68 results found
Echeverria-Londono S, Li X, Toor J, et al., 2021, How can the public health impact of vaccination be estimated?, BMC PUBLIC HEALTH, Vol: 21
Hamlet A, Ramos DG, Gaythorpe K, et al., 2021, Seasonality of agricultural exposure as an important predictor of seasonal yellow fever spillover in Brazil, Nature Communications, Vol: 12, Pages: 1-11, ISSN: 2041-1723
Yellow fever virus (YFV) is a zoonotic arbovirus affecting both humans and non-human primates (NHP’s) in Africa and South America. Previous descriptions of YF’s seasonality have relied purely on climatic explanations, despite the high proportion of cases occurring in people involved in agriculture. We use a series of random forest classification models to predict the monthly occurrence of YF in humans and NHP’s across Brazil, by fitting four classes of covariates related to the seasonality of climate and agriculture (planting and harvesting), crop output and host demography. We find that models captured seasonal YF reporting in humans and NHPs when they considered seasonality of agriculture rather than climate, particularly for monthly aggregated reports. These findings illustrate the seasonality of exposure, through agriculture, as a component of zoonotic spillover. Additionally, by highlighting crop types and anthropogenic seasonality, these results could directly identify areas at highest risk of zoonotic spillover.
Jean K, Raad H, Gaythorpe KAM, et al., 2021, Assessing the impact of preventive mass vaccination campaigns on yellow fever outbreaks in Africa: A population-level self-controlled case series study, PLOS MEDICINE, Vol: 18, ISSN: 1549-1277
Li X, Mukandavire C, Cucunuba ZM, et al., 2021, Estimating the health impact of vaccination against ten pathogens in 98 low-income and middle-income countries from 2000 to 2030: a modelling study, The Lancet, Vol: 397, Pages: 398-408, ISSN: 0140-6736
BackgroundThe past two decades have seen expansion of childhood vaccination programmes in low-income and middle-income countries (LMICs). We quantify the health impact of these programmes by estimating the deaths and disability-adjusted life-years (DALYs) averted by vaccination against ten pathogens in 98 LMICs between 2000 and 2030.Methods16 independent research groups provided model-based disease burden estimates under a range of vaccination coverage scenarios for ten pathogens: hepatitis B virus, Haemophilus influenzae type B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, Streptococcus pneumoniae, rotavirus, rubella, and yellow fever. Using standardised demographic data and vaccine coverage, the impact of vaccination programmes was determined by comparing model estimates from a no-vaccination counterfactual scenario with those from a reported and projected vaccination scenario. We present deaths and DALYs averted between 2000 and 2030 by calendar year and by annual birth cohort.FindingsWe estimate that vaccination of the ten selected pathogens will have averted 69 million (95% credible interval 52–88) deaths between 2000 and 2030, of which 37 million (30–48) were averted between 2000 and 2019. From 2000 to 2019, this represents a 45% (36–58) reduction in deaths compared with the counterfactual scenario of no vaccination. Most of this impact is concentrated in a reduction in mortality among children younger than 5 years (57% reduction [52–66]), most notably from measles. Over the lifetime of birth cohorts born between 2000 and 2030, we predict that 120 million (93–150) deaths will be averted by vaccination, of which 58 million (39–76) are due to measles vaccination and 38 million (25–52) are due to hepatitis B vaccination. We estimate that increases in vaccine coverage and introductions of additional vaccines will result in a 72% (59–81) reduction in lifetime mortality in t
Londono SE, Li X, Toor J, et al., 2021, How can the public health impact of vaccination be estimated?
