London School of Hygiene and Tropical Medicine
Recent publications
BACKGROUND Adolescents experiencing multiple vulnerabilities, including poverty, curtailed education, transactional sex and early childbearing, are at risk of poor mental health. In Zimbabwe, girls who are pregnant or new mothers and involved in selling sex struggle to cope with the combined pressures of parenthood, financial insecurity, and social stigma. A pilot intervention brought such girls together into self-help groups to increase peer support, resources and skills. OBJECTIVE This study aimed to explore whether and how participation in a self-help group intervention affected vulnerable young mothers’ experiences and perceptions of mental health stressors. METHODS Self-help groups received 12 participatory sessions over 6 months. Eighteen semi-structured interviews and three focus group discussions were held with participants and drop-outs. Before and after the intervention, participants completed the locally validated 14-item Shona Symptom Questionnaire tool to indicate the probable prevalence of common mental health disorders. RESULTS Adolescent girls described mutually reinforcing stressors in their lives and reported low self-esteem and anxiety. Key themes emerging from qualitative data centred around girls’ struggles with adverse life events, the burden of new motherhood, social isolation related to sex work and self-help groups as a source of hope. Participants joined groups to obtain support and felt their mental well-being improved due to new social networks, feelings of solidarity with peers, and increased confidence for positive action, that is, seeking health services for themselves and their babies. Prior to enrolment 16% showed signs of possible common mental disorders falling to 2% at follow-up. CONCLUSIONS Participants believed involvement in interactive self-help groups improved their mental health by strengthening peer support and engendering hope for the future. Although reduced mental distress cannot be attributed to the programme, the pilot intervention offers a low-cost approach that could be rigorously tested and adapted to a wide range of community settings.
Background: Climate change is having significant impacts on health and mental health across Europe and globally. Such effects are likely to be more severe in climate change hotspots such as the Mediterranean region, including Italy. Objective: To review existing literature on the relationship between climate change and mental health in Italy, with a particular focus on trauma and PTSD. Methods: A scoping review methodology was used. We followed guidance for scoping reviews and the PRISMA Extension for Scoping Reviews (PRISMA-ScR) checklist. We searched for literature in MEDLINE, Global Health, Embase and PsycINFO. Following screening, data was extracted from individual papers and a quality assessment was conducted. Given the heterogeneity of studies, findings were summarized narratively. Results: We identified 21 original research articles investigating the relationship between climate change and mental health in Italy. Climate change stressors (heat and heatwaves in particular) were found to have several negative effects on various mental health outcomes, such as a higher risk of mortality among people with mental health conditions, suicide and suicidal behaviour and psychiatric morbidity (e.g. psychiatric hospitalization and symptoms of mental health conditions). However, there is little research on the relationship between climate change and trauma or PTSD in the Italian context. Conclusions: More attention and resources should be directed towards understanding the mental health implications of climate change to prevent, promote, and respond to the mental health needs of Italy and the wider Mediterranean region. Highlights: • Climate change stressors in Italy were found to have detrimental impacts on various mental health outcomes, such as psychiatric mortality and morbidity. • Little research on the relationship between climate change stressors and PTSD exists in Italy.
Background: Disasters can have long-lasting impacts on mental health. Intrusive memories have been found to be common and persistent in the aftermath of earthquakes. Objective: To explore, using diaries, intrusive memories' presence, content, characteristics, and relationship with probable post-traumatic stress disorder (PTSD) in a small rural community exposed to mass destruction and loss of life. Methods: Survivors of the 2016-2017 Central Italy earthquakes (N = 104) were first interviewed to investigate the presence of intrusive memories of the disaster. Those that reported intrusive memories were subsequently asked to complete a 7-day paper-and-pen diary tracking their spontaneous memories of the earthquake events. Results: Twenty months after the earthquakes, 49% (n = 51) of the sample reported having experienced intrusive memories post-earthquake and 38% (n = 39) reported at least one intrusive memory in their diaries. Memories were rated as being distressing, vivid, and experienced as a mixture of images and thoughts. The content of intrusive memories generally focused on sensations and experiences during the earthquake. Other common categories of content were the material environment and physical objects as well as human loss & death. Several memories had a social focus. A minority of memories contained more positive content as well as content from before and after the earthquake. Some participants (28%) experienced repeated intrusive memories of the same content. Memories of participants with and without probable PTSD did not significantly differ on characteristics or content. Conclusions: Intrusive memories can be common, distressing, and persistent occurrences following disasters, even in survivors not suffering from probable PTSD. Highlights: Intrusive memories were common, distressing, and vivid more than 1-year post-disaster.They captured peri-earthquake sensations, material destruction, death, and social interactions.No difference in content or characteristics was found between participants with and without probable PTSD.
