ahs: Australian Health Survey Data
In mcglm: Multivariate Covariance Generalized Linear Models

ahs	R Documentation

Australian Health Survey Data

Description

The Australian Health Survey (AHS) was used by Bonat and Jorgensen (2016) as an example of multivariate count regression modeling. The dataset contains five count response variables related to health system usage and nine covariates related to social conditions in Australia for the years 1987-88.

Usage

data(ahs)

Format

A data.frame with 5190 observations and 15 variables:

sex: Factor with levels male and female.
age: Respondent's age in years divided by 100.
income: Respondent's annual income in Australian dollars divided by 1000.
levyplus: Factor indicating coverage by private health insurance for private patients in public hospital with doctor of choice (1) or otherwise (0).
freepoor: Factor indicating government coverage due to low income, recent immigration, or unemployment (1) or otherwise (0).
freerepa: Factor indicating government coverage due to old-age/disability pension, veteran status, or family of deceased veteran (1) or otherwise (0).
illnes: Number of illnesses in the past two weeks, capped at 5.
actdays: Number of days of reduced activity in the past two weeks due to illness or injury.
hscore: General health questionnaire score (Goldberg's method); higher scores indicate poorer health.
chcond: Factor with levels: limited (chronic condition with activity limitation), nonlimited (chronic condition without limitation), otherwise (reference level).
Ndoc: Number of consultations with a doctor or specialist (response variable).
Nndoc: Number of consultations with health professionals (response variable).
Nadm: Number of admissions to hospital, psychiatric hospital, nursing, or convalescence home in the past 12 months (response variable).
Nhosp: Number of nights in a hospital during the most recent admission.
Nmed: Total number of prescribed and non-prescribed medications used in the past two days.

Source

Deb, P. and Trivedi, P. K. (1997) "Demand for medical care by the elderly: A finite mixture approach." Journal of Applied Econometrics, 12(3):313–336.

Bonat, W. H. and Jorgensen, B. (2016) "Multivariate covariance generalized linear models." Journal of the Royal Statistical Society: Series C, 65:649–675.

Examples

library(mcglm)
data(ahs, package = "mcglm")
form1 <- Ndoc ~ income + age
form2 <- Nndoc ~ income + age
Z0 <- mc_id(ahs)
fit.ahs <- mcglm(linear_pred = c(form1, form2),
                 matrix_pred = list(Z0, Z0),
                 link = c("log", "log"),
                 variance = c("poisson_tweedie", "poisson_tweedie"),
                 data = ahs)
summary(fit.ahs)

mcglm documentation built on Jan. 9, 2026, 1:07 a.m.