fysio {R2MLwiN}

Data on physiotherapy referrals from 100 general practices in the Netherlands, collected in 1987

Description

These data were collected in 1987 as part of a large national survey of general practice (Van der Velden 1999).

Usage

fysio

Format

A data frame with 16700 observations on the following 14 variables:

gpid

GP identifier.

patid

Patient identifier.

patage

Patient age in years.

pagegrp

Patient age group, with ages grouped into 7 categories (ordered factor with levels page<35, 35<=page<45, 45<=page<55, 55<=page<65, 65<=page<75, 75<=page<85, 85<=page).

patsex

Patient gender (factor with levels female, male).

patinsur

Patient insurance indicator (factor with levels privateins (privately insured), publicins (publically insured)).

patedu

Patient education level (ordered factor with levels none (no formal education), primary (primary education), secondary (secondary and lower/middle vocational education), higher (higher vocational and university education)).

diag

Primary diagnosis resulting from care episodes (factor with levels 1 (symptoms/complaints neck), 2 (symptoms/complaints back), 3 (myalgia/fibrositis), 4 (symptoms of multiple muscles), 5 (disabilities related to the locomotive system), 6 (impediments of the cervical spine), 7 (arthrosis cervical spine), 8 (lumbago), 9 (ischialgia), 10 (hernia nuclei pulposi), 11 (impediments of the shoulder), 12 (epicondylitis lateralis), 13 (tendinitis/synovitis)).

gpexper

GP experience (number of years working as a GP divided by ten).

gpworkload

GP workload (number of contacts in the 3-month registration period divided by 1000).

practype

Practice type (factor with levels solo, duo, group, healthcentre).

location

Practice location (factor with levels rural, suburban, urban, bigcity).

gpphysifr

Indicator of whether the GP has physiotherapists in their social network (factor with levels no, yes).

referral

Indicator of whether the patient was referred to a physiotherapist (factor with levels no, yes).

Details

The fysio dataset is one of the example datasets analysed in Leyland and Groenewegen (2020), and provided with the multilevel-modelling software package MLwiN (Charlton et al., 2024).

Source

Charlton, C., Rasbash, J., Browne, W.J., Healy, M. and Cameron, B. (2024) MLwiN Version 3.08 Centre for Multilevel Modelling, University of Bristol.

Leyland, A.H., Groenewegen, P.P. (2020). Multilevel Logistic Regression Using MLwiN: Referrals to Physiotherapy. In: Multilevel Modelling for Public Health and Health Services Research. Springer, Cham. doi:10.1007/978-3-030-34801-4_12

Van der Velden, K. (1999). General practice at work: its contribution to epidemiology and health policy. NIVEL, PhD thesis Erasmus University, Utrecht

Examples

## Not run: 

data(fysio, package = "R2MLwiN")

# Example taken from Leyland and Groenewegen (2020)

# Change contrasts if wish to avoid warning indicating that, by default,
# specified contrasts for ordered predictors will be ignored by runMLwiN
# (they will be fitted as "contr.treatment" regardless of this setting). To
# enable specified contrasts, set allowcontrast to TRUE (this will be the
# default in future package releases).
my_contrasts <- options("contrasts")$contrasts
options(contrasts = c(unordered = "contr.treatment",
                      ordered = "contr.treatment"))

# As an alternative to changing contrasts, can instead use C() to specify
# contrasts for ordered predictors in formula object, e.g.:

# F1 <- logit(referral) ~ 1 + C(pagegrp, "contr.treatment") + patsex + diag +
#   C(patedu, "contr.treatment") + patinsur + gpexper + gpworkload +
#   practype + location + gpphysifr +
#   (1 | gpid)
# 
# (mod_MQL1 <- runMLwiN(Formula = F1,
#                       D = "Binomial",
#                       data = fysio,
#                       allowcontrast = TRUE))

F1 <- logit(referral) ~ 1 + pagegrp + patsex + diag +
  patedu + patinsur + gpexper + gpworkload +
  practype + location + gpphysifr +
  (1 | gpid)

(mod_MQL1 <- runMLwiN(Formula = F1,
                      D = "Binomial",
                      data = fysio))

(mod_PQL2 <- runMLwiN(Formula = F1,
                      estoptions = list(nonlinear = c(N = 1, M = 2),
                                        startval = list(FP.b = mod_MQL1@FP,
                                                        FP.v = [e-mail],
                                                        RP.b = mod_MQL1@RP,
                                                        RP.v = [e-mail])),
                      D = "Binomial",
                      data = fysio))
                      
# Change contrasts back to pre-existing:
options(contrasts = my_contrasts)

## End(Not run)