Emerging Themes in Epidemiology最新文献

Background: The principal aim of this study was to explore if biological differences between men and women can be explained by gendered mechanisms.

Methods: We used data from the 1958 National Child Development Study, including all the living subjects of the cohort at the outcome collection wave (44-45 years). We explored several biomarkers as outcomes: systolic blood pressure, triglycerides, LDL cholesterol, HbA1c, CRP, and cortisol. Three conceptualizations of gender have been used to define methodological strategies: (a) Gender as an individual characteristic; (b) Gender as an effect of sex on socio-behavioural characteristics; (c) Gender as an interaction between sex and the social environment, here the early-life social environment. We estimated the total effect of sex and the proportion of total effect of sex at birth eliminated by gender, measured by 3 different ways according to these 3 concepts, using g-computation.

Results: The average level of each biomarker was significantly different according to sex at birth, higher in men for cardiometabolic biomarkers and higher in women for inflammatory and neuroendocrine biomarkers. The sizes of the differences were always smaller than one standard deviation but were larger than differences due to early-life deprivation, except for CRP. We observed gender mechanisms underlying these differences between men and women, even if the mediation effects were rarely statistically significant. These mechanisms were of three kinds: (1) mediation by socio-behavioural characteristics; (2) attenuation by gendered mechanisms; (3) interaction with early social environment. Indeed, we observed that being born into a deprived rather than non-deprived family increased metabolic and inflammatory biomarkers levels more strongly in females than in males.

Conclusions: The biological differences between men and women seem to not be purely explained by biological mechanisms. The exploration of gender mechanisms opens new perspectives, in terms of methodology, understanding and potential applications.

Low and middle-income countries continue to use Verbal autopsies (VAs) as a World Health Organisation-recommended method to ascertain causes of death in settings where coverage of vital registration systems is not yet comprehensive. Whilst the adoption of VA has resulted in major improvements in estimating cause-specific mortality in many settings, well documented limitations have been identified relating to the standardisation of the processes involved. The WHO has invested significant resources into addressing concerns in some of these areas; there however remains enduring challenges particularly in operationalising VA surveys for deaths amongst women and children, challenges which have measurable impacts on the quality of data collected and on the accuracy of determining the final cause of death. In this paper we describe some of our key experiences and recommendations in conducting VAs from over two decades of evaluating seminal trials of maternal and child health interventions in rural Ghana. We focus on challenges along the entire VA pathway that can impact on the success rates of ascertaining the final cause of death, and lessons we have learned to optimise the procedures. We highlight our experiences of the value of the open history narratives in VAs and the training and skills required to optimise the quality of the information collected. We describe key issues in methods for ascertaining cause of death and argue that both automated and physician-based methods can be valid depending on the setting. We further summarise how increasingly popular information technology methods may be used to facilitate the processes described. Verbal autopsy is a vital means of increasing the coverage of accurate mortality statistics in low- and middle-income settings, however operationalisation remains problematic. The lessons we share here in conducting VAs within a long-term surveillance system in Ghana will be applicable to researchers and policymakers in many similar settings.

The pandemic progression is a dynamic process, in which measures yield outcomes, and outcomes in turn influence subsequent measures and outcomes. Due to the dynamics of pandemic progression, it is challenging to analyse the long-term influence of an individual measure in the sequence on pandemic outcomes. To demonstrate the problem and find solutions, in this article, we study the first wave of the pandemic-probably the most dynamic period-in the Nordic countries and analyse the influences of the Swedish measures relative to the measures adopted by its neighbouring countries on COVID-19 mortality, general mortality, COVID-19 incidence, and unemployment. The design is a longitudinal observational study. The linear regressions based on the Poisson distribution or the binomial distribution are employed for the analysis. To show that analysis can be timely conducted, we use table data available during the first wave. We found that the early Swedish measure had a long-term and significant causal effect on public health outcomes and a certain degree of long-term mitigating causal effect on unemployment during the first wave, where the effect was measured by an increase of these outcomes under the Swedish measures relative to the measures adopted by the other Nordic countries. This information from the first wave has not been provided by available analyses but could have played an important role in combating the second wave. In conclusion, analysis based on table data may provide timely information about the dynamic progression of a pandemic and the long-term influence of an individual measure in the sequence on pandemic outcomes.

Background: Menstrual health (MH) is a recognised global public health challenge. Poor MH may lead to absence from school and work, and adverse health outcomes. However, reviews suggest a lack of rigorous evidence for the effectiveness of MH interventions on health and education outcomes. The objective of this paper is to describe the methods used in a cluster-randomised controlled trial to estimate the effect of a multi-component intervention to improve MH and school attendance in The Gambia.

Methods: The design ensured half the schools (25) were randomised to receive the intervention which comprised of the following components: (i) Peer education camps and menstrual hygiene laboratories in schools, (ii) Mother's outreach sessions, (iii) Community meetings, and (iv) minor improvements of school Water Sanitation and Hygiene (WASH) facilities and maintenance. The intervention was run over a three-month period, and the evaluation was conducted at least three months after the last intervention activity was completed in the school or community. The other 25 schools acted as controls. Of these 25 control schools one Arabic school dropped out due to COVID-19. The primary outcome was the prevalence of girls missing at least one day of school during their last period. Secondary outcomes included: Urinary Tract Infection (UTI) symptoms, biochemical markers of UTI in urine, Reproductive Tract Infection symptoms, self-reported menstruation related wellbeing, social support and knowledge, perceptions and practices towards menstruation and MH in target school girls. In addition, a process evaluation using observations, routine monitoring data, survey data and interviews was undertaken to assess dose and reach (quantitative data) and assess acceptability, fidelity, context and possible mechanisms of impact (qualitative data). Cost and cost-effectiveness of the intervention package will also be assessed.

Conclusion: Results will add to scarce resources available on effectiveness of MH interventions on school attendance. A positive result may encourage policy makers to increase their commitment to improve operation and maintenance of school WASH facilities and include more information on menstruation into the curriculum and help in the reporting and management of infections related to adolescent menstruation. Trial Registration PACTR, PACTR201809769868245, Registered 14th August 2018, https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=3539.

Conducting qualitative research within public health trials requires balancing timely data collection with the need to maintain data quality. Verbatim transcription of interviews is the conventional way of recording qualitative data, but is time consuming and can severely delay the availability of research findings. Expanding field notes into fair notes is a quicker alternative method, but is not usually recommended as interviewers select and interpret what they record. We used the fair note methodology in Ghana, and found that where research questions are relatively simple, and interviewers undergo sufficient training and supervision, fair notes can decrease data collection and analysis time, while still providing detailed and relevant information to the study team. Interviewers liked the method and felt it made them more reflective and analytical and improved their interview technique. The exception was focus group discussions, where the fair note approach failed to capture the interaction and richness of discussions, capturing group consensus rather than the discussions leading to this consensus.

Background: Successful implementation of community-based research is dominantly influenced by participation and engagement from the local community without which community members will not want to participate in research and important knowledge and potential health benefits will be missed. Therefore, maximising community participation and engagement is key for the effective conduct of community-based research. In this paper, we present lessons learnt over two decades of conducting research in 7 rural districts in the Brong Ahafo region of Ghana with an estimated population of around 600,000. The trials which were mainly in the area of Maternal, Neonatal and Child Health were conducted by the Kintampo Health Research Centre (KHRC) in collaboration with the London School of Hygiene and Tropical Medicine (LSHTM).

Methods: The four core strategies which were used were formative research methods, the formation of the Information, Education and Communication (IEC) team to serve as the main link between the research team and the community, recruitment of field workers from the communities within which they lived, and close collaboration with national and regional stakeholders.

Results: These measures allowed trust to be built between the community members and the research team and ensured that potential misconceptions which came up in the communities were promptly dealt with through the IEC team. The decision to place field workers in the communities from which they came and their knowledge of the local language created trust between the research team and the community. The close working relationship between the District health authorities and the Kintampo Health Research Centre supported the acceptance of the research in the communities as the District Health Authorities were respected and trusted.

Conclusion: The successes achieved during the past 2 decades of collaboration between LSHTM and KHRC in conducting community-based field trials were based on involving the community in research projects. Community participation and engagement helped not only to identify the pertinent issues, but also enabled the communities and research team to contribute towards efforts to address challenges.

Background: Risk ratio is a popular effect measure in epidemiological research. Although previous research has suggested that logistic regression may provide biased odds ratio estimates when the number of events is small and there are multiple confounders, the performance of risk ratio estimation has yet to be examined in the presence of multiple confounders.

Methods: We conducted a simulation study to evaluate the statistical performance of three regression approaches for estimating risk ratios: (1) risk ratio interpretation of logistic regression coefficients, (2) modified Poisson regression, and (3) regression standardization using logistic regression. We simulated 270 scenarios with systematically varied sample size, the number of binary confounders, exposure proportion, risk ratio, and outcome proportion. Performance evaluation was based on convergence proportion, bias, standard error estimation, and confidence interval coverage.

Results: With a sample size of 2500 and an outcome proportion of 1%, both logistic regression and modified Poisson regression at times failed to converge, and the three approaches were comparably biased. As the outcome proportion or sample size increased, modified Poisson regression and regression standardization yielded unbiased risk ratio estimates with appropriate confidence intervals irrespective of the number of confounders. The risk ratio interpretation of logistic regression coefficients, by contrast, became substantially biased as the outcome proportion increased.

Conclusions: Regression approaches for estimating risk ratios should be cautiously used when the number of events is small. With an adequate number of events, risk ratios are validly estimated by modified Poisson regression and regression standardization, irrespective of the number of confounders.

Background: One of the emerging themes in epidemiology is the use of interval estimates. Currently, three interval estimates for confidence (CI), prediction (PI), and tolerance (TI) are at a researcher's disposal and are accessible within the open access framework in R. These three types of statistical intervals serve different purposes. Confidence intervals are designed to describe a parameter with some uncertainty due to sampling errors. Prediction intervals aim to predict future observation(s), including some uncertainty present in the actual and future samples. Tolerance intervals are constructed to capture a specified proportion of a population with a defined confidence. It is well known that interval estimates support a greater knowledge gain than point estimates. Thus, a good understanding and the use of CI, PI, and TI underlie good statistical practice. While CIs are taught in introductory statistical classes, PIs and TIs are less familiar.

Results: In this paper, we provide a concise tutorial on two-sided CI, PI and TI for binary variables. This hands-on tutorial is based on our teaching materials. It contains an overview of the meaning and applicability from both a classical and a Bayesian perspective. Based on a worked-out example from veterinary medicine, we provide guidance and code that can be directly applied in R.

Conclusions: This tutorial can be used by others for teaching, either in a class or for self-instruction of students and senior researchers.