Violence Against Children And Youth Surveys (VACS)

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The global surveillance system to track and monitor the burden of violence against children.

  • One table per country with one row per sampled respondent.

  • Nationally representative cross-sectional household surveys of children and youth ages 13–24.

  • No listed update frequency across the participating nations.

  • Led by the CDC through funding from PEPFAR, in partnership with Together for Girls.

Please skim before you begin:

  1. Sampling design and methodology of the Violence Against Children and Youth Surveys

  2. Violence Against Children Surveys (VACS): Towards a global surveillance system

  3. This human-composed haiku or a bouquet of artificial intelligence-generated limericks

# enable us to
# lodge cane between each spoke of
# cycles of abuse

Download, Import, Preparation

  1. Request public VACS data at https://www.togetherforgirls.org/en/analyzing-public-vacs-data.

  2. Select the Mozambique 2019 dataset and Stata option.

  3. Download and unzip the Mozambique VACS Public Use Dataset files:

library(haven)

vacs_tbl <- read_stata( file.path( path.expand( "~" ) , "mozambique_public use data.dta" ) )

vacs_df <- data.frame( vacs_tbl )

names( vacs_df ) <- tolower( names( vacs_df ) )

Save locally  

Save the object at any point:

# vacs_fn <- file.path( path.expand( "~" ) , "VACS" , "this_file.rds" )
# saveRDS( vacs_df , file = vacs_fn , compress = FALSE )

Load the same object:

# vacs_df <- readRDS( vacs_fn )

Survey Design Definition

Construct a complex sample survey design:

library(survey)

options( survey.lonely.psu = "adjust" )

vacs_design <- 
    svydesign(
        ids = ~cluster ,
        strata = ~strata ,
        weights = ~sampleweight ,
        data = subset( vacs_df , sampleweight > 0 ) ,
        nest = TRUE
    )

Variable Recoding

Add new columns to the data set:

vacs_design <- 
    update( 
        vacs_design , 
        
        one = 1 ,
                
        age_sex_group =
            factor( 
                ifelse( agegrp == 1 , sex , sex + 2 ) , 
                levels = 1:4 , 
                labels = c( 'male 13-17' , 'female 13-17' , 'male 18-24' , 'female 18-24' ) 
            ) ,
        
        sex = factor( sex , levels = 1:2 , labels = c( 'male' , 'female' ) ) ,
        
        agegrp = factor( agegrp , levels = 1:2 , labels = c( '13-17' , '18-24' ) ) ,
        
        ever_attended_school = ifelse( eversch %in% 1:2 , as.numeric( eversch == 1 ) , NA ) ,
        
        childhood_physical_violence = as.numeric( pv18 == 1 ) ,
        
        marry =
            factor(
                marry ,
                levels = 1:3 ,
                labels = 
                    c( 'Yes, ever married' , 'Yes, ever lived with a partner' , 
                    'No, never married or lived with a partner' )

            ) ,
            
        age_at_first_pregnancy = ifelse( prage < 98 , prage , NA ) ,
            
        age_at_first_cohabitation = ifelse( marage < 98 , marage , NA )
    )

Analysis Examples with the survey library  

Unweighted Counts

Count the unweighted number of records in the survey sample, overall and by groups:

sum( weights( vacs_design , "sampling" ) != 0 )

svyby( ~ one , ~ age_sex_group , vacs_design , unwtd.count )

Weighted Counts

Count the weighted size of the generalizable population, overall and by groups:

svytotal( ~ one , vacs_design )

svyby( ~ one , ~ age_sex_group , vacs_design , svytotal )

Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

svymean( ~ age_at_first_cohabitation , vacs_design , na.rm = TRUE )

svyby( ~ age_at_first_cohabitation , ~ age_sex_group , vacs_design , svymean , na.rm = TRUE )

Calculate the distribution of a categorical variable, overall and by groups:

svymean( ~ marry , vacs_design )

svyby( ~ marry , ~ age_sex_group , vacs_design , svymean )

Calculate the sum of a linear variable, overall and by groups:

svytotal( ~ age_at_first_cohabitation , vacs_design , na.rm = TRUE )

svyby( ~ age_at_first_cohabitation , ~ age_sex_group , vacs_design , svytotal , na.rm = TRUE )

Calculate the weighted sum of a categorical variable, overall and by groups:

svytotal( ~ marry , vacs_design )

svyby( ~ marry , ~ age_sex_group , vacs_design , svytotal )

Calculate the median (50th percentile) of a linear variable, overall and by groups:

svyquantile( ~ age_at_first_cohabitation , vacs_design , 0.5 , na.rm = TRUE )

svyby( 
    ~ age_at_first_cohabitation , 
    ~ age_sex_group , 
    vacs_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE , na.rm = TRUE
)

Estimate a ratio:

svyratio( 
    numerator = ~ age_at_first_pregnancy , 
    denominator = ~ age_at_first_cohabitation , 
    vacs_design ,
    na.rm = TRUE
)

Subsetting

Restrict the survey design to :

sub_vacs_design <- subset( vacs_design , childhood_physical_violence == 1 )

Calculate the mean (average) of this subset:

svymean( ~ age_at_first_cohabitation , sub_vacs_design , na.rm = TRUE )

Measures of Uncertainty

Extract the coefficient, standard error, confidence interval, and coefficient of variation from any descriptive statistics function result, overall and by groups:

this_result <- svymean( ~ age_at_first_cohabitation , vacs_design , na.rm = TRUE )

coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )

grouped_result <-
    svyby( 
        ~ age_at_first_cohabitation , 
        ~ age_sex_group , 
        vacs_design , 
        svymean ,
        na.rm = TRUE 
    )
    
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )

Calculate the degrees of freedom of any survey design object:

degf( vacs_design )

Calculate the complex sample survey-adjusted variance of any statistic:

svyvar( ~ age_at_first_cohabitation , vacs_design , na.rm = TRUE )

Include the complex sample design effect in the result for a specific statistic:

# SRS without replacement
svymean( ~ age_at_first_cohabitation , vacs_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ age_at_first_cohabitation , vacs_design , na.rm = TRUE , deff = "replace" )

Compute confidence intervals for proportions using methods that may be more accurate near 0 and 1. See ?svyciprop for alternatives:

svyciprop( ~ ever_attended_school , vacs_design ,
    method = "likelihood" , na.rm = TRUE )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( age_at_first_cohabitation ~ ever_attended_school , vacs_design )

Perform a chi-squared test of association for survey data:

svychisq( 
    ~ ever_attended_school + marry , 
    vacs_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        age_at_first_cohabitation ~ ever_attended_school + marry , 
        vacs_design 
    )

summary( glm_result )

Replication Example

This example matches statistics and confidence intervals within 0.1% from the Final Report of the Mozambique Violence Against Children and Youth Survey (VACS), 2019, Table 4.1.1. Prevalence of different types of sexual violence[1] before age 18, among 18-24-year-olds:

females_18_to_24_design <- subset( vacs_design , sex == 'female' & agegrp == '18-24' )

# define a function to check unweighted N, prevalence, confidence interval for each estimate
check_sv <- 
    function( this_variable , this_design = females_18_to_24_design , N , prevalence , lb , ub ){
    
        this_formula <- as.formula( paste( "~ as.numeric(" , this_variable , "== 1 )" ) )
    
        stopifnot( coef( unwtd.count( this_formula , this_design ) ) == N )
        
        this_result <- svymean( this_formula , this_design , na.rm = TRUE )
        
        stopifnot( round( coef( this_result ) , 3 ) == prevalence )
        
        stopifnot( abs( confint( this_result )[1] - lb ) < 0.0015 )
        
        stopifnot( abs( confint( this_result )[2] - ub ) < 0.0015 )
        
        invisible( TRUE )
    }
    
# sexual touching in childhood
check_sv( "sv1_only18" , N = 1232 , prevalence = 0.066 , lb = 0.039 , ub = 0.093 )

# unwanted attempted sex in childhood
check_sv( "sv2_only18" , N = 1232 , prevalence = 0.061 , lb = 0.035 , ub = 0.087 )

# pressured or coerced sex in childhood
check_sv( "sv4_only18" , N = 1221 , prevalence = 0.056 , lb = 0.035 , ub = 0.077 )

# physically forced sex in childhood
check_sv( "sv3_only18" , N = 1231 , prevalence = 0.035 , lb = 0.020 , ub = 0.051 )

Analysis Examples with srvyr  

The R srvyr library calculates summary statistics from survey data, such as the mean, total or quantile using dplyr-like syntax. srvyr allows for the use of many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, the tidyverse style of non-standard evaluation and more consistent return types than the survey package. This vignette details the available features. As a starting point for VACS users, this code replicates previously-presented examples:

library(srvyr)
vacs_srvyr_design <- as_survey( vacs_design )

Calculate the mean (average) of a linear variable, overall and by groups:

vacs_srvyr_design %>%
    summarize( mean = survey_mean( age_at_first_cohabitation , na.rm = TRUE ) )

vacs_srvyr_design %>%
    group_by( age_sex_group ) %>%
    summarize( mean = survey_mean( age_at_first_cohabitation , na.rm = TRUE ) )