Abstracts

Introduction

The Study of Cardiovascular Risks in Adolescents (ERICA) would in principle aim to provide national estimates about the prevalence of cardiovascular risk factors and metabolic syndrome by sex and age in adolescents aged 12 to 17 years.

To that end, it would be necessary to base the study on a national household sample, so that information could be collected in the subset of households with adolescents. However, considering the prohibitive cost of such an operation, it was decided to carry out the survey via schools and to limit the target population to adolescents enrolled in the last three grades of Basic Education and in all three grades of Secondary (High-School) Education of public or private schools in municipalities with population of over 100,000 inhabitants, grouped in 32 geographic strata described in this article.

A study based on a geographically stratified sample of students, clustered by school, shift and grade, and class, in addition to allowing inference to the set of most populated cities of Brazil, and to geographic strata that allow visualization of regional differences within the country was the solution to match ERICA’s estimated costs to the available budget. It was thus decided to select the sample in three stages: schools, combinations of shift and grade, and classes. In the selected classes, all eligible students were invited to take part in the survey.

This article describes the study population and its geographic stratification; the calculation of the sample size and its allocation to the strata; the methods for the selection of schools, combinations of shift and grade, and classes; the treatment of non-response among the adolescents; calculation and calibration of sampling weights; and the estimation techniques recommended for analyzing the survey data.

Study population

The study population ¹1 Cochran WG. Sampling techniques. 3rd Ed. New York: John Wiley & Sons; 1977. includes the set of 12 to 17-year-old adolescents, with no temporary or permanent disability, enrolled in one of the last three grades of Basic Education or of the three grades of Secondary Education (High-School), in the morning or afternoon shifts, in public or private schools located in one of the 273 municipalities with population of over 100,000 inhabitants on July 1^st, 2009 (the most recent population data available at the time ERICA’s basic definitions were made).

The characterization of the study population is based on a file provided by the Anísio Teixeira National Institute of Educational Studies and Research (INEP/MEC), which was developed from data of the 2011 Educational Census, (whose microdata are available at ftp://ftp.inep.gov.br/microdados/micro_censo_escolar_2011.zip, accessed on 16/Feb/2012), since this was used to select the combinations of shift and grade in each sampled school.

Considering students who are not lagging behind, adolescents in the 12 to 17 year-old range are expected to be enrolled in classes of one of the last three grades of Basic Education (grades 7^th to 9^th) or one of the three grades of Secondary Education, which were therefore defined as the eligible grades. Thus, from a total of 237,438 schools covered by the 2011 Educational Census, there were 389,315 combinations of eligible shifts (morning or afternoon) and grades.

However, 63,912 of these combinations were related to remedial classes (8,114), non-serialized or mixed grade classes in Basic Education (54,973), and non-serialized classes in Secondary Education (825), which were discarded for not having a clear association with the eligible grades, and therefore with the target adolescent age range. In addition, 51,953 combinations were related to the evening shift (10,559 in Basic Education; 41,394 in Secondary Education), and were ruled out for operational reasons. Thus, in the whole country, there were 273,450 eligible combinations of shift and grade in 61,325 schools.

When considering only the schools located in the 273 municipalities with a population of over 100,000 inhabitants on July 1^st, 2009, the number of combinations dropped to 117,726, and the number of schools to 24,441.

Table 1 shows the totals of schools, classes and students of the eligible shifts and grades, according to the 2011 Educational Census, in the country and in the 273 municipalities considered (those with population over 100,000 inhabitants). It shows that limiting the geographic coverage of the study to these 273 municipalities accounts for coverage of a little over 54% (8.2 million) of the Brazilian population of adolescents enrolled in schools (15.3 million) in the eligible shifts and grades.

Stratification of the study population

The study population was stratified in 32 geographic strata, one for each of the 27 state capitals, and five strata with the schools in the set of municipalities with population over 100,000 in each of Brazil’s five macro-regions.

The distribution of the study population over the geographic strata is presented, abridged, in Table 2. It shows that 44% of the students of the study population attended schools located in the capitals, whereas the remainder 56% were enrolled in schools of the other strata.

