Background: Parabens are commonly used as antimicrobial preservatives in cosmetics, pharmaceuticals, and food and beverage processing. Widespread human exposure to parabens has been recently documented, and some parabens have demonstrated adverse effects on male reproduction in animal studies. However, human epidemiologic studies are lacking. Objective: We investigated relationships between urinary concentrations of parabens and markers of male reproductive health in an ongoing reproductive epidemiology study. Methods: Urine samples collected from male partners attending an infertility clinic were analyzed for methyl paraben (MP), propyl paraben (PP), butyl paraben (BP), and bisphenol A (BPA). Associations with serum hormone levels (n = 167), semen quality parameters (n = 190), and sperm DNA damage measures (n = 132) were assessed using multivariable linear regression. Results: Detection rates in urine were 100% for MP, 92% for PP, and 32% for BP. We observed no statistically significant associations between MP or PP and the outcome measures. Categories of urinary BP concentration were not associated with hormone levels or conventional semen quality parameters, but they were positively associated with sperm DNA damage (p for trend = 0.03). When urinary BPA quartiles were added to the model, BP and BPA were both positively associated with sperm DNA damage (p for trend = 0.03). Assessment of paraben concentrations measured on repeated urine samples from a subset of the men (n = 78) revealed substantial temporal variability. Conclusions: We found no evidence for a relationship between urinary parabens and hormone levels or semen quality, although intraindividual variability in exposure and a modest sample size could have limited our ability to detect subtle relationships. Our observation of a relationship between BP and sperm DNA damage warrants further investigation.

It is unknown whether the quantity or quality of green space is more important for mental wellbeing. We aimed to explore associations between availability of, satisfaction with, and use of green space and mental wellbeing among children aged 4 years in a multi-ethnic sample. We did a 4-year follow-up assessment of participants in the Born in Bradford longitudinal cohort study, which recruited children and mothers at the city's main maternity unit from 2007 to 2011. The primary outcome was parent-reported mental wellbeing for children aged 4 years, assessed with the standardised Strengths and Difficulties Questionnaire. Total, internalising, and externalising behavioural difficulties and prosocial behaviour scales were computed (with higher scores indicating greater difficulties or more prosocial behaviour). Residential green space around participants' home addresses and distance to major green spaces were computed with the normalised difference vegetation index (NDVI). A subsample of participants completed additional questionnaires on measures of satisfaction with, and use of, local green spaces. Multiple regressions examined associations between green space and children's mental wellbeing and explored moderation by ethnicity (white British vs south Asian) and socioeconomic status. Between Oct 1, 2012, and June 30, 2015, 2594 mothers attended a follow-up appointment during which they completed a detailed questionnaire assessing the health of their child. 1519 (58%) participants were of south Asian origin, 740 (29%) of white British origin, and 333 (13%) of another ethnicity. Data on ethnicity were missing for two participants. 832 (32%) of 2594 participants completed additional questionnaires. Ethnicity moderated associations between residential green space and mental wellbeing (p<0·05 for total and internalising difficulties). After adjusting for all relevant variables, more green space was associated with fewer internalising behavioural difficulties (mean NDVI 100 m: β -2·35 [95% CI -4·20 to -0·50]; 300 m: -3·15 [-5·18 to -1·13]; 500 m: -2·85 [-4·91 to -0·80]) and with fewer total behavioural difficulties (100 m: -4·27 [-7·65 to -0·90]; 300 m: -5·22 [-8·91 to -1·54]; 500 m: -4·82 [-8·57 to -1·07]) only for south Asian children across all three buffer zones. In the subsample of participants, the effect of NDVI on wellbeing was rendered non-significant after controlling for satisfaction with, and use of, green space. Among south Asian children, satisfaction with green space was significantly associated with fewer total behavioural difficulties across all three buffer zones (β -0·59 [95% CI -1·11 to -0·07]), fewer internalising behavioural difficulties within 100 m (-0·28 [95% CI -0·56 to -0·003]) and 300 m buffer zones (-0·28 [-0·56 to -0·002]), and greater prosocial behaviour across all three buffer zones (0·20 [0·02 to 0·38]); no such associations were observed among white British children. Positive effects of green space on wellbeing differ by ethnicity. Satisfaction with the quality of green space appears to be a more important predictor of wellbeing than does quantity of green space. Public health professionals and urban planners need to focus on both quality and quantity of urban green spaces to promote health, particularly among ethnic minority groups. European Community's Seventh Framework Programme.

