Dataset Information

Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study.

ABSTRACT:

Background

Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Multilevel models assume independent geographical areas, whereas spatial models explicitly incorporate geographical correlation, often via a conditional autoregressive prior. However the relative merits of these methods for large population-based studies have not been explored. Using a case-study approach, we report on the implications of using multilevel and spatial survival models to study geographical inequalities in all-cause survival.

Methods

Multilevel discrete-time and Bayesian spatial survival models were used to study geographical inequalities in all-cause survival for a population-based colorectal cancer cohort of 22,727 cases aged 20-84 years diagnosed during 1997-2007 from Queensland, Australia.

Results

Both approaches were viable on this large dataset, and produced similar estimates of the fixed effects. After adding area-level covariates, the between-area variability in survival using multilevel discrete-time models was no longer significant. Spatial inequalities in survival were also markedly reduced after adjusting for aggregated area-level covariates. Only the multilevel approach however, provided an estimation of the contribution of geographical variation to the total variation in survival between individual patients.

Conclusions

With little difference observed between the two approaches in the estimation of fixed effects, multilevel models should be favored if there is a clear hierarchical data structure and measuring the independent impact of individual- and area-level effects on survival differences is of primary interest. Bayesian spatial analyses may be preferred if spatial correlation between areas is important and if the priority is to assess small-area variations in survival and map spatial patterns. Both approaches can be readily fitted to geographically enabled survival data from international settings.

SUBMITTER: Dasgupta P

PROVIDER: S-EPMC4197252 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Publications

Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study.

Dasgupta Paramita P Cramb Susanna M SM Aitken Joanne F JF Turrell Gavin G Baade Peter D PD

International journal of health geographics 20141004

<h4>Background</h4>Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Multilevel models assume independent geographical areas, whereas spatial models explicitly incorporate geographical correlation, often via a conditional autoregressive prior. However the relative merits of these methods for large population-based studies have not been explored. Using a case-study approach, we report on the implications of u ...[more]

PMID: 25280499

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Project description:OBJECTIVES:To investigate potential geographical and socioeconomic patterning of allostatic load (AL) in China. DESIGN:Multilevel longitudinal study of the 2010 Chronic Disease Risk Factor Surveillance linked to the National Death Surveillance up to 31 December 2015. SETTING:All 31 provinces in China, not including Hong Kong, Macao or Taiwan. PARTICIPANTS:96 466 ≥ 18 years old (women=54.3%). EXPOSURES:Person-level educational attainment and mean years of education in counties. OUTCOME:AL was measured using clinical guidelines for nine biomarkers: body mass index; waist circumference; systolic blood pressure; diastolic blood pressure; fasting blood glucose; total cholesterol; triglycerides; high-density lipoprotein cholesterol; low-density lipoprotein cholesterol. RESULTS:Multilevel logistic regressions adjusted for sex, age, marital status, person-level education, county mean years of education and urban/rural reported ORs of 1.22 (95% CI 1.08 to 1.38) for 5-year all-cause mortality (n=3284) and 1.20 (1.04-1.37) for deaths from non-communicable diseases (n=2891) among people in AL quintile 5 (high) compared with quintile 1 (low). The median rate ratio estimated from unadjusted multilevel negative binomial regression showed AL clustered geographically (province=1.14; county=1.12; town=1.11; village=1.14). After adjusting for aforementioned confounders, AL remained higher with age (rate ratio 1.02, 95% CI 1.02 to 1.02), higher in women compared with men (1.17, 1.15 to 1.19), lower among singletons (0.83, 0.81 to 0.85) and widowers (0.96, 0.94 to 0.98). AL was lower among people with university-level compared with no education (0.92, 0.89 to 0.96), but higher in counties with higher mean education years (1.03, 1.01 to 1.05). A two-way interaction suggested AL was higher (1.04, 1.02 to 1.06) among those with university-level compared with no education within counties with higher mean years of education. Similar results were observed for alternative constructions of AL using 75th and 80th percentile cut-points. CONCLUSIONS:AL in China is patterned geographically. The degree of association between AL and person-level education seems to be dependent on area-level education, which may be a proxy for other contextual factors that warrant investigation.

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Dataset Information

Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study.

Background

Methods

Results

Conclusions

Publications

Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study.

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