Effects of mobility, immunity and vaccination on SARS-CoV-2 transmission in the Dominican Republic: a modelling study.

Background: COVID-19 dynamics are driven by a complex interplay of factors including population behaviour, new variants, vaccination and immunity from prior infections. We quantify drivers of SARS-CoV-2 transmission in the Dominican Republic, an upper-middle income country of 10.8 million people. We then assess the impact of the vaccination campaign implemented in February 2021, primarily using CoronaVac, in saving lives and averting hospitalisations. Methods: We fit an age-structured, multi-variant transmission dynamic model to reported deaths, hospital bed occupancy, and seroprevalence data until December 2021, and simulate epidemic trajectories under different counterfactual scenarios. Findings: We estimate that vaccination averted 7210 hospital admissions (95% credible interval, CrI: 6830-7600), 2180 intensive care unit admissions (95% CrI: 2080-2280) and 766 deaths (95% CrI: 694-859) in the first 6 months of the campaign. If no vaccination had occurred, we estimate that an additional decrease of 10-20% in population mobility would have been required to maintain equivalent death and hospitalisation outcomes. We also found that early vaccination with CoronaVac was preferable to delayed vaccination using a product with higher efficacy. Interpretation: SARS-CoV-2 transmission dynamics in the Dominican Republic were driven by a substantial accumulation of immunity during the first two years of the pandemic but, despite this, vaccination was essential in enabling a return to pre-pandemic mobility levels without considerable additional morbidity and mortality. Funding: Medical Research Council, Wellcome Trust, Royal Society, US CDC and Australian National Health and Medical Research Council.

MulTI-domain self-management in older People wiTh OstEoarthritis and multi-morbidities: protocol for the TIPTOE randomised controlled trial.

BACKGROUND: Four out of five people living with osteoarthritis (OA) also suffer with at least one other long-term health condition. The complex interaction between OA and multiple long-term conditions (MLTCs) can result in difficulties with self-care, restricted mobility, pain, anxiety, depression and reduced quality of life. The aim of the MulTI-domain Self-management in Older People wiTh OstEoarthritis and Multi-Morbidities (TIPTOE) trial is to evaluate the clinical and cost-effectiveness of the Living Well self-management support intervention, co-designed with people living with OA, integrated into usual care, in comparison to usual care alone. METHODS: TIPTOE is a multi-centre, two-arm, individually randomised controlled trial where 824 individuals over 65 years old with knee and/or hip joint pain from their OA affected joint and at least one other long-term health condition will be randomised to receive either the Living Well Self-Management support intervention or usual care. Eligible participants can self-refer onto the trial via a website or be referred via NHS services across Wales and England. Those randomised to receive the Living Well support intervention will be offered up to six one-to-one coaching sessions with a TIPTOE-trained healthcare practitioner and a co-designed book. Participants will be encouraged to nominate a support person to assist them throughout the study. All participants will complete a series of self-reported outcome measures at baseline and 6- and 12-month follow-up. The primary outcome is symptoms and quality of life as assessed by the Musculoskeletal Health Questionnaire (MSK-HQ). Routine data will be used to evaluate health resource use. A mixed methods process evaluation will be conducted alongside the trial to inform future implementation should the TIPTOE intervention be found both clinically and cost-effective. An embedded 'Study Within A Project' (SWAP) will explore and address barriers to the inclusion of under-served patient groups (e.g. oldest old, low socioeconomic groups, ethnic groups). DISCUSSION: TIPTOE will evaluate the clinical and cost-effectiveness of a co-designed, living well personalised self-management support intervention for older individuals with knee and/or hip OA and MLTCs. The trial has been designed to maximise inclusivity and access. TRIAL REGISTRATION: ISRCTN 16024745 . Registered on October 16, 2023.

A climatic suitability indicator to support Leishmania infantum surveillance in Europe: a modelling study.