Sample size

Assuming that the prevalence of metabolic syndrome in adolescents is 4%, and specifying a maximum estimation error of 0.9% and a 95% confidence level, the required size for a simple random sample would be 1,821 students. Considering that the sample is clustered by school, shift and grade, and class, a design effect of 2.97 was calculated for the average of body mass. This design effect was obtained by processing the data from the 2007 survey of the surveillance system for risk factors to the health of adolescents developed in the city of Rio de Janeiro, Brazil (details in Castro et al. ²2 Castro IRR, Cardoso LO, Engstrom EM, Levy RB, Monteiro CA. Vigilância de fatores de risco para doenças não transmissíveis entre adolescentes: a experiência da cidade do Rio de Janeiro, Brasil. Cad Saúde Pública 2008; 24:2279-88.). As the design effect changes according to the variable considered, and it is, therefore, a precaution for calculating the size of clustered samples, it was decided to use this figure, which leads to a sample size of 5,408 (≈ 1,821 x 2.97) students. In addition, to make up for expected non-response and other losses of up to 15%, the sample size was increased accordingly, reaching 6,219 adolescents. As the survey should produce estimates with the specified precision for each of the 12 domains (= 6 ages x 2 sexes), this led to a total sample size of 74,628 adolescents, which was rounded to 75,060 adolescents after allocation, as it was necessary to have multiples of 60 students sampled in each stratum, as indicated in Table 3.

Sample allocation

The total size of the sample of adolescents was calculated to allow the estimation with controlled precision for 12 domains defined according to the sex and age of the adolescents. Now the sample of adolescents is clustered by school, shift and grade, and class, because there were no frames of adolescents available for direct, unclustered selection of adolescents. Thus, the allocation of the total sample into the 32 geographic strata had to be made taking into account the characteristics of the 2009 Educational Census (ftp://ftp.inep.gov.br/microdados/micro_censo_escolar2009.zip, accessed on 25/Feb/2010), the last one available at the time.

Four different alternatives were tested for the allocation of the total sample size to the strata: equal allocation (same sample size for each stratum); proportional allocation (the stratum sample size is proportional to the stratum population size); power allocation (the stratum sample size is proportional to a power of the stratum population size – two powers 1/3 and 1/2 were tried).

Of the four sample size allocations tested, power allocation with power 1/3 (cubic root) was the one that presented the best balance between precision and sample size available per estimation domain in each stratum.

In fact, equal allocation would ensure the same level of precision for all strata, whereas proportional allocation would ensure distribution proportional to the size of the stratum, but would imply in different precision levels per stratum, and an undesirable sample concentration in the most populated areas of Brazil. Finally, power allocation (with powers of 1/2 and 1/3) reduces the difference between the sample sizes of the various strata, while leading to a compatible size, which allows higher data disaggregation in the larger strata.

Considering that most schools have three or more classes of the grades and shifts being investigated, it was decided to limit the selection to three classes per school. To achieve this goal while spreading the sample over the eligible shifts and grades, three combinations of shift and grade were selected in each school. The average number of students per class, considering a 15% loss, was approximately twenty students. With these parameters, the sizes of the samples of schools and classes for each stratum were determined, by dividing the sample size of students by sixty and by twenty, respectively, as presented in Table 3.

Selection of school sample

The selection of the sample of schools was made using data from the 2009 Educational Census, in order to determine the parameters for the cost of the project, so that the budget for the study could be prepared. Schools were sampled using probability proportional to size (PPS), with the measure of the size equal to the ratio between the number of the students of the school, in 2009, in the shifts and grades considered, and the distance, in kilometers, between the municipality where the school is located and the capital of the state. This composite size measure aimed to decrease travel costs between the capital and the municipalities with sample schools, by reducing the probability of selecting schools in municipalities that are farther away from the capital. To prevent excessive variability in the size measurements, which would lead to an undesirable variability in the sampling weights, distance ranges in kilometers were converted to numeric scores: (1) up to 10km, score 1; (2) between 11 and 50km, score 10; (3) between 51 and 200km, score 50; (4) between 201 and 400km, score 100; (5) between 401 and 600km, score 150; (6) between 601 and 800km, score 200; (7) between 801 and 1,000km, score 250; and (8) over 1,000km, score 300. The inclusion probabilities of schools are defined in equation (1) of Figure 1.

In order to ascertain the distribution of schools per location (urban or rural area) and administrative dependency (public or private), within each geographic stratum, the systematic PPS selection method was used, with schools in the frame sorted by geographic stratum, location (urban x rural) and administrative dependency (public or private). This corresponds to an implicit stratification by location and administrative dependency within each geographic stratum.

In the school selection process, 23 schools were large enough to be included in the sample with certainty. These schools were included in the sample, and the PPS sampling process was applied for sampling the remaining schools and with the remainder sample size. Note that these 23 schools included with certainty are no longer primary sampling units (PSUs) but become selection strata (Figure 1). For these schools, the PSUs are the combinations of shift and grade.

Once the schools were selected according to the above-mentioned criteria, schools from 124 (45.1%) municipalities were selected out of the 273 municipalities in the study population. This implied that the criteria allowed the sample of schools to be concentrated in less than half of the municipalities considered without loss of regional representation.

