R: Medicaid Utilization Data

Medicaid1986 {AER} | R Documentation

## Medicaid Utilization Data

### Description

Cross-section data originating from the 1986 Medicaid Consumer Survey. The data comprise two groups of Medicaid eligibles at two sites in California (Santa Barbara and Ventura counties): a group enrolled in a managed care demonstration program and a fee-for-service comparison group of non-enrollees.

### Usage

    data("Medicaid1986")

### Format

A data frame containing 996 observations on 14 variables.

visits

  Number of doctor visits.

exposure

  Length of observation period for ambulatory care (days).

children

  Total number of children in the household.

age

  Age of the respondent.

income

  Annual household income (average of income range in million USD).

health1

  The first principal component (divided by 1000) of three health-status variables: functional limitations, acute conditions, and chronic conditions.

health2

  The second principal component (divided by 1000) of three health-status variables: functional limitations, acute conditions, and chronic conditions.

access

  Availability of health services (0 = low access, 1 = high access).

married

  Factor. Is the individual married?

gender

  Factor indicating gender.

ethnicity

  Factor indicating ethnicity (`"cauc"` or `"other"`).

school

  Number of years completed in school.

enroll

  Factor. Is the individual enrolled in a demonstration program?

program

  Factor indicating the managed care demonstration program: Aid to Families with Dependent Children (`"afdc"`) or non-institutionalized Supplementary Security Income (`"ssi"`).

### Source

Journal of Applied Econometrics Data Archive.

http://qed.econ.queensu.ca/jae/1997-v12.3/gurmu/

### References

Gurmu, S. (1997). Semi-Parametric Estimation of Hurdle Regression Models with an Application to Medicaid Utilization. Journal of Applied Econometrics, 12, 225–242.

### Examples

    ## data and packages
    data("Medicaid1986")
    library("MASS")
    library("pscl")

    ## scale regressors
    Medicaid1986$age2 <- Medicaid1986$age^2 / 100
    Medicaid1986$school <- Medicaid1986$school / 10
    Medicaid1986$income <- Medicaid1986$income / 10

    ## subsets
    afdc <- subset(Medicaid1986, program == "afdc")[, c(1, 3:4, 15, 5:9, 11:13)]
    ssi <- subset(Medicaid1986, program == "ssi")[, c(1, 3:4, 15, 5:13)]

    ## Gurmu (1997):
    ## Table VI., Poisson and negbin models
    afdc_pois <- glm(visits ~ ., data = afdc, family = poisson)
    summary(afdc_pois)
    coeftest(afdc_pois, vcov = sandwich)

    afdc_nb <- glm.nb(visits ~ ., data = afdc)
    ssi_pois <- glm(visits ~ ., data = ssi, family = poisson)
    ssi_nb <- glm.nb(visits ~ ., data = ssi)

    ## Table VII., Hurdle models (without semi-parametric effects)
    afdc_hurdle <- hurdle(visits ~ . | . - access, data = afdc, dist = "negbin")
    ssi_hurdle <- hurdle(visits ~ . | . - access, data = ssi, dist = "negbin")

    ## Table VIII., Observed and expected frequencies
    round(cbind(
      Observed = table(afdc$visits)[1:8],
      Poisson = sapply(0:7, function(x) sum(dpois(x, fitted(afdc_pois)))),
      Negbin = sapply(0:7, function(x) sum(dnbinom(x, mu = fitted(afdc_nb), size = afdc_nb$theta))),
      Hurdle = colSums(predict(afdc_hurdle, type = "prob")[,1:8])
      )/nrow(afdc), digits = 3) * 100
    round(cbind(
      Observed = table(ssi$visits)[1:8],
      Poisson = sapply(0:7, function(x) sum(dpois(x, fitted(ssi_pois)))),
      Negbin = sapply(0:7, function(x) sum(dnbinom(x, mu = fitted(ssi_nb), size = ssi_nb$theta))),
      Hurdle = colSums(predict(ssi_hurdle, type = "prob")[,1:8])
      )/nrow(ssi), digits = 3) * 100

[Package AER version 1.2-5 Index]