Background: Leishmaniases are neglected diseases transmitted by sand flies. They disproportionately affect vulnerable groups globally. Understanding the relationship between climate and disease transmission allows the development of relevant decision-support tools for public health policy and surveillance. The aim of this modelling study was to develop an indicator that tracks climatic suitability for Leishmania infantum transmission in Europe at the subnational level. Methods: Historical records of sand fly vectors, human leishmaniasis, bioclimatic indicators, and environmental variables were integrated in a machine learning framework (XGBoost) to predict suitability in two past periods (2001-2010 and 2011-2020). We further assessed if predictions were associated with human and animal disease data from selected countries (France, Greece, Italy, Portugal, and Spain). Findings: An increase in the number of climatically suitable regions for leishmaniasis was detected, especially in southern and eastern countries, coupled with a northward expansion towards central Europe. The final model had excellent predictive ability (AUC = 0.970 [0.947-0.993]), and the suitability predictions were positively associated with human leishmaniasis incidence and canine seroprevalence for Leishmania. Interpretation: This study demonstrates how key epidemiological data can be combined with open-source climatic and environmental information to develop an indicator that effectively tracks spatiotemporal changes in climatic suitability and disease risk. The positive association between the model predictions and human disease incidence demonstrates that this indicator could help target leishmaniasis surveillance to transmission hotspots. Funding: European Union Horizon Europe Research and Innovation Programme (European Climate-Health Cluster), United Kingdom Research and Innovation.

Climate change, malaria and neglected tropical diseases: a scoping review.

To explore the effects of climate change on malaria and 20 neglected tropical diseases (NTDs), and potential effect amelioration through mitigation and adaptation, we searched for papers published from January 2010 to October 2023. We descriptively synthesised extracted data. We analysed numbers of papers meeting our inclusion criteria by country and national disease burden, healthcare access and quality index (HAQI), as well as by climate vulnerability score. From 42 693 retrieved records, 1543 full-text papers were assessed. Of 511 papers meeting the inclusion criteria, 185 studied malaria, 181 dengue and chikungunya and 53 leishmaniasis; other NTDs were relatively understudied. Mitigation was considered in 174 papers (34%) and adaption strategies in 24 (5%). Amplitude and direction of effects of climate change on malaria and NTDs are likely to vary by disease and location, be non-linear and evolve over time. Available analyses do not allow confident prediction of the overall global impact of climate change on these diseases. For dengue and chikungunya and the group of non-vector-borne NTDs, the literature privileged consideration of current low-burden countries with a high HAQI. No leishmaniasis papers considered outcomes in East Africa. Comprehensive, collaborative and standardised modelling efforts are needed to better understand how climate change will directly and indirectly affect malaria and NTDs.

Climate change, thermal anomalies, and the recent progression of dengue in Brazil.

Dengue is rapidly expanding its transmission area across Brazil and much of South America. In this study, data-mining techniques were used to identify climatic and demographic indicators that could explain the recent (2014-2020) and simultaneous trends of expansion and exacerbation of the incidence in some regions of Brazil. The previous circulation of the virus (dengue incidence rates between 2007 and 2013), urbanization, and the occurrence of temperature anomalies for a prolonged period were the main factors that led to increased incidence of dengue in the central region of Brazil. Regions with high altitudes, which previously acted as a barrier for dengue transmission, became areas of high incidence rates. The algorithm that was developed during this study can be utilized to assess future climate scenarios and plan preventive actions.

Seeking support for abortion online: a qualitative study of women's experiences.

INTRODUCTION: Social support can mitigate the impact of stress and stigma before or after an abortion. However, stigma anticipation can limit access to in-person support. Informal online spaces can offer opportunities to address unmet support needs including supplementing in-person support lacking within stigmatised contexts. While earlier studies have explored content of posts comprising personal accounts of abortion, little is known about the nuances of how and to what end online spaces are navigated. METHODS: Semi-structured interviews were conducted remotely (online or by telephone) with 23 women living in Scotland (aged 20-54 years) recruited through social media and online advertisements. Reflexive thematic analysis was supported by NVivo12 software. RESULTS: Key themes: obtaining support that was unavailable from in-person networks; preparation for abortion; reducing feelings of isolation. The majority of participants independently searched online for accounts of abortion, with only three receiving any signposting to specific resources. Without guidance, finding relevant, supportive content was not straightforward. The search process was additionally complicated by the prevalence of abortion stigma online, which generated an additional burden at a potentially challenging time. Those who received direction towards particular resources reported primarily positive online experiences. CONCLUSIONS: While online content could address perceived in-person support gaps, the process of finding supportive content without guidance can be complex. Online searching may also expose women to stigmatising material and interactions. Signposting by abortion services towards well-moderated and trustworthy online resources could be constructive in limiting exposure to stigma and misinformation, while allowing those seeking it to access better support.

A reproducible ensemble machine learning approach to forecast dengue outbreaks.

Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model's qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, we showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast system can aid local governments in implementing targeted control measures. The study advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergovernmental organizations and public health institutions. The innovation lies not only in the algorithms themselves but in their application to a domain marked by data scarcity and operational scalability challenges. We bridge the gap by integrating well-curated ground data with advanced analytical methods, addressing a significant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showcase its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excels where accurate predictions are critical. The study not only contributes to advancing DIR forecasting but also represents a paradigm shift in integrating advanced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressing global health challenges. It not only enhances our understanding of factors triggering dengue outbreaks but also serves as a template for the effective implementation of advanced analytical methods in public health.

Ethical considerations related to drone use for environment and health research: A scoping review protocol.

INTRODUCTION: The use of drones in environment and health research is a relatively new phenomenon. A principal research activity drones are used for is environmental monitoring, which can raise concerns in local communities. Existing ethical guidance for researchers is often not specific to drone technology and practices vary between research settings. Therefore, this scoping review aims to gather the evidence available on ethical considerations surrounding drone use as perceived by local communities, ethical considerations reported on by researchers implementing drone research, and published ethical guidance related to drone deployment. METHODS AND ANALYSIS: This scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI) guidelines. The literature search will be conducted using academic databases and grey literature sources. After pilot testing the inclusion criteria and data extraction tool, two researchers will double-screen and then chart available evidence independently. A content analysis will be carried out to identify patterns of categories or terms used to describe ethical considerations related to drone usage for environmental monitoring in the literature using the R Package RQDA. Discrepancies in any phase of the project will be solved through consensus between the two reviewers. If consensus cannot be reached, a third arbitrator will be consulted. ETHICS AND DISSEMINATION: Ethical approval is not required; only secondary data will be used. This protocol is registered on the Open Science Framework (osf.io/a78et). The results will be disseminated through publication in a scientific journal and will be used to inform drone field campaigns in the Wellcome Trust funded HARMONIZE project. HARMONIZE aims to develop cost-effective and reproducible digital infrastructure for stakeholders in climate change hotspots in Latin America & the Caribbean and will use drone technology to collect data on fine scale landscape changes.

The effect of temporal data aggregation to assess the impact of changing temperatures in Europe: an epidemiological modelling study.

Background: Daily time-series regression models are commonly used to estimate the lagged nonlinear relation between temperature and mortality. A major impediment to this type of analysis is the restricted access to daily health records. The use of weekly and monthly data represents a possible solution unexplored to date. Methods: We temporally aggregated daily temperatures and mortality records from 147 contiguous regions in 16 European countries, representing their entire population of over 400 million people. We estimated temperature-lag-mortality relationships by using standard time-series quasi-Poisson regression models applied to daily data, and compared the results with those obtained with different degrees of temporal aggregation. Findings: We observed progressively larger differences in the epidemiological estimates with the degree of temporal data aggregation. The daily data model estimated an annual cold and heat-related mortality of 290,104 (213,745-359,636) and 39,434 (30,782-47,084) deaths, respectively, and the weekly model underestimated these numbers by 8.56% and 21.56%. Importantly, differences were systematically smaller during extreme cold and heat periods, such as the summer of 2003, with an underestimation of only 4.62% in the weekly data model. We applied this framework to infer that the heat-related mortality burden during the year 2022 in Europe may have exceeded the 70,000 deaths. Interpretation: The present work represents a first reference study validating the use of weekly time series as an approximation to the short-term effects of cold and heat on human mortality. This approach can be adopted to complement access-restricted data networks, and facilitate data access for research, translation and policy-making. Funding: The study was supported by the ERC Consolidator Grant EARLY-ADAPT (https://www.early-adapt.eu/), and the ERC Proof-of-Concept Grants HHS-EWS and FORECAST-AIR.

Evaluating the prescribing and monitoring of medications associated with QTc-prolongation in the ambulatory care setting.