Issues related to controlled human infection studies using Schistosoma mansoni (CHI-S) were explored to ensure the ethical and voluntary participation of potential CHI-S volunteers in an endemic setting in Uganda. We invited volunteers from a fishing community and a tertiary education community to guide the development of informed consent procedures. Consultative group discussions were held to modify educational materials on schistosomiasis, vaccines and the CHI-S model and similar discussions were held with a test group. With both groups, a mock consent process was conducted. Fourteen in-depth key informant interviews and three group discussions were held to explore perceptions towards participating in a CHI-S. Most of the participants had not heard of the CHI-S. Willingness to take part depended on understanding the study procedures and the consenting process. Close social networks were key in deciding to take part. The worry of adverse effects was cited as a possible hindrance to taking part. Volunteer time compensation was unclear for a CHI-S. Potential volunteers in these communities are willing to take part in a CHI-S. Community engagement is needed to build trust and time must be taken to share study procedures and ensure understanding of key messages.
Coronavirus disease 2019 (COVID-19) emerged in late 2019, with the first case identified in Wuhan City, Hubei Province, China, on 12 December 2019. In order to perceive the comprehensive impact of this pandemic, we have to know that misinformation and denials about COVID-19 have surely exacerbated its diffusion and hindered the response against it. Turkmenistan remains one of the very few countries in the world that lacks reports about emerging cases of the novel coronavirus. Turkmen authorities claim that they have adopted all attainable measures required in order to combat the virus, asserting that COVID-19 has yet to reach their country. Despite the government’s reported absence of COVID-19 in the country, rumors, media reports and independent sources suggest the spread of the pandemic in Turkmenistan. By mid-June 2020, the outbreak was referred to as being serious with patients suffering extreme health risks, and following its state of disrepair and unethical practices, many of those anticipated to be COVID-19 infected tend to suffer at home, discouraging any interaction with the healthcare system. The civil society in Turkmenistan, for the time being, takes full part of the government’s duty in the process of informing and educating the public regarding the COVID-19 pandemic, and endeavors to keep the government and WHO accountable for behaving in such repressive ways that could lead to rather preventable loss of human life in Turkmenistan. Yet, efforts hang fire before unveiling the real situation, and Turkmenistan’s government owning up to the negations and roaming speculations, not only regarding the coronavirus crisis, but every public-related issue itself.
Background Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear. Methods Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. Results Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rate < 5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at p < 0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions. Conclusion This report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.
