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Abstract Background Globally too many mothers and babies die during childbirth; 98% of maternal deaths are avoidable. Skilled clinicians can reduce these deaths; however, there is a world-wide shortage of maternity healthcare workers. Malawi has enough to deliver 20% of its maternity care. A motivating work environment is important for healthcare worker retention. To inform a future trial, we aimed to assess the feasibility of implementing a motivational intervention (Appreciative Inquiry) to improve the working lives of maternity healthcare workers and patient satisfaction in Malawi. Methods Three government hospitals participated over 1 year. Its effectiveness was assessed through: a monthly longitudinal survey of working life using psychometrically validated instruments (basic psychological needs, job satisfaction and work-related quality of life); a before and after questionnaire of patient satisfaction using a patient satisfaction tool validated in low-income settings with a maximum score of 80; and a qualitative template analysis encompassing ethnographic data, semi-structured interviews and focus groups with staff. Results The intervention was attended by all 145 eligible staff, who also participated in the longitudinal study. The general trend was an increase in the scores for each scale except for the basic psychological needs score in one site. Only one site demonstrated strong evidence for the intervention working in the work-related quality of life scales. Pre-intervention, 162 postnatal women completed the questionnaire; post-intervention, 191 postnatal women participated. Patient satisfaction rose in all three sites; referral hospital 4.41 rise (95% CI 1.89 to 6.95), district hospital 10.22 (95% CI 7.38 to 13.07) and community hospital 13.02 (95% CI 10.48 to 15.57). The qualitative data revealed that staff felt happier, that their skills (especially communication) had improved, behaviour had changed and systems had developed. Conclusions We have shown that it is possible to implement Appreciative Inquiry in government facilities in Malawi, which has the potential to change the way staff work and improve patient satisfaction. The mixed methods approach revealed important findings including the importance of staff relationships. We have identified clear implementation elements that will be important to measure in a future trial such as implementation fidelity and inter-personal relationship factors.

Abstract Background Regression analyses of time series of disease counts on environmental determinants are a prominent component of environmental epidemiology. For planning such studies, it can be useful to predict the precision of estimated coefficients and power to detect associations of given magnitude. Existing generic approaches for this have been found somewhat complex to apply and do not easily extend to multiple series studies analysed in two stages. We have sought a simpler approximate approach which can easily extend to multiple series and give insight into factors determining precision. Methods We derive approximate expressions for precision and hence power in single and multiple time series studies of counts from basic statistical theory, compare the precision predicted by these with that estimated by analysis in real data from 51 cities of varying size, and illustrate the use of these estimators in a realistic planning scenario. Results In single series studies with Poisson outcome distribution, precision and power depend only on the usable variation of exposure (i.e. that conditional on covariates) and the total number of disease events, regardless of how many days those are spread over. In multiple time series (eg multi-city) studies focusing on the meta-analytic mean coefficient, the usable exposure variation and the total number of events (in all series) are again the sole determinants if there is no between-series heterogeneity or within-series overdispersion. With heterogeneity, its extent and the number of series becomes important. For all but the crudest approximation the estimates of standard errors were on average within + 20% of those estimated in full analysis of actual data. Conclusions Predicting precision in coefficients from a planned time series study is possible simply and given limited information. The total number of disease events and usable exposure variation are the dominant factors when overdispersion and between-series heterogeneity are low.

Abstract Background Vitamin D deficiency has been associated with an increased risk of abnormal pregnancy implantation leading to obstetric complications such as pre-eclampsia and fetal growth restriction. However, the effect of vitamin D on reproductive treatment outcomes in couples undergoing assisted reproductive treatment is poorly understood. This study investigates the association between vitamin D and reproductive treatment outcomes in women undergoing assisted reproductive treatments? Methods A prospective cohort study conducted at a large tertiary teaching hospital, United Kingdom. Five hundred women undergoing assisted reproductive treatment were recruited between September 2013 and September 2015. All participants had their serum vitamin D measured and their reproductive treatment outcomes collated. Women were categorised in to three groups: vitamin D replete (> 75 nmol/L), insufficient (50-75 nmol/L) and deficient (

Abstract Background Although adaptation to continuously rising ambient temperatures is an emerging topic and has been widely studied at a global scale, detailed analysis of the joint indicators for long-term adaptation in Spain are scarce. This study aims to explore temporal variations of the minimum mortality temperature and mortality burden from heat and cold between 1979 and 2018. Methods We collected individual all-cause mortality and climate reanalysis data for 4 decades at a daily time step. To estimate the temperature-mortality association for each decade, we fitted a quasi-Poisson time-series regression model using a distributed lag non-linear model with 21 days of lag, controlling for trends and day of the week. We also calculated attributable mortality fractions by age and sex for heat and cold, defined as temperatures above and below the optimum temperature, which corresponds to the minimum mortality in each period. Results We analysed over 14 million deaths registered in Spain between 1979 and 2018. The optimum temperature estimated at a nationwide scale declined from 21 °C in 1979–1988 to 16 °C in 1999–2008, and raised to 18 °C in 2009–2018. The mortality burden from moderate cold showed a 3-fold reduction down to 2.4% in 2009–2018. Since 1988–1999, the mortality risk attributable to moderate (extreme) heat reduced from 0.9% (0.8%) to 0.6% (0.5%). The mortality risk due to heat in women was almost 2 times larger than in men, and did not decrease over time. Conclusion Despite the progressively warmer temperatures in Spain, we observed a persistent flattening of the exposure-response curves, which marked an expansion of the uncertainty range of the optimal temperatures. Adaptation has been produced to some extent in a non-uniform manner with a substantial decrease in cold-related mortality, while for heat it became more apparent in the most recent decade only.

Abstract Background More than half of the working population in Latin American and Caribbean (LAC) countries is engaged in informal employment. The few previous studies indicate that this employment condition could have negative consequences for workers’ health. The aim of the present study was to estimate the association between self-perceived health and informality in LAC countries according to gender and welfare state type. Methods The cross-sectional study based on different working conditions and health national surveys was carried out in 13 LAC countries between 2012 and 2018. A sample of 176,786 workers was selected from these surveys. The association between health and informality was estimated using Poisson regression. Finally, a random effects meta-analysis was carried out by country. All results were stratified by sex and type of welfare state (statalist or familialist). Results Informal workers reported significantly worse health than formal workers, for both women (1.28 [95% CI 1.14-1.43]) and men (1.30 [1.12-1.50]). This difference was broader and more significant in countries with statalist welfare state regimes, among both women (1.40 [1.22-1.60]) and men (1.51 [1.30-1.74]), than in familialist regime countries (1.19 [1.03-1.38] and 1.24 [1.03-1.49], respectively). Conclusions This study provides strong evidence of the association between informal employment and worker health. Welfare states appear to have a modifying effect on this association. The transition from the informal to the formal labour market in LAC is essential to improving the health of the population.

Abstract Background Data analysis and visualization is an essential tool for exploring and communicating findings in medical research, especially in epidemiological surveillance. Results Data on COVID-19 diagnosed cases and mortality, from January 1st, 2020, onwards is collected automatically from the European Centre for Disease Prevention and Control (ECDC). We have developed a Shiny application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using ECDC data. A country-specific tool for basic epidemiological surveillance, in an interactive and user-friendly manner. The available analyses cover time trends and projections, attack rate, population fatality rate, case fatality rate, and basic reproduction number. Conclusions The COVID19-World online web application systematically produces daily updated country-specific data visualization and analysis of the SARS-CoV-2 epidemic worldwide. The application may help for a better understanding of the SARS-CoV-2 epidemic worldwide.

Abstract Background Autism spectrum disorder (ASD) is a complex developmental disorder characterised by impaired social interaction and communication, and restrictive and repetitive behaviour. Previous systematic reviews have traditionally assessed the prevalence of ASD on global or regional context, with very few meta-analyses at the country level. The objective of this study will be to systematically evaluate published and unpublished observational studies that present prevalence and comorbidity of ASD among children, adolescent and adult population in Spain. Methods/design We designed and registered a study protocol for a systematic review and meta-analysis of descriptive epidemiology data. Observational studies (cohort, cross-sectional) reporting the prevalence of ASD and conducted in a wide range of people (e.g. general population, outpatient and/or school settings) will be included. The primary outcome will be the prevalence of ASD. Secondary outcomes will be the prevalence of any physical or mental comorbidity in association with ASD. No limitations will be imposed on publication status, study conduct period, and language of dissemination. Comprehensive literature searches will be conducted in seven electronic databases (from January 1980 onwards), including PubMed/MEDLINE, EMBASE, Scopus, Web of Science, PsycINFO, IME—Spanish Medical Index and IBECS—Spanish Bibliographic Index of Health Sciences. Grey literature will be identified through searching dissertation databases, Google Scholar and conference abstracts. Two team members will independently screen all citations, full-text articles, and abstract data. Potential conflicts will be resolved through discussion. The study methodological quality (or bias) will be appraised using an appropriate tool. If feasible, we will conduct random effects meta-analysis of observational data. Prevalence estimates will be stratified according to gender, age and geographical location. Additional analyses will be conducted to explore the potential sources of heterogeneity (e.g. methodological quality, sample size, diagnostic criteria). Discussion This systematic review and meta-analysis of observational data will identify, evaluate and integrate the epidemiological knowledge underlying the prevalence of ASD in Spain. The results of this study will be of interest to multiple audiences including patients, their families, caregivers, healthcare professional, scientists and policy makers. Results will be published in a peer-reviewed journal. Implications for future epidemiological research will be discussed. Systematic review registration PROSPERO CRD42018090372

Abstract Background Meta-analysis is very useful to summarize the effect of a treatment or a risk factor for a given disease. Often studies report results based on log-transformed variables in order to achieve the principal assumptions of a linear regression model. If this is the case for some, but not all studies, the effects need to be homogenized. Methods We derived a set of formulae to transform absolute changes into relative ones, and vice versa, to allow including all results in a meta-analysis. We applied our procedure to all possible combinations of log-transformed independent or dependent variables. We also evaluated it in a simulation based on two variables either normally or asymmetrically distributed. Results In all the scenarios, and based on different change criteria, the effect size estimated by the derived set of formulae was equivalent to the real effect size. To avoid biased estimates of the effect, this procedure should be used with caution in the case of independent variables with asymmetric distributions that significantly differ from the normal distribution. We illustrate an application of this procedure by an application to a meta-analysis on the potential effects on neurodevelopment in children exposed to arsenic and manganese. Conclusions The procedure proposed has been shown to be valid and capable of expressing the effect size of a linear regression model based on different change criteria in the variables. Homogenizing the results from different studies beforehand allows them to be combined in a meta-analysis, independently of whether the transformations had been performed on the dependent and/or independent variables.