RATIONALE: Little is known about the prescribing of medications with potential to cause QTc-prolongation in the ambulatory care settings. Understanding real-world prescribing of QTc-prolonging medications and actions taken to mitigate this risk will help guide strategies to optimize safety and appropriate prescribing among ambulatory patients. OBJECTIVE: To evaluate the frequency of clinician action taken to monitor and mitigate modifiable risk factors for QTc-prolongation when indicated. METHODS: This retrospective, cross-sectional study evaluated clinician action at the time of prescribing prespecified medications with potential to prolong QTc in adult patients in primary care. The index date was defined as the date the medication was ordered. Electronic health record (EHR) data were evaluated to assess patient, clinician and visit characteristics. Clinician action was determined if baseline or follow-up monitoring was ordered or if action was taken to mitigate modifiable risk factors (laboratory abnormalities or electrocardiogram [ECG] monitoring) within 48 h of prescribing a medication with QTc-prolonging risk. Descriptive statistics were used to describe current practice. RESULTS: A total of 399 prescriptions were prescribed to 386 patients, with a mean age of 51 ± 18 years, during March 2021 from a single-centre, multisite health system. Of these, 17 (4%) patients had a known history of QTc-prolongation, 170 (44%) did not have a documented history of QTc-prolongation and 199 (52%) had an unknown history (no ECG documented). Thirty-nine patients (10%) had at least one laboratory-related risk factor at the time of prescribing, specifically hypokalemia (16 patients), hypomagnesemia (8 patients) or hypocalcemia (19 patients). Of these 39 patients with laboratory risk factors, only 6 patients (15%) had their risk acknowledged or addressed by a clinician. Additionally, eight patients' most recent QTc was ≥500 ms and none had an ECG checked at the time the prescription was ordered. CONCLUSION: Despite national recommendations, medication monitoring and risk mitigation is infrequent when prescribing QTc-prolonging medications in the ambulatory care setting. These findings call for additional research to better understand this gap, including reasons for the gap and consequences on patient outcomes.

Interactions between climate change, urban infrastructure and mobility are driving dengue emergence in Vietnam.

Dengue is expanding globally, but how dengue emergence is shaped locally by interactions between climatic and socio-environmental factors is not well understood. Here, we investigate the drivers of dengue incidence and emergence in Vietnam, through analysing 23 years of district-level case data spanning a period of significant socioeconomic change (1998-2020). We show that urban infrastructure factors (sanitation, water supply, long-term urban growth) predict local spatial patterns of dengue incidence, while human mobility is a more influential driver in subtropical northern regions than the endemic south. Temperature is the dominant factor shaping dengue's distribution and dynamics, and using long-term reanalysis temperature data we show that warming since 1950 has expanded transmission risk throughout Vietnam, and most strongly in current dengue emergence hotspots (e.g., southern central regions, Ha Noi). In contrast, effects of hydrometeorology are complex, multi-scalar and dependent on local context: risk increases under either short-term precipitation excess or long-term drought, but improvements in water supply mitigate drought-associated risks except under extreme conditions. Our findings challenge the assumption that dengue is an urban disease, instead suggesting that incidence peaks in transitional landscapes with intermediate infrastructure provision, and provide evidence that interactions between recent climate change and mobility are contributing to dengue's expansion throughout Vietnam.

Quantifying climatic and socioeconomic drivers of urban malaria in Surat, India: a statistical spatiotemporal modelling study.

BACKGROUND: Cities are becoming increasingly important habitats for mosquito vectors of disease. The pronounced heterogeneity of urban landscapes challenges our understanding of the effects of climate and socioeconomic factors on mosquito-borne disease dynamics at different spatiotemporal scales. Here, we quantify the impact of climatic and socioeconomic factors on urban malaria risk, using an extensive dataset in both space and time for reported Plasmodium falciparum cases in the city of Surat, northwest India. METHODS: We analysed 10 years of monthly P falciparum cases resolved at three nested spatial resolutions (seven zones, 32 units, and 478 worker units) with a Bayesian hierarchical mixed model that incorporates the effects of population density, poverty, relative humidity, and temperature, in addition to random effects (structured and unstructured). To reduce dimensionality and avoid correlation of covariates, socioeconomic variables from survey data were summarised into main axes of variation using principal component analysis. With model selection, we identified the main drivers of spatiotemporal variation in malaria incidence rates at each of the three spatial resolutions. We also compared observations to model-fitted cases by quantifying the percentage of predictions within five discrete levels of malaria risk. FINDINGS: The spatial variation of urban malaria cases was stationary over time, whereby locations with high and low yearly cases remained largely consistent across years. Local socioeconomic variation could be summarised with three principal components accounting for approximately 80% of the variance. The model that incorporated local temperature and relative humidity together with two of these principal components, largely representing population density and poverty, best explained monthly malaria patterns in models formulated at the three different spatial scales. As model resolution increased, the effect size of humidity decreased, whereas those of temperature and the principal component associated with population density increased. Model predictions accurately captured aggregated total monthly cases for the city; in space-time, they more closely matched observations at the intermediate scale, with around 57% of units estimated to fall in the observed category on average across years. The mean absolute error was lower at the intermediate level, showing that this is the best aggregation level to predict the space-time dynamics of malaria incidence rates across the city with the selected model. INTERPRETATION: This statistical modelling framework provides a basis for development of a climate-driven early warning system for urban malaria for the units of Surat, including spatially explicit prediction of malaria risk several weeks to months in advance. Results indicate environmental and socioeconomic covariates for which further measurement at high resolution should lead to model improvement. Advanced warning combined with local surveillance and knowledge of disease hotspots within the city could inform targeted intervention as part of urban malaria elimination efforts. FUNDING: US National Institutes of Health.

Correction: Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines.

[This corrects the article DOI: 10.1371/journal.pmed.1003793.].

Quantifying the relationship between climatic indicators and leptospirosis incidence in Fiji: A modelling study.

Leptospirosis, a global zoonotic disease, is prevalent in tropical and subtropical regions, including Fiji where it's endemic with year-round cases and sporadic outbreaks coinciding with heavy rainfall. However, the relationship between climate and leptospirosis has not yet been well characterised in the South Pacific. In this study, we quantify the effects of different climatic indicators on leptospirosis incidence in Fiji, using a time series of weekly case data between 2006 and 2017. We used a Bayesian hierarchical mixed-model framework to explore the impact of different precipitation, temperature, and El Niño Southern Oscillation (ENSO) indicators on leptospirosis cases over a 12-year period. We found that total precipitation from the previous six weeks (lagged by one week) was the best precipitation indicator, with increased total precipitation leading to increased leptospirosis incidence (0.24 [95% CrI 0.15-0.33]). Negative values of the Niño 3.4 index (indicative of La Niña conditions) lagged by four weeks were associated with increased leptospirosis risk (-0.2 [95% CrI -0.29 --0.11]). Finally, minimum temperature (lagged by one week) when included with the other variables was positively associated with leptospirosis risk (0.15 [95% CrI 0.01-0.30]). We found that the final model was better able to capture the outbreak peaks compared with the baseline model (which included seasonal and inter-annual random effects), particularly in the Western and Northern division, with climate indicators improving predictions 58.1% of the time. This study identified key climatic factors influencing leptospirosis risk in Fiji. Combining these results with demographic and spatial factors can support a precision public health framework allowing for more effective public health preparedness and response which targets interventions to the right population, place, and time. This study further highlights the need for enhanced surveillance data and is a necessary first step towards the development of a climate-based early warning system.

Projecting the future incidence and burden of dengue in Southeast Asia.

The recent global expansion of dengue has been facilitated by changes in urbanisation, mobility, and climate. In this work, we project future changes in dengue incidence and case burden to 2099 under the latest climate change scenarios. We fit a statistical model to province-level monthly dengue case counts from eight countries across Southeast Asia, one of the worst affected regions. We project that dengue incidence will peak this century before declining to lower levels with large variations between and within countries. Our findings reveal that northern Thailand and Cambodia will show the biggest decreases and equatorial areas will show the biggest increases. The impact of climate change will be counterbalanced by income growth, with population growth having the biggest influence on increasing burden. These findings can be used for formulating mitigation and adaptation interventions to reduce the immediate growing impact of dengue virus in the region.

Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond.

Climate change is one of several drivers of recurrent outbreaks and geographical range expansion of infectious diseases in Europe. We propose a framework for the co-production of policy-relevant indicators and decision-support tools that track past, present, and future climate-induced disease risks across hazard, exposure, and vulnerability domains at the animal, human, and environmental interface. This entails the co-development of early warning and response systems and tools to assess the costs and benefits of climate change adaptation and mitigation measures across sectors, to increase health system resilience at regional and local levels and reveal novel policy entry points and opportunities. Our approach involves multi-level engagement, innovative methodologies, and novel data streams. We take advantage of intelligence generated locally and empirically to quantify effects in areas experiencing rapid urban transformation and heterogeneous climate-induced disease threats. Our goal is to reduce the knowledge-to-action gap by developing an integrated One Health-Climate Risk framework.