The impact of long COVID is increasingly recognised, but risk factors are poorly characterised. We analysed questionnaire data on symptom duration from 10 longitudinal study (LS) samples and electronic healthcare records (EHR) to investigate sociodemographic and health risk factors associated with long COVID, as part of the UK National Core Study for Longitudinal Health and Wellbeing. Methods Analysis was conducted on 6,899 adults self-reporting COVID-19 from 45,096 participants of the UK LS, and on 3,327 cases assigned a long COVID code in primary care EHR out of 1,199,812 adults diagnosed with acute COVID-19. In LS, we derived two outcomes: symptoms lasting 4+ weeks and symptoms lasting 12+ weeks. Associations of potential risk factors (age, sex, ethnicity, socioeconomic factors, smoking, general and mental health, overweight/obesity, diabetes, hypertension, hypercholesterolaemia, and asthma) with these two outcomes were assessed, using logistic regression, with meta-analyses of findings presented alongside equivalent results from EHR analyses. Results Functionally limiting long COVID for 12+ weeks affected between 1.2% (age 20), and 4.8% (age 63) of people reporting COVID-19 in LS. The proportion reporting symptoms overall for 12+ weeks ranged from 7.8 (mean age 28) to 17% (mean age 58) and for 4+ weeks 4.2% (age 20) to 33.1% (age 56). Age was associated with a linear increase in long COVID between age 20-70. Being female (LS: OR=1.49; 95%CI:1.24-1.79; EHR: OR=1.51 [1.41-1.61]), poor pre-pandemic mental health (LS: OR=1.46 [1.17-1.83]; EHR: OR=1.57 [1.47-1.68]) and poor general health (LS: OR=1.62 [1.25-2.09]; EHR: OR=1.26; [1.18-1.35]) were associated with higher risk of long COVID. Individuals with asthma also had higher risk (LS: OR=1.32 [1.07-1.62]; EHR: OR=1.56 [1.46-1.67]), as did those categorised as overweight or obese (LS: OR=1.25 [1.01-1.55]; EHR: OR=1.31 [1.21-1.42]) though associations for symptoms lasting 12+ weeks were less pronounced. Non-white ethnic minority groups had lower 4+ week symptom risk (LS: OR=0.32 [0.22-0.47]), a finding consistent in EHR. Associations were not observed for other risk factors. Few participants in the studies had been admitted to hospital (0.8-5.2%). Conclusions Long COVID is clearly distributed differentially according to several sociodemographic and pre-existing health factors. Establishing which of these risk factors are causal and predisposing is necessary to further inform strategies for preventing and treating long COVID.

There is widespread metabolic disruption in women upon becoming pregnant. South Asians (SA) compared to White Europeans (WE) have more fat mass and are more insulin-resistant at a given body mass index (BMI). Whether these are reflected in other gestational metabolomic differences is unclear. Our aim was to compare gestational metabolic profiles and their determinants between WE and SA women. We used data from a United Kingdom (UK) cohort to compare metabolic profiles and associations of maternal age, education, parity, height, BMI, tricep skinfold thickness, gestational diabetes (GD), pre-eclampsia, and gestational hypertension with 156 metabolic measurements in WE (n = 4072) and SA (n = 4702) women. Metabolic profiles, measured in fasting serum taken between 26–28 weeks gestation, were quantified by nuclear magnetic resonance. Distributions of most metabolic measures differed by ethnicity. WE women had higher levels of most lipoprotein subclasses, cholesterol, glycerides and phospholipids, monosaturated fatty acids, and creatinine but lower levels of glucose, linoleic acid, omega-6 and polyunsaturated fatty acids, and most amino acids. Higher BMI and having GD were associated with higher levels of several lipoprotein subclasses, triglycerides, and other metabolites, mostly with stronger associations in WEs. We have shown differences in gestational metabolic profiles between WE and SA women and demonstrated that associations of exposures with these metabolites differ by ethnicity.

Economic, physical, built, cultural, learning, social and service environments have a profound effect on lifelong health. However, policy thinking about health research is dominated by the ‘biomedical model’ which promotes medicalisation and an emphasis on diagnosis and treatment at the expense of prevention. Prevention research has tended to focus on ‘downstream’ interventions that rely on individual behaviour change, frequently increasing inequalities. Preventive strategies often focus on isolated leverage points and are scattered across different settings. This paper describes a major new prevention research programme that aims to create City Collaboratory testbeds to support the identification, implementation and evaluation of upstream interventions within a whole system city setting. Prevention of physical and mental ill-health will come from the cumulative effect of multiple system-wide interventions. Rather than scatter these interventions across many settings and evaluate single outcomes, we will test their collective impact across multiple outcomes with the goal of achieving a tipping point for better health. Our focus is on early life (ActEarly) in recognition of childhood and adolescence being such critical periods for influencing lifelong health and wellbeing.