United Kingdom

Life expectancy in England & Wales has diverged from that of its high-income peers since 2011, raising concerns that the country may become an international laggard like the United States (US). Using the contour method, we decompose gaps in life expectancy between England & Wales and 20 individual high-income countries for males and females in 2019 into: (a) pre-existing differences in age- and cause-specific death rates in 2011; and (b) diverging trends in these rates between 2011 and 2019. Focusing on trends, we find that external mortality at young-to-middle ages, cardiometabolic mortality at middle-to-older ages, and dementia mortality at older ages contributed most to life expectancy gaps. Although England & Wales and the US shared adverse trends in cardiometabolic and external mortality, England & Wales experienced larger life expectancy gains. Our findings underscore the urgency of improving population health in England & Wales to avoid life expectancy falling further behind.

Full birth histories that enable analysis of mothers' ages at birth and birth intervals by parity are rare in historical data. Partial birth histories can be obtained from retrospective fertility surveys and census data that record the numbers of children ever born, children deceased, and ages of surviving co-resident children. In a 1988 article published in Population Studies, Luther and Cho proposed a method for reconstructing full birth histories by probabilistically imputing the ages of deceased and non-co-resident children, and in a 2020 article in Historical Methods, Hacker extended this method to historical census data from the United States. In this paper I further adapt the reconstruction method using a sample from the 1911 Census of England and Wales and census records linked from 1881 onwards; this reduces the number of missing observations in the partial birth histories. Birth histories based on linked data particularly improve estimates of duration-specific marital fertility for women who married and had children more than 15 years before the survey date.

ABSTRACT Background Life expectancy improved dramatically during the 20 th century. Whether more recent generations are also living longer in good health has serious implications for healthcare systems and the economy. Aim To synthesise evidence on cohort differences in physical and mental health from the British birth cohort studies, born 1946 to 2000-02. Method Electronic databases (MEDLINE, EMBASE, PsycInfo, Web of Science, up to 25 June 2024) were searched for pairwise combinations of the six cohort studies of interest or for terms indicating the use of at least two cohorts in the same study. Articles were eligible for inclusion if they compared the health of two or more included cohorts at similar ages (within 3 years). Results Results were summarised narratively. There was little evidence for improving health across successive cohorts born since 1946 when compared at the same age. For several outcomes – particularly obesity, mental ill-health and diabetes – prevalence of poor health was higher in more recent generations, a pattern we term “Generational Health Drift”. Many outcomes were self-reported, but studies using observer-measured outcomes (anthropometric measures and blood biomarkers) tended to support conclusions based on self-reports. Conclusion More research is needed to understand the drivers of this trend, shaped by changing exposure to preventable social and environmental risk factors across the lifecourse, and to monitor future trends in disability and functional limitation. The Generational Health Drift has serious implications for policy, planning, and funding allocation to be able to support a growing number of people living with chronic health conditions.

BACKGROUNDSince World War II, Britain has witnessed significant societal changes, including in relation to fertility.Robust longitudinal and cross-cohort research requires data harmonisation to create comparable fertility measures to understand the predictors and consequences of these changes across generations. OBJECTIVEThis paper describes newly created datasets on fertility histories that have been derived and harmonised across four longitudinal British studies of cohorts born between 1946 and 1990.The consistency with national statistics on fertility are examined. METHODSThe birth cohorts are: National Survey of Health and Development, born in 1946; the 1958 National Child Development Study; the 1970 British Cohort Study; Next Steps, born in 1989/90. RESULTSThe harmonised datasets include information on the cohort members' biological children at each survey sweep during childbearing age, such as whether they have children and if so how many, age of eldest and youngest child, number of boys and girls, and children by a previous partner.Additional variables related to non-biological children have been derived where possible.The percentage of female cohort members who have had at least one live birth and the number of children show a good degree of consistency with national statistics on fertility.

Summary A major reason why the difference between the number of baptisms recorded in Anglican registers and the number of births occurring was becoming greater was the increasing delay between birth and baptism. More and more children died before they had been baptised, or in circumstances which caused their baptism to go unrecorded. As long as burial registration was complete, estimates of the extent of the shortfall between births and baptisms may be made from a comparison of baptism and burial registers. When burial registration, too, began to falter, resort must be had to indirect methods of estimating underregistration both of births and deaths. Estimates are given of the changing levels of underregistration arising from this cause at half-century intervals between the late sixteenth and late eighteenth centuries. The exercise also brings to light evidence of the fluctuating level of infant mortality over the period and of changes in the relative importance of endogenous and exogenous mortality within the total infant mortality rate.