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There has been a recent increase in interest among evolutionary researchers in the hypothesis that humans evolved as cooperative breeders, using extended family support to help decrease offspring mortality and increase the number of children that can be successfully reared. In this study, data drawn from the 1970 longitudinal British cohort study were analysed to determine whether extended family support encourages fertility in contemporary Britain. The results showed that at age 30, reported frequency that participants saw their own parents (but not in-laws) and the closeness of the bond between the participant and their own parents were associated with an increased likelihood of having a child between ages 30 and 34. Financial help and reported grandparental childcare were not significantly positively associated with births from age 30 to 34. Men's income was positively associated with likelihood of birth, whereas women's income increased likelihood of birth only for working women with at least one child. While it was predicted that grandparental financial and childcare help would increase the likelihood of reproduction by lowering the cost to the parent of having a child, it appears that the mere physical presence of supportive parents rather than their financial or childcare help encouraged reproduction in the 1970 British birth cohort sample.

Life-history theoretical models show that a typical evolutionarily optimal response of a juvenile organism to high mortality risk is to reach reproductive maturity earlier. Experimental studies in a range of species suggest the existence of adaptive flexibility in reproductive scheduling to maximize fitness just as life-history theory predicts. In humans, supportive evidence has come from studies comparing neighbourhoods with different mortality rates, historical and cross-cultural data. Here, the prediction is tested in a novel way in a large (n = 9099), longitudinal sample using data comparing age at first reproduction in individuals with and without life-expectancy-reducing chronic disease diagnosed during childhood. Diseases selected for inclusion as chronic illnesses were those unlikely to be significantly affected by shifting allocation of effort away from reproduction towards survival; those which have comparatively large effects on mortality and life expectancy; and those which are not profoundly disabling. The results confirmed the prediction that chronic disease would associate with early age at first reproduction: individuals growing up with a serious chronic disease were 1.6 times more likely to have had a first child by age 30. Analysis of control variables also confirmed past research findings on links between being raised father-absent and early pubertal development and reproduction.

Prior research on causes of preterm birth has tended to focus on pathophysiological processes while acknowledging the role of socioeconomic indicators. The present research explored a wide range of factors plausibly associated with preterm birth informed by pathophysiological and evolutionary life history perspectives on gestation length. To achieve this, a machine learning ensemble classification data analysis approach, random forest (RF), was applied to the UK Millennium Cohort (18,201 births). The results highlighted the importance of socioeconomic variables and parental age in predicting preterm (before 37 completed weeks) and very preterm (before 32 weeks) birth. Infants born in households with low income and with young fathers had an increased risk of both very preterm and preterm birth. Maternal health and health problems during pregnancy were not found to be useful predictors. The best-performing algorithm was for very preterm birth and had 93% sensitivity and 100% specificity using six variables. Algorithms predicting preterm birth before 37 weeks showed increased error, with out-of-bag error rates of about 7% versus only 1% for those predicting very preterm birth. The poorer performance of algorithms predicting preterm births to 37 weeks of gestation suggests that some preterm birth may not result from pathology related to poor maternal health or social or economic disadvantage, but instead represents normal life-history variation.

Psychomotor developmental delay in infants includes failure to acquire abilities such as sitting, walking, grasping objects and communication at the ages when most infants have acquired these abilities. Known risk factors include a large number of aspects of family environment, socioeconomic position, problems in pregnancy and birth and maternal health. It is clinically useful to be able to screen for developmental delay so that healthcare interventions can be considered. The present research used machine learning (random forest) to create an algorithm predicting psychomotor delay in 9-month-old infants using information ascertainable at birth and in early infancy. The dataset was the UK longitudinal Millennium Cohort study. In total, 53 predictors measuring socioeconomic indicators, paternal, family and social support for the mother, beliefs about good parenting, maternal health, pregnancy and birth were included in the initial algorithm. Feature reduction showed that of the 53 variables, birthweight, gestational age at birth, pre-pregnancy BMI, family income and parents’ ages had the highest feature importance scores and could alone correctly predict developmental delay with over 99% sensitivity and 100% specificity. No features measuring aspects of early infant care or environment meaningfully added to algorithm performance. The relationships between delay and some of the predictors, particularly income, were nonlinear and complex. The results suggest that the risk of psychomotor developmental delay can be identified in early infancy using machine learning, and that the best predictors are factors present prior to and at birth.

Jobs for life have become increasingly rare in industrialized economies, and have been replaced by shorter-term employment contracts and freelancing. This labour market change is likely to be accompanied by physiological changes in individuals who have experienced little job stability. Evolved responses to increased environmental instability or stochasticity include increased fat deposition and fight-or-flight responses, such as glucose mobilization and increased blood pressure. These responses may have evolved by natural selection as beneficial to individuals in the short-term, but are damaging in the longer term. This study tested whether job losses experienced between ages 30 and 42 are associated with increased body weight, hypertension and diabetes diagnosis in the 1970 British Birth Cohort, which consists of all registered births in a one-week period in April 1970. Each job loss experienced increased the odds of developing diabetes by 1.39 times (CI 1.08-1.80), and of hypertension by 1.28 times (CI 1.07-1.53). Another economic variable, higher personal debt, was associated with all three of these health outcomes: every £100 000 of debt roughly doubled the odds of gaining at least 5 kg between ages 30 and 42. These associations between job loss and health-risk factors suggest that our changing economy results in increases in the prevalence of risk factors for cardiovascular disease. At a broader level, they are consistent with evolutionary understandings of environmental stochasticity, and are a reminder that economic policy is also health policy.

