Research report
Socioeconomic determinants for compliance to colorectal cancer screening. A multilevel analysis C Pornet, O Dejardin, F Morlais, V Bouvier, G Launoy ‘Cancers & Populations’, ERI 3 INSERM, CHU Caen, Faculty of Medicine, Caen Cedex, France Correspondence to Professor Guy Launoy, ‘Cancers & Populations’, ERI 3 INSERM, CHU Caen, Faculty of Medicine, Avenue Coˆte de Nacre 14032 Caen Cedex, France;
[email protected] Accepted 20 May 2009
ABSTRACT Background Compliance in cancer screening among socially disadvantaged persons is known to be lower than among more socially advantaged persons. However, most of the studies regarding compliance proceed via a questionnaire and are thus limited by self-reported measures of participation and by participation bias. This study aimed at investigating the influence of socioeconomic characteristics on compliance to an organised colorectal cancer screening programme on an unbiased sample based on data from the entire target population within a French geographical department, Calvados (n¼180 045). Methods Individual data of participation and aggregate socioeconomic data, from the structure responsible for organising screening and the French census, respectively, were analysed simultaneously by a multilevel model. Results Uptake was significantly higher in women than in men (OR¼1.33; 95% CI 1.21 to 1.45), and significantly lower in the youngest (50e59 years) and in the oldest (70e74 years) persons, compared with intermediate ages (60e69 years), with OR¼0.70 (95% CI 0.63 to 0.77) and OR¼0.82 (95% CI 0.72 to 0.93), respectively. Uptake fell with increasing level of deprivation. There was a significant difference of uptake probability between the least deprived and the most deprived areas (OR¼0.68; 95% CI 0.59 to 0.79). No significant influence of the general practitioners density was found. Conclusion Multilevel analysis allowed to detect areas of weak uptake linked to areas of strong deprivation. These results suggest that targeting populations with a risk of low compliance, as identified both socially and geographically in our study, could be adopted to minimise inequalities in screening.
Colorectal cancer (CRC) is the second leading cause of death from cancer in France,1 the United Kingdom2 and the USA.3 Randomised controlled trials have demonstrated that CRC mortality can be reduced by screening using the faecal occult blood test.4e6 In the UK, a pilot campaign of organised screening for CRC was set up in 2000, followed by a national screening programme in 2006.2 In France, a pilot campaign was launched in 2002, throughout 23 of 96 geographical departments.7 Local screening management structures invited the target population by post to consult the general practitioner (GP) of their choice, to obtain tests that were to be conducted at home. Individuals forwarded the tests to the analysis centre that, in turn, informed the management structure of test results. A few months later, individuals who had failed to respond to the initial invitation directly received tests by post. 318
Among countries having established an organised CRC screening programme, France occupies an intermediate position in terms of participation rate (42%)8 between Japan (17%)9 and England (60.6% for the first round of screening).10 Compliance to screening is known to be favoured by a high individual socioeconomic level.11e17 However, most of the studies regarding compliance proceed via a questionnaire and are thus limited by self-reported measures of participation and by participation bias. Some studies regarding social inequalities in cancer have used data aggregated into a geographical level10; however, although this method avoids selection bias, no study has investigated the social determinants of screening by combining individual participation data and aggregate socioeconomic data, within the entire target population, and at a sufficiently accurate geographical level. This study aimed at investigating the influence of socioeconomic characteristics on compliance to an organised colorectal cancer screening programme on an unbiased sample based on data from the entire target population within a French geographical department (Calvados).
MATERIALS AND METHODS Study population The first campaign of CRC screening took place from June 2004 to June 2006 in the department of the Calvados. The Association Mathilde was in charge of organising screening in Calvados. This association was therefore given the target population data from the various health insurance systems, concerning particularly identity and address. The study’s target population, aged from 50 to 74 years and located within the department, included 180 045 individuals (figure 1). The availability of exact addresses enabled a geographical unit to be assigned to each participant (geocoding). The geographical units used were Ilôts Regroupés pour l’Information Statistique (IRIS, or regrouped statistical information block), as defined by the National Institute for Statistics and Economic Studies (INSEE), and are the smallest geographical census units available in France. The regional capital and other major towns are divided into several IRISs, and small towns form one IRIS. Our study zone, the department of Calvados, has a total of 829 IRISs (INSEE website, http://www. insee.fr). Because geocoding procedures were not completely automated, it would appear unreasonable to consider that the study concerns the entire population. Sampling of 10 000 individuals was conducted, by simply drawing lots, without handing over, and according to the law of uniform
J Epidemiol Community Health 2010;64:318e324. doi:10.1136/jech.2008.081117
Research report Figure 1 Flow diagram.
distribution. The final study population comprised 8758 individuals after geocoding (135 not geocoded), 1107 individuals having been excluded (figure 1). Among these 1107 excluded individuals, 762 individuals were excluded for medical reasons. The medical exclusions were collected by the Association Mathilde after the medical visit. The GP checked the following medical exclusions: < Patients with a recent digestive symptomatology; < Patients with a normal complete colonoscopy within the last 5 years; < Patients with a high risk or very high of CRC, as those having a history of CRC. Final sample representativeness was checked in terms of participation rate, age and sex (table 1). However, there was a significant difference of insurance coverage between the final sample and the target population, the civil servant system being overrepresented in the final sample.
Measures Participant or non-participant status was known for each individual (table 1). Participants were defined as having undergone a screening test within the duration of the study. Two types of variables were used: individual and aggregate.
Level 1: individual variables Sex, age and insurance coverage were obtained from exhaustive registries held by the Association Mathilde and via all insurers within Calvados (table 1). In France, medical insurance coverage is virtually universal. Homeless people were excluded from our population study (as well as people living in mobile homes) because of the lack of address that was crucial for sending them the invitation to the screening. Unemployed and retired persons were included in our population study because had an available address. Different systems of insurance coverage exist in France: the most common is the general medical insurance scheme, the J Epidemiol Community Health 2010;64:318e324. doi:10.1136/jech.2008.081117
Mutuelle Sociale Agricole covering agricultural occupations, other coverage including civil servant systems, schemes for the selfemployed or other specific systems. The special coverage systems concerns persons belonging to major French enterprises such as Electricité De France and Société Nationale des Chemins de Fer.
Level 2: aggregate variables Each individual was attributed the socioeconomic characteristics of his/her appropriate neighbourhood (IRIS level) using French census data provided by the INSEE (1999). The INSEE produces a large number of socioeconomic indicators. Consequently, because deprivation is multifactorial,18 the selection of relevant variables for the determination of the socioeconomic status of each IRIS proved difficult. We therefore used a composite index of deprivation, less sensitive to measurement bias than individual variables considered independently.19 20 We chose the Townsend index,18 which is widely used and acknowledged in Anglo-Saxon countries.20 This index is based on the unweighted sum of four centred and reduced socioeconomic variables, after log transformation of the first three: percentage of overcrowned households, percentage of households without a car, percentage of unemployed individuals of an economically active age and the square root of the percentage of non-homeowner households. To allow international comparisons, a standardised division into quintiles of the distribution of the Townsend index score for each IRIS was carried out: quintile 1 represented the most privileged IRIS and, conversely, quintile 5, the most deprived. GP density per 100 000 inhabitants for each IRIS was used as a proxy variable determining access to healthcare.
Statistical analysis Multilevel statistical models provide a technically robust framework with which to analyse the correlated nature of the outcome variable and are pertinent when predictor variables are measured simultaneously at different levels.21 319
Research report Table 1 Comparison between the final sample (n¼8758) and the target population (n¼159 014*) Final sample
Target population
n
%
n
%
Participation Yes No
3026 5732
34.55 65.45
54219 104795
34.10 65.90
Sex Female Male Not known
4775 3976 7
54.52 45.40 0.08
86202 72692 120
54.21 45.71 0.08
Age (years) 50e54 55e59 60e64 65e69 70e74 Not known
2213 2178 1474 1338 1553 2
25.27 24.87 16.83 15.28 17.73 0.02
41493 40209 26104 23668 27526 14
26.09 25.29 16.42 14.88 17.31 0.01
6160 251 844 695
70.34 2.87 9.64 7.94
113853 3988 13824 12491
71.60 2.51 8.69 7.86
743 65
8.48 0.74
13509 1349
8.50 0.85
Insurance coverage CPAM Special coverage systems Civil servant systems Self-employed, independent occupations MSA Not known
Pearson c2 0.79; p>0.50
0.34; p>0.90
6.95; p>0.20
17.43; p