A quantitative approach was adopted to study participants with diverse backgrounds. As no fitting questionnaire was identified, the decision to develop a new questionnaire was made. To capture the relevant constructs of politicians’ role in health promotion, a subject with no conceptual basis, an inductive approach to item construction was chosen, with qualitative interviews as the first step (Hinkin 1998).
Questionnaire development
For the initial interviews, six politicians were randomly chosen from a list of available politicians in two municipalities. The politicians had been divided into pre-determined groups to facilitate the invitations to participate to be based on the aim to include participants with diverse backgrounds in terms of political party representation, representation from both majority and opposition parties, and gender representation. The included participants satisfied these criteria: four were female, and three were under the age of 45 years; their political experience ranged from four to more than 30 years, and they represented both majority and opposition parties and the entire political spectrum.
The interviews were semi-structured and followed an interview guide (see supplemental information — interview guide). The questions centered on the politicians’ views on health and health promotion and on their roles, responsibility, and possibility to promote health. A dual focus on the general population and newly arrived migrants was adopted. The interviews lasted 20–62 min and were recorded using a smart phone and transcribed verbatim.
The data were analyzed using inductive (Patton 2015) manifest qualitative content analysis according to Graneheim and Lundman’s (2004) description of the method. The data were divided into meaning units and coded. The codes were grouped into categories and subcategories. The categories, which later constituted the different parts of the questionnaire, were politicians’ views on health and health promotion, affecting health as a politician, collaboration between actors, the politics of health, and municipality and regional organization and their prerequisites (see supplemental information — qualitative results). A battery of self-rated questions were constructed from the categories and subcategories (Grant and Ferris 2012).
The final version of the questionnaire consisted of 49 items, all derived from the interview data and judged to be of relevance to the research projects’ aims. Three of the questions were open-ended questions, and another three were aimed at clarifying open questions, offering respondents the opportunity to provide examples to illustrate their answers. Eight background questions related to age, gender, political affiliation, and political and public health experience were also added to the questionnaire. When a 5-point Likert scale was not fitting for an item, other logical answers discussed by the politicians in the interviews were used (such as having experience as a politician, other experience, or no experience).
To test the face validity of the final version, the previously interviewed politicians completed the questionnaire (Rattray and Jones 2007), thinking aloud as they proceeded (Streiner et al. 2015, p. 123). Minor adjustments to the instructions and wording were subsequently made. The questionnaire was then reviewed by experienced health promotion researchers (Rattray and Jones 2007). A few more minor adjustments were made.
Data collection and participants
The questionnaire was electronic and was emailed to politicians who were members of the 44 municipality councils or the four regional councils of northern Sweden. The politicians’ email addresses were collected from the municipalities and regions. Politicians with seats in both municipality and regional councils were asked to choose one and respond to the questionnaire as such. The questionnaire was sent to a total of 2744 politicians and was open for 6 weeks during April–May 2019.
A total of 667 politicians (24.3%) responded to the questionnaire and were included in the study. Their background characteristics are displayed in Table 1. For the analysis of nonresponse bias, official data on the ordinary members of municipality and regional councils were used (data were only available for ordinary members). In this study, alternates were also included and were listed with ordinary members. The nonresponse bias analysis was therefore performed by comparing both the included ordinary members and the total sample to the official data.
Table 1 Participants’ background data A comparison between the study participants and the total study population revealed significant differences in party representation (p = 0.028) and age groups (p < 0.001) but not in gender (p = 0.862) and municipality or regional council membership (p = 0.139). However, the effect sizes were small: Cramér’s V was 0.093 for party representation and 0.110 for age group, indicating small differences between the groups. A post-hoc analysis of adjusted residuals showed that only a difference regarding the Sweden Democrats party was statistically significant (adjusted Z = −3.30, χ2 = 10.89, p = 0.00097), and it was only significant for the entire sample and not between ordinary members, indicating that politicians from the Sweden Democrats party were underrepresented in the study in relation to their representation in municipalities and regions. A comparison between age groups showed that all age groups below 55 years were underrepresented and older age groups were overrepresented in the study. However, a post-hoc analysis showed no statistically significant differences in any age groups when analyzing the adjusted residuals.
Quantitative data analysis
The data extracted from the questionnaires (Table 2) were exported from Netigate to IBM SPSS Statistics 25 for statistical analysis. Univariate data were presented in frequencies and percentages. Bivariate analyses were performed using χ2 tests, the independent samples t-test, and the Wilcoxon signed-rank test. The significance level was set to p < 0.05. For post-hoc analyses, Bonferroni adjustment was used for the α level (Pallant 2016, p. 240). To assess the effect size, Cramér’s V (Pallant 2016, pp. 221–222) and Cohen’s coefficient were used (Cohen 1988, cited in Pallant 2016, p. 137).
Table 2 Variables, index, and coding For multivariate analyses, binary logistic regression was used to evaluate associations between predictor variables (knowledge, attitude, and general health-related knowledge) and outcome variables. Ordinal and categorical variables were dichotomized before entered into the analysis (Table 2). The analysis was performed in three steps of variable entry, and adjusted odds ratios (OR) and 95% confidence intervals (CI) were calculated. Cox and Snell’s R2 and Nagelkerke’s R2 were calculated for the model explanation degree. Omnibus tests of model coefficients were performed for model and step significance. The Hosmer–Lemeshow test was used to evaluate the goodness of fit (Hosmer et al. 2013; Pallant 2016, pp. 169–181).
Two indexes were created for the analysis. An index called General Health Knowledge was composed of five items measured on a 5-point Likert scale, which was reversed to where 1 represented fully disagree and 5 represented fully agree. The items included were “I consider myself to have enough knowledge to judge whether a political decision that I make affects the population’s health,” “I consider myself to have enough knowledge to judge how a political decision that I make affects the population’s health,” “I understand the differences between health promotion and disease prevention,” “I understand the municipality’s/region’s responsibility for the population’s health according to the new national public health political goals established in 2018,” and “I consider myself to have a clear picture of the health status of the general population in the municipality/region where I am a politician.” The index had a Cronbach’s α of 0.741 and ranged from 1 to 18, where 18 was the highest-rated knowledge.
The second index, Considering Health Effects (Cronbach’s α = 0.771), was composed of two items: “In the last 3 months, have you, on your own, reflected on how political decisions that you make affect newly arrived migrants’ health?” and “In the last 3 months, have you, in your council, discussed how decisions that you make affect newly arrived migrants’ health?” The coding was 1 = Yes, in a large proportion of the decisions, 2 = Yes, a few times, and 3 = No, yielding a range of 2–6. In the logistic regression analysis, the coding was expressed as 0 = 4–6 (not considering health effects) and 1 = 2–3 (considering health effects).
Exploratory Factor Analysis was then used to further explore the indexes. The variables were subjected to principal component analysis (PCA) and varimax rotation. The minimum factor loading was set to 0.5. First, the suitability for a factor analysis was examined. The Kaiser–Meyer–Olkin measure of sampling adequacy was 0.764 and the Bartlett’s test of sphericity was significant (< .001), indicating a fit of using factor analysis (Pallant 2016, pp. 182–203). Two components with eigenvalues exceeding 1 was present, explaining 44.01% and 15.78% of variance respectively. In total, the two dimensions explained 59.79% of variance among the items in the study. The two factors identified in this exploratory factor analysis align with the assumptions made in the item and questionnaire construction. Factor 1 refers to the index named General Health Knowledge (including five items) and Factor 2 refers to the index named Considering Health Effects (including two items). Factor loadings are presented in Table 3.
Table 3 Exploratory Factor Analysis results