yrbss.Rmd

# Youth Risk Behavior Surveillance System (YRBSS) {-}

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The high school edition of the Behavioral Risk Factor Surveillance System (BRFSS).

* One table with one row per sampled youth respondent.

* A complex sample survey designed to generalize to all public and private school students in grades 9-12 in the United States.

* Released biennially since 1993.

* Administered by the [Centers for Disease Control and Prevention](http://www.cdc.gov/).

---

Please skim before you begin:

1. [Methodology of the Youth Risk Behavior Surveillance System](https://www.cdc.gov/mmwr/pdf/rr/rr6201.pdf)

2. [Wikipedia Entry](https://en.wikipedia.org/wiki/Youth_Risk_Behavior_Surveillance_System)

3. A haiku regarding this microdata:

```{r}
# maladolescence
# epidemiology
# sex, drugs, rock and roll
```

---

## Download, Import, Preparation {-}

Load the `SAScii` library to interpret a SAS input program, and also re-arrange the SAS input program:
```{r eval = FALSE , results = "hide" }
library(SAScii)

sas_url <-
	"https://www.cdc.gov/yrbs/files/2023/2023XXH_SAS_Input_Program.sas"

sas_text <- tolower( readLines( sas_url ) )

# find the (out of numerical order)
# `site` location variable's position
# within the SAS input program
site_location <- which( sas_text == '@1 site $3.' )

# find the start field's position
# within the SAS input program
input_location <- which( sas_text == "input" )

# create a vector from 1 to the length of the text file
sas_length <- seq( length( sas_text ) )

# remove the site_location
sas_length <- sas_length[ -site_location ]

# re-insert the site variable's location
# immediately after the starting position
sas_reorder <- 
	c( 
		sas_length[ seq( input_location ) ] , 
		site_location , 
		sas_length[ seq( input_location + 1 , length( sas_length ) ) ] 
	)

# re-order the sas text file
sas_text <- sas_text[ sas_reorder ]

sas_tf <- tempfile()

writeLines( sas_text , sas_tf )
```

Download and import the national file:
```{r eval = FALSE , results = "hide" }
dat_tf <- tempfile()

dat_url <-
	"https://www.cdc.gov/yrbs/files/2023/XXH2023_YRBS_Data.dat"
	
download.file( dat_url , dat_tf , mode = 'wb' )

yrbss_df <- read.SAScii( dat_tf , sas_tf )

names( yrbss_df ) <- tolower( names( yrbss_df ) )

yrbss_df[ , 'one' ] <- 1
```

### Save Locally \ {-}

Save the object at any point:

```{r eval = FALSE , results = "hide" }
# yrbss_fn <- file.path( path.expand( "~" ) , "YRBSS" , "this_file.rds" )
# saveRDS( yrbss_df , file = yrbss_fn , compress = FALSE )
```

Load the same object:

```{r eval = FALSE , results = "hide" }
# yrbss_df <- readRDS( yrbss_fn )
```

### Survey Design Definition {-}
Construct a complex sample survey design:

```{r eval = FALSE , results = "hide" }
library(survey)

yrbss_design <- 
	svydesign( 
		~ psu , 
		strata = ~ stratum , 
		data = yrbss_df , 
		weights = ~ weight , 
		nest = TRUE 
	)
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE , results = "hide" }
yrbss_design <- 
	update( 
		yrbss_design , 
		did_not_always_wear_seat_belt = as.numeric( qn8 == 1 ) ,
		ever_used_marijuana = as.numeric( qn46 == 1 ) ,
		tried_to_quit_tobacco_past_year = as.numeric( qn40 == 1 ) ,
		used_tobacco_past_year = as.numeric( q40 > 1 )
	)
```

---

## Analysis Examples with the `survey` library \ {-}

### Unweighted Counts {-}

Count the unweighted number of records in the survey sample, overall and by groups:
```{r eval = FALSE , results = "hide" }
sum( weights( yrbss_design , "sampling" ) != 0 )

svyby( ~ one , ~ ever_used_marijuana , yrbss_design , unwtd.count )
```

### Weighted Counts {-}
Count the weighted size of the generalizable population, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ one , yrbss_design )

svyby( ~ one , ~ ever_used_marijuana , yrbss_design , svytotal )
```

### Descriptive Statistics {-}

Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ bmipct , yrbss_design , na.rm = TRUE )

svyby( ~ bmipct , ~ ever_used_marijuana , yrbss_design , svymean , na.rm = TRUE )
```

Calculate the distribution of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ q2 , yrbss_design , na.rm = TRUE )

svyby( ~ q2 , ~ ever_used_marijuana , yrbss_design , svymean , na.rm = TRUE )
```

Calculate the sum of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ bmipct , yrbss_design , na.rm = TRUE )

svyby( ~ bmipct , ~ ever_used_marijuana , yrbss_design , svytotal , na.rm = TRUE )
```