Background Risk communication interventions during epidemics aim to modify risk perceptions to achieve rapid shifts in population health behaviours. Exposure to frequent and often concurrent epidemics may influence how the public and health professionals perceive and respond to epidemic risks. This review aimed to systematically examine the evidence on risk perceptions of epidemic-prone diseases in countries highly vulnerable to epidemics. Methods We conducted a systematic review using PRISMA standards. We included peer-reviewed studies describing or measuring risk perceptions of epidemic-prone diseases among the general adult population or health professionals in 62 countries considered highly vulnerable to epidemics. We searched seven bibliographic databases and applied a four-stage screening and selection process, followed by quality appraisal. We conducted a narrative meta-synthesis and descriptive summary of the evidence, guided by the Social Amplification of Risk Framework. Results Fifty-six studies were eligible for the final review. They were conducted in eighteen countries and addressed thirteen epidemic-prone diseases. Forty-five studies were quantitative, six qualitative and five used mixed methods. Forty-one studies described epidemic risk perceptions in the general public and nineteen among health professionals. Perceived severity of epidemic-prone diseases appeared high across public and health professional populations. However, perceived likelihood of acquiring disease varied from low to moderate to high among the general public, and appeared consistently high amongst health professionals. Other occupational groups with high exposure to specific diseases, such as bushmeat handlers, reported even lower perceived likelihood than the general population. Among health professionals, the safety and effectiveness of the work environment and of the broader health system response influenced perceptions. Among the general population, disease severity, familiarity and controllability of diseases were influential factors. However, the evidence on how epidemic risk perceptions are formed or modified in these populations is limited. Conclusions The evidence affords some insights into patterns of epidemic risk perception and influencing factors, but inadequately explores what underlies perceptions and their variability, particularly among diseases, populations and over time. Approaches to defining and measuring epidemic risk perceptions are relatively underdeveloped. Graphical Abstract
Background The prevalence of cardiometabolic disease (CMD) is rising globally, with environmentally induced epigenetic changes suggested to play a role. Few studies have investigated epigenetic associations with CMD risk factors in children from low- and middle-income countries. We sought to identify associations between DNA methylation (DNAm) and CMD risk factors in children from India and The Gambia. Results Using the Illumina Infinium HumanMethylation 850 K Beadchip array, we interrogated DNAm in 293 Gambian (7–9 years) and 698 Indian (5–7 years) children. We identified differentially methylated CpGs (dmCpGs) associated with systolic blood pressure, fasting insulin, triglycerides and LDL-Cholesterol in the Gambian children; and with insulin sensitivity, insulinogenic index and HDL-Cholesterol in the Indian children. There was no overlap of the dmCpGs between the cohorts. Meta-analysis identified dmCpGs associated with insulin secretion and pulse pressure that were different from cohort-specific dmCpGs. Several differentially methylated regions were associated with diastolic blood pressure, insulin sensitivity and fasting glucose, but these did not overlap with the dmCpGs. We identified significant cis-methQTLs at three LDL-Cholesterol-associated dmCpGs in Gambians; however, methylation did not mediate genotype effects on the CMD outcomes. Conclusion This study identified cardiometabolic biomarkers associated with differential DNAm in Indian and Gambian children. Most associations were cohort specific, potentially reflecting environmental and ethnic differences.
Background Clinical endpoints for upcoming therapeutic trials in frontotemporal dementia (FTD) are increasingly urgent. Cognitive composite scores are often used as endpoints but are lacking in genetic FTD. We aimed to create cognitive composite scores for genetic frontotemporal dementia (FTD) as well as recommendations for recruitment and duration in clinical trial design. Methods A standardized neuropsychological test battery covering six cognitive domains was completed by 69 C9orf72 , 41 GRN , and 28 MAPT mutation carriers with CDR® plus NACC-FTLD ≥ 0.5 and 275 controls. Logistic regression was used to identify the combination of tests that distinguished best between each mutation carrier group and controls. The composite scores were calculated from the weighted averages of test scores in the models based on the regression coefficients. Sample size estimates were calculated for individual cognitive tests and composites in a theoretical trial aimed at preventing progression from a prodromal stage (CDR® plus NACC-FTLD 0.5) to a fully symptomatic stage (CDR® plus NACC-FTLD ≥ 1). Time-to-event analysis was performed to determine how quickly mutation carriers progressed from CDR® plus NACC-FTLD = 0.5 to ≥ 1 (and therefore how long a trial would need to be). Results The results from the logistic regression analyses resulted in different composite scores for each mutation carrier group (i.e. C9orf72 , GRN , and MAPT ). The estimated sample size to detect a treatment effect was lower for composite scores than for most individual tests. A Kaplan-Meier curve showed that after 3 years, ~ 50% of individuals had converted from CDR® plus NACC-FTLD 0.5 to ≥ 1, which means that the estimated effect size needs to be halved in sample size calculations as only half of the mutation carriers would be expected to progress from CDR® plus NACC FTLD 0.5 to ≥ 1 without treatment over that time period. Discussion We created gene-specific cognitive composite scores for C9orf72 , GRN , and MAPT mutation carriers, which resulted in substantially lower estimated sample sizes to detect a treatment effect than the individual cognitive tests. The GENFI-Cog composites have potential as cognitive endpoints for upcoming clinical trials. The results from this study provide recommendations for estimating sample size and trial duration.