<jats:title>ABSTRACT</jats:title><jats:p>Deaths due to vaccine preventable diseases cause a notable proportion of mortality worldwide. To quantify the importance of vaccination, it is necessary to estimate the burden averted through vaccination. The Vaccine Impact Modelling Consortium (VIMC) was established to estimate the health impact of vaccination. We describe the methods implemented by the VIMC to estimate impact by calendar year, birth year and year of vaccination (YoV). The calendar and birth year methods estimate impact in a particular year and over the lifetime of a particular birth cohort, respectively. The YoV method estimates the impact of a particular year’s vaccination activities through the use of impact ratios which have no stratification and stratification by activity type and/or birth cohort. Furthermore, we detail an impact extrapolation (IE) method for use between coverage scenarios. We compare the methods, focusing on YoV for hepatitis B, measles and yellow fever. We find that the YoV methods estimate similar impact with routine vaccinations but have greater yearly variation when campaigns occur with the birth cohort stratification. The IE performs well for the YoV methods, providing a time-efficient mechanism for updates to impact estimates. These methods provide a robust set of approaches to quantify vaccination impact.</jats:p>
Hamlet A, Gaythorpe KAM, Garske T, et al., 2021, Seasonal and inter-annual drivers of yellow fever transmission in South America, PLOS NEGLECTED TROPICAL DISEASES, Vol: 15, ISSN: 1935-2735
Gaythorpe KAM, Hamlet A, Cibrelus L, et al., 2020, The effect of climate change on yellow fever disease burden in Africa, eLife, Vol: 9, ISSN: 2050-084X
Yellow Fever (YF) is an arbovirus endemic in tropical regions of South America and Africa and it is estimated to cause 78,000 deaths a year in Africa alone. Climate change may have substantial effects on the transmission of YF and we present the first analysis of the potential impact on disease burden. We extend an existing model of YF transmission to account for rainfall and a temperature suitability index and project transmission intensity across the African endemic region in the context of four climate change scenarios. We use these transmission projections to assess the change in burden in 2050 and 2070. We find disease burden changes heterogeneously across the region. In the least severe scenario, we find a 93.0%[95%CI(92.7, 93.2%)] chance that annual deaths will increase in 2050. This change in epidemiology will complicate future control efforts. Thus, we may need to consider the effect of changing climatic variables on future intervention strategies.
Ozawa S, Clark S, Portnoy A, et al., 2020, Economic impact of vaccination against 10 vaccine‐preventable diseases across 73 low‐ and middle‐income countries supported by Gavi, 2001‐2020, Bulletin of the World Health Organization, Vol: 95, Pages: 629-638, ISSN: 1564-0604
Jean K, Hamlet A, Benzler J, et al., 2020, Eliminating yellow fever epidemics in Africa: Vaccine demand forecast and impact modelling, PLoS Neglected Tropical Diseases, Vol: 14, Pages: 1-16, ISSN: 1935-2727
BackgroundTo counter the increasing global risk of Yellow fever (YF), the World Health Organisation initiated the Eliminate Yellow fever Epidemics (EYE) strategy. Estimating YF burden, as well as vaccine impact, while accounting for the features of urban YF transmission such as indirect benefits of vaccination, is key to informing this strategy.Methods and findingsWe developed two model variants to estimate YF burden in sub-Saharan Africa, assuming all infections stem from either the sylvatic or the urban cycle of the disease. Both relied on an ecological niche model fitted to the local presence of any YF reported event in 34 African countries. We calibrated under-reporting using independent estimates of transmission intensity provided by 12 serological surveys performed in 11 countries. We calculated local numbers of YF infections, deaths and disability-adjusted life years (DALYs) lost based on estimated transmission intensity while accounting for time-varying vaccination coverage. We estimated vaccine demand and impact of future preventive mass vaccination campaigns (PMVCs) according to various vaccination