs for NCDs, positively associated with age, region, and perceived health status. This underscores the necessity for healthcare promotional interventions throughout the life cycle, as these behaviors may persist into adulthood.

Clustering of Lifestyle Risk Factors among Algerian Adolescents: Comparison between Urban and Rural Areas: GSHS Data

Objective: Compare the clustering of LBRs between urban and rural Algerian adolescents. Design: Data of this cross-sectional study was derived from the Global School-based Health Survey (GSHS). A self-administered, anonymous questionnaire was filled out by 4532 adolescents (11–16 years), which addressed LBRs of NCDs. Life style behavioral risk factors (LBRs) clustering was measured by the ratios of observed (O) and expected (E) prevalence of one or more simultaneously occurring LBRs for urban and rural areas separately. Multivariate logistic regression was performed to examine the association of LBRs as dependent variable with demographic variables (location, age, gender). Results: The most common LBR was physical inactivity (84.6%: 50.9% for urban and 49.1% for rural). Adolescents in urban areas had a higher prevalence of two (56.8% vs. 43.2%) and three and more (61.3% vs. 38.7%) LBRs than in rural areas. In urban areas, a significant positive association was found between (low fruit and vegetable consumption + physical inactivity) [2.06 (1.61–2.64)] and (high SB + smoking) [2.10 (1.54–2.76)], while (physical inactivity + high SB) [0.70 (0.54–0.91)] showed a significant negative association. In rural areas, (high SB + overweight/obesity) [1.49 (1.09–2.04)] had a significant positive association. While, (low fruit and vegetable consumption + high SB) [0.75 (0.60–0.94)], (physical inactivity + high SB) [0.65 (0.49–0.86)], and (physical inactivity + smoking) [0.70 (0.49–0.99)] had a negative association. Conclusions: Several socio-demographic factors have been identified to play a role in LBRs clustering among Algerian adolescents. Results of the study suggest the development of intervention aiming to tackle different LBRs rather than focusing on a single LBR.