Higher availability of administrative data and better infrastructure for electronic surveys allow for large sample sizes in evaluations of national and other large scale policies. Although larger datasets have many advantages, the use of big disaggregate data (e.g., on individuals, households, stores, municipalities) can be challenging in terms of statistical inference. Measurements made at the same point in time may be jointly influenced by contemporaneous factors and produce more variation across time than suggested by the model. This excess variation, or co-movement over time, produce observations that are not truly independent (i.e., cross-sectional dependence). If this dependency is not accounted for, statistical uncertainty will be underestimated, and studies may indicate reform effects where there is none. In the context of interrupted time series (segmented regression), we illustrate the potential for bias in inference when using large disaggregate data, and we describe two simple solutions that are available in standard statistical software.
Although it is widely recognized that strong program management is essential to achieving better health outcomes, this priority is not recognized in malaria programmatic practices. Increased management precision offers the opportunity to improve the effectiveness of malaria interventions, overcoming operational barriers to intervention coverage and accelerating the path to elimination. Here we propose a combined approach involving quality improvement, quality management, and participative process improvement, which we refer to as Combined Quality and Process Improvement (CQPI), to improve upon malaria program management. We draw on evidence from other areas of public health, as well as pilot implementation studies in Eswatini, Namibia and Zimbabwe to support the proposal. Summaries of the methodological approaches employed in the pilot studies, overview of activities and an outline of lessons learned from the implementation of CQPI are provided. Our findings suggest that a malaria management strategy that prioritizes quality and participative process improvements at the district-level can strengthen teamwork and communication while enabling the empowerment of subnational staff to solve service delivery challenges. Despite the promise of CQPI, however, policy makers and donors are not aware of its potential. Investments are therefore needed to allow CQPI to come to fruition.
Noncommunicable diseases (NCDs) are the leading cause of death and disability worldwide. They exact a disproportionate toll in low and middle-income countries, and the world is not on-track to meet international targets for reductions in premature NCD mortality. Largely, we know which policies work for tackling NCDs, and the World Health Organization (WHO) has developed a package of ‘best buy’ policies that are highly cost effective. However, we don’t necessarily know how to adapt and implement these policies in new populations and cultures. Implementation Research (IR) is emerging as a potent tool for gearing the international response, providing a scientific approach to study the processes used to implement policies and interventions and the contextual factors that affect these processes. Amidst growing interest from policymakers, we identify four main areas for action: high-level engagement with IR among international NCD leaders; domestic investment in technical capacity-building; the creation of new financing streams for IR research; and the development of multi-stakeholder engagement mechanisms that can convene and leverage the perspectives and resources of multiple actors with overlapping aims.
We previously proposed that realist randomised controlled trials could be used to evaluate how, for whom and under what conditions complex interventions can be used to activate mechanisms to improve health. While this idea was accepted by some, it was also met with resistance, particularly from some realist evaluators who believe that trials are inextricably positivist and dependent on constant conjunctions to understand causation, and that realist trials are unfeasible because participants and contexts will be insufficiently diverse to enable the testing of context-mechanism-outcome configurations. In this paper, we reflect on analyses of qualitative and quantitative data from the Initiating Change Locally in Bullying and Aggression through the School Environment (INCLSUIVE) trial, and whether these are useful and aligned with realism. We summarise the concerns expressed by realists and reflect on the philosophical and practical challenges that we encountered and whether or not they are related to the trial’s design. Finally, we reflect on the trial’s weaknesses and highlight areas that future researchers might consider when running realist trials. We conclude that realist randomised controlled trials are philosophically coherent, practically feasible, and can produce nuanced findings.