scenarios.Vaccination activities conducted in Africa between 2005 and 2017 were estimated to prevent from 3.3 (95% CI 1.2–7.7) to 6.1 (95% CI 2.4–13.2) millions of deaths over the lifetime of vaccinees, representing extreme scenarios of none or maximal herd effects, respectively. By prioritizing provinces based on the risk of urban YF transmission in future PMVCs, an average of 37.7 million annual doses for PMVCs over eight years would avert an estimated 9,900,000 (95% CI 7,000,000–13,400,000) infections and 480,000 (180,000–1,140,000) deaths over the lifetime of vaccinees, corresponding to 1.7 (0.7–4.1) deaths averted per 1,000 vaccine doses.ConclusionsBy estimating YF burden and vaccine impact over a range of spatial and temporal scales, while accounting for the specificity of urban transmission, our model can be used to inform th
Dorigatti I, Morrison S, Donnelly C, et al., 2019, Risk of yellow fever virus importation into the United States from Brazil, outbreak years 2016–2017 and 2017–2018, Scientific Reports, Vol: 9, ISSN: 2045-2322
Southeast Brazil has experienced two large yellow fever (YF) outbreaks since 2016. While the 2016–2017 outbreak mainly affected the states of Espírito Santo and Minas Gerais, the 2017–2018 YF outbreak primarily involved the states of Minas Gerais, São Paulo, and Rio de Janeiro, the latter two of which are highly populated and popular destinations for international travelers. This analysis quantifies the risk of YF virus (YFV) infected travelers arriving in the United States via air travel from Brazil, including both incoming Brazilian travelers and returning US travelers. We assumed that US travelers were subject to the same daily risk of YF infection as Brazilian residents. During both YF outbreaks in Southeast Brazil, three international airports—Miami, New York-John F. Kennedy, and Orlando—had the highest risk of receiving a traveler infected with YFV. Most of the risk was observed among incoming Brazilian travelers. Overall, we found low risk of YFV introduction into the United States during the 2016-2017 and 2017-2018 outbreaks. Decision makers can use these results to employ the most efficient and least restrictive actions and interventions.
Eneanya O, Fronterre C, Anagbogu I, et al., 2019, Mapping the baseline prevalence of lymphatic filariasis across Nigeria, Parasites & Vectors, Vol: 12, ISSN: 1756-3305
Introduction: The baseline endemicity profile of lymphatic filariasis (LF) is a keybenchmark for planning control programmes, monitoring their impact on transmissionand assessing the feasibility of achieving elimination. Presented in this work is themodelled serological and parasitological prevalence of LF prior to the scale-up of massdrug administration (MDA) in Nigeria using a machine learning based approach.Methods: LF prevalence data generated by the Nigeria Lymphatic Filariasis ControlProgramme during country-wide mapping surveys conducted between 2000 and 2013were used to build the models. The dataset comprised of 1103 community-levelsurveys based on the detection of filarial antigenaemia using rapidimmunochromatographic card tests (ICT) and 184 prevalence surveys testing for thepresence of microfilaria (Mf) in blood. Using a suite of climate and environmentalcontinuous gridded variables and compiled site-level prevalence data, a quantileregression forest (QRF) model was fitted for both antigenaemia and microfilaraemia LFprevalence. Model predictions were projected across a continuous 5 × 5 km griddedmap of Nigeria. The number of individuals potentially infected by LF prior to MDAinterventions was subsequently estimated.Results: Maps presented predict a heterogeneous distribution of LF antigenaemia andmicrofilaraemia in Nigeria. The North-Central, North-West, and South-East regionsdisplayed the highest predicted LF seroprevalence, whereas predicted Mf prevalencewas highest in the southern regions. Overall, 8.7 million and 3.3 million infections werepredicted for ICT and Mf, respectively.Conclusions: QRF is a machine learning-based algorithm capable of handling high-dimensional data and fitting complex relationships between response and predictorvariables. Our models provide a benchmark through which the progress of ongoing LF control efforts can be monitored.
