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BackgroundWe conducted spatial analyses to determine the geographic variation of cancer at the neighbourhood level (dissemination areas or DAs) within the area of a single Ontario public health unit, Wellington-Dufferin-Guelph, covering a population of 238,326 inhabitants. Cancer incidence data between 1999 and 2003 were obtained from the Ontario Cancer Registry and were geocoded down to the level of DA using the enhanced Postal Code Conversion File. The 2001 Census of Canada provided information on the size and age-sex structure of the population at the DA level, in addition to information about selected census covariates, such as average neighbourhood income.ResultsAge standardized incidence ratios for cancer and the prevalence of census covariates were calculated for each of 331 dissemination areas in Wellington-Dufferin-Guelph. The standardized incidence ratios (SIR) for cancer varied dramatically across the dissemination areas. However, application of the Moran's I statistic, a popular index of spatial autocorrelation, suggested significant spatial patterns for only two cancers, lung and prostate, both in males (p < 0.001 and p = 0.002, respectively). Employing Bayesian hierarchical models, areas in the urban core of the City of Guelph had significantly higher SIRs for male lung cancer than the remainder of Wellington-Dufferin-Guelph; and, neighbourhoods in the urban and surrounding rural areas of Orangeville exhibited significantly higher SIRs for prostate cancer. After adjustment for age and spatial dependence, average household income attenuated much of the spatial pattern of lung cancer, but not of prostate cancer.ConclusionThis paper demonstrates the feasibility and utility of a systematic approach to identifying neighbourhoods, within the area served by a public health unit, that have significantly higher risks of cancer. This exploratory, ecologic study suggests several hypotheses for these spatial patterns that warrant further investigations. To the best of our knowledge, this is the first Canadian study published in the peer-reviewed literature estimating the risk of relatively rare public health outcomes at a very small areal level, namely dissemination areas.

Background Premature mortality is a meaningful indicator of both population health and health system performance, which varies by geography in Ontario. We used the Local Health Integration Network (LHIN) sub-regions to conduct a spatial analysis of premature mortality, adjusting for key population-level demographic and behavioural characteristics. Methods We used linked vital statistics data to identify 163,920 adult premature deaths (deaths between ages 18 and 74) registered in Ontario between 2011 and 2015. We compared premature mortality rates, population demographics, and prevalence of health-relevant behaviours across 76 LHIN sub-regions. We used Bayesian hierarchical spatial models to quantify the contribution of these population characteristics to geographic disparities in premature mortality. Results LHIN sub-region premature mortality rates ranged from 1.7 to 6.6 deaths per 1000 per year in males and 1.2 to 4.8 deaths per 1000 per year in females. Regions with higher premature mortality had fewer immigrants and higher prevalence of material deprivation, excess body weight, inadequate fruit and vegetable consumption, sedentary behaviour, and ever-smoked status. Adjusting for all variables eliminated close to 90% of geographic variation in premature mortality, but did not fully explain the spatial pattern of premature mortality in Ontario. Conclusions We conducted the first spatial analysis of mortality in Ontario, revealing large geographic variations. We demonstrate that well-known risk factors explain most of the observed variation in premature mortality. The result emphasizes the importance of population health efforts to reduce the burden of well-known risk factors to reduce variation in premature mortality. Electronic supplementary material The online version of this article (10.1186/s12963-019-0193-9) contains supplementary material, which is available to authorized users.

BackgroundObserved breast, cervical and colon cancer screening rates are below provincial targets for the province of Ontario, Canada. The populations who are under- or never-screened for these cancers have not been described at the Ontario provincial level. Our objective was to use qualitative methods of inquiry to explore who are the never- or under-screened populations of Ontario.MethodsQualitative data were collected from two rounds of focus group discussions conducted in four communities selected using maps of screening rates by dissemination area. The communities selected were archetypical of the Ontario context: urban, suburban, small city and rural. The first phase of focus groups was with health service providers. The second phase of focus groups was with community members from the under- and never- screened population. Guided by a grounded theory methodology, data were collected and analyzed simultaneously to enable the core and related concepts about the under- and never-screened to emerge.ResultsThe core concept that emerged from the data is that the under- and never-screened populations of Ontario are characterized by diversity. Group level characteristics of the under- and never- screened included: 1) the uninsured (e.g., Old Order Mennonites and illegal immigrants); 2) sexual abuse survivors; 3) people in crisis; 4) immigrants; 5) men; and 6) individuals accessing traditional, alternative and complementary medicine for health and wellness. Under- and never-screened could have one or multiple group characteristics.ConclusionThe under- and never-screened in Ontario comprise a diversity of groups. Heterogeneity within and intersectionality among under- and never-screened groups adds complexity to cancer screening participation and program planning.

