# Data on physiotherapy referrals from 100 general practices in the Netherlands, collected in 1987 These data were collected in 1987 as part of a large national survey of general practice (Van der Velden 1999). data ## 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`). ## 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. tools:::Rd_expr_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 ```r fysio ``` ## 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). ## Examples ```r ## 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 = mod_MQL1@FP.cov, RP.b = mod_MQL1@RP, RP.v = mod_MQL1@RP.cov)), D = "Binomial", data = fysio)) # Change contrasts back to pre-existing: options(contrasts = my_contrasts) ## End(Not run) ```

fysio dataset