This study simultaneously estimated the variance in the lack of several cancer screening tests apportioned at the individual and MMSA level. The size of the variance among MMSAs was modest and varied by type of screening test, with ICCs ranging from 4.9% (never having had a Pap smear) to 1.2% (never having had a colonoscopy/sigmoidoscopy). This suggests much greater heterogeneity within MMSAs than between MMSAs. Future research could examine the magnitude of the variation within MMSAs at the county level or even smaller geographical areas such as census tracts as the intra‐MMSA variation is large. Although there have been many studies using multilevel methods in the USA,27
most have quantified the association between area level factors and individual level outcomes in terms of odds ratios and have neglected to examine the variance apportioned to individual and area levels as shown to be distinctly different and important.15,16
This study shows that contextual effects exist and are important even in the presence of low area level variance.
Increasing MMSA level poverty rate was associated with higher prevalence of never having been screened for all tests. After adjustment for the individual level factors, the association between poverty rate and screening using CBE, FOBT, and Pap smear was attenuated, but did not affect the results for mammogram, and colonoscopy/sigmoidoscopy. The compositional effect of the individual level factors for CBE and Pap smear may not be attributable to confounding, but such factors could actually be in the pathway between area level factors and health outcomes. Controlling for these factors in the analysis is considered to be overadjustment.28,29
However, based on the similarities between the crude and adjusted odds ratios for the association between MMSA level poverty rate with mammogram, CBE, colonoscopy/sigmoidoscopy, and FOBT use, there is little evidence that the included individual level factors are mediators. Other individual level factors, not included in the models, may exert residual confounding or be mediators between MMSA level poverty and screening use. This lack of screening seen in our study in areas with higher poverty rates may result in lower rates of early stage cancer, as well as higher rates of advanced stage cancer and mortality.10,11
In this study, we investigated the compositional effect of access to medical care and socioeconomic position as possible mediators of the association between area level poverty rates and cancer screening. This mechanism is not intended to replace other mechanisms but rather to complement and add to existing understanding of the factors that influence screening. This is by no means the only pathway by which persons who reside in socioeconomically deprived areas may be less likely to be screened for cancer. Other pathways include availability of the existing medical infrastructure (for example, physicians and mammography facilities), social capital and collective efficacy, and “compositional” confounding by psychosocial characteristics.30,31,32,33
A separate issue is whether or not we can or should identify specific MMSAs to direct additional resources aimed at increasing screening. As the variation among MMSAs is much smaller than the intra‐MMSA level variation, it is not efficient to target specific MMSAs and allocate screening resources locally to reduce the prevalence of never having been screened.16
Therefore, if MMSAs were selected based on high poverty rates, then a large low income population would be missed because they reside in MMSAs with low poverty rates.
The results of this study need to be considered in light of its limitations. Firstly, self reported screening use may overestimate actual cancer screening.34
However, most discrepancies in self reported screening use have been attributable to inaccurate recall of the interval because the procedure, which was not an issue in this study. The overall accuracy of the BRFSS and similar surveys has been reported in previous studies.35,36
Secondly, generalisability of the findings is limited to persons with telephones. Telephone coverage (households with telephones) varies by state and also by sub‐population. Telephone coverage averages 97.6% for US states as a whole in 2002, but non‐coverage ranges from 1.1% to 6.6% across states. Although persons without telephones are more likely to be of lower income and therefore not included in the analysis, this fact is unlikely to have affected the findings because the variation among MMSAs is much smaller than the intra‐MMSA level variation and a large low income population resided in MMSAs with low poverty rates.
Area level poverty rate was associated with never having been screened for breast and colorectal cancer, but not associated with screening for cervical cancer. As the variation among communities is much smaller than the intra‐community level variation, it is not efficient to target specific communities and allocate screening resources locally to reduce the prevalence of never having been screened. While poverty rate was associated with screening independent of individual level factors, if communities were selected based on high poverty rates, then a large low income population would be missed because they reside in communities with low poverty rates.
Thirdly, one of the frequent criticisms of multilevel models using geographical areas at the second level is that in some cases the choice of the geographical areas is somewhat arbitrary and may not reflect communities. In our study, we used MMSAs, which have a high degree of social and economic integration as measured by commuting ties and, as such, may represent communities. Nevertheless, it is understood that for some MMSAs, substantial intra‐MMSA variation may still exist as was evidenced by the modest ICCs.
Note that because our response variable is dichotomous, level 2 (MMSA level) variation is normally distributed, but level 1 (individual level) variation follows a logistic distribution, where the variance depends on the probability of screening.25,37
Our ICC estimates assume that the observed response variable reflects a threshold effect on an underlying continuous variable,37
in this case, propensity to undergo cancer screening. Depending on how they are estimated, ICCs in multilevel logistic regression models may not accurately represent the partitioning of variance between level 1 and level 2.37
In general, such measures of variance explained in logistic regression models tend to be low relative to those seen in normal response models.38
Other approaches to quantifying the variation at level 2 are described by Goldstein and colleagues37
and Larsen and Merlo.39
Fourthly, selective residential mobility (for example, low income persons move to MMSAs with higher poverty rates where there is availability of cheap and affordable housing) may, in part, drive the compositional effects of the individual level factors. Although this may be true in some cases, it may be more likely that more affluent persons move from urban areas, leaving low income persons behind.40
Regardless of the mechanism, some compositional effect of individual level factors was present in the study.
In conclusion, persons who resided in MMSAs with higher poverty rates were more likely never to have been screened for breast and for colorectal cancer over and above the individual level factors considered. The magnitude of the geographical variation in cancer screening between MMSAs was limited to no more than 5% of the total variance, suggesting that most of the variation in screening is within MMSAs. Future research should examine the pathways by which poverty rate exerts its influence on screening for various cancers.