The Inter99 study
Inter99 study was a population-based randomized lifestyle intervention with a catchment area covering 73 neighborhoods (census districts) in the south-western part of Copenhagen County, Denmark. Mean adult neighborhood population size was 2457 persons (range: 464–5412). The adult working-age population (25–65 years) of this area included 179,359 persons. The design of the study has previously been described in detail [11]. The study population consisted of a stratified sample of all inhabitants born in 1939–40, 1944–45, 1949–50, 1954–55, 1959–60, 1964–65, and 1969–70 (n = 61,301) and these were pre-randomized (December 2nd 1998) to either control (n = 48,285) or intervention (n = 13,016) group. At baseline all persons in the intervention group were invited to health checks and assessment of 10-year risk of ischemic heart disease at the Research Centre for Prevention and Health taking place between March 15th 1999 and January 31st 2001. They all had lifestyle counseling of varying intensity according to their assessed risk [11] and persons at high risk of IHD were additionally over a four to six month period offered six sessions of group-based counselling. All persons in the intervention group received questionnaires regarding health and lifestyle.
The analyses of this paper are based on all persons in the intervention group. A total of 88 persons in the intervention group emigrated, were lost to follow-up or died in the period between date of randomization and baseline. Furthermore, between date of randomization and January 1st 1999, when data on census district was retrieved from the registers a total of 76 persons moved to a municipality outside the study area and we were not able to identify the census district of 84 persons (1 %). Additionally, educational attainment was missing for 204 persons (2 %), leaving 12,564 persons for analyses.
All participants gave a written consent before taking part in the study. In Denmark researchers have permission to use registers for research purposes without persons’ informed consent as long as they comply with predefined research regulations, which made it possible to obtain register information on participants as well as non-participants. The study was approved by the Regional Scientific Ethics Committee (KA 98 155) and the Danish Data Protection Agency and is registered at ClinicalTrials.gov (registration no. NCT00289237).
Individual level factors
Participation: Persons were categorized as participating if they attended the health check.
Educational attainment was categorized into basic education (up to high school), low education (<2 years of vocational training), middle education (2–4 years of vocational training/education), and high education (>4 years; academic degree).
Income (equalized disposable income) was calculated as the average household income after taxation and interest, divided by the number of equivalent adults in the household. Equalized family size was calculated as follows; the first adult was given a weight of 1.0, each subsequent adult was given a weight of 0.5 and each child under 14 years was given a weight of 0.3 [4]. Furthermore, income was corrected for inflation by adjusting to the year 2000 price index. As income was not normally distributed, the variable was divided into quartiles.
Employment status was categorized into wage earners, and persons out of workforce (e.g. students, retired, unemployed).
Covariates: Data on personal identification number, age and sex was retrieved from the Central Personal Registry. In Denmark each person is assigned a unique identification number at birth which enables citizens to be followed for the rest of their life.
Neighborhood level factors
Neighborhood informal socializing: A total of 6537 persons responded to two items regarding their social networks;
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“How often do you have contact with family members, with whom you do not live?”
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“How often do you have contact with friends or acquaintances?”
For both questions, the five response categories were given scores as follows: daily (4), several times a week (3), several times a month (2), hardly ever (1) and never (0). All respondents were assigned to their respective neighborhoods and the mean contact frequency with family and friends respectively was calculated at the neighborhood level. On average 91 persons (range: 18–240 persons) from each neighborhood completed the two questions. The 73 neighborhoods were ranked according to their mean friend contact frequency and were divided into quartiles. Corresponding procedure was conducted for family contacts. In order to construct a variable for informal socializing we summed the quartile-scores of the two variables which resolved in a score with values ranking from 2 to 8 (8 being high socializing). This score was divided in four levels of informal socializing; very low (scores 2 + 3, n = 19 neighborhoods), low (score 4 = 15 neighborhoods), middle (scores 5 + 6, n = 22 neighborhoods) and high (scores 7 + 8, n = 17 neighborhoods).
Neighborhood voting turnout: Voting turnout for each of the 73 neighborhoods was used as the second measure of social capital. Voting turnout (in %) in the elections for the Danish parliament on November 20th 2001 was used for this purpose [17] and was based on all persons residing in the neighborhoods. In the multilevel analyses, all of the 73 neighborhoods were ranked and divided into quartiles according to their level of voting participation (very low (<83 %), low (83–85 %), middle (86–89 %), high (>89 %)).
Neighborhood deprivation: The income of all persons between the ages of 25 and 65 who by January 1st 1999 were living in the Inter99 study area (n = 179,097) was ranked and divided into quartiles. All persons were grouped into their respective census districts (n = 73) and the districts were ranked according to the proportion of persons with an income within the lowest quartile (family disposable income <16,500$/year). We then divided the districts into quartiles, creating a neighborhood deprivation variable with four levels of deprivation; very low, low, middle and high.
Statistical analyses
Data from registers was merged with data from the Inter99 study by using census districts and individual identification numbers as key variables. Descriptive statistics include mean neighborhood participation in relation both to voting turnout and informal socializing (defined as proportion never/hardly ever seeing friends or family). Also, contingency tables were conducted showing distribution of baseline characteristics (sex, age, educational attainment, employment status, income, neighborhood deprivation) in neighborhoods with very low and high informal socializing and likewise in neighborhoods with very low and high voting turnout. Additionally, a p-value for chi-square was calculated for each covariate, indicating if the distribution of persons within the categories of each covariate varied significantly between neighborhoods with different level of social capital. As there were only marginal differences in the baseline analyses between men and women, all analyses were conducted for men and women combined.
We estimated the relative risks (RRs) of health check participation according to the level of informal socializing and voting turnout by conducting multilevel analyses with binomial distributions and log links. Census district code was included in the random statement to account for intra-neighborhood correlation and the intraclass correlation coefficient (ICC) was calculated as: \( \frac{\sigma^2}{\sigma^2+1} \). P-value for difference between each category and the reference category was calculated together with 95 % confidence intervals and a p-value for a trend between level of social capital and participation. Model 0 included sex and age in addition to the two measures of social capital. In model 1 three measures of individual socioeconomic position (education, income and employment status) were added as they could confound the effect of social capital on participation. Model 2 included only neighborhood deprivation, age and sex as confounders and model 3 included all three measures of socioeconomic position, neighborhood deprivation, age and sex.
All analyses were performed through the use of the statistical software SAS (version 9.3; SAS Institute).