Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on sleep in brain health

LIVE: Thursday 2nd May 2024, 18:00-19:30 (CEST)

Quality sleep is essential for health. But what happens to our brains when sleep patterns are disturbed? Join our experts to explore the interplay between sleep disruption and neurological diseases, and the questions that you need to be asking your patients to help you prevent the harmful effects of sleep deprivation.
ADVANCED SEARCH
Log in
Top
BMC Public Health
Published in: BMC Public Health 1/2016

Open Access 01-12-2016 | Research article

Estimating micro area behavioural risk factor prevalence from large population-based surveys: a full Bayesian approach

Authors: L. Seliske, T. A. Norwood, J. R. McLaughlin, S. Wang, C. Palleschi, E. Holowaty

Published in: BMC Public Health | Issue 1/2016

Login to get access

Abstract

Background

An important public health goal is to decrease the prevalence of key behavioural risk factors, such as tobacco use and obesity. Survey information is often available at the regional level, but heterogeneity within large geographic regions cannot be assessed. Advanced spatial analysis techniques are demonstrated to produce sensible micro area estimates of behavioural risk factors that enable identification of areas with high prevalence.

Methods

A spatial Bayesian hierarchical model was used to estimate the micro area prevalence of current smoking and excess bodyweight for the Erie-St. Clair region in southwestern Ontario. Estimates were mapped for male and female respondents of five cycles of the Canadian Community Health Survey (CCHS). The micro areas were 2006 Census Dissemination Areas, with an average population of 400–700 people. Two individual-level models were specified: one controlled for survey cycle and age group (model 1), and one controlled for survey cycle, age group and micro area median household income (model 2). Post-stratification was used to derive micro area behavioural risk factor estimates weighted to the population structure. SaTScan analyses were conducted on the granular, postal-code level CCHS data to corroborate findings of elevated prevalence.

Results

Current smoking was elevated in two urban areas for both sexes (Sarnia and Windsor), and an additional small community (Chatham) for males only. Areas of excess bodyweight were prevalent in an urban core (Windsor) among males, but not females. Precision of the posterior post-stratified current smoking estimates was improved in model 2, as indicated by narrower credible intervals and a lower coefficient of variation. For excess bodyweight, both models had similar precision. Aggregation of the micro area estimates to CCHS design-based estimates validated the findings.

Conclusions

This is among the first studies to apply a full Bayesian model to complex sample survey data to identify micro areas with variation in risk factor prevalence, accounting for spatial correlation and other covariates. Application of micro area analysis techniques helps define areas for public health planning, and may be informative to surveillance and research modeling of relevant chronic disease outcomes.
Appendix
Available only for authorised users
Literature
1.
2.
3.
4.
Reid JL, Hammond D, Burkhalter R, Rynard VL, Ahmed R. Tobacco use in Canada: patterns and trends. Waterloo ON: Propel Centre for Population Health Impact, University of Waterloo; 2013 Edition.
5.
6.
Economos CD, Irish-Hauser S. Community interventions: a brief overview and their application to the obesity epidemic. J Law Med Ethics. 2007;35(1):131–7.CrossRefPubMed
7.
Li W, Kelsey JL, Zhang Z, Lemon SC, Mezgebu S, Boddie-Willis C, et al. Small-area estimation and prioritizing communities for obesity control in Massachusetts. Am J Public Health. 2009;99(3):511–9.CrossRefPubMedPubMedCentral
8.
Statistics Canada. Health Statistics Division. CCHS cycle 1.1 (2000–2001): public use microdata file documentation. Ottawa. 2002.
9.
Mokdad AH. The behavioral risk factors surveillance system: past, present, and future. Annu Rev Public Health. 2009;30:43–54.CrossRefPubMed
10.
Morabia A, Beer-Borst S, Hercberg S. Locally based surveys, unite! The EURALIM example. EURALIM Study Group. European information campaign on diet and nutrition. Am J Public Health. 1998;88(8):1153–5.CrossRefPubMedPubMedCentral
11.
Baker D, Eyeson-Annan M. Monitoring health behaviours and health status in New South Wales: release of the Adult Health Survey 2002. NSW Public Health Bull. 2004;15(4):63–7.CrossRef
12.
Baldissera S, Campostrini S, Binkin N, Minardi V, Minelli G, Ferrante G, et al. Features and initial assessment of the Italian Behavioral Risk Factor Surveillance System (PASSI), 2007–2008. Prev Chronic Dis. 2011;8(1):A24.PubMed
13.
Puska P, Helasoja V, Prattala R, Kasmel A, Klumbiene J. Health behaviour in Estonia, Finland and Lithuania 1994–1998: standardized comparison. Eur J Public Health. 2003;13(1):11–7.CrossRefPubMed
14.
Ontario Rapid Risk Factor Surveillance System (RFSS): Waves 1–96. Institute for Social Research, York University, Toronto. http://​www.​rrfss.​ca/​. Accessed 20 Oct 2014.
15.
16.
Holowaty EJ, Norwood TA, Wanigaratne S, Abellan JJ, Beale L. Feasibility and utility of mapping disease risk at the neighbourhood level within a Canadian public health unit: an ecological study. Int J Health Geogr. 2010;9:21.CrossRefPubMedPubMedCentral
17.
Lawson AB. Bayesian disease mapping: hierarchical modeling in spatial epidemiology. 2nd ed. Boca Raton, Florida: CRC Press; 2013.
18.
Elliott P, Wakefield JC, Best N, Briggs DJ. Bayesian approaches to disease mapping. In: Wakefield JC, Best N, Waller L, editors. Spatial epidemiology: methods and applications. Oxford: Oxford University Press; 2000. p. 104.
19.
Waller LA, Gotway CA. Applied spatial statistics for public health data. New Jersey: John Wiley and Sons; 2004.CrossRef
20.
Statistics Canada. Health Statistics Division. Canadian Community Health Survey (CCHS) Cycle 2.1 (2003): Public use microdata file documentation. Ottawa. 2005
21.
Statistics Canada. Health Statistics Division. Canadian Community Health Survey (CCHS) Cycle 3.1 (2005): Public use microdata file documentation. Ottawa. 2006
22.
Statistics Canada. Health Statistics Division. Canadian Community Health Survey (CCHS)–annual component: user guide 2007–2008 microdata files. Ottawa. 2009.
23.
Statistics Canada. Health Statistics Division. Canadian Community Health Survey (CCHS)–annual component: user guide 2010 and 2009–2010 microdata files. Ottawa. 2011.
24.
Statistics Canada. 2006 Census of Population. Profile of age and sex for Canada, provinces, territories, census divisions, census subdivisions and dissemination areas, 2006 Census. 2007 August. Ottawa: Statistics Canada Catalogue no. 94-575-XCB2006002.
25.
Shen W, Louis TA. Triple-goal estimates for disease mapping. Stat Med. 2000;19(17–18):2295–308.CrossRefPubMed
26.
Lawson AB, Biggeri AB, Boehning D, Lesaffre E, Viel JF, Clark A, et al. Disease mapping models: an empirical evaluation. Disease Mapping Collaborative Group. Stat Med. 2000;19(17–18):2217–41.PubMed
27.
Statistics Canada. GeoSuite: Census year 2006 (publication 92-150-X). Ottawa: Statistics Canada; Accessed 31 Mar 2007.
28.
Statistics Canada. 2006 Census of Population. Profile of income and earnings, 2006 Census–profile for Canada, provinces, territories, census divisions, census subdivisions and dissemination areas, 2006 Census. 2008 May. Ottawa: Statistics Canada Catalogue no. 94-581-XCB2006002.
29.
World Health Organization. Obesity: preventing and managing the global epidemic: report of a WHO consultation. Geneva. 2000.
30.
Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320(7244):1240–3.CrossRefPubMedPubMedCentral
31.
Wilkins R, Khan S. PCCF+ version 5H user’s guide: automated geographic coding based on the Statistics Canada postal code conversion files including postal codes through October 2010. Ottawa: Health Statistics Division, Statistics Canada; 2011.
32.
Thomas S, Wannell B. Combining cycles of the Canadian Community Health Survey. Health Rep. 2009;20(1):53–8.PubMed
33.
34.
Kanjilal S, Gregg EW, Cheng YJ, Zhang P, Nelson DE, Mensah G, et al. Socioeconomic status and trends in disparities in 4 major risk factors for cardiovascular disease among US adults, 1971–2002. Arch Intern Med. 2006;166(21):2348–55.CrossRefPubMed
35.
Diez Roux AV, Merkin SS, Hannan P, Jacobs DR, Kiefe CI. Area characteristics, individual-level socioeconomic indicators, and smoking in young adults: the coronary artery disease risk development in young adults study. Am J Epidemiol. 2003;157(4):315–26.CrossRefPubMed
36.
Duncan C, Jones K, Moon G. Smoking and deprivation: are there neighbourhood effects? Soc Sci Med. 1999;48(4):497–505.CrossRefPubMed
37.
Choiniere R, Lafontaine P, Edwards AC. Distribution of cardiovascular disease risk factors by socioeconomic status among Canadian adults. CMAJ. 2000;162(9 Suppl):S13–24.PubMedPubMedCentral
38.
Wang Y, Beydoun MA. The obesity epidemic in the United States–gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol Rev. 2007;29:6–28.CrossRefPubMed
39.
Zhang Q, Wang Y. Trends in the association between obesity and socioeconomic status in U.S. adults: 1971 to 2000. Obes Res. 2004;12(10):1622–32.CrossRefPubMed
40.
R Core Team. R. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://​R-project.​org. Accessed 12 Nov 2014.
41.
Tiefelsdorf M, Boots B. The exact distribution of Moran’s I. Environ Planning A. 1995;27(6):985–9.CrossRef
42.
Besag J, York J, Mollie A. Bayesian image restoration, with two applications in spatial statistics. Ann Inst Statist Math. 1991;43:1–59.CrossRef
43.
Bernardinelli L, Clayton D, Pascutto C, Montomoli C, Ghislandi M, Songini M. Bayesian analysis of space-time variation in disease risk. Stat Med. 1995;14(21–22):2433–43.CrossRefPubMed
44.
MacNab YC, Dean CB. Spatio-temporal modelling of rates for the construction of disease maps. Stat Med. 2002;21(3):347–58.CrossRefPubMed
45.
Lee D. A comparison of conditional autoregressive models used in Bayesian disease mapping. Spat Spatiotemporal Epidemiol. 2011;2(2):79–89.CrossRefPubMed
46.
Richardson S, Best N. Bayesian hierarchical models in ecological studies of health-environment effects. Environmetrics. 2003;14(2):129–47.CrossRef
47.
Zhang X, Holt JB, Wheaton AG, Ford ES, Greenlund KJ, Croft JB. Multilevel regression and poststratification of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the behavioural risk factors surveillance system. Am J Epidemiol. 2014;179(8):1025–33.CrossRefPubMed
48.
Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS–A Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput. 2000;10:325–37.CrossRef
49.
Smith AFM, Roberts GO. Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. R Stat Soc Series B Stat Methodol. 1993;55(1):3–23.
50.
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol. 2002;64(4):583–639.CrossRef
51.
Spiegelhalter DJ, Best N, Carlin BP, Van der Linde A. Bayesian deviance, the effective number of parameters, and the comparison of arbitrarily complex models. J Roy Statist Soc Ser B. 2002;64:583–640.CrossRef
52.
Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York: Springer; 2002.
53.
Hobbs NT, Hooten MB. A statistical primer for ecologists. Princeton, NJ: Princeton University Press; 2015.
54.
Clayton D, Bernardinelli L. Bayesian methods for mapping disease risk. In: Elliott P, Cuzick J, English D, Stern R, editors. Geographical and environmental epidemiology: methods for small-area studies. New York: Oxford University Press; 1996. p. 205–20.CrossRef
55.
Kuldorff M. A spatial scan statistic. Commun Stat A Theory Methods. 1997;26:1481–96.CrossRef
56.
Meng G, Law J, Thompson ME. Small-scale health-related indicator acquisition using secondary data spatial interpolation. Int J Health Geogr. 2010;9:50.CrossRefPubMedPubMedCentral
57.
58.
Richardson S, Thomson A, Best N, Elliott P. Interpreting posterior relative risk estimates in disease-mapping studies. Environ Health Perspect. 2004;112(9):1016–25.CrossRefPubMedPubMedCentral
59.
Studts JL, Ghate SR, Gill JL, Studts CR, Barnes CN, LaJoie AS, et al. Validity of self-reported smoking status among participants in a lung cancer screening trial. Cancer Epidemiol Biomarkers Prev. 2006;15(10):1825–8.CrossRefPubMed
60.
Wagenknecht LE, Burke GL, Perkins LL, Haley NJ, Friedman GD. Misclassification of smoking status in the CARDIA study: a comparison of self-report with serum cotinine levels. Am J Public Health. 1992;82(1):33–6.CrossRefPubMedPubMedCentral
61.
Shields M, Connor Gorber S, Tremblay MS. Estimates of obesity based on self-report versus direct measures. Health Rep. 2008;19(2):61–76.PubMed
62.
Heisz A, McLeod L. Low-income in census metropolitan areas, 1980–2000. Ottawa: Business and Labour Market Analysis Division, Statistics Canada; 2004.
63.
Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J Roy Stat Soc B Met. 2009;71(2):319–92.CrossRef
64.
Lamarre MC. The International Union for Health Promotion and Education. Health Educ Res. 2000;15(3):243–8.CrossRefPubMed
65.
Campostrini S, McQueen DV, Evans L. Health promotion and surveillance: the establishment of an IUHPE global working group. Glob Health Promot. 2009;16(4):58–60.CrossRefPubMed
Metadata
Title
Estimating micro area behavioural risk factor prevalence from large population-based surveys: a full Bayesian approach
Authors
L. Seliske
T. A. Norwood
J. R. McLaughlin
S. Wang
C. Palleschi
E. Holowaty
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2016
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-016-3144-4

Other articles of this Issue 1/2016

BMC Public Health 1/2016 Go to the issue

Research article

Gender relations, sexual behaviour, and risk of contracting sexually transmitted infections among women in union in Uganda

Research article

Healthy Food Intake Index (HFII) – Validity and reproducibility in a gestational-diabetes-risk population

Research article

Suicide attempts and behavioral correlates among a nationally representative sample of school-attending adolescents in the Republic of Malawi

Research article

Uptake of HIV testing and counseling, risk perception and linkage to HIV care among Thai university students

Research article

Validity and reliability of a brief self-reported questionnaire assessing fruit and vegetable consumption among pregnant women

Study protocol

The effect of financial incentives on top of behavioral support on quit rates in tobacco smoking employees: study protocol of a cluster-randomized trial