Gaythorpe KAM, Jean K, Cibrelus L, et al., 2019, Quantifying model evidence for yellow fever transmission routes in Africa, PLoS Computational Biology, Vol: 15, Pages: 1-18, ISSN: 1553-734X
Yellow fever is a vector-borne disease endemic in tropical regions of Africa, where 90% of the global burden occurs, and Latin America. It is notoriously under-reported with uncertainty arising from a complex transmission cycle including a sylvatic reservoir and non-specific symptom set. Resulting estimates of burden, particularly in Africa, are highly uncertain. We examine two established models of yellow fever transmission within a Bayesian model averaging framework in order to assess the relative evidence for each model’s assumptions and to highlight possible data gaps. Our models assume contrasting scenarios of the yellow fever transmission cycle in Africa. The first takes the force of infection in each province to be static across the observation period; this is synonymous with a constant infection pressure from the sylvatic reservoir. The second model assumes the majority of transmission results from the urban cycle; in this case, the force of infection is dynamic and defined through a fixed value of R0 in each province. Both models are coupled to a generalised linear model of yellow fever occurrence which uses environmental covariates to allow us to estimate transmission intensity in areas where data is sparse. We compare these contrasting descriptions of transmission through a Bayesian framework and trans-dimensional Markov chain Monte Carlo sampling in order to assess each model’s evidence given the range of uncertainty in parameter values. The resulting estimates allow us to produce Bayesian model averaged predictions of yellow fever burden across the African endemic region. We find strong support for the static force of infection model which suggests a higher proportion of yellow fever transmission occurs as a result of infection from an external source such as the sylvatic reservoir. However, the model comparison highlights key data gaps in serological surveys across the African endemic region. As such, conclusions concerning the most prevale
den Boon S, Jit M, Brisson M, et al., 2019, Guidelines for multi-model comparisons of the impact of infectious disease interventions, BMC Medicine, Vol: 17, ISSN: 1741-7015
BackgroundDespite the increasing popularity of multi-model comparison studies and their ability to inform policy recommendations, clear guidance on how to conduct multi-model comparisons is not available. Herein, we present guidelines to provide a structured approach to comparisons of multiple models of interventions against infectious diseases. The primary target audience for these guidelines are researchers carrying out model comparison studies and policy-makers using model comparison studies to inform policy decisions.MethodsThe consensus process used for the development of the guidelines included a systematic review of existing model comparison studies on effectiveness and cost-effectiveness of vaccination, a 2-day meeting and guideline development workshop during which mathematical modellers from different disease areas critically discussed and debated the guideline content and wording, and several rounds of comments on sequential versions of the guidelines by all authors.ResultsThe guidelines provide principles for multi-model comparisons, with specific practice statements on what modellers should do for six domains. The guidelines provide explanation and elaboration of the principles and practice statements as well as some examples to illustrate these. The principles are (1) the policy and research question – the model comparison should address a relevant, clearly defined policy question; (2) model identification and selection – the identification and selection of models for inclusion in the model comparison should be transparent and minimise selection bias; (3) harmonisation – standardisation of input data and outputs should be determined by the research question and value of the effort needed for this step; (4) exploring variability – between- and within-model variability and uncertainty should be explored; (5) presenting and pooling results – results should be presented in an appropriate way to support decision-making; and (6)
Watson OJ, Verity R, Ghani AC, et al., 2019, Impact of seasonal variations in Plasmodium falciparum malaria transmission on the surveillance of pfhrp2 gene deletions, eLife, Vol: 8, ISSN: 2050-084X
Ten countries have reported pfhrp2/pfhrp3 gene deletions since the first observation of pfhrp2-deleted parasites in 2012. In a previous study (Watson et al., 2017) we characterised the drivers selecting for pfhrp2/3 deletions, and mapped the regions in Africa with the greatest selection pressure. In February 2018, the World Health Organization issued guidance on investigating suspected false-negative rapid diagnostic tests (RDTs) due to pfhrp2/3 deletions. However, no guidance is provided regarding the timing of investigations. Failure to consider seasonal variation could cause premature decisions to switch to alternative RDTs. In response, we have extended our methods and predict that the prevalence of false-negative RDTs due to pfhrp2/3 deletions is highest when sampling from younger individuals during the beginning of the rainy season. We conclude by producing a map of the regions impacted by seasonal fluctuations in pfhrp2/3 deletions and a database identifying optimum sampling intervals to support malaria control programmes.
