Healthcare providers' perceived barriers and facilitators to screening for intimate partner violence in pregnant women attending prenatal clinics.

AIM: To examine healthcare providers' extent of and perceived barriers and facilitators to screening for intimate partner violence in pregnant women attending prenatal clinics. DESIGN: Cross-sectional descriptive design was used to collect data from 130 healthcare providers. METHODS: Seventeen healthcare providers from 17 prenatal clinics in Kanungu district, Uganda, were recruited via convenience sampling to participate in an online survey implementing a modified Normalization Measure Development instrument. Data were collected between February 2023 and March 2023 (02/8/2023 to 03/12/2023) and analysed using descriptive and Mann-Whitney U test and chi-square tests. RESULTS: Slightly more than half (56%) of healthcare providers report screening pregnant women for intimate partner violence. There was a statistically significant relationship between healthcare providers screening for intimate partner violence and having previous training on intimate partner violence screening. The only barrier to screening identified was a lack of understanding of how intimate partner violence screening affects the nature of participant's own work. There were numerous potential facilitators identified for healthcare providers' intimate partner violence screening. CONCLUSION: Although higher-than-expected number of healthcare providers reported screening of pregnant women for intimate partner violence, the extent of screening is still suboptimal. The barrier to screening identified needs to be addressed and facilitators promoted. Receiving training among healthcare providers on intimate partner violence screening was associated with higher levels of screening; thus, this needs to be enhanced to optimize screening rates. Future studies should assess screening practices objectively and implement interventions to improve healthcare providers' intimate partner violence screening rates. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Screening for intimate partner violence should be part of standard care provided by healthcare providers to all pregnant women during prenatal clinic visits. The study supports the need for more training for healthcare providers in aspects related to intimate partner violence screening in order to ensure prompt diagnosis and treatment of those affected, identify those at risk and increase awareness. There is a need to enhance healthcare providers' capacity for intimate partner violence screening through education by integrating intimate partner violence screening pre- and post-registration courses and preparation programs or curriculum. IMPACT: Intimate partner violence (IPV) in pregnancy is a global health problem. Screening for IPV by healthcare providers is suboptimal. This study found that only 56% of healthcare providers were routinely screening for IPV in Ugandan prenatal clinics. This study identified the main facilitators and one barrier to IPV screening. REPORTING METHOD: This study has adhered to the relevant EQUATOR guidelines for quantitative studies. PATIENT AND PUBLIC CONTRIBUTION: No patient was involved in this study.

Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions?

Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets.

The Hidden Hand of Asymptomatic Infection Hinders Control of Neglected Tropical Diseases: A Modeling Analysis.

BACKGROUND: Neglected tropical diseases are responsible for considerable morbidity and mortality in low-income populations. International efforts have reduced their global burden, but transmission is persistent and case-finding-based interventions rarely target asymptomatic individuals. METHODS: We develop a generic mathematical modeling framework for analyzing the dynamics of visceral leishmaniasis in the Indian sub-continent (VL), gambiense sleeping sickness (gHAT), and Chagas disease and use it to assess the possible contribution of asymptomatics who later develop disease (pre-symptomatics) and those who do not (non-symptomatics) to the maintenance of infection. Plausible interventions, including active screening, vector control, and reduced time to detection, are simulated for the three diseases. RESULTS: We found that the high asymptomatic contribution to transmission for Chagas and gHAT and the apparently high basic reproductive number of VL may undermine long-term control. However, the ability to treat some asymptomatics for Chagas and gHAT should make them more controllable, albeit over relatively long time periods due to the slow dynamics of these diseases. For VL, the toxicity of available therapeutics means the asymptomatic population cannot currently be treated, but combining treatment of symptomatics and vector control could yield a quick reduction in transmission. CONCLUSIONS: Despite the uncertainty in natural history, it appears there is already a relatively good toolbox of interventions to eliminate gHAT, and it is likely that Chagas will need improvements to diagnostics and their use to better target pre-symptomatics. The situation for VL is less clear, and model predictions could be improved by additional empirical data. However, interventions may have to improve to successfully eliminate this disease.

Demographic, environmental and physiological predictors of gastrointestinal parasites in urban raccoons.

Raccoons are host to diverse gastrointestinal parasites, but little is known about the ecology of these parasites in terms of their interactions with each other during coinfections, their interactions with host physiology and environmental factors, and their impact on raccoon health and survival. As a first step, we investigated the patterns of parasite infection and their demographic distribution in an urban-suburban population of raccoons trapped in the summers and autumns of 2018 and 2019. We collected faecal samples, demographic data, morphometric measurements, and blood smears, and used GPS data to classify trapping location by land cover type. Faecal floats were performed to detect and quantify gastrointestinal nematode eggs and coccidia oocysts, and white blood cell differentials were performed on blood smears to characterise white blood cell distributions. Data were analysed cross-sectionally and, where possible, longitudinally, using generalised linear models. Overall, 62.6% of sampled raccoons were infected with gastrointestinal nematodes, and 82.2% were infected with gastrointestinal coccidia. We analysed predictors of infection status and faecal egg count for three different morphotypes of nematode-Baylisascaris, strongyle, and capillariid nematodes-and found that infection status and egg count varied with Year, Month, Age class, Land cover, and coinfection status, though the significance of these predictors varied between nematode types. Gastrointestinal coccidia prevalence varied with Year, Month, Age class, strongyle infection status, and capillariid infection status. Coccidia oocyst counts were lower in adults and in October, but higher in females and in raccoons trapped in areas with natural land cover; furthermore, coccidia oocysts were positively associated with capillariid faecal egg counts. We found no evidence that gastrointestinal parasites influenced raccoon body condition or overwinter mortality, and so conclude that raccoons, though harbouring diverse and abundant gastrointestinal parasites, may be relatively tolerant of these parasites.

Establishing community mental health clinics increased the number of patients receiving care in rural Western Uganda.

Background: Mental, neurological, and substance-use disorders cause medium to long term disability in all countries. They are amenable to treatment but often treatment is only available in hospitals, as few staff feel competent to give treatment. The WHO developed the "Mental Health GAP" (mhGAP) course to train non-specialist clinical staff in basic diagnosis and treatment. At Bwindi Community Hospital, in south-west Uganda, mental health care was initially only provided at the hospital. It was extended outside the hospital in two implementation phases, initially by establishing 17 clinics in the community, run by qualified mental health staff from the hospital. In the second implementation phase staff in 12 health centers were trained using mhGAP and ran their own clinics under supervision. Methods: Using routine data the defined data variables for the individuals attending the clinics was extracted. Results: A total of 2,617 people attended a mental health care clinic in the study period between January 2016 and March 2020. Of these 1,051 people attended more than once. The number of patients attending clinics increased from 288 during the baseline to 693 in the first implementation phase then to 839 patients in the second implementation phase. After mhGAP training, about 30% of patients were seen locally by mhGAP trained healthcare personnel. The average number of mental health patients seen each month increased from 12 to 65 over the time of the study. The number of patients living >20 km from the hospital increased from 69 in the baseline to 693 in the second implementation phase. The proportion of patients seen at the hospital clinic dropped from 100% to 27%. Conclusions: Providing mental health care in the community at a distance from the hospital substantially increased the number of people accessing mental health care. Training health center-based staff in mhGAP contributed to this. Not all patients could appropriately be managed by non-specialist clinical staff, who only had the five-day training in mhGAP. Supplies of basic medicines were not always adequate, which probably contributed to patients being lost to follow-up. About 50% of patients only attend the clinic once. Further work is required to understand the reasons.

From serological surveys to disease burden: a modelling pipeline for Chagas disease.