Calculate the weighted sum of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ q2 , yrbss_design , na.rm = TRUE )

svyby( ~ q2 , ~ ever_used_marijuana , yrbss_design , svytotal , na.rm = TRUE )
```

Calculate the median (50th percentile) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svyquantile( ~ bmipct , yrbss_design , 0.5 , na.rm = TRUE )

svyby( 
	~ bmipct , 
	~ ever_used_marijuana , 
	yrbss_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE , na.rm = TRUE
)
```

Estimate a ratio:
```{r eval = FALSE , results = "hide" }
svyratio( 
	numerator = ~ tried_to_quit_tobacco_past_year , 
	denominator = ~ used_tobacco_past_year , 
	yrbss_design ,
	na.rm = TRUE
)
```

### Subsetting {-}

Restrict the survey design to youths who ever drank alcohol:
```{r eval = FALSE , results = "hide" }
sub_yrbss_design <- subset( yrbss_design , qn40 > 1 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ bmipct , sub_yrbss_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:
```{r eval = FALSE , results = "hide" }
this_result <- svymean( ~ bmipct , yrbss_design , na.rm = TRUE )

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

grouped_result <-
	svyby( 
		~ bmipct , 
		~ ever_used_marijuana , 
		yrbss_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:
```{r eval = FALSE , results = "hide" }
degf( yrbss_design )
```

Calculate the complex sample survey-adjusted variance of any statistic:
```{r eval = FALSE , results = "hide" }
svyvar( ~ bmipct , yrbss_design , na.rm = TRUE )
```

Include the complex sample design effect in the result for a specific statistic:
```{r eval = FALSE , results = "hide" }
# SRS without replacement
svymean( ~ bmipct , yrbss_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ bmipct , yrbss_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:
```{r eval = FALSE , results = "hide" }
svyciprop( ~ did_not_always_wear_seat_belt , yrbss_design ,
	method = "likelihood" , na.rm = TRUE )
```

### Regression Models and Tests of Association {-}

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( bmipct ~ did_not_always_wear_seat_belt , yrbss_design )
```

Perform a chi-squared test of association for survey data:
```{r eval = FALSE , results = "hide" }
svychisq( 
	~ did_not_always_wear_seat_belt + q2 , 
	yrbss_design 
)
```

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		bmipct ~ did_not_always_wear_seat_belt + q2 , 
		yrbss_design 
	)

summary( glm_result )
```

---

## Replication Example {-}

This example matches statistics, standard errors, and confidence intervals from the "did not always wear a seat belt" row of [PDF page 29 of this CDC analysis software document](https://www.cdc.gov/yrbs/media/pdf/2023/2023_YRBS_analysis_software.pdf#page=29):

```{r eval = FALSE , results = "hide" }

unwtd_count_result <-
	unwtd.count( ~ did_not_always_wear_seat_belt , yrbss_design )

stopifnot( coef( unwtd_count_result ) == 15071 )

wtd_n_result <-
	svytotal( 
		~ one , 
		subset(
			yrbss_design , 
			!is.na( did_not_always_wear_seat_belt ) 
		)
	)

stopifnot( round( coef( wtd_n_result ) , 0 ) == 16917 )

share_result <-
	svymean(
		~ did_not_always_wear_seat_belt ,
		yrbss_design ,
		na.rm = TRUE 
	)

stopifnot( round( coef( share_result ) , 4 ) == .3958 )

stopifnot( round( SE( share_result ) , 4 ) == .0172 )

ci_result <-
	svyciprop(
		~ did_not_always_wear_seat_belt ,
		yrbss_design , 
		na.rm = TRUE
	)

stopifnot( round( confint( ci_result )[1] , 4 ) == 0.3621 )

stopifnot( round( confint( ci_result )[2] , 4 ) == 0.4304 )
```

---

## Analysis Examples with `srvyr` \ {-}

The R `srvyr` library calculates summary statistics from survey data, such as the mean, total or quantile using [dplyr](https://github.com/tidyverse/dplyr/)-like syntax. [srvyr](https://github.com/gergness/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](https://cran.r-project.org/web/packages/srvyr/vignettes/srvyr-vs-survey.html) details the available features. As a starting point for YRBSS users, this code replicates previously-presented examples:

```{r eval = FALSE , results = "hide" }
library(srvyr)
yrbss_srvyr_design <- as_survey( yrbss_design )
```
Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
yrbss_srvyr_design %>%
	summarize( mean = survey_mean( bmipct , na.rm = TRUE ) )

yrbss_srvyr_design %>%
	group_by( ever_used_marijuana ) %>%
	summarize( mean = survey_mean( bmipct , na.rm = TRUE ) )
```