Receiving social support from community and extended family has been typical for mothers with infants in human societies past and present. In non-industrialised contexts, infants of mothers with extended family support often have better health and higher survival through the vulnerable infant period, and hence shared infant care has a clear fitness benefit. However, there is scant evidence that these benefits continue in industrialised contexts. Better infant health and development with allocare support would indicate continued evolutionary selection for allocare. The research reported here used multiple logistic regression analysis to test whether a lack of family and other social support for mothers was associated with an increased risk of developmental delay in 9-month-old infants in the UK Millennium Cohort (analysis sample size, 15,696 infants). Extended family-based childcare during work hours and more maternal time spent with friends were the most influential kin and social support variables: infants of mothers with kin-based childcare versus all other childcare arrangements had a lower risk of developmental delay (OR = 0.61, 95% CIs: 0.46–0.82). Infants of mothers who spent no time with friends when compared with those who saw friends every day had double the odds of delay. Greater paternal involvement in infant care was associated with a lower odds of developmental delay. In conclusion, shared care of infants and social support for mothers may influence fitness-related traits in industrialised societies rather than being factors that influenced selection only in the past and in societies which retain close kin networks and a strong local community focus.

Long-term sequelae of coronavirus disease 2019 (COVID-19) infection are common and can have debilitating consequences. There is a need to understand risk factors for Long COVID-19 to give impetus to the development of targeted yet holistic clinical and public health interventions to reduce its associated healthcare and economic burden. Given the large number and variety of predictors implicated spanning health-related and sociodemographic factors, machine learning becomes a valuable tool. As such, this study aims to employ machine learning to produce an algorithm to predict Long COVID-19 risk, and thereby identify key predisposing factors. Longitudinal cohort data were sourced from the UK’s “Understanding Society: COVID-19 Study” (n = 601 participants with past symptomatic COVID-19 infection confirmed by serology testing). The random forest classification algorithm demonstrated good overall performance with 97.4% sensitivity and modest specificity (65.4%). Significant risk factors included early timing of acute COVID-19 infection in the pandemic, greater number of hours worked per week, older age and financial insecurity. Loneliness and having uncommon health conditions were associated with lower risk. Sensitivity analysis suggested that COVID-19 vaccination is also associated with lower risk, and asthma with an increased risk. The results are discussed with emphasis on evaluating the value of machine learning; potential clinical utility; and some benefits and limitations of machine learning for health science researchers given its availability in commonly used statistical software.

Mother–infant co-sleeping or bed sharing is discouraged by health organisations due to evidence that it is associated with unexplained sudden infant death. On the other hand, there is evidence that it should theoretically be beneficial for infants. One line of this evidence concerns breathing regulation, which at night is influenced by the rocking movement of the mother’s chest as she breathes. Here, the hypothesis that mother–infant co-sleeping will be associated with a lower probability of infant breathing distress is tested in the UK Millennium Cohort Study (n = 18,552 infants). Maternal, infant, family, and socio-economic covariates were included in logistic regression analysis, and in a machine learning algorithm (Random Forest) to make full use of the number of variables available in the birth cohort study data. Results from logistic regression analysis showed that co-sleeping was associated with a reduced risk of breathing difficulties (OR = 0.69, p = 0.027). The Random Forest algorithm placed high importance on socio-economic aspects of infant environment, and indicated that a number of maternal, child, and environmental variables predicted breathing distress. Co-sleeping by itself was not high in the Random Forest variable importance ranking. Together, the results suggest that co-sleeping may be associated with a modest reduction in risk of infant breathing difficulties.

Millions of infants around the world have been born as a result of assisted reproductive technologies (ART), and in the past three decades ART has become increasingly effective and technologically sophisticated. At the same time, advances have been made in understanding the evolutionary biology of mate choice and post-copulatory processes. These advances have relevance for ART as ART methods to a greater or lesser extent circumvent potentially important natural processes determining which fertilized embryo is successfully implanted. Here, using UK Millennium cohort data, the hypothesis that ART methods which circumvent both natural selection of ova and sperm (for example fertilization) lead to poorer child health and developmental outcomes than ART methods in which fertilization occurs naturally after fertility treatment using drugs or diathermy. The results showed that both groups of ART were associated with the number of infant health problems from birth through the first week of life when compared with naturally conceived infants. Methods with artificial fertilization were associated with two of the four most common health conditions: respiratory distress (OR 1.80; 95% CI 1.12-2.91) and infections (OR 1.77; 95% CI 1.96-2.06). ART methods with artificial fertilization were associated with delayed achievement of developmental milestones at nine months, and when contrasted with ART using fertility drugs or diathermy only, were significantly more likely to be associated with slower child development. This suggests that evolved processes that determine which egg and sperm lead to successful pregnancy may be important for offspring quality as indicated by infant development. Clinically, the results suggest that women should avoid ART with artificial gamete selection if they can conceive using other ART methods.

It has been hypothesised that facial traits such as masculinity and a healthy appearance may indicate heritable qualities in males (e.g. immunocompetence) and that, consequently, female preferences for such traits may function to increase offspring viability and health. However, the putative link between paternal facial features and offspring health has not previously been tested empirically in humans. Here we present data from two traditional societies with little or no access to modern medicine and family planning technologies. Data on offspring number and offspring survival were analysed for the Agta of the Philippines and the Maya of Belize, and archive facial photographs were assessed by observers for attractiveness and masculinity. While there was no association between attractiveness and offspring survival in either population, a quadratic relationship was observed between masculinity and offspring survival in both populations, such that intermediate levels of masculinity were associated with the lowest offspring mortality, with both high and low levels of masculinity being associated with increased mortality. Neither attractiveness nor masculinity were related to fertility (offspring number) in either population. We consider how these data may or may not reconcile with current theories of female preferences for masculinity in male faces and argue that further research and replication in other traditional societies should be a key priority for the field.