Eneanya O, Garske T, Donnelly C, 2019, The social, physical and economic impact of lymphedema and hydrocele: A matched cross-sectional study in rural Nigeria, BMC Infectious Diseases, Vol: 19, ISSN: 1471-2334
BackgroundLymphatic filariasis (LF) is a mosquito-borne parasitic disease and a major cause of disability worldwide. To effectively plan morbidity management programmes, it is important to estimate disease burden and evaluate the needs of patients. This study aimed to estimate patient numbers and characterise the physical, social and economic impact of LF in in rural Nigeria.MethodsThis is a matched cross-sectional study which identified lymphedema and hydrocele patients with the help of district health officers and community-directed distributors of mass drug administration programmes. A total of 52 cases were identified and matched to 52 apparently disease-free controls, selected from the same communities and matched by age and sex. Questionnaires and narrative interviews were used to characterise the physical, social and economic impact of lymphedema and hydrocele.ResultsForty-eight cases with various stages of lower limb lymphedema, and 4 with hydrocele were identified. 40% of all cases reported feeling stigma and were 36 times (95% CI: 5.18–1564.69) more likely to avoid forms of social participation. Although most cases engaged in some form of income-generating activity, these were low paid employment, and on average cases spent significantly less time than controls working. The economic effects of lower income were exacerbated by increased healthcare spending, as cases were 86 times (95% CI: 17.48–874.90) more likely to spend over US $125 on their last healthcare payment.ConclusionThis study highlights the importance of patient-search as a means of estimating the burden of LF morbidity in rural settings. Findings from this work also confirm that LF causes considerable psychosocial and economic suffering, all of which adversely affect the mental health of patients. It is therefore important to incorporate mental health care as a major component of morbidity management programmes.
Hamlet A, Jean K, Yactaco S, et al., 2019, POLICI: A web application for visualising and extracting yellow fever vaccination coverage in Africa, Vaccine, Vol: 37, Pages: 1384-1388, ISSN: 0264-410X
Recent yellow fever (YF) outbreaks have highlighted the increasing global risk of urban spread of the disease. In context of recurrent vaccine shortages, preventive vaccination activities require accurate estimates of existing population-level immunity. We present POLICI (POpulation-Level Immunization Coverage – Imperial), an interactive online tool for visualising and extracting YF vaccination coverage estimates in Africa.We calculated single year age-disaggregated sub-national population-level vaccination coverage for 1950–2050 across the African endemic zone by collating vaccination information and inputting it into a demographic model. This was then implemented on an open interactive web platform.POLICI interactively displays age-disaggregated, population-level vaccination coverages at the first subnational administrative level, through numerous downloadable and customisable visualisations. POLICI is available at https://polici.shinyapps.io/yellow_fever_africa/.POLICI offers an accessible platform for relevant stakeholders in global health to access and explore vaccination coverages. These estimates have already been used to inform the WHO strategy to Eliminate Yellow fever Epidemics (EYE).
Cori A, Nouvellet P, Garske T, et al., 2018, A graph-based evidence synthesis approach to detecting outbreak clusters: An application to dog rabies, PLoS Computational Biology, Vol: 14, ISSN: 1553-734X
Early assessment of infectious disease outbreaks is key to implementing timely and effective control measures. In particular, rapidly recognising whether infected individuals stem from a single outbreak sustained by local transmission, or from repeated introductions, is crucial to adopt effective interventions. In this study, we introduce a new framework for combining several data streams, e.g. temporal, spatial and genetic data, to identify clusters of related cases of an infectious disease. Our method explicitly accounts for underreporting, and allows incorporating preexisting information about the disease, such as its serial interval, spatial kernel, and mutation rate. We define, for each data stream, a graph connecting all cases, with edges weighted by the corresponding pairwise distance between cases. Each graph is then pruned by removing distances greater than a given cutoff, defined based on preexisting information on the disease and assumptions on the reporting rate. The pruned graphs corresponding to different data streams are then merged by intersection to combine all data types; connected components define clusters of cases related for all types of data. Estimates of the reproduction number (the average number of secondary cases infected by an infectious individual in a large population), and the rate of importation of the disease into the population, are also derived. We test our approach on simulated data and illustrate it using data on dog rabies in Central African Republic. We show that the outbreak clusters identified using our method are consistent with structures previously identified by more complex, computationally intensive approaches.
