17-chis.Rmd

# California Health Interview Survey (CHIS) {-}

[![Build Status](https://travis-ci.org/asdfree/chis.svg?branch=master)](https://travis-ci.org/asdfree/chis) [![Build status](https://ci.appveyor.com/api/projects/status/github/asdfree/chis?svg=TRUE)](https://ci.appveyor.com/project/ajdamico/chis)

*Contributed by Carl Ganz <<carlganz@gmail.com>>*

The State of California's edition of the National Health Interview Survey (NHIS), a regional healthcare survey for the nation's largest state.

* One adult, one teenage, and one child table, each with one row per sampled respondent.

* A complex sample survey designed to generalize to the civilian non-institutionalized population of California.

* Released annually since 2011, and biennially since 2001.

* Administered by the [UCLA Center for Health Policy Research](http://healthpolicy.ucla.edu/).

## Simplified Download and Importation {-}

The R `lodown` package easily downloads and imports all available CHIS microdata by simply specifying `"chis"` with an `output_dir =` parameter in the `lodown()` function. Depending on your internet connection and computer processing speed, you might prefer to run this step overnight.

```{r eval = FALSE }
library(lodown)
lodown( "chis" , output_dir = file.path( path.expand( "~" ) , "CHIS" ) , 
	your_username = "username" , 
	your_password = "password" )
```

`lodown` also provides a catalog of available microdata extracts with the `get_catalog()` function. After requesting the CHIS catalog, you could pass a subsetted catalog through the `lodown()` function in order to download and import specific extracts (rather than all available extracts).

```{r eval = FALSE , results = "hide" }
library(lodown)
# examine all available CHIS microdata files
chis_cat <-
	get_catalog( "chis" ,
		output_dir = file.path( path.expand( "~" ) , "CHIS" ) , 
		your_username = "username" , 
		your_password = "password" )

# 2014 only
chis_cat <- subset( chis_cat , year == 2014 )
# download the microdata to your local computer
chis_cat <- lodown( "chis" , chis_cat , 
	your_username = "username" , 
	your_password = "password" )
```

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

Construct a complex sample survey design:

```{r eval = FALSE }

```

```{r eval = FALSE }
library(survey)

child <- readRDS( file.path( path.expand( "~" ) , "CHIS" , "2014 child.rds" ) )

child$ak7_p1 <- child$ak10_p <- NA
child$agecat <- "1 - child"
child$no_usual_source_of_care <- as.numeric( child$cd1 == 2 )

# four-category srhs (excellent / very good / good / fair+poor)
child$hlthcat <- child$ca6_p1

# load adolescents ages 12-17
teen <- readRDS( file.path( path.expand( "~" ) , "CHIS" , "2014 teen.rds" ) )

teen$ak7_p1 <- teen$ak10_p <- NA
teen$agecat <- "2 - adolescent"
teen$no_usual_source_of_care <- as.numeric( teen$tf1 == 2 )

# four-category srhs (excellent / very good / good / fair+poor)
teen$hlthcat <- teen$tb1_p1

# load adults ages 18+
adult <- readRDS( file.path( path.expand( "~" ) , "CHIS" , "2014 adult.rds" ) )

adult$agecat <- ifelse( adult$srage_p1 >= 65 , "4 - senior" , "3 - adult" )
adult$no_usual_source_of_care <- as.numeric( adult$ah1 == 2 )

# four-category srhs (excellent / very good / good / fair+poor)
adult$hlthcat <- c( 1 , 2 , 3 , 4 , 4 )[ adult$ab1 ]

# construct a character vector with only the variables needed for the analysis
vars_to_keep <- 
	c( grep( "rakedw" , names( adult ) , value = TRUE ) , 
		'hlthcat' , 'agecat' , 'ak7_p1' , 'ak10_p' ,
		'povgwd_p' , 'no_usual_source_of_care' )

chis_df <- 
	rbind( 
		child[ vars_to_keep ] , 
		teen[ vars_to_keep ] , 
		adult[ vars_to_keep ] 
	)

# remove labelled classes
labelled_cols <- 
	sapply( 
		chis_df , 
		function( w ) class( w ) == 'labelled' 
	)

chis_df[ labelled_cols ] <- 
	sapply( 
		chis_df[ labelled_cols ] , 
		as.numeric
	)

chis_design <- 
	svrepdesign( 
		data = chis_df , 
		weights = ~ rakedw0 , 
		repweights = "rakedw[1-9]" , 
		type = "other" , 
		scale = 1 , 
		rscales = 1 , 
		mse = TRUE 
	)
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE }
chis_design <- 
	update( 
		chis_design , 
		one = 1 ,
		hlthcat = 
			factor( hlthcat , 
				labels = c( 'excellent' , 'very good' , 'good' , 'fair or poor' ) 
			)
	)
```

### Unweighted Counts {-}

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

svyby( ~ one , ~ hlthcat , chis_design , unwtd.count )
```

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

svyby( ~ one , ~ hlthcat , chis_design , svytotal )
```

### Descriptive Statistics {-}

Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ povgwd_p , chis_design )

svyby( ~ povgwd_p , ~ hlthcat , chis_design , svymean )
```

Calculate the distribution of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ agecat , chis_design )

svyby( ~ agecat , ~ hlthcat , chis_design , svymean )
```

Calculate the sum of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ povgwd_p , chis_design )

svyby( ~ povgwd_p , ~ hlthcat , chis_design , svytotal )
```

Calculate the weighted sum of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ agecat , chis_design )

svyby( ~ agecat , ~ hlthcat , chis_design , svytotal )
```

Calculate the median (50th percentile) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svyquantile( ~ povgwd_p , chis_design , 0.5 )

svyby( 
	~ povgwd_p , 
	~ hlthcat , 
	chis_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE ,
	keep.var = TRUE 
)
```

Estimate a ratio:
```{r eval = FALSE , results = "hide" }
svyratio( 
	numerator = ~ ak10_p , 
	denominator = ~ ak7_p1 , 
	chis_design ,
	na.rm = TRUE
)
```

### Subsetting {-}

Restrict the survey design to seniors:
```{r eval = FALSE , results = "hide" }
sub_chis_design <- subset( chis_design , agecat == "4 - senior" )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ povgwd_p , sub_chis_design )
```

### 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( ~ povgwd_p , chis_design )

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

grouped_result <-
	svyby( 
		~ povgwd_p , 
		~ hlthcat , 
		chis_design , 
		svymean 
	)
	
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( chis_design )
```

Calculate the complex sample survey-adjusted variance of any statistic:
```{r eval = FALSE , results = "hide" }
svyvar( ~ povgwd_p , chis_design )
```

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

# SRS with replacement
svymean( ~ povgwd_p , chis_design , 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( ~ no_usual_source_of_care , chis_design ,
	method = "likelihood" )
```

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

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( povgwd_p ~ no_usual_source_of_care , chis_design )
```

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

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		povgwd_p ~ no_usual_source_of_care + agecat , 
		chis_design 
	)

summary( glm_result )
```

## 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 CHIS users, this code replicates previously-presented examples:

```{r eval = FALSE , results = "hide" }
library(srvyr)
chis_srvyr_design <- as_survey( chis_design )
```
Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
chis_srvyr_design %>%
	summarize( mean = survey_mean( povgwd_p ) )

chis_srvyr_design %>%
	group_by( hlthcat ) %>%
	summarize( mean = survey_mean( povgwd_p ) )
```

---

## Replication Example {-}

The example below matches statistics and confidence intervals from this table pulled from the [AskCHIS](http://ask.chis.ucla.edu/) online table creator:

`r knitr::include_graphics("images/askchis.png")`

Match the bottom right weighted count:

```{r eval = FALSE , results = "hide" }
stopifnot( round( coef( svytotal( ~ one , chis_design ) ) , -3 ) == 37582000 )
```
	
Compute the statistics and standard errors for excellent, very good, and good in the rightmost column:

```{r eval = FALSE , results = "hide" }
( total_population_ex_vg_good <- svymean( ~ hlthcat , chis_design ) )

# confirm these match
stopifnot( 
	identical( 
		as.numeric( round( coef( total_population_ex_vg_good ) * 100 , 1 )[ 1:3 ] ) ,
		c( 23.2 , 31.4 , 28.4 )
	)
)
```

Compute the confidence intervals in the rightmost column:

```{r eval = FALSE , results = "hide" }
( total_pop_ci <- confint( total_population_ex_vg_good , df = degf( chis_design ) ) )

# confirm these match
stopifnot(
	identical(
		as.numeric( 
			round( total_pop_ci * 100 , 1 )[ 1:3 , ] 
		) ,
		c( 22.1 , 30.1 , 27.1 , 24.2 , 32.7 , 29.6 )
	)
)
```