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1.
Ann Pharmacother ; : 1060028020905846, 2020 Feb 13.
Artigo em Inglês | MEDLINE | ID: mdl-32052651

RESUMO

Objective: To review the safety, efficacy, and administration of intranasal (IN) glucagon for the management of hypoglycemia. Data Source: A literature search of PubMed/MEDLINE (1995 to November 2019) using the terms intranasal glucagon, nasal glucagon, glucagon, hypoglycemia treatment, and hypoglycemia management was completed. Study Selection and Data Extraction: English-language studies evaluating IN glucagon were evaluated. Data Synthesis: IN glucagon is a newly approved product for the treatment of hypoglycemia in patients with diabetes, 4 years and older. Administered as a 3-mg dose, it was shown to be noninferior to intramuscular (IM) glucagon. In comparison trials, more than 98% of hypoglycemic events were treated successfully with IN glucagon in both pediatric and adult patients. In simulated and real-world studies, IN glucagon was administered in less than a minute for the majority of scenarios. IM glucagon took longer to administer, ranging from 1 to 4 minutes, and often, patients did not receive the intended full dose. Nausea and vomiting, known adverse events for glucagon, as well as local adverse events were most commonly reported with IN glucagon. Relevance to Patient Care and Clinical Practice: IN glucagon is safe, effective, easy to use, and does not require reconstitution prior to use, which can lead to faster delivery in a severe hypoglycemic event. It does not require age- or weight-based dosing. This delivery method offers an option for someone who fears needles or is uncomfortable with injections. Conclusion: IN glucagon is a safe, effective, easy to use, needle-free treatment option for severe hypoglycemia.

2.
Malar J ; 19(1): 5, 2020 Jan 06.
Artigo em Inglês | MEDLINE | ID: mdl-31906963

RESUMO

BACKGROUND: Malaria transmission is influenced by a complex interplay of factors including climate, socio-economic, environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progress. A better understanding of the association with other climatic and non-climatic drivers of malaria incidence over time and space may help guide and interpret the impact of interventions. METHODS: Routine monthly paediatric outpatient clinical malaria case data were compiled from 27 districts in Malawi between 2004 and 2017, and analysed in combination with data on climatic, environmental, socio-economic and interventional factors and district level population estimates. A spatio-temporal generalized linear mixed model was fitted using Bayesian inference, in order to quantify the strength of association of the various risk factors with district-level variation in clinical malaria rates in Malawi, and visualized using maps. RESULTS: Between 2004 and 2017 reported childhood clinical malaria case rates showed a slight increase, from 50 to 53 cases per 1000 population, with considerable variation across the country between climatic zones. Climatic and environmental factors, including average monthly air temperature and rainfall anomalies, normalized difference vegetative index (NDVI) and RDT use for diagnosis showed a significant relationship with malaria incidence. Temperature in the current month and in each of the 3 months prior showed a significant relationship with the disease incidence unlike rainfall anomaly which was associated with malaria incidence at only three months prior. Estimated risk maps show relatively high risk along the lake and Shire valley regions of Malawi. CONCLUSION: The modelling approach can identify locations likely to have unusually high or low risk of malaria incidence across Malawi, and distinguishes between contributions to risk that can be explained by measured risk-factors and unexplained residual spatial variation. Also, spatial statistical methods applied to readily available routine data provides an alternative information source that can supplement survey data in policy development and implementation to direct surveillance and intervention efforts.

4.
Proc Natl Acad Sci U S A ; 116(48): 24268-24274, 2019 11 26.
Artigo em Inglês | MEDLINE | ID: mdl-31712420

RESUMO

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

5.
PLoS Biol ; 17(11): e3000526, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31730640

RESUMO

The Amazon is Brazil's greatest natural resource and invaluable to the rest of the world as a buffer against climate change. The recent election of Brazil's president brought disputes over development plans for the region back into the spotlight. Historically, the development model for the Amazon has focused on exploitation of natural resources, resulting in environmental degradation, particularly deforestation. Although considerable attention has focused on the long-term global cost of "losing the Amazon," too little attention has focused on the emergence and reemergence of vector-borne diseases that directly impact the local population, with spillover effects to other neighboring areas. We discuss the impact of Amazon development models on human health, with a focus on vector-borne disease risk. We outline policy actions that could mitigate these negative impacts while creating opportunities for environmentally sensitive economic activities.

6.
Epidemics ; 29: 100361, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31668494

RESUMO

Bayesian inference using Gibbs sampling (BUGS) is a set of statistical software that uses Markov chain Monte Carlo (MCMC) methods to estimate almost any specified model. Originally developed in the late 1980s, the software is an excellent introduction to applied Bayesian statistics without the need to write a MCMC sampler. The software is typically used for regression-based analyses, but any model that can be specified using graphical nodes are possible. Advanced topics such as missing data, spatial analysis, model comparison and dynamic infectious disease models can be tackled. Three examples are provided; a linear regression model to illustrate parameter estimation, the steps to ensure that the estimates have converged and a comparison of run-times across different computing platforms. The second example describes a model that estimates the probability of being vaccinated from cross-sectional and surveillance data, and illustrates the specification of different models, model comparison and data augmentation. The third example illustrates estimation of parameters within a dynamic Susceptible-Infected-Recovered model. These examples show that BUGS can be used to estimate parameters from models relevant for infectious diseases, and provide an overview of the relative merits of the approach taken.

7.
PLoS Negl Trop Dis ; 13(10): e0007772, 2019 10.
Artigo em Inglês | MEDLINE | ID: mdