The source population is defined as all people with PD in the Netherlands, divided in either the PRIME or the usual care region receiving hospital-based neurological care [7]. From both source populations, we recruited a questionnaire sample containing an unmatched and self-enrolled group of people with PD (convenience sampling). To examine the research questions, we 1) investigated the regional differences in baseline characteristics between the source population in the PRIME and UC region in the healthcare claims data, 2) determined the generalizability of the questionnaire sample by comparing their characteristics to the source population, and 3) tested for the presence of selection and confounding bias by comparing the PRIME and UC questionnaire sample at baseline and 1-year follow-up (Fig. 2).

Overview of the comparisons made. First, we assessed regional baseline differences between the PRIME and the usual care (UC) region using healthcare claims data of almost all people with Parkinson’s disease in the Netherlands (the source population) (1). Second, we compared the combined questionnaire sample of participants from both regions to the source population to determine if the questionnaire sample findings will be generalizable to all people with PD in the Netherlands (2). Third, to assess selection and confounding bias between the two regions, we compared the PRIME and UC questionnaire sample on baseline characteristics (3a) and investigated whether there is differential 1-year compliance (3b and 3c)

People with PD were identified in the national healthcare claims data of Vektis, which contains the data of more than 99% of all people with PD in the Netherlands. For this specific analysis, we included only people with PD, based on diagnostic hospital code DBC501, because the diagnostic hospital code for atypical parkinsonism is also used for other types of movement disorders. The inclusion criteria were: 1) received the 501 code in 2018, 2019 or 2020, 2) alive in 2020, and 3) primarily received outpatient care at a regional hospital instead of a university medical centre, because PRIME Parkinson care is restricted to regional hospitals as they better reflect usual care for the majority of people with parkinsonism. The hospital of care was classified as regional if people with PD received more than 75% of their care in a regional hospital in the years 2018, 2019 and 2020. In our analysis of baseline data, we examined regional differences in age, sex, disease duration, socio-economic status, Charlson comorbidity index, hospital admissions for orthopaedic fractures and pneumonia’s, as well as prescribed medication for anxiety, depression, and cognitive impairments.

Furthermore, we leveraged data from the Central Bureau of Statistics (CBS) of the Netherlands to determine regional differences regarding variables not included in the healthcare claims database [9]. This includes migratory background, overweight based on body mass index (BMI), COVID-19 occurrence, smoking behaviour, alcohol consumption, education, and living situation. Although these data are extracted from the general population instead of the PD specific population, they are the only and best proxy for determining regional differences at baseline for the PD population for the variables missing in the healthcare claims data. If a relationship between these variables and PD exists [10], we assumed that such a relationship will be similar between both regions. We extracted data on a provincial level because no municipality-level data were available (see supplementary file S1 for details). Therefore, in this analysis only, we used the provinces Gelderland, Noord-Brabant and Limburg as a proxy for the PRIME region, because they cover the population of the PRIME hospitals [7]. We are mindful that these provinces also include considerable subregions that are not part of the PRIME region, so we interpret this analysis with caution.

People with a clinical diagnosis of parkinsonism, which was confirmed by a letter of the general practitioner or neurologist, were eligible to participate in the questionnaire study, irrespective of whether the specific diagnosis was PD or atypical parkinsonism. People with medication-induced parkinsonism and those who received their treatment in university medical centres were excluded. Potential participants must have visited the neurology outpatient clinic of a regional hospital at least once during the last year for inclusion in questionnaire-based assessments [7].

The questionnaire consisted of various tailor-made sub-questionnaires aimed at retrieving socio-demographic characteristics as well as several existing (clinical) questionnaires to measure, e.g., depression or anxiety. For this paper, the following variables were examined: region, recruitment procedure, sex, age, disease duration, COVID-19 burden, education, work situation, living situation, smoking behaviour, alcohol consumption, BMI, comorbidities, anxiety, depression, cognition, complications, motor symptoms, disease stage based on the Hoehn and Yahr score, and quality of life. All items in the questionnaire were mandatory to complete for participants. An overview of included variables and associated questionnaires is provided in Supplementary Table 1.

Prior to study inclusion, potential participants were called by one of the well-trained research assistants of the assessment team to inform them about study procedures and screen on inclusion criteria. When eligible for the study, participants were sent an informed consent form. Participants had up to 10 days to think about participating in the study. They were called again to discuss any questions and, if they were still interested, to sign the informed consent and to assess cognitive performance using the telephone Montreal Cognitive Assessment (t-MoCA). Afterwards, participants could either self-complete questionnaires electronically or on paper or answer the questions during a phone call with one of the research assistants. Only the paper version of the questionnaire allowed participants to not complete questions. If this was the case, the assessment team called, e-mailed or sent a letter to the participant to complete the questionnaire(s). When the questionnaire was administered via a phone call, the research assistant would encourage the participant to answer all questions.

We implemented identical recruitment strategies in both regions, except for an additional information letter sent by the treating neurologists to the persons with parkinsonism in the PRIME region because recruitment was lagging behind (Table 1).

The healthcare claims and CBS data were used to examine the regional demographic differences at baseline (2020) between persons with PD in the PRIME and UC region (Table 2A). We used t-tests for age, disease duration, socioeconomic status and comorbidities. For each outcome, we inspected histograms and standard deviations per group to assess the assumptions of normality and homoscedasticity. If these assumptions were violated, we performed the Mann–Whitney U-test instead of the t-test. We performed Chi-square tests for sex, anti-anxiety medication, anti-depressive or cognitive medication, orthopedic fractures and pneumonia’s to compare both regions. For the CBS data comparisons (Table 2B), we performed no statistical tests as these data reflect population-measures. We adhered to a 5% difference as cut-off for meaningful differences.

We tested whether the source population and questionnaire sample, both with combined regions, were different in age and disease duration with t-tests. For sex and the number of pneumonia’s, we performed Chi-square tests to compare the source population and questionnaire sample. To make a fair comparison to the source population, we excluded the people with atypical parkinsonism from the questionnaire sample for this analysis. Furthermore, we adjusted the combined questionnaire sample estimates through inverse probability weighting. This was necessary to account for the selective overrepresentation of PRIME participants in the questionnaire sample, as we recruited 27% of the PRIME source population versus 2% of the UC source population.

To examine the presence of selection bias and the potential for confounding bias in the questionnaire sample, we tested whether the PRIME region and the UC region (predictor) differed with respect to baseline characteristics (outcome). Furthermore, to assess whether the recruitment procedure introduced selection bias, we compared people within the PRIME region who were recruited by their neurologist with people who were not recruited by their neurologist (predictor) on baseline characteristics (outcome). For both analyses, we used linear regression for continuous outcomes and multinomial or binary logistic regression for nominal and ordinal outcomes, adjusting all analyses for age, sex and disease duration. Outliers were included. Continuous variables that did not meet the assumptions for linear regression were log transformed before conducting linear regression.

To examine if the loss to follow-up caused selection bias, differences between participants who remained in the study and who were lost were assessed with linear regression for continuous outcomes (age, motor symptoms, depression, anxiety, cognition, quality of life, disease duration) and with multinomial (education and disease stage) or binomial (sex) logistic regression, using compliance as predictor in all models. We performed these analyses for each region separately as we expect a test for interaction across all outcomes and regions to be underpowered given the low number of drop-outs. We log-transformed continuous outcomes that did not meet the assumptions for linear regression. We define a loss to follow-up as a participant who no longer provided questionnaire data for any reason. Therefore, the loss to follow-up numbers contain both deceased participants as well as actively dropped-out participants. All p-values were adjusted according to the Benjamini–Hochberg method [11].

All data analyses were conducted in R Studio version 2022.02.1 [12]. We https://osf.io/wugkc/?view_only=5f8d8725072a46deb6fcc2ce77fb7881 pre-registered our analyses at the Open Science Framework. In our interpretation of all analyses, we consider both p-values, effect estimates and confidence intervals to judge whether differences between groups are meaningful.