hiv {GJRM.data} | R Documentation |
HIV Zambian data
Description
HIV Zambian data by region, together with polygons describing the regions' shapes.
Usage
data(hiv)
data(hiv.polys)
Format
hiv
is a 6416 row data frame with the following columns:
- consent
binary variable indicating consent to test for HIV.
- status
binary variable indicating whether an individual is HIV positive (status = 1) or not (status = 0).
- age
age in years.
- education
years of education.
- wealth
wealth index.
- region
code identifying region, and matching
names(hiv.polys)
. It can take nine possible values: 1 central, 2 copperbelt, 3 eastern, 4 luapula, 5 lusaka, 6 northwestern, 7 northern, 8 southern, 9 western.- marital
never married, currently married, formerly married.
- std
had a sexually transmitted disease.
- highhiv
had high risk sex.
- partner
number of partners.
- condom
used condom during last intercourse.
- aidscare
equal to 1 if would care for an HIV-infected relative.
- knowsdiedofaids
equal to 1 if know someone who died of HIV.
- evertestedHIV
equal to 1 if previously tested for HIV.
- smoke
smoker or not.
- ethnicity
bemba, lunda (luapula), lala, ushi, lamba, tonga, luvale, lunda (northwestern), mbunda, kaonde, lozi, chewa, nsenga, ngoni, mambwe, namwanga, tumbuka, other.
- language
English, Bemba, Lozi, Nyanja, Tonga, other.
- interviewerID
interviewer identifier.
- agehadsex
age the individual had sex.
- religion
four categories.
- sw
survey weights.
hiv.polys
contains the polygons defining the areas in the format described below.
Details
The data frame hiv
relates to the regions whose boundaries are coded in hiv.polys
.
hiv.polys[[i]]
is a 2 column matrix, containing the vertices of the polygons defining the boundary of the ith
region. names(hiv.polys)
matches hiv$region
(order unimportant).
Source
The data have been produced as described in:
McGovern M.E., Barnighausen T., Marra G. and Radice R. (2015), On the Assumption of Joint Normality in Selection Models: A Copula Approach Applied to Estimating HIV Prevalence. Epidemiology, 26(2), 229-237.
References
Marra G., Radice R., Barnighausen T., Wood S.N. and McGovern M.E. (2017), A Simultaneous Equation Approach to Estimating HIV Prevalence with Non-Ignorable Missing Responses. Journal of the American Statistical Association, 112(518), 484-496.