“…The three most remarkable correlations were as follows: First, early age at first childbirth was associated with an increased number of pregnancies. This association was consistent with that reported in a previous study [ 26 ]. Second, a younger age of starting hormone replacement therapy correlated to a longer duration of hormonal treatment.…”
This study aimed to investigate the important predictors related to predicting positive mammographic findings based on questionnaire-based demographic and obstetric/gynecological parameters using the proposed integrated machine learning (ML) scheme. The scheme combines the benefits of two well-known ML algorithms, namely, least absolute shrinkage and selection operator (Lasso) logistic regression and extreme gradient boosting (XGB), to provide adequate prediction for mammographic anomalies in high-risk individuals and the identification of significant risk factors. We collected questionnaire data on 18 breast-cancer-related risk factors from women who participated in a national mammographic screening program between January 2017 and December 2020 at a single tertiary referral hospital to correlate with their mammographic findings. The acquired data were retrospectively analyzed using the proposed integrated ML scheme. Based on the data from 21,107 valid questionnaires, the results showed that the Lasso logistic regression models with variable combinations generated by XGB could provide more effective prediction results. The top five significant predictors for positive mammography results were younger age, breast self-examination, older age at first childbirth, nulliparity, and history of mammography within 2 years, suggesting a need for timely mammographic screening for women with these risk factors.
“…The three most remarkable correlations were as follows: First, early age at first childbirth was associated with an increased number of pregnancies. This association was consistent with that reported in a previous study [ 26 ]. Second, a younger age of starting hormone replacement therapy correlated to a longer duration of hormonal treatment.…”
This study aimed to investigate the important predictors related to predicting positive mammographic findings based on questionnaire-based demographic and obstetric/gynecological parameters using the proposed integrated machine learning (ML) scheme. The scheme combines the benefits of two well-known ML algorithms, namely, least absolute shrinkage and selection operator (Lasso) logistic regression and extreme gradient boosting (XGB), to provide adequate prediction for mammographic anomalies in high-risk individuals and the identification of significant risk factors. We collected questionnaire data on 18 breast-cancer-related risk factors from women who participated in a national mammographic screening program between January 2017 and December 2020 at a single tertiary referral hospital to correlate with their mammographic findings. The acquired data were retrospectively analyzed using the proposed integrated ML scheme. Based on the data from 21,107 valid questionnaires, the results showed that the Lasso logistic regression models with variable combinations generated by XGB could provide more effective prediction results. The top five significant predictors for positive mammography results were younger age, breast self-examination, older age at first childbirth, nulliparity, and history of mammography within 2 years, suggesting a need for timely mammographic screening for women with these risk factors.
“…The preference for large families tends to reinforce adolescent childbearing and vice versa, particularly in West Africa (Bongaarts 2017;Fenn et al 2015;Mbacké 2017). The earlier a woman becomes a mother, the more children she is likely to have: not just historically, but also in high-income, low-fertility societies today (Casterline and Trussell 1980;Morgan and Rindfuss 1999;Tomkinson 2019). In West Africa, married young women report a higher ideal number of children than unmarried women of the same age (MacQuarrie 2014).…”
Although recent studies examine overall fertility trends in West Africa, few using advanced demographic techniques focus on adolescents. This study explores long-term patterns of adolescent childbearing in 12 West African countries using 51 Demographic and Health Surveys covering birth cohorts that span 54 years (1940-1994). We employ classic demographic measures as well as disaggregation by early-(10-14 years old), middle-(15-17), and late adolescence (18-19). Cohort-based estimates of total adolescent births, parity progression ratios, and rapid repeat birth probabilities reveal little change over time. Most women begin childbearing in adolescence, the progression to additional adolescent births remains common, and the incidence of rapid repeat births is high. In recent cohorts, women exit adolescence with an average of between 0.4 (Ghana) to 1.3 (Niger) births. Contrary to common assumptions, it is women commencing motherhood in early-and middle-, not later adolescence, who account for most West African adolescent fertility.
“…All the studies we have cited so far show that the number of children of mothers decreases with age at first birth (e.g., Kohler, Billari, and Ortega 2002). Although women who have a first child later have a second child faster (Tomkinson 2019), biological constraints and normative limits can shorten their time to have children. Indeed, at later ages, lessened perceived expectations or even disapproval among peers to proceed to a further child, as well as a lack of physical energy to carry and care for another child while older, can lead to keeping the family small (Wagner, Huinink, and Liefbroer 2019).…”
Section: Mothers' Completed Fertility Conditional On Age At First Birthmentioning
confidence: 99%
“…Indeed, at later ages, lessened perceived expectations or even disapproval among peers to proceed to a further child, as well as a lack of physical energy to carry and care for another child while older, can lead to keeping the family small (Wagner, Huinink, and Liefbroer 2019). Selection effects can also be at play: Women in the median range of age at first birth generally behave with a median behaviour, but those who start earlier or later than the rest of their birth cohort can be at odds with that behaviour (Tomkinson 2019). Hence, in the pre-postponement cohorts, women who started having children in their 30s possibly had fewer children because they were specific.…”
Section: Mothers' Completed Fertility Conditional On Age At First Birthmentioning
BACKGROUNDThe rise in the age at first birth has been universal in low-fertility countries in the last decades. Mothers who have their first child later tend to have fewer children, and in the absence of fertility catch-up at older ages, delayed fertility contributes to cohort fertility decline.
OBJECTIVEWe aim to study how changes in completed cohort fertility (quantum) relate to delayed age at first birth (tempo) across birth cohorts.
METHODSWe use birth histories collected in surveys or censuses in ten high-income countries. We rely on a decomposition analysis that quantifies how much the changes in age at first birth, mothers' completed fertility conditional on age at first birth, and childlessness contribute to the total change in cohort fertility over the 1940-1969 birth cohorts.
RESULTSIn many countries and cohorts, the fertility intensity of mothers increased more at later ages than at earlier ages, reflecting the catching up of those who had delayed childbearing. However, in most countries studied, the increased fertility intensity of mothers at older ages was not sufficient to offset the depressing effect of delayed first births on cohort fertility rates.
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