In 2012, the World Health Organization (WHO) set the elimination of Chagas disease intradomiciliary vectorial transmission as a goal by 2020. After a decade, some progress has been made, but the new 2021-2030 WHO roadmap has set even more ambitious targets. Innovative and robust modelling methods are required to monitor progress towards these goals. We present a modelling pipeline using local seroprevalence data to obtain national disease burden estimates by disease stage. Firstly, local seroprevalence information is used to estimate spatio-temporal trends in the Force-of-Infection (FoI). FoI estimates are then used to predict such trends across larger and fine-scale geographical areas. Finally, predicted FoI values are used to estimate disease burden based on a disease progression model. Using Colombia as a case study, we estimated that the number of infected people would reach 506 000 (95% credible interval (CrI) = 395 000-648 000) in 2020 with a 1.0% (95%CrI = 0.8-1.3%) prevalence in the general population and 2400 (95%CrI = 1900-3400) deaths (approx. 0.5% of those infected). The interplay between a decrease in infection exposure (FoI and relative proportion of acute cases) was overcompensated by a large increase in population size and gradual population ageing, leading to an increase in the absolute number of Chagas disease cases over time. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.

Disease outbreaks select for mate choice and coat color in wolves.

We know much about pathogen evolution and the emergence of new disease strains, but less about host resistance and how it is signaled to other individuals and subsequently maintained. The cline in frequency of black-coated wolves (Canis lupus) across North America is hypothesized to result from a relationship with canine distemper virus (CDV) outbreaks. We tested this hypothesis using cross-sectional data from wolf populations across North America that vary in the prevalence of CDV and the allele that makes coats black, longitudinal data from Yellowstone National Park, and modeling. We found that the frequency of CDV outbreaks generates fluctuating selection that results in heterozygote advantage that in turn affects the frequency of the black allele, optimal mating behavior, and black wolf cline across the continent.

Too few, too late: U.S. Endangered Species Act undermined by inaction and inadequate funding.

This year, the Conference of Parties to the Convention on Biological Diversity will meet to finalize a post 2020-framework for biodiversity conservation, necessitating critical analysis of current barriers to conservation success. Here, we tackle one of the enduring puzzles about the U.S. Endangered Species Act, often considered a model for endangered species protection globally: Why have so few species been successfully recovered? For the period of 1992-2020, we analyzed trends in the population sizes of species of concern, trends in the time between when species are first petitioned for listing and when they actually receive protection, and trends in funding for the listing and recovery of imperiled species. We find that small population sizes at time of listing, coupled with delayed protection and insufficient funding, continue to undermine one of the world's strongest laws for protecting biodiversity.

Evidence gaps and diversity among potential win-win solutions for conservation and human infectious disease control.

As sustainable development practitioners have worked to "ensure healthy lives and promote well-being for all" and "conserve life on land and below water", what progress has been made with win-win interventions that reduce human infectious disease burdens while advancing conservation goals? Using a systematic literature review, we identified 46 proposed solutions, which we then investigated individually using targeted literature reviews. The proposed solutions addressed diverse conservation threats and human infectious diseases, and thus, the proposed interventions varied in scale, costs, and impacts. Some potential solutions had medium-quality to high-quality evidence for previous success in achieving proposed impacts in one or both sectors. However, there were notable evidence gaps within and among solutions, highlighting opportunities for further research and adaptive implementation. Stakeholders seeking win-win interventions can explore this Review and an online database to find and tailor a relevant solution or brainstorm new solutions.

Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease.

BACKGROUND: Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues. METHODOLOGY/PRINCIPAL FINDINGS: We compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty. CONCLUSIONS/SIGNIFICANCE: The choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to other infectious diseases for which serosurveys are regularly conducted for surveillance.

Association of Environmental Factors with Seasonal Intensity of Erysipelothrix rhusiopathiae Seropositivity among Arctic Caribou.

Several caribou (Rangifer tarandus) populations have been declining concurrently with increases in infectious diseases in the Arctic. Erysipelothrix rhusiopathiae, a zoonotic bacterium, was first described in 2015 as a notable cause of illness and death among several Arctic wildlife species. We investigated epidemiologic and environmental factors associated with the seroprevalence of E. rhusiopathiae in the Arctic and found that seropositivity was highest during warmer months, peaking in September, and was highest among adult males. Summer seroprevalence increases tracked with the oestrid index from the previous year, icing and snowing events, and precipitation from the same year but decreased with growing degree days in the same year. Seroprevalence of E. rhusiopathiae varied more during the later years of the study. Our findings provide key insights into the influence of environmental factors on disease prevalence that can be instrumental for anticipating and mitigating diseases associated with climate change among Arctic wildlife and human populations.

The costs and benefits of primary prevention of zoonotic pandemics.

The lives lost and economic costs of viral zoonotic pandemics have steadily increased over the past century. Prominent policymakers have promoted plans that argue the best ways to address future pandemic catastrophes should entail, "detecting and containing emerging zoonotic threats." In other words, we should take actions only after humans get sick. We sharply disagree. Humans have extensive contact with wildlife known to harbor vast numbers of viruses, many of which have not yet spilled into humans. We compute the annualized damages from emerging viral zoonoses. We explore three practical actions to minimize the impact of future pandemics: better surveillance of pathogen spillover and development of global databases of virus genomics and serology, better management of wildlife trade, and substantial reduction of deforestation. We find that these primary pandemic prevention actions cost less than 1/20th the value of lives lost each year to emerging viral zoonoses and have substantial cobenefits.

Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study.

Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty.In Colombia, 76 serosurveys (1980-2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia.A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions.Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.

Dietary abundance distributions: Dominance and diversity in vertebrate diets.

Diet composition is among the most important yet least understood dimensions of animal ecology. Inspired by the study of species abundance distributions (SADs), we tested for generalities in the structure of vertebrate diets by characterising them as dietary abundance distributions (DADs). We compiled data on 1167 population-level diets, representing >500 species from six vertebrate classes, spanning all continents and oceans. DADs near-universally (92.5%) followed a hollow-curve shape, with scant support for other plausible rank-abundance-distribution shapes. This strong generality is inherently related to, yet incompletely explained by, the SADs of available food taxa. By quantifying dietary generalisation as the half-saturation point of the cumulative distribution of dietary abundance (sp50, minimum number of foods required to account for 50% of diet), we found that vertebrate populations are surprisingly specialised: in most populations, fewer than three foods accounted for at least half the diet. Variation in sp50 was strongly associated with consumer type, with carnivores being more specialised than herbivores or omnivores. Other methodological (sampling method and effort, taxonomic resolution), biological (body mass, frugivory) and biogeographic (latitude) factors influenced sp50 to varying degrees. Future challenges include identifying the mechanisms underpinning the hollow-curve DAD, its generality beyond vertebrates, and the biological determinants of dietary generalisation.

Challenges in modelling the dynamics of infectious diseases at the wildlife-human interface.

The Covid-19 pandemic is of zoonotic origin, and many other emerging infections of humans have their origin in an animal host population. We review the challenges involved in modelling the dynamics of wildlife-human interfaces governing infectious disease emergence and spread. We argue that we need a better understanding of the dynamic nature of such interfaces, the underpinning diversity of pathogens and host-pathogen association networks, and the scales and frequencies at which environmental conditions enable spillover and host shifting from animals to humans to occur. The major drivers of the emergence of zoonoses are anthropogenic, including the global change in climate and land use. These, and other ecological processes pose challenges that must be overcome to counterbalance pandemic risk. The development of more detailed and nuanced models will provide better tools for analysing and understanding infectious disease emergence and spread.

A solution scan of societal options to reduce transmission and spread of respiratory viruses: SARS-CoV-2 as a case study.

Societal biosecurity - measures built into everyday society to minimize risks from pests and diseases - is an important aspect of managing epidemics and pandemics. We aimed to identify societal options for reducing the transmission and spread of respiratory viruses. We used SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) as a case study to meet the immediate need to manage the COVID-19 pandemic and eventually transition to more normal societal conditions, and to catalog options for managing similar pandemics in the future. We used a 'solution scanning' approach. We read the literature; consulted psychology, public health, medical, and solution scanning experts; crowd-sourced options using social media; and collated comments on a preprint. Here, we present a list of 519 possible measures to reduce SARS-CoV-2 transmission and spread. We provide a long list of options for policymakers and businesses to consider when designing biosecurity plans to combat SARS-CoV-2 and similar pathogens in the future. We also developed an online application to help with this process. We encourage testing of actions, documentation of outcomes, revisions to the current list, and the addition of further options.

Towards an ecosystem model of infectious disease.

Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics.