Browsing by Author "Perelman, Julian"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
- Change in the Prevalence and Social Patterning of First- and Second-Hand Smoking in PORTUGAL: a Repeated Cross-Sectional Study (2005 and 2014)Publication . Alves, Joana; Filipe, Rita; Machado, João; Nunes, Baltazar; Perelman, JulianBetween 2005 and 2007, important reinforcements of the tobacco legislation have been implemented in Portugal, which may have affected smoking patterns. The aim of this study was to measure the change in prevalence of first- and second-hand smoking (SHS) among adults, and its socio-demographic patterning in Portugal from 2005 to 2014. Data from the last two Portuguese National Health Interview Surveys (2005 and 2014) were used. The changes in daily smoking and SHS were measured using Poisson regressions, stratifying by sex and survey year. The inequalities were measured using relative inequality indexes (RII). From 2005 to 2014, there was a reduction in SHS (75%-54% among men, and 52%-38% among women), and a reduction in smoking among men (27%-26%), and an increase among women (9%-12%). SHS reduction was more marked among less privileged people. Among Portuguese men, inequalities in daily smoking have increased slightly, while among women the gap favoring low-educated reduced. Between 2005 and 2014, SHS decreased, but not daily smoking, particularly among women. Additionally, socioeconomic inequalities in smoking increased. Future policies should simultaneously tackle smoking and SHS prevalence, and their socioeconomic patterning. More comprehensive policies such as comprehensive national (non-partial) bans, combined with price increases could be more effective.
- Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experiencePublication . Kislaya, Irina; Leite, Andreia; Perelman, Julian; Machado, Ausenda; Torres, Ana Rita; Tolonen, Hanna; Nunes, BaltazarBackground: Accurate data on hypertension is essential to inform decision-making. Hypertension prevalence may be underestimated by population-based surveys due to misclassification of health status by participants. Therefore, adjustment for misclassification bias is required when relying on self-reports. This study aims to quantify misclassification bias in self-reported hypertension prevalence and prevalence ratios in the Portuguese component of the European Health Interview Survey (INS2014), and illustrate application of multiple imputation (MIME) for bias correction using measured high blood pressure data from the first Portuguese health examination survey (INSEF). Methods: We assumed that objectively measured hypertension status was missing for INS2014 participants (n = 13,937) and imputed it using INSEF (n = 4910) as auxiliary data. Self-reported, objectively measured and MIME-corrected hypertension prevalence and prevalence ratios (PR) by sex, age group and education were estimated. Bias in self-reported and MIME-corrected estimates were computed using objectively measured INSEF data as a gold-standard. Results: Self-reported INS2014 data underestimated hypertension prevalence in all population subgroups, with misclassification bias ranging from 5.2 to 18.6 percentage points (pp). After MIME-correction, prevalence estimates increased and became closer to objectively measured ones, with bias reduction to 0 pp - 5.7 pp. Compared to objectively measured INSEF, self-reported INS2014 data considerably underestimated prevalence ratio by sex (PR = 0.8, 95CI = [0.7, 0.9] vs. PR = 1.2, 95CI = [1.1, 1.4]). MIME successfully corrected direction of association with sex in bivariate (PR = 1.1, 95CI = [1.0, 1.3]) and multivariate analyses (PR = 1.2, 95CI = [1.0, 1.3]). Misclassification bias in hypertension prevalence ratios by education and age group were less pronounced and did not require correction in multivariate analyses. Conclusions: Our results highlight the importance of misclassification bias analysis in self-reported hypertension. Multiple imputation is a feasible approach to adjust for misclassification bias in prevalence estimates and exposure-outcomes associations in survey data.
- Do self-reported data accurately measure health inequalities in risk factors for cardiovascular disease?Publication . Kislaya, Irina; Perelman, Julian; Tolonen, Hanna; Nunes, BaltazarObjectives: This study aimed to compare the magnitude of educational inequalities in self-reported and examination-based hypertension and hypercholesterolemia and to assess the impact of self-reported measurement error on health inequality indicators. Methods: We used the Portuguese National Health Examination Survey data (n = 4911). The slope index of inequality (SII) and the relative index of inequality (RII) were used to determine the magnitude of absolute and relative education-related inequalities. Results: Among the 25-49-year-old (yo) men, absolute and relative inequalities were smaller for self-reported than for examination-based hypertension (SIIeb = 0.18 vs. SIIsr = - 0.001, p < 0.001; RIIeb = 1.99 vs. RIIsr = 0.86, p = 0.031). For women, the relative inequalities were similar despite differences in self-reported and examination-based hypertension prevalence. For hypercholesterolemia, self-reported relative inequalities were larger than examination-based inequalities among the 50-74-yo men (RIIsr = 2.28 vs. RIIeb = 1.21, p = 0.004) and women (RIIsr = 1.22 vs. RIIeb= 0.87, p = 0.045), while no differences were observed among 25-49-yo. Conclusions: Self-reported data underestimated educational inequalities among 25-49-yo men and overestimated them in older individuals. Inequality indicators derived from self-report should be interpreted with caution, and examination-based values should be preferred, when available.
- The social patterning of measurement errors in self-reports: Impact on socioeconomic inequalities estimatesPublication . Kislaya, Irina; Perelman, Julian; Tolonen, Hanna; Nunes, BaltazarReduction of unfair differences in health between socioeconomic groups and countries constitutes an important public health challenge in the 21st century. To monitor progress on this goal, health inequalities are most frequently estimated based on self-reported data from population surveys. However, it has been shown that self-reported data on cardiovascular disease risk factors is prompt to reporting error. If errors occur more often in specific socioeconomic groups (due to under-diagnosis or lower literacy) they are likely to seriously bias health inequality estimates. This study aims at comparing measurement errors between socioeconomic categories in self-reported hypertension, and their consequences on health inequality estimates. We used data from the Portuguese National Health Examination Survey (INSEF), a cross-sectional nationwide study conducted in 2015 on a probabilistic sample (n = 4911) of community-dwelling individuals aged between 25 and 74-years-old. Inequalities in hypertension between the highest and lowest socioeconomic status groups were measured using relative indexes of inequality (RII) and respective confidence intervals (95% CI), estimated by Poisson regression. Estimates of inequalities were stratified by age and sex, using four population groups (male 25–49-years-old, female 25–49-years-old, male 50–75-years-old, female 50–75-years-old). Age- and sex-specific results showed considerable discrepancies in inequality indicators between self-reported and examination-based data. Namely, differences in estimated gradients were more pronounced among 25–49-years-old males, with RII = 0.67 (95% CI: 0.29 to 1.54) for self-reported and RII = 1.90 (95% CI: 1.22 to 2.96) for examination-based hypertension. In 25–49-years-old females inequalities in self-reported hypertension were not statistically significant (RII = 3.18; 95% CI: 0.94 to 10.73), while females with the lowest education were 4.35 (95% CI: 2.60 to 7.27) times more likely to have examination-based hypertension then compared to the most educated. Our results illustrated the significant effect of measurement error in self-reported hypertension on estimates of socioeconomic inequalities. Use of self-reported data led to underestimation of educational inequalities among young and middle-aged individuals. Inequality indicators derived from self-report should be interpreted with caution