Toronto's syphilis epidemic is mature. Response, resources, and intervention activities should target core and noncore areas.

Background Our objective was to determine the extent to which geographical core areas for gonorrhea and syphilis are located in rural areas, as compared to urban areas. Methods Incident gonorrhea (January 1, 2005 to December 31, 2010) and syphilis (January 1, 1999 to December 31, 2010) rates were estimated and mapped by census tract and quarter. Rurality was measured using percent rural and rural-urban commuting area (RUCA; rural, small town, micropolitan, or urban). SaTScan was used to identify spatiotemporal clusters of significantly elevated rates of infection. Clusters lasting five years or longer were considered core areas; clusters of shorter duration were considered outbreaks. Clusters were overlaid on maps of rurality and qualitatively assessed for correlation. Results Twenty gonorrhea core areas were identified; 65% in urban centers, 25% in micropolitan areas, and the remaining 10% were geographically large capturing combinations of urban, micropolitan, small town and rural environments. Ten syphilis core areas were identified with 80% in urban centers and 20% capturing two or more RUCAs. All ten of the syphilis core areas (100%) overlapped with gonorrhea core areas. Conclusions Gonorrhea and syphilis rates were high for rural parts of North Carolina; however, no core areas were identified exclusively for small towns or rural areas. The main pathway of rural STI transmission may be through the interconnectedness of urban, micropolitan, small town and rural areas. Directly addressing STIs in urban and micropolitan communities may also indirectly help address STI rates in rural and small town communities.

Objectives Existing Canadian social determinants of health (SDOH) indicators do not quantify uncertainty to identify priority areas. The objectives of this methodologic study were: (1) to estimate and map small area (dissemination area) shared and variable-specific SDOH indicators with measures of uncertainty using a Bayesian model that accounts for spatial dependence; (2) to quantify geographic variation in the SDOH indicators and their contribution to a shared indicator; and (3) to assess the SDOH indicators' associations with behavioural risk factors and their consistency with the Ontario Marginalization Index (ON-Marg). Methods Lower education-, income-, unemployment-, living alone-and visible minority-related variables used in existing Canadian SDOH indices were fit as dependent variables to a Bayesian model to produce area-based SDOH indicators that were mapped with measures of uncertainty in two study areas. The fractions of spatial variation explained by the model components were computed. Bayesian analysis of variance was used to examine the SDOH indicator associations with behavioural risk factors and their consistency with ON-Marg examined using Pearson's correlation coefficient. Results The shared component was strongly associated with material deprivation (i.e., income) in each study area; however, variable-specific SDOH indicators were important too. The SDOH indicators were associated with behavioural risk factors for chronic disease, particularly alcohol consumption and smoking, and the shared component estimates were consistent with the ON-Marg material deprivation. Conclusions The Bayesian approach to produce SDOH indicators met the three study objectives and as such provides a new approach to prioritize areas that may experience health inequalities. Résumé Objectifs Les indicateurs des déterminants sociaux de la santé canadiens existants ne quantifient pas l'incertitude pour déterminer les aires prioritaires. Voici les objectifs de cette étude méthodologique: 1) estimer et cartographier les indicateurs des déterminants sociaux de la santé communs et spécifiques à des variables de petites régions (aires de diffusion) en y intégrant des mesures de l'incertitude à l'aide d'un modèle bayésien qui tient compte de la dépendance spatiale; 2) quantifier la variation géographique des indicateurs des déterminants sociaux de la santé et leur contribution à l'établissement d'un indicateur commun; et, 3) évaluer les associations des indicateurs des déterminants sociaux de la santé avec les facteurs de risque comportementaux et leur conformité à l'indice de marginalisation ontarien (ON-Marg).

BackgroundAn 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.MethodsA 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.ResultsCurrent 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.ConclusionsThis 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-3144-4) contains supplementary material, which is available to authorized users.