1,720,996 research outputs found
(Un)Healthy migrants: unpacking the relationship between health and migration within Great Britain
No full textThis thesis is the first attempt at creating a comprehensive geographical understanding of the relationship between health and internal migration within Great Britain for working age adults. Drawing on international literature, theories and mechanisms driving the high rates of internal migration among those with poor mental health, and the low rates among those with poor physical health are assessed, and these are then tested in three distinct empirical analyses. Previous attempts at modelling these interrelationships fail to account for realistic place influences on migration behaviour, which are also known to affect health behaviours and outcomes, and this shortfall is overcome with the use of multilevel modelling. Throughout, evidence is presented that, although moderated by place of residence, both physical and mental health have an effect on the likelihood of moving and of long-distance migration within Great Britain, and further avenues for research are suggeste
Place and preference effects on the association between mental health and internal migration within Great Britain
No full textIndividuals with mental health needs are more likely to migrate than the general population, but the effects of migration preference and place of residence are often overlooked. These issues are addressed through the application of a novel origin and destination multilevel model to survey data. In comparison to those with good mental health, individuals with poor mental health are more likely to make undesired moves and this is moderated, but not explained by place of residence. Implications for understanding the mental health and migration relationship, and its impact on service provision are then proposed
The impact of limiting long term illness on internal migration in England and Wales: New evidence from census microdata
No full textPrevious research has suggested that poor health is associated with reduced migration; this knowledgestems from models based on past censuses, or longitudinal studies which imply that the factors influencing migration are the same between those in good and poor health. This paper addresses these issuesby utilising health-stratified analyses on the 2011 Census Individual Secure Sample for England and Wales.Multilevel models predict the odds of moving for working age adults, controlling for key predictors of migration, estimating the effect of health status on the odds of moving and the destination specific variance in migration. We find that those in poor health are less likely to move, after controlling for individual level characteristics. In contrast with expectations, economic inactivity, marriage and being in African, Caribbean, Black, Other or Mixed ethnic groups were not significant predictors ofmigration among the unhealthy sample, but were for the healthy sample. We conclude that migration is health-selective and propose implications for understanding area level concentrations of poor health in England and Wales
Socioeconomic differences in the incidence of small-for-gestational-age birth and their time trend
No full textBackground: Children born small for gestational age (<10th percentile for birthweight at a particular gestational age; SGA) are at higher risk of morbidity and later disease risk. This study aims to quantify socioeconomic differences in SGA incidence and test whether these inequalities are shifting over time in an English population-based cohort.Methods: We used 70,818 antenatal care and delivery records of singleton births to mothers aged ≥18 years at University Hospital Southampton, UK, utilising logistic regression modelling to investigate the risk of SGA by maternal educational qualification, employment, partner’s employment status and lone motherhood recorded at the first antenatal appointment. We also adjusted for maternal age, ethnicity, parity, blood pressure, gestational diabetes and baby’s sex. We tested mediation by maternal body mass index (BMI) category and smoking status. Interactions between social indicators and year estimate change in inequality over the study period (2004-2016).Results: Mothers with no university degree were more likely to give birth to an SGA baby (adjusted odds ratio (aOR) 1.22, 99% CI 1.03, 1.45) than mothers with a degree. Maternal (aOR 1.26, CI 1.09, 1.45) and paternal unemployment (aOR 1.29, CI 1.03, 1.61) were also associated with higher SGA risk compared to employed mothers and partners respectively. There was no evidence of mediation by maternal smoking or BMI for those associations. However, despite lone motherhood being associated with higher risk of SGA (aOR 1.27, CI 1.01, 1.59), this was attenuated by adding maternal smoking status in the model (aOR 1.15, CI 0.92, 1.45). All of the linear trends in ORs by year showed no evidence of narrowing inequalities over time at the 1% statistical significance level.Conclusion: Social inequalities in SGA incidence are evident, and have remained stable over the 12-year study period. Pre-conception and antenatal interventions targeting socially disadvantaged mothers are needed
Predicting the risk of childhood overweight and obesity at 4–5 years using pregnancy and early life healthcare data
No full textBackground: in England, 9.5% of children aged 4–5 years and 20.1% aged 10–11 years are obese, with the prevalence in the most deprived areas being more than twice as that in the least deprived. There is evidence illustrating the developmental origins of obesity, but it focuses on individual risk factors and comes mostly from research birth cohorts which are not necessarily representative of the wider population. There is no system-based early identification of childhood obesity risk at pregnancy stage and onwards.The aim was to develop and validate a risk identification system for childhood obesity using existing routinely collected maternal and early-life population-level healthcare data in Hampshire.Methods: studying Lifecourse Obesity PrEdictors (SLOPE) study is an anonymised population-based linked cohort of maternal antenatal and delivery records for all births taking place at University Hospital Southampton 2003–2018, and child health records including