Exploring the demographic history of populations with enhanced Lexis surfaces

Cimentada, Jorge; Kluesener, Sebastian; Riffe, Tim

doi:10.4054/demres.2020.42.6

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…In cancer surveillance research [ 1 ], a basic unit of analysis is a matrix of incidence or mortality hazard rates jointly indexed by age group and calendar period [ 2 ]. Alongside classical [ 3 , 4 ] and contemporary [ 5 – 8 ] descriptive methods, the age-period-cohort (APC) model provides an established paradigm to quantify rate patterns and trends along each temporal direction – age, period, and birth cohort – adjusted for the other two [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative age-period-cohort analysis

Rosenberg,

Miranda-Filho,

Whiteman

2023

BMC Med Res Methodol

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Background Cancer surveillance researchers analyze incidence or mortality rates jointly indexed by age group and calendar period using age-period-cohort models. Many studies consider age- and period-specific rates in two or more strata defined by sex, race/ethnicity, etc. A comprehensive characterization of trends and patterns within each stratum can be obtained using age-period-cohort (APC) estimable functions (EF). However, currently available approaches for joint analysis and synthesis of EF are limited. Methods We develop a new method called Comparative Age-Period-Cohort Analysis to quantify similarities and differences of EF across strata. Comparative Analysis identifies whether the stratum-specific hazard rates are proportional by age, period, or cohort. Results Proportionality imposes natural constraints on the EF that can be exploited to gain efficiency and simplify the interpretation of the data. Comparative Analysis can also identify differences or diversity in proportional relationships between subsets of strata (“pattern heterogeneity”). We present three examples using cancer incidence from the United States Surveillance, Epidemiology, and End Results Program: non-malignant meningioma by sex; multiple myeloma among men stratified by race/ethnicity; and in situ melanoma by anatomic site among white women. Conclusions For studies of cancer rates with from two through to around 10 strata, which covers many outstanding questions in cancer surveillance research, our new method provides a comprehensive, coherent, and reproducible approach for joint analysis and synthesis of age-period-cohort estimable functions.

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Section: Introductionmentioning

confidence: 99%

Comparative age-period-cohort analysis

Rosenberg,

Miranda-Filho,

Whiteman

2023

BMC Med Res Methodol

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“…The scientific literature review indicates that the research topic we are studying is not new. Several attempts have been made to address particular aspects, that is: data visualization in sociology [2]; exploring the demographic history of populations with enhanced Lexis surfaces [3]; reaching the potential of visualization of statistical data on the POWER BI platform [4]. There is a growing body of literature that recognizes the importance of data and statistics visualization 1049 (2022) 012076 IOP Publishing doi:10.1088/1755-1315/1049/1/012076 2 to a wide audience [5], and explores some aspects of visualization of statistical and scientific data [6].…”

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confidence: 99%

Visualization of demographic statistical data

Bondarenko

Hanchuk

Pakhomova

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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In this publication, the authors address the content and objectives of demographic data visualization, generalize the most common techniques and tools for demographic data visualization, and share their own experience of testing various statistical visualization practices, namely World Population Dashboard of United Nations Population Fund and Population Reference Bureau. Special emphasis is placed on the implementation of Trendalyzer software, developed by the Swedish Gapminder Foundation, and the experience of demographic statistics visualization via interactive infographics made by Oxford Martin School (a structural unit of the Department of Social Sciences at Oxford University). The presented article evaluates the contribution to the visualization of statistical data made by such Ukrainian organizations as State Statistics Service, public organization “Ukrainian Center for Public Data”, BusinessViews and Top Lead. Outlining the advantages and disadvantages of various visualization practices, the authors argue that “dry” statistics being represent by means of visualization tools such as Trendalyzer and Our World Data may constitute a clear and expressive language of convincing facts that contribute to a holistic demographic worldview.

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