Eneanya OA, Cano J, Dorigatti I, et al., 2018, Environmental suitability for lymphatic filariasis in Nigeria, Parasites & Vectors, Vol: 11, ISSN: 1756-3305
BackgroundLymphatic filariasis (LF) is a mosquito-borne parasitic disease and a major cause of disability worldwide. It is one of the neglected tropical diseases identified by the World Health Organization for elimination as a public health problem by 2020. Maps displaying disease distribution are helpful tools to identify high-risk areas and target scarce control resources.MethodsWe used pre-intervention site-level occurrence data from 1192 survey sites collected during extensive mapping surveys by the Nigeria Ministry of Health. Using an ensemble of machine learning modelling algorithms (generalised boosted models and random forest), we mapped the ecological niche of LF at a spatial resolution of 1 km2. By overlaying gridded estimates of population density, we estimated the human population living in LF risk areas on a 100 × 100 m scale.ResultsOur maps demonstrate that there is a heterogeneous distribution of LF risk areas across Nigeria, with large portions of northern Nigeria having more environmentally suitable conditions for the occurrence of LF. Here we estimated that approximately 110 million individuals live in areas at risk of LF transmission.ConclusionsMachine learning and ensemble modelling are powerful tools to map disease risk and are known to yield more accurate predictive models with less uncertainty than single models. The resulting map provides a geographical framework to target control efforts and assess its potential impacts.
Donnelly CA, Garske T, 2018, How deadly is Ebola?, Biomedical Science Journal for teens
The Ebola Outbreak Epidemiology Team, Bhatia S, Cori A, et al., 2018, Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April–May, 2018: an epidemiological study, The Lancet, Vol: 392, Pages: 213-221, ISSN: 0140-6736
BackgroundOn May 8, 2018, the Government of the Democratic Republic of the Congo reported an outbreak of Ebola virus disease in Équateur Province in the northwest of the country. The remoteness of most affected communities and the involvement of an urban centre connected to the capital city and neighbouring countries makes this outbreak the most complex and high risk ever experienced by the Democratic Republic of the Congo. We provide early epidemiological information arising from the ongoing investigation of this outbreak.MethodsWe classified cases as suspected, probable, or confirmed using national case definitions of the Democratic Republic of the Congo Ministère de la Santé Publique. We investigated all cases to obtain demographic characteristics, determine possible exposures, describe signs and symptoms, and identify contacts to be followed up for 21 days. We also estimated the reproduction number and projected number of cases for the 4-week period from May 25, to June 21, 2018.FindingsAs of May 30, 2018, 50 cases (37 confirmed, 13 probable) of Zaire ebolavirus were reported in the Democratic Republic of the Congo. 21 (42%) were reported in Bikoro, 25 (50%) in Iboko, and four (8%) in Wangata health zones. Wangata is part of Mbandaka, the urban capital of Équateur Province, which is connected to major national and international transport routes. By May 30, 2018, 25 deaths from Ebola virus disease had been reported, with a case fatality ratio of 56% (95% CI 39–72) after adjustment for censoring. This case fatality ratio is consistent with estimates for the 2014–16 west African Ebola virus disease epidemic (p=0·427). The median age of people with confirmed or probable infection was 40 years (range 8–80) and 30 (60%) were male. The most commonly reported signs and symptoms in people with confirmed or probable Ebola virus disease were fever (40 [95%] of 42 cases), intense general fatigue (37 [90%] of 41 cases), an
Hamlet A, Jean K, Perea W, et al., 2018, The seasonal influence of climate and environment on yellow fever transmission across Africa, PLoS Neglected Tropical Diseases, Vol: 12, ISSN: 1935-2727
Background:Yellow fever virus (YFV) is a vector-borne flavivirus endemic to Africa and Latin America. Ninety per cent of the global burden occurs in Africa where it is primarily transmitted by Aedes spp, with Aedes aegypti the main vector for urban yellow fever (YF). Mosquito life cycle and viral replication in the mosquito are heavily dependent on climate, particularly temperature and rainfall. We aimed to assess whether seasonal variations in climatic factors are associated with the seasonality of YF reports.Methodology/Principal findings:We constructed a temperature suitability index for YFV transmission, capturing the temperature dependence of mosquito behaviour and viral replication within the mosquito. We then fitted a series of multilevel logistic regression models to a dataset of YF reports across Africa, considering location and seasonality of occurrence for seasonal models, against the temperature suitability index, rainfall and the Enhanced Vegetation Index (EVI) as covariates alongside further demographic indicators. Model fit was assessed by the Area Under the Curve (AUC), and models were ranked by Akaike’s Information Criterion which was used to weight model outputs to create combined model predictions. The seasonal model accurately captured both the geographic and temporal heterogeneities in YF transmission (AUC = 0.81), and did not perform significantly worse than the annual model which only captured the geographic distribution. The interaction between temperature suitability and rainfall accounted for much of the occurrence of YF, which offers a statistical explanation for the spatio-temporal variability in transmission.Conclusions/Significance:The description of seasonality offers an explanation for heterogeneities in the West-East YF burden across Africa. Annual climatic variables may indicate a transmission suitability not always reflected in seasonal interactions. This finding, in conjunction with forecasted data, could highlight areas of
Chang AY, Riumallo-Herl C, Perales NA, et al., 2018, The equity impact vaccines may have on averting deaths and medical impoverishment in developing countries, Health Affairs, Vol: 37, Pages: 316-324, ISSN: 0278-2715
With social policies increasingly directed toward enhancing equity through health programs, it is important that methods for estimating the health and economic benefits of these programs by subpopulation be developed, to assess both equity concerns and the programs’ total impact. We estimated the differential health impact (measured as the number of deaths averted) and household economic impact (measured as the number of cases of medical impoverishment averted) of ten antigens and their corresponding vaccines across income quintiles for forty-one low- and middle-income countries. Our analysis indicated that benefits across these vaccines would accrue predominantly in the lowest income quintiles. Policy makers should be informed about the large health and economic distributional impact that vaccines could have, and they should view vaccination policies as potentially important channels for improving health equity. Our results provide insight into the distribution of vaccine-preventable diseases and the health benefits associated with their prevention.
Hamlet A, Jean K, Ferguson N, et al., 2017, THE SEASONAL INFLUENCE OF CLIMATE AND ENVIRONMENT ON YELLOW FEVER TRANSMISSION ACROSS AFRICA, 65th Annual Meeting of the American-Society-of-Tropical-Medicine-and-Hygiene (ASTMH), Publisher: AMER SOC TROP MED & HYGIENE, Pages: 44-44, ISSN: 0002-9637
Garske T, 2017, BIAS ADJUSTMENT OF CASE FATALITY RATE ESTIMATES IN THE EBOLA OUTBREAK IN WEST AFRICA, 65th Annual Meeting of the American-Society-of-Tropical-Medicine-and-Hygiene (ASTMH), Publisher: AMER SOC TROP MED & HYGIENE, Pages: 210-210, ISSN: 0002-9637
Jean K, Ferguson NM, Van Kerkhove MD, et al., 2017, THE DIFFERENTIAL IMPACT OF YELLOW FEVER VACCINE ACROSS TRANSMISSION CYCLES: ACCOUNTING FOR HERD IMMUNITY IN THE FACE OF ZOONOTIC TRANSMISSION, 65th Annual Meeting of the American-Society-of-Tropical-Medicine-and-Hygiene (ASTMH), Publisher: AMER SOC TROP MED & HYGIENE, Pages: 42-43, ISSN: 0002-9637
Dorigatti I, Hamlet A, Aguas R, et al., 2017, International risk of yellow fever spread from the ongoing outbreak in Brazil, December 2016 to May 2017, EUROSURVEILLANCE, Vol: 22, Pages: 1-4, ISSN: 1560-7917
States in south-eastern Brazil were recently affected by the largest Yellow Fever (YF) outbreak seen in a decade in Latin America. Here we provide a quantitative assessment of the risk of travel-related international spread of YF indicating that the United States, Argentina, Uruguay, Spain, Italy and Germany may have received at least one travel-related YF case capable of seeding local transmission. Mitigating the risk of imported YF cases seeding local transmission requires heightened surveillance globally.