Selection of the combinations of shift and grade and classes

In the second stage of sampling, three combinations of shift and grade were sampled amongst those existing in the sampled school. This stage was necessary for two reasons: (1) to enable the blood testing of the students, considering that the 12-hour fasting made it infeasible for students enrolled in the afternoon shift; (2) to represent, in the sample, the different ages of the eligible adolescents, using the grades as approximations to the different ages. The design attempted to allocate about 2/3 of the sample in morning-shift classes, and about 1/3 in the afternoon-shift classes, with an equal allocation for the grades under consideration. The use of these fractions was defined in accordance with the resources available for the blood testing, which would not cover more than 2/3 of the size of the student sample.

Next, a selection algorithm was designed to ensure the selection of precisely three combinations of shift and grade per school, so that the size of the sample per shift and, whenever possible, per grade was respected. Using this selection algorithm, three combinations of shift and grade were selected from each sampled school, in accordance with the information about the existence of classes of the selected shifts and grades, as indicated in the formula (2) of Figure 1. The first ratio in expression (2) indicates the inclusion probability of the combination of shift and grade, while the second indicates the inclusion probability of the class in the combination of shift and grade. The selection of classes among the existing ones in each combination of shift and grade was made in the field, using Microsoft Excel spreadsheets (Microsoft Corp., United States) prepared for each sampled school (example in Figure 2).

These spreadsheets provided the full identification of the selected schools, and two tables for the selection of classes and the sub-sample of two students who should repeat the 24-hour food recall form. In the first Table, by typing, in the third column, the number of classes of the selected shift and grade indicated in the first two columns, the serial number of the selected class was automatically displayed, according to pre-programmed formulas and random numbers assigned for that school. The serial numbers were always associated to the list of eligible classes sorted by name in the selected shift and grade. The second Table was used to enter: the number of students attending classes at the time of data collection; the numbers of delivered, signed and accepted Parental Informed Consent Forms (ICF) and Adolescent Consent Forms; and the numbers of students who took part in each subsample of the investigation (filling out the questionnaire in the personal digital assistant (PDA), anthropometry, blood pressure, blood test, and 24-hour food recall). From the selected random numbers, pre-programmed formulas, and the total number of students who filled out the food recall form, the serial numbers of the two selected students to repeat the 24-hour food recall were displayed in the last two columns on the right. The serial numbers were always associated with the alphabetic order of the names of the students who did the first 24-hour food recall.

Student selection

In principle, all eligible students of the sampled classes were included in the sample. However, due to absences in the day of the survey, or lack of parental consent for blood testing, the observed sample size may be smaller than the number of eligible students per class. Therefore, the inclusion probability of students in the class, presented in formula (3) of Figure 1, implies treating the sample of adolescents actually interviewed as an equi-probable subsample of the eligible students of the class, assuming there are no major differences between those who participated and those who did not participate in each class.

Treatment for non-response and for sub-sampling

The treatment used to compensate for non-response in each part of the study is based on the assumption that the surveyed adolescents are not significantly different from the non-surveyed adolescents in the same class. Thus, the set of surveyed adolescents may be treated as a random subsample of adolescents in each part of the study: filling out PDA questionnaires; anthropometry; blood pressure; blood testing (cases in which blood was collected and results were obtained), and 24-hour food recall.

Although Figure 1 shows a single conditional inclusion probability for an adolescent in his/her class, the formula (3) was calculated independently for each subsample. Therefore, formulas (4a) to (6) may also vary according to the subsample considered.

Item non-response and inconsistent values detected during data editing that occurred in each part of the study were treated by means of automatic correction or probabilistic imputation.

Calculation of sampling weights and their calibration

As indicated in the equations (5a) and (5b) of Figure 1, the design weight ¹1 Cochran WG. Sampling techniques. 3rd Ed. New York: John Wiley & Sons; 1977. is equal to the product of the reciprocals of the inclusion probabilities in each of the sampling stages, considering the treatment of the subsamples as selection stage.

However, like all clustered samples, these design weights do not reflect the distribution of the population of student adolescents (12 to 17 years in full) by sex and age. This occurs also in household surveys, because the inclusion probabilities reflect the total numbers of units in the different selection stages, and provide a good representation of the total (of residents or students), but distort the distribution by sex and age. According to Silva ³3 Silva PLN. Calibration estimation: when and why, how much and how. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2004., this is the main reason that leads official statistics agencies to calibrate the sampling weights of their household surveys: to ensure that the estimates reflect population data of the elementary sampling units, known from exogenous sources to the investigation.