information on postnatal growth, type of feeding and childhood body mass index (BMI) up to 14 years. Childhood age- and sex- adjusted BMI at 4–5 years was used to define the outcome of overweight and obesity in the models. Logistic regression models together with multivariable fractional polynomials were used to select model predictors and to identify transformations of continuous predictors that best predict the outcome. Predictive accuracy was evaluated by assessing model discrimination and calibration.Results: childhood BMI was available for approximately 30000 children aged 4–5 years (9% obese). Models were developed in stages, incorporating data collected at first antenatal booking appointment, birth and early life predictors. The area under the curve (AUC) was lowest (0.64) for the model only incorporating maternal predictors from the booking appointment and highest for the model incorporating all factors up to weight at 2 years for predicting outcome at 4–5 years (0.82 for overweight and obesity and 0.89 for obesity excluding overweight). Maternal predictors included BMI, smoking status at first antenatal appointment, age and ethnicity. Early life predictors included birthweight, gender, breastfeeding and weight at 1 or 2 years of age. Although AUC was lower for the booking models, maternal predictors remained consistent across the models, thus high-risk groups could be identified at an early stage with more precise estimation as the child grows.Conclusion: this prediction modelling can be used to identify and quantify clustering of risk for childhood obesity as early as the first trimester of pregnancy, and can strengthen the long-term preventive element of antenatal and early years care
A prediction model for childhood overweight and obesity at 10–11 years using pregnancy and early life longitudinal healthcare data
No full textSocioeconomic inequalities in risk of small for gestational age birth in primiparous and multiparous women: analysis of a population-based cohort in the south of England
No full textHow far is a long distance? An assessment of the issue of scale in the relationship between limiting long term illness and long distance migration in England and Wales
No full textResearch consistently shows that those in poor health are less likely to migrate over long distances, but analyses rarely consider what constitutes a long distance in this context. Additionally, the migration literature often fails to account for place of residence effects on migration behaviour. This paper addresses these issues through analysis on the distance of residential moves by working age adults in the year preceding the 2011 Census. Multilevel logistic regression models predict the odds of having moved long distance relative to short distance, for different definitions of long distance: 10km+, 20km+ and 50km+. We test whether those reporting a Limiting Long Term Illness (LLTI) are less likely to move long distance in all models, controlling for local authority at the time of the 2011 Census. We find no evidence for health-selectivity in long distance migration in the 10 and 20km models, but uncover a significant effect in the 50km model. By age, the odds of having moved long distance do not vary for middle-working age adults (25-54) by LLTI, whilst those with an LLTI in the pre-retirement age group (55-64) are less likely to move long distance in all models. We uncover clusters of local authorities where those with an LLTI are more likely to have moved long distance in the 10km and 20km models, but in the 50km model only two of these areas remain significantly positive. We conclude that health selection in distances moved occurs above a cut-off somewhere between 20km and 50km
Predicting the risk of childhood overweight and obesity at 4-5 years using population-level pregnancy and early-life healthcare data
No full textBackground: nearly a third of children in the UK are overweight, with the prevalence in the most deprived areas more than twice that in the least deprived. The aim was to develop a risk identification model for childhood overweight/obesity applied during pregnancy and early life using routinely collected population-level healthcare data.Methods: a population-based anonymised linked cohort of maternal antenatal records (January 2003 to September 2013) and birth/early-life data for their children with linked body mass index (BMI) measurements at 4-5 years (n=29060 children) in Hampshire, UK. Childhood age- and sex- adjusted BMI at 4-5 years, measured between September 2007 and November 2018, using a clinical cut-off of ≥91st centile for overweight/obesity. Logistic regression models together with multivariable fractional polynomials were used to select model predictors and to identify transformations of continuous predictors that best predict the outcome. Results: fifteen percent of children had a BMI≥91st centile. Models were developed in stages, incorporating data collected at first antenatal booking appointment, later pregnancy/birth and early life predictors (1 and 2 years). The area under the curve (AUC) was lowest (0.64) for the model only incorporating maternal predictors from early pregnancy and highest for the model incorporating all factors up to weight at 2 years for predicting outcome at 4-5 years (0.83). The models were well calibrated. The prediction models identify 21% (at booking) to 24% (at ~2 years) of children as being at high risk of overweight or obese by the age of 4-5 years (as defined by a ≥20% risk score). Early pregnancy predictors included maternal BMI, smoking status, maternal age and ethnicity. Early life predictors included birthweight, baby’s sex and weight at 1 or 2 years of age. Conclusions: although predictive ability was lower for the early pregnancy models, maternal predictors remained consistent across the models, thus high-risk groups could be identified at an early stage with more precise estimation as the child grows. A tool based on these models can be used to quantify clustering of risk for childhood obesity as early as the first trimester of pregnancy, and can strengthen the long-term preventive element of antenatal and early-years care.<br/
Modifiable maternal exposures between pregnancies and offspring adiposity: A systematic review
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