Background Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources. Methods We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the weighted interval score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known. Results All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons. Conclusions Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.
Background COVID-19 pandemic has a devastating impact on the economies and health care system of sub-Saharan Africa. Healthcare workers (HWs), the main actors of the health system, are at higher risk because of their occupation. Serology-based estimates of SARS-CoV-2 infection among HWs represent a measure of HWs’ exposure to the virus and could be used as a guide to the prevalence of SARS-CoV-2 in the community and valuable in combating COVID-19. This information is currently lacking in Ethiopia and other African countries. This study aimed to develop an in-house antibody testing assay, assess the prevalence of SARS-CoV-2 antibodies among Ethiopian high-risk frontline HWs. Methods We developed and validated an in-house Enzyme-Linked Immunosorbent Assay (ELISA) for specific detection of anti-SARS-CoV-2 receptor binding domain immunoglobin G (IgG) antibodies. We then used this assay to assess the seroprevalence among HWs in five public hospitals located in different geographic regions of Ethiopia. From consenting HWs, blood samples were collected between December 2020 and February 2021, the period between the two peaks of COVID-19 in Ethiopia. Socio-demographic and clinical data were collected using questionnaire-based interviews. Descriptive statistics and bivariate and multivariate logistic regression were used to determine the overall and post-stratified seroprevalence and the association between seropositivity and potential risk factors. Results Our successfully developed in-house assay sensitivity was 100% in serum samples collected 2- weeks after the first onset of symptoms whereas its specificity in pre-COVID-19 pandemic sera was 97.7%. Using this assay, we analyzed a total of 1997 sera collected from HWs. Of 1997 HWs who provided a blood sample, and demographic and clinical data, 51.7% were females, 74.0% had no symptoms compatible with COVID-19, and 29.0% had a history of contact with suspected or confirmed patients with SARS-CoV-2 infection. The overall seroprevalence was 39.6%. The lowest (24.5%) and the highest (48.0%) seroprevalence rates were found in Hiwot Fana Specialized Hospital in Harar and ALERT Hospital in Addis Ababa, respectively. Of the 821 seropositive HWs, 224(27.3%) of them had a history of symptoms consistent with COVID-19 while 436 (> 53%) of them had no contact with COVID-19 cases as well as no history of COVID-19 like symptoms. A history of close contact with suspected/confirmed COVID-19 cases is associated with seropositivity (Adjusted Odds Ratio (AOR) = 1.4, 95% CI 1.1–1.8; p = 0.015). Conclusion High SARS-CoV-2 seroprevalence levels were observed in the five Ethiopian hospitals. These findings highlight the significant burden of asymptomatic infection in Ethiopia and may reflect the scale of transmission in the general population.
Digital approaches are increasingly common in clinical trial recruitment, retention, analysis, and dissemination. Community engagement processes have contributed to the successful implementation of clinical trials and are crucial in enhancing equity in trials. However, few studies focus on how digital approaches can be implemented to enhance community engagement in clinical trials. This narrative review examines three key areas for digital approaches to deepen community engagement in clinical trials—the use of digital technology for trial processes to decentralize trials, digital crowdsourcing to develop trial components, and digital qualitative research methods. We highlight how digital approaches enhanced community engagement through a greater diversity of participants, and deepened community engagement through the decentralization of research processes. We discuss new possibilities that digital technologies offer for community engagement, and highlight potential strengths, weaknesses, and practical considerations. We argue that strengthening community engagement using a digital approach can enhance equity and improve health outcomes.
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3,099 members
Charles Graham Clark
  • Faculty of Infectious and Tropical Diseases
Ifedayo M Adetifa
  • Department of Infectious Disease Epidemiology
Sedona Sweeney
  • Department of Global Health and Development
Susannah Mayhew
  • Department of Global Health and Development
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Keppel street, WC1E 7HT, London, United Kingdom
Head of institution
Professor Peter Piot CMG MD PhD DTMH FRCP FMedSci, Director & Professor of Global Health
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www.lshtm.ac.uk
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