In the ERICA study, the population data of adolescents enrolled in public or private schools may be estimated considering the information from the last two population censuses held in the Brazil ⁴4 Instituto Brasileiro de Geografia e Estatística. Censo demográfico 2000: educação. Resultados da amostra. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2003.^,55 Instituto Brasileiro de Geografia e Estatística. Censo demográfico 2010: educação e deslocamento. Resultados da amostra. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2012., and the linear trend model that the Brazilian Institute of Geography and Statistics (IBGE) uses to estimate the population of Brazilian municipalities ⁶6 Instituto Brasileiro de Geografia e Estatística. Estimativas da população residente nos municípios brasileiros com data de referência em 1o de julho de 2013. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2013., described by Madeira & Simões ⁷7 Madeira JL, Simões CCS. Estimativas preliminares da população urbana e rural segundo as Unidades da Federação, de 1960/1980 por uma nova metodologia. Revista Brasileira de Estatística 1972; 33: 3-11.. The estimates were made for the date of December 31^st, 2013, considering that part of the sample was investigated in 2013, and part in 2014. In this case, the same type of strategy used by IBGE in its household budget surveys was adopted for ERICA: the chosen date is close to the mid-point of the data-collection period of the investigation.

However, the above method does not enable estimating the number of students in the morning shift. For this purpose, two files from the 2013 Educational Census (ftp://ftp.inep.gov.br/microdados/micro_censo_escolar_2013.zip, accessed on 04/Mar/2014), namely the files with records for classes and the files with the enrolled students, were processed to produce some required estimates. In the classes file, the same criteria applied to define the eligible classes were applied, and in the second the age at December 31^st, 2013, was calculated for all enrolled students. After merging the two files, the proportion of eligible students enrolled in the morning shift was calculated by geographic stratum, sex and age. This vector or proportions was applied to the projected population totals for December 31^st, 2013, providing the vector of population totals of students in the morning shift by geographic stratum, sex and age.

A post-stratification estimator was used, which is a particular case of regression estimator described in Särndal et al. ⁸8 Särndal CE, Swensson B, Wretman JH. Model assisted survey sampling. New York: Springer Verlag; 1992., which multiplies the design weight by a calibration factor that corresponds to the ratio between the known population total and its estimate obtained using by the design weights in each post-stratum. Twelve post-strata were defined, corresponding to the cross-classification of adolescents by six ages and two sexes considered.

Estimation from the ERICA data

The sample of ERICA is considered a complex sample ⁹9 Skinner CJ, Holt D, Smith TMF. Analysis of complex surveys. Chichester: John Wiley & Sons; 1989., since it uses stratification, clustering and unequal probabilities in its selection stages. Unbiased (or at least approximately unbiased) point estimates of target population parameters may be calculated with the use of calibrated sampling weights by any statistical system that accepts weighting.

However, the usual estimates of variance and other statistics that depend on these (such as standard-deviations, standard errors, confidence intervals of point estimates, regression model parameter significance tests, among others) require special estimation procedures. This is due to the fact that two variability sources interfere in the estimates: (1) one is the complex sample design; and (2) the other concerns the residuals of the calibration equations.

The procedure suggested in the literature ⁸8 Särndal CE, Swensson B, Wretman JH. Model assisted survey sampling. New York: Springer Verlag; 1992.^,99 Skinner CJ, Holt D, Smith TMF. Analysis of complex surveys. Chichester: John Wiley & Sons; 1989.^,1010 Lumley T. Complex surveys: a guide to analysis using R. Hoboken: John Wiley & Sons; 2010. (Wiley Series in Survey Methodology). to estimate variances from complex samples is the Ultimate Cluster method, which consists in the estimation of variance from the means calculated per PSU in each (geographic) stratum. Most statistics systems already incorporate routines for the estimation of variances using the Ultimate Cluster method. This method, however, does not consider the source of variability from the calibration, only from the complex sample design.

To consider all the variability sources of the ERICA’s sample, the use survey library of the R language (The R Foundation for Statistical Computing, Vienna, Austria; http://www.r-project.org) is recommended. This library, developed by Thomas Lumley, allows replication of the sample-weight calibration, saving in an object of sample design both the complex sample design structural variables and the residuals of calibration ¹⁰10 Lumley T. Complex surveys: a guide to analysis using R. Hoboken: John Wiley & Sons; 2010. (Wiley Series in Survey Methodology)..

For this purpose, the structural design variables of each sub-sample of data will be saved in the files: selection stratum, PSU code, and sampling weight (both the design and the calibrated ones), in addition to the poststratum codes and population totals used in the calibration of sampling weights. The selection strata are the 23 schools included with certainty in the sample and, for the other schools, the selection stratum matches the geographic stratum. For the 23 certainty schools, the PSUs are the eligible combinations of shift and grade. For all the other strata, the PSUs are the schools themselves. The recommended sampling weight is the calibrated weight, but the design weight is included in the files to allow replication of the calibration process in case the survey library is used.

Acknowledgments

We thank the field team, principals and teachers of the selected schools and classes, and, particularly, to the students, boys and girls, who responded the investigation, without whom ERICA would not exist. To the Brazilian Ministry of Health for their financial support.

References

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