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1.
Eur Child Adolesc Psychiatry ; 2022 Jun 16.
Article in English | MEDLINE | ID: covidwho-1888891

ABSTRACT

Growing use of mobiles phones (MP) and other wireless devices (WD) has raised concerns about their possible effects on children and adolescents' wellbeing. Understanding whether these technologies affect children and adolescents' mental health in positive or detrimental ways has become more urgent following further increase in use since the COVID-19 outbreak. To review the empirical evidence on associations between use of MP/WD and mental health in children and adolescents. A systematic review of literature was carried out on Medline, Embase and PsycINFO for studies published prior to July 15th 2019, PROSPERO ID: CRD42019146750. 25 observational studies published between January 1st 2011 and 2019 were reviewed (ten were cohort studies, 15 were cross-sectional). Overall estimated participant mean age and proportion female were 14.6 years and 47%, respectively. Substantial between-study heterogeneity in design and measurement of MP/WD usage and mental health outcomes limited our ability to infer general conclusions. Observed effects differed depending on time and type of MP/WD usage. We found suggestive but limited evidence that greater use of MP/WD may be associated with poorer mental health in children and adolescents. Risk of bias was rated as 'high' for 16 studies, 'moderate' for five studies and 'low' for four studies. More high-quality longitudinal studies and mechanistic research are needed to clarify the role of sleep and of type of MP/WD use (e.g. social media) on mental health trajectories in children and adolescents.

2.
Lancet Public Health ; 6(11): e805-e816, 2021 11.
Article in English | MEDLINE | ID: covidwho-1467001

ABSTRACT

BACKGROUND: High-resolution data for how mortality and longevity have changed in England, UK are scarce. We aimed to estimate trends from 2002 to 2019 in life expectancy and probabilities of death at different ages for all 6791 middle-layer super output areas (MSOAs) in England. METHODS: We performed a high-resolution spatiotemporal analysis of civil registration data from the UK Small Area Health Statistics Unit research database using de-identified data for all deaths in England from 2002 to 2019, with information on age, sex, and MSOA of residence, and population counts by age, sex, and MSOA. We used a Bayesian hierarchical model to obtain estimates of age-specific death rates by sharing information across age groups, MSOAs, and years. We used life table methods to calculate life expectancy at birth and probabilities of death in different ages by sex and MSOA. FINDINGS: In 2002-06 and 2006-10, all but a few (0-1%) MSOAs had a life expectancy increase for female and male sexes. In 2010-14, female life expectancy decreased in 351 (5·2%) of 6791 MSOAs. By 2014-19, the number of MSOAs with declining life expectancy was 1270 (18·7%) for women and 784 (11·5%) for men. The life expectancy increase from 2002 to 2019 was smaller in MSOAs where life expectancy had been lower in 2002 (mostly northern urban MSOAs), and larger in MSOAs where life expectancy had been higher in 2002 (mostly MSOAs in and around London). As a result of these trends, the gap between the first and 99th percentiles of MSOA life expectancy for women increased from 10·7 years (95% credible interval 10·4-10·9) in 2002 to reach 14·2 years (13·9-14·5) in 2019, and for men increased from 11·5 years (11·3-11·7) in 2002 to 13·6 years (13·4-13·9) in 2019. INTERPRETATION: In the decade before the COVID-19 pandemic, life expectancy declined in increasing numbers of communities in England. To ensure that this trend does not continue or worsen, there is a need for pro-equity economic and social policies, and greater investment in public health and health care throughout the entire country. FUNDING: Wellcome Trust, Imperial College London, Medical Research Council, Health Data Research UK, and National Institutes of Health Research.


Subject(s)
Life Expectancy/trends , Mortality/trends , Adolescent , Adult , Aged , Aged, 80 and over , Bayes Theorem , Child , Child, Preschool , England/epidemiology , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Registries , Residence Characteristics/statistics & numerical data , Risk Assessment , Spatio-Temporal Analysis , Young Adult
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