Ozawa S, Clark S, Portnoy A, et al., 2017, Estimated economic impact of vaccinations in 73 low- and middle-income countries, 2001-2020, Bulletin of the World Health Organization, Vol: 95, Pages: 629-638, ISSN: 0042-9686
Objective To estimate the economic impact likely to be achieved by efforts to vaccinate against 10 vaccine-preventable diseases between 2001 and 2020 in 73 low- and middle-income countries largely supported by Gavi, the Vaccine Alliance.Methods We used health impact models to estimate the economic impact of achieving forecasted coverages for vaccination against Haemophilus influenzae type b, hepatitis B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, rotavirus, rubella, Streptococcus pneumoniae and yellow fever. In comparison with no vaccination, we modelled the costs – expressed in 2010 United States dollars (US$) – of averted treatment, transportation costs, productivity losses of caregivers and productivity losses due to disability and death. We used the value-of-a-life-year method to estimate the broader economic and social value of living longer, in better health, as a result of immunization.Findings We estimated that, in the 73 countries, vaccinations given between 2001 and 2020 will avert over 20 million deaths and save US$ 350 billion in cost of illness. The deaths and disability prevented by vaccinations given during the two decades will result in estimated lifelong productivity gains totalling US$ 330 billion and US$ 9 billion, respectively. Over the lifetimes of the vaccinated cohorts, the same vaccinations will save an estimated US$ 5 billion in treatment costs. The broader economic and social value of these vaccinations is estimated at US$ 820 billion.Conclusion By preventing significant costs and potentially increasing economic productivity among some of the world’s poorest countries, the impact of immunization goes well beyond health.
Cori A, Donnelly CA, dorigatti, et al., 2017, Key data for outbreak evaluation: building on the Ebola experience, Philosophical Transactions of the Royal Society B: Biological Sciences, Vol: 372, ISSN: 1471-2970
Following the detection of an infectious disease outbreak, rapid epidemiological assessmentis critical to guidean effectivepublic health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained inthe West AfricanEbolaepidemic and prior emerging infectious disease outbreaksto set out a checklist of data needed to: 1) quantify severity and transmissibility;2) characterise heterogeneities in transmission and their determinants;and 3) assess the effectiveness of different interventions.We differentiate data needs into individual-leveldata (e.g. a detailed list of reported cases), exposure data(e.g.identifying where / howcases may have been infected) and populationlevel data (e.g.size/demographicsof the population(s)affected andwhen/where interventions were implemented). A remarkable amount of individual-level and exposuredata was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However,gaps in population-level data (particularly around which interventions were applied whenand where)posed challenges to the assessment of (3).Herewehighlight recurrent data issues, give practical suggestions for addressingthese issues and discuss priorities for improvements in data collection in future outbreaks.
Garske T, Cori A, Ariyarajah A, et al., 2017, Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013 – 2016, Philosophical Transactions of the Royal Society B: Biological Sciences, Vol: 372, ISSN: 1471-2970
The 2013–2016 Ebola outbreak in West Africa is the largest on record with 28 616 confirmed, probable and suspected cases and 11 310 deaths officially recorded by 10 June 2016, the true burden probably considerably higher. The case fatality ratio (CFR: proportion of cases that are fatal) is a key indicator of disease severity useful for gauging the appropriate public health response and for evaluating treatment benefits, if estimated accurately. We analysed individual-level clinical outcome data from Guinea, Liberia and Sierra Leone officially reported to the World Health Organization. The overall mean CFR was 62.9% (95% CI: 61.9% to 64.0%) among confirmed cases with recorded clinical outcomes. Age was the most important modifier of survival probabilities, but country, stage of the epidemic and whether patients were hospitalized also played roles. We developed a statistical analysis to detect outliers in CFR between districts of residence and treatment centres (TCs), adjusting for known factors influencing survival and identified eight districts and three TCs with a CFR significantly different from the average. From the current dataset, we cannot determine whether the observed variation in CFR seen by district or treatment centre reflects real differences in survival, related to the quality of care or other factors or was caused by differences in reporting practices or case ascertainment.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.