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Funnel Plots and Choropleth Maps in Cancer Risk Communication: a Comparison of Tools for Disseminating Population-Based Incidence Data to Stakeholders

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Funnel Plots and Choropleth Maps in Cancer Risk Communication: a Comparison of Tools for Disseminating Population-Based Incidence Data to Stakeholders Downloaded from http://bmjopen.bmj.com/ on April 12, 2017 - Published by group.bmj.com Open Access Research Funnel plots and choropleth maps in cancer risk communication: a comparison of tools for disseminating population-based incidence data to stakeholders Walter Mazzucco,1,2 Rosanna Cusimano,1,3 Maurizio Zarcone,1,4 Sergio Mazzola,1,4 Francesco Vitale1,2,4 To cite: Mazzucco W, ABSTRACT Strengths and limitations of this study Cusimano R, Zarcone M, Background: Population-based cancer registries et al. Funnel plots and provide epidemiological cancer information, but the ▪ choropleth maps in cancer To the best of our knowledge, this study indicators are often too complex to be interpreted by risk communication: explores for the first time the application of the a comparison of tools for local authorities and communities, due to numeracy funnel plot methodology to represent standar- disseminating population- and literacy limitations. The aim of this paper is to dised cancer incidence ratio at the municipal based incidence data to compare the commonly used visual formats to funnel level through a comparison with the commonly stakeholders. BMJ Open plots to enable local public health authorities and used visual format, as choropleth map. 2017;7:e011502. communities to access valid and understandable ▪ The results of this study support the use of doi:10.1136/bmjopen-2016- cancer incidence data obtained at the municipal level. funnel plot as a complement to choropleth map 011502 Methods: A funnel plot representation of standardised for disseminating epidemiological data of cancer incidence ratio (SIR) was generated for the 82 registries to local communities and authorities. ▸ Prepublication history and municipalities of the Palermo Province with the 2003– ▪ The proposed communication approach needs to additional material is 2011 data from the Palermo Province Cancer Registry be further validated in the field. To this end, the available. To view please visit (Sicily, Italy). The properties of the funnel plot and Palermo Province Cancer Registry has generated the journal (http://dx.doi.org/ choropleth map methodologies were compared within 82 municipal risk maps, one for each municipal- 10.1136/bmjopen-2016- the context of disseminating epidemiological data to 011502). ity of the province, and for a period of 1 year, stakeholders. qualified personnel from the registry will be Results: The SIRs of all the municipalities remained involved in on-site meetings to share cancer inci- Received 3 April 2016 within the control limits, except for Palermo city area dence data with stakeholders (citizens, local Revised 28 October 2016 (SIR=1.12), which was sited outside the upper control authorities, general practitioners, specialised Accepted 22 December 2016 limit line of 99.8%. The Palermo Province SIRs funnel physicians, pharmacists, etc) using funnel plots. plot representation was congruent with the choropleth The Delphi consensus process will be explored map generated from the same data, but the former as well by involving public health operators. resulted more informative as shown by the comparisons of the weaknesses and strengths of the 2 visual formats. develop risk communication plans that Conclusions: Funnel plot should be used as a address cancer incidence, survival and the complementary valuable tool to communicate potential impact of environmental exposure.2 epidemiological data of cancer registries to Apart from the presumed effects of lifestyle communities and local authorities, visually conveying changes and environmental factors on an efficient and simple way to interpret cancer – cancer trends,3 6 the global increase in incidence data. cancer prevalence could be largely attribut- able to a combination of improved cancer survival7 and ageing population.8 Local com- munities possess a variable degree of literacy BACKGROUND fl For numbered affiliations see and numeracy, which, in turn, in uence end of article. Cancer is the second major cause of death in their understanding of such demographical the developed countries.1 In the past few and epidemiological concepts.910Local Correspondence to decades, the increasing burden of disease public health and political authorities regu- Dr Walter Mazzucco; has caused major concerns in local commu- larly engage in finding better ways to satisfy [email protected] nities, requiring local health authorities to the growing demand for information on the Mazzucco W, et al. BMJ Open 2017;7:e011502. doi:10.1136/bmjopen-2016-011502 1 Downloaded from http://bmjopen.bmj.com/ on April 12, 2017 - Published by group.bmj.com Open Access 11 23 impact of cancer by the general public. In particular, between observed cases (Oi) and expected cases (Ei). citizens often question if they live in an area at high risk The Oi were assumed to follow a homogeneous Poisson 2 λ θ Á for environmental exposure. distribution with parameter = 0 Ei. The Ei were esti- The Centers for Diseases Control and Prevention mated by indirect method,24 considering the entire (CDC) define public health surveillance as the population time under study (the PP) as the reference “ Σ Σ 25 Ongoing, systematic collection, analysis, interpretation, population, with Oi= Ei. The resident population was and dissemination of data regarding a health-related reported using the intercensus estimates, provided by event for use in public health action to reduce morbidity the Italian National Statistical Institute (ISTAT), also and mortality and to improve health.”12 Population-based considering the annual municipal data on migration.22 cancer registries (PBCRs) carry out cancer surveillance For each SIR, the 95% CI was calculated by using the by continuously collecting and classifying information on normal approximation method.26 all new cancer cases within a defined population, and Graphic FP representation26 was used to highlight any providing statistics on its occurrence for the purpose of municipality with a higher cancer incidence compared assessing and controlling the impact of this disease on with the reference population (entire PP population). the community.13 The mission of PBCRs includes the The following elements were included to generate the FP translation and dissemination of evidences to enable (figure 1A): the SIRs of the 82 municipalities, on the θ informed decision-making and to empower the general y-axis; the target line ( 0=1), representing the reference population or other stakeholders, while preserving a value for the indicator of interest (Oi=Ei); the Ei precision rigorous methodological approach and facilitating a parameter, measuring the accuracy of the indicator of truthful interpretation of the data obtained. PBCR publi- interest (Poisson variance parameter, using the hypothesis cations use validated and internationally shared measure- θ0=1), represented on the x-axis; the 95% and 99.8% CIs, ments systems and employ terminology and visual calculated with the normal approximation method, defin- formats that are easily understood by the scientific com- ing the control limits.26 The two sets of control limit lines munity, but often difficult to interpret for other stake- define three different areas within the graph (figure 1B): holders, particularly at the local level.14 15 the ‘undercontrol’ area (in green), the ‘warning’ area (in The most commonly used format for reporting geo- yellow) and the ‘alert’ area (in red).27 graphic comparisons of cancer epidemiological data is As the data distribution was not congruent with the an atlas, which includes thematic maps, such as choro- underlying assumption (variance equal to the expected pleth maps (CMs), representing cancer incidence rates value), in order to check for any potential overdispersion28 (standardised rates, standardised ratios, etc) computed both additive and multiplicative approaches were adopted. for specific areas.16 17 Overdispersion coefficients (τ for the additive approach While data are available on how the context18 and the and ϕ for the multiplicative approach) were calculated. content of such communications influence individual Overdispersion was addressed by considering the win- risk perception,19 little is known about the effects of risk sorised estimates too.27 Moreover, Z-score29 and the winsori- communications at a group level, particularly in small sation method (by testing for different levels of Z-score communities.20 quantiles28) were applied for the direct selection of The Italian Association of Cancer Registries extreme values. Furthermore, to define the level of winsori- (AIRTum), a national network of 41 local PBCRs, includ- sation, an R-script routine was developed to set a cut-off for ing the Palermo Province Cancer Registry (PPCR), has thequantilebetweentheacceptanceandrejectionofthe greatly emphasised improving communication tools.21 overdispersion test (see online supplementary material). The aim of this paper is to propose the use of funnel The map representing the PP municipalities was gen- plots (FPs) for reporting local cancer incidence data, as erated by using the ISTAT Shapefile vector format,30 a complement to the more common visual formats released in the ED50 (European Datum - 1950) UTM employed by the PPCR to address local public health Zone 32N reference system, and converted in plane authorities and communities, in order to facilitate the coordinates (decimal degrees), providing georeferenced dissemination and interpretation of measures of cancer data in addition to the coordinates of geographic statistics at the municipal level.
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