Hexamaps for Age-Period-Cohort Data Visualization and Implementation in R.
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ABSTRACT:
SUBMITTER: Jalal H
PROVIDER: S-EPMC8022848 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
Publications
Hexamaps for Age-Period-Cohort Data Visualization and Implementation in R.
Jalal Hawre H Burke Donald S DS
Epidemiology (Cambridge, Mass.) 20201101 6
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Project description:BackgroundCancer 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.MethodsWe 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.ResultsProportionality 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.ConclusionsFor 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.
| S-EPMC10585891 | biostudies-literature
Project description:Social scientists have frequently attempted to assess the relative contribution of age, period, and cohort variables to the overall trend in an outcome. We develop an R package APCI (and Stata command apci) to implement the age-period-cohort-interaction (APC-I) model for estimating and testing age, period, and cohort patterns in various types of outcomes for pooled cross-sectional data and multi-cohort panel data. Package APCI also provides a set of functions for visualizing the data and modeling results. We demonstrate the usage of package APCI with empirical data from the Current Population Survey. We show that package APCI provides useful visualization and analytical tools for understanding age, period, and cohort trends in various types of outcomes.
| S-EPMC10237519 | biostudies-literature
Project description:BackgroundRecently, the multiphase method was proposed to estimate cohort effects after removing the effects of age and period in age-period contingency table data. Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver and is strongly associated with cirrhosis, due to both alcohol and viral etiologies. In epidemiology, age-period-cohort (APC) model can be used to describe (or predict) the secular trend in HCC mortality.ResultsThe confidence interval (CI) of the weighted estimates was found to be relatively narrow (compared to unweighted estimates). Moreover, for males, the mortality trend reverses itself during 2006-2010 was found from an increasing trend into a slightly deceasing trend. For females, the increasing trend reverses (earlier than males) itself during 2001-2005.ConclusionsThe weighted estimation of the regression model is recommended for the multiphase method in estimating the cohort effects in age-period contingency table data.ImpactThe regression model can be modified through the weighted average estimate of the effects with narrower CI of each cohort.MethodsAfter isolating the residuals during the median polish phase, the final phase is to estimate the magnitude of the cohort effects using the regression model of these residuals on the cohort category with the weight equal to the occupied proportion according to the number of death of HCC in each cohort.
| S-EPMC5929429 | biostudies-literature
Project description:This study aims to provide a systematical introduction of age-period-cohort (APC) analysis to South Korean readers who are unfamiliar with this method (we provide an extended version of this study in Korean). As health data in South Korea has substantially accumulated, population-level studies that explore long-term trends of health status and health inequalities and identify macrosocial determinants of the trends are needed. Analyzing long-term trends requires to discern independent effects of age, period, and cohort using APC analysis. Most existing health and aging literature have used cross-sectional or short-term available panel data to identify age or period effects ignoring cohort effects. This under-use of APC analysis may be attributed to the identification (ID) problem caused by the perfect linear dependency across age, period, and cohort. This study explores recently developed three APC models to address the ID problem and adequately estimate the effects of A-P-C: intrinsic estimator-APC models for tabular age by period data; hierarchical cross-classified random effects models for repeated cross-sectional data; and hierarchical APC-growth curve models for accelerated longitudinal panel data. An analytic exemplar for each model was provided. APC analysis may contribute to identifying biological, historical, and socioeconomic determinants in long-term trends of health status and health inequalities as well as examining Korean's aging trajectories and temporal trends of period and cohort effects. For designing effective health policies that improve Korean population's health and reduce health inequalities, it is essential to understand independent effects of the three temporal factors by using the innovative APC models.
| S-EPMC5790985 | biostudies-literature
Project description:BackgroundSri Lanka has experienced major changes in its suicide rates since the 1970s, and in 1995 it had one of the highest rates in the world. Subsequent reductions in Sri Lanka's suicide rates have been attributed to the introduction of restrictions on the availability of highly toxic pesticides. We investigate these changes in suicide rates in relation to age, gender, method specific trends and birth-cohort and period effects, with the aim of informing preventative strategies.MethodsSecular trends of suicide in relation to age, sex, method, birth-cohort and period effects were investigated graphically using police data (1975-2012). Poisoning case-fatality was investigated using national hospital admission data (2004-2010).ResultsThere were marked changes to the age-, gender- and method-specific incidence of suicide over the study period. Year on year declines in rates began in 17-25 year olds in the early 1980s. Reduction in older age groups followed and falls in all age groups occurred after all class I (the most toxic) pesticides were banned. Distinct changes in the age/gender pattern of suicide are observed: in the 1980s suicide rates were highest in 21-35 year old men; by the 2000s, this pattern had reversed with a stepwise increase in male rates with increasing age. Throughout the study period female rates were highest in 17-25 year olds. There has been a rise in suicide by hanging, though this rise is relatively small in relation to the marked decline in self-poisoning deaths. The patterns of suicides are more consistent with a period rather than birth-cohort effect.ConclusionsThe epidemiology of suicide in Sri Lanka has changed noticeably in the last 30 years. The introduction of pesticide regulations in Sri Lanka coincides with a reduction in suicide rates, with evidence of limited method substitution.
| S-EPMC4148962 | biostudies-literature
Project description:BackgroundDuring the last decades, the prevalence of obesity [body mass index (BMI): weight/height2), ≥30.00 kg/m2] among adults has increased considerably. We examined whether this increase in a high-income, welfare state, like Denmark was driven by age, period or cohort effects, which would inform preventive strategies aiming at reducing the prevalence.MethodsWe used data from the National Representative Health and Morbidity Studies, which are representative surveys of the Danish adult population (age 16 years and above), conducted in 1987, 1994, 2000, 2005, 2010, 2013, 2017 and 2021 (N = 91 684). Participants reported height and weight, from which BMI was calculated after correction for systematic bias in self-reported data and non-response. Age, survey year and birth cohorts were mutually adjusted and adjusted for sex in generalized linear models.ResultsThe obesity prevalence increased from 6.1% in 1987 to 18.4% in 2021, similarly in men (18.8%) and women (18.0%) and in all age groups. Age had an inverted u-shaped effect on the prevalence. Compared with individuals aged 16-24 years, the highest rate of obesity was seen for the age group 55-64 years [rate ratio 3.27, 95% confidence interval (CI): 2.58; 4.14]. The increasing rate for each recent survey year over time was compatible with a period effect without any birth cohort effects. The rate for obesity in 2021 was 4.16 in 1987 vs. 1987 (95% CI: 3.10; 5.59).ConclusionsObesity prevalence in Denmark increased steadily during the period 1987 through 2021, primarily driven by secular changes over time across all ages and birth cohorts.
| S-EPMC10234665 | biostudies-literature
Project description:BackgroundTuberculosis remains a major public health challenge worldwide. This study aimed to determine the long-term trends in the notification rate of tuberculosis in Zhejiang Province, and to assess the potential independent risks associated with age, time period, and birth cohort.MethodsData on all pulmonary tuberculosis (PTB) cases in Zhejiang Province from 2008 to 2022 were collected from the Tuberculosis Information Management System. Long-term trends in the notification rate and independent risks associated with quantitatively decomposed variables were determined using joinpoint regression model and age-period-cohort model.ResultsBetween 2008 and 2022, a total of 323,745 PTB cases were notified in Zhejiang Province. Overall, the notification rate declined, with notable turning points in 2010 and 2019. Net drift analysis revealed an average annual decrease of 5.7% (95% CI: -6.8%, -4.6%; P < 0.01), with faster declines among males than females. Age effects showed peaks in notification rates among people aged 15-29 and 65-74. Period effects peaked during 2008-2012 (RR = 1.4; 95% CI: 1.3, 1.5; P < 0.01). Cohort effects indicated decreasing risks in later birth cohorts, with the highest risk observed in the 1928-1932 birth cohort (RR = 11.0, 95% CI: 7.2, 16.8; P < 0.01) and relative protective effects in cohorts born after 1978-1982.ConclusionsNotification rates of PTB declined consistently across various age groups in Zhejiang Province. Young individuals aged 15-29 and older individuals aged 65-74 were identified as high-risk groups requiring active intervention. Additionally, pre-1978 birth cohorts had a relatively higher risk of PTB. These findings provided valuable insights into the age, period, and birth cohort characteristics of patients with PTB in Zhejiang Province, aiding relevant authorities in formulating appropriate policies and implementing targeted preventive control measures.
| S-EPMC11834198 | biostudies-literature
Project description:Up to now, tuberculosis (TB) remains a global public health problem, posing a serious threat to human health. Traditional methods for analyzing time-varying trends, such as age and period, tend to ignore the poor impact of birth cohorts, which is an important factor in the development of TB. The age-period-cohort (APC) model, a statistical method widely used in recent decades in economics, sociology, and epidemiology, can quantitatively estimate the efficacy of different age, period, and birth cohort groups for TB by separating the effects of these three dimensions and controlling for confounding factors among the time variables. The purpose of this paper is to briefly review the model, focus on the application of the existing APC model in the field of TB, and explain its advantages and disadvantages. This study will help to provides a theoretical basis and reference for using the APC model in TB analysis and prediction.
| S-EPMC11814438 | biostudies-literature
Project description:Age-period-cohort analysis of incidence and/or mortality data has received much attention in the literature. To circumvent the non-identifiability problem inherent in the age-period-cohort model, additional constraints are necessary on the parameters estimates. We propose setting the constraint to reflect the different nature of the three temporal variables: age, period, and birth cohort. There are two assumptions in our method. Recognizing age effects to be deterministic (first assumption), we do not explicitly incorporate the age parameters into constraint. For the stochastic period and cohort effects, we set a constant-relative-variation constraint on their trends (second assumption). The constant-relative-variation constraint dictates that between two stochastic effects, one with a larger curvature gets a larger (absolute) slope, and one with zero curvature gets no slope. We conducted Monte-Carlo simulations to examine the statistical properties of the proposed method and analyzed the data of prostate cancer incidence for whites from 1973-2012 to illustrate the methodology. A driver for the period and/or cohort effect may be lacking in some populations. In that case, the CRV method automatically produces an unbiased age effect and no period and/or cohort effect, thereby addressing the situation properly. However, the method proposed in this paper is not a general purpose model and will produce biased results in many other real-life data scenarios. It is only useful in situations when the age effects are deterministic and dominant, and the period and cohort effects are stochastic and minor.
| S-EPMC6922428 | biostudies-literature
Project description:The aim of this study was to investigate the long-term trends of human immunodeficiency virus (HIV) mortality in China and its associations with age, period and birth cohort. We used HIV mortality data obtained from the Global Burden of Disease Study (GBD) 2016 and analysed the data with an age-period-cohort framework. Age effects indicate different risks of different outcomes at specific periods in life; period effects reflect population- wide exposure at a circumscribed point in time; and cohort effects generally reflect differences in risk across birth cohorts.Our results showed that the overall annual percentage change (net drift) of HIV mortality was 11.3% (95% CI: 11.0% to 11.6%) for males and 7.2% (95% CI: 7.0% to 7.5%) for females, and the annual percentage changes in each age group (local drift) were greater than 5% (p < 0.01 for all) in both sexes. In the same birth cohort, the risk of death from HIV increased with age in both sexes after controlling for period effects, and the risk for each five-year period was 1.98 for males and 1.57 for females compared to their previous life stage. Compared to the period of 2002-2006, the relative risk (RR) of HIV mortality in 2012-2016 increased by 56.1% in males and 3.7% in females, and compared to the 1955-1959 birth cohort, the cohort RRs increased markedly, by 82.9 times in males and 34.8 times in females. Considering the rapidly increasing risk of HIV mortality, Chinese policymakers should take immediate measures to target the key age group of 15-44 years in both sexes.
| S-EPMC7184615 | biostudies-literature