Analysis of the effects of the age-period-birth cohort on cervical cancer mortality in the Brazilian Northeast

Meira, Karina Cardoso; Silva, Glauber Weder dos Santos; Santos, Juliano dos; Guimarães, Raphael Mendonça; Souza, Dyego Leandro Bezerra de; Ribeiro, Gilcilene Pretta Cani; Dantas, Eder Samuel Oliveira; Carvalho, Jovanka Bittencourt Leite de; Jomar, Rafael Tavares; Simões, Taynãna César

doi:10.1371/journal.pone.0226258

Cited by 10 publications

(38 citation statements)

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“…The greater access to health care services and socioeconomic income of the population in this region further explains the findings of this study [ 3 , 7 ]. Similarly, studies in Brazil and the United States reported a lower mortality rates in states with both higher human development indexes and economic incomes [ 38 , 39 ]. Nevertheless, it is to be expected that rapid access to healthcare facilities and the availability of therapy would result in lower mortality trends in the coastal region.…”

Section: Discussionmentioning

confidence: 91%

Cervical cancer mortality in Peru: regional trend analysis from 2008–2017

et al. 2021

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Background Cervical cancer is the third leading cause of cancer-related death among Latin American women. Peru has the sixth highest mortality rate for cervical cancer in the region with regional variations. We aimed to determine overall and regional cervical cancer mortality rates and trends in Peru between 2008 and 2017. Methods We performed an ecological study on the number of deaths by cervical cancer in Peru. Deaths were extracted from the Peruvian Ministry of Health mortality database. Age-standardized mortality rates (ASMR) were estimated per 100,000 women-years using the world standard Segi population. We computed mortality trends using the Joinpoint regression program, estimating the annual percent change (APC). For spatial analysis, GeoDA software was used. Results Peru showed downward trends in the last decade (from 11.62 in 2008 to 9.69 in 2017 (APC = − 2.2, 95% CI: − 4.3, − 0.1, p < 0.05). According to regional-specific analysis, the highest ASMR was in the rainforest region, although this declined from 34.16 in 2008 to 17.98 in 2017 (APC = − 4.3, 95% CI: − 7.2, − 1.3, p < 0.01). Concerning spatial analysis and clustering, the mortality rates from 2008 to 2017 showed a positive spatial autocorrelation and significant clustering (Moran’s I: 0.35, p < 0.001) predominantly in the neighboring North-East departments (Loreto, Ucayali, and San Martin). Conclusions Although mortality trends in the entire population are decreasing, mortality rates remain very high, mainly in the rainforest region. Our results encourage a need for further development and improvement of the current health care delivery system in Peru.

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Section: Discussionmentioning

confidence: 91%

Cervical cancer mortality in Peru: regional trend analysis from 2008–2017

et al. 2021

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“…Res. Public Health 2021, 18, 5607 2 of 11 factors influencing disease incidence and mortality [8]. Considering that previous studies in China possibly ignored the interaction between age, period and cohort effects [9,10], we used an age-period-cohort (APC) model to simultaneously calculate these three variables [11].…”

Section: Introductionmentioning

confidence: 99%

“…This study also aimed to estimate age, period and cohort effects on TB notification and mortality in mainland Chinese residents. Age, period and cohort are usually three key factors influencing disease incidence and mortality [ 8 ]. Considering that previous studies in China possibly ignored the interaction between age, period and cohort effects [ 9 , 10 ], we used an age–period–cohort (APC) model to simultaneously calculate these three variables [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Temporal Trends in Notification and Mortality of Tuberculosis in China, 2004–2019: A Joinpoint and Age–Period–Cohort Analysis

Wang

2021

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Tuberculosis (TB) remains a major public health problem in China and worldwide. In this article, we used a joinpoint regression model to calculate the average annual percent change (AAPC) of TB notification and mortality in China from 2004 to 2019. We also used an age–period–cohort (APC) model based on the intrinsic estimator (IE) method to simultaneously distinguish the age, period and cohort effects on TB notification and mortality in China. A statistically downward trend was observed in TB notification and mortality over the period, with AAPCs of −4.2% * (−4.9%, −3.4%) and −5.8% (−7.5%, −4.0%), respectively. A bimodal pattern of the age effect was observed, peaking in the young adult (aged 15–34) and elderly (aged 50–84) groups. More specifically, the TB notification risk populations were people aged 20–24 years and 70–74 years; the TB mortality risk population was adults over the age of 60. The period effect suggested that TB notification and mortality risks were nearly stable over the past 15 years. The cohort effect on both TB notification and mortality presented a continuously decreasing trend, and it was no longer a risk factor after 1978. All in all, the age effect should be paid more attention.

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“…The risk of death was estimated by relative risk (RR) estimates and 95% confidence intervals according to period and cohort effects. Estimates for the Age Period and Cohort models were made using the Epi library 1.1.18 (R Foundation of Computational Statistics, Vienna, Austria http://www.r-project.org) of the R program (version 4.1) [25].…”

Section: Discussionmentioning

confidence: 99%

Mortality due to breast cancer in a region of high socioeconomic vulnerability in Brazil: Analysis of the effect of age-period and cohort

et al. 2021

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Introduction Breast cancer is an important public health problem worldwide, with important disparities in incidence, mortality, and survival rates between developed and developing countries due to inequalities regarding access to measures for the prevention and treatment of the disease. In Brazil, there are higher rates of incidence and a downward trend in mortality in regions of greater socioeconomic development. Objective To evaluate the effect of age, period, and birth cohort on breast cancer mortality in women aged 20 years and older in the states of the Northeast Region of Brazil, an area of high socioeconomic vulnerability, from 1980 to 2019. Methods The death records were extracted from the DATASUS Mortality Information System website (Department of National Health Informatics) from the Ministry of Health of Brazil. Estimable functions were used to estimate the age-period and cohort models (APC) using the Epi library from the R statistical software version 6.4.1. Results The average breast cancer mortality rate for the period was 20.45 deaths per 100,000 women. The highest coefficients per 100,000 women were observed in the states of Pernambuco (21.09 deaths) and Ceará (20.85 deaths), and the lowest in Maranhão (13.58 deaths) and Piauí (15.43 deaths). In all of the locations, there was a progressive increase in mortality rates in individuals over 40 years of age, with higher rates in the last five-year period (2015–2019). There was an increase in the risk of death for the five-year period of the 2000s in relation to the reference period (1995–1999) in the Northeast region and in the states of Alagoas, Bahia, Maranhão, Paraíba, and Piauí. In addition, there was an increased risk of death for women born after the 1950s in all locations. Conclusion The highest mortality rates in all five-year periods analyzed were observed in states with greater socioeconomic development, with an increase in mortality rates in the 2000s, and a higher risk of death in the younger cohorts.

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Analysis of the effects of the age-period-birth cohort on cervical cancer mortality in the Brazilian Northeast

Cited by 10 publications

References 55 publications

Cervical cancer mortality in Peru: regional trend analysis from 2008–2017

Cervical cancer mortality in Peru: regional trend analysis from 2008–2017

Temporal Trends in Notification and Mortality of Tuberculosis in China, 2004–2019: A Joinpoint and Age–Period–Cohort Analysis

Mortality due to breast cancer in a region of high socioeconomic vulnerability in Brazil: Analysis of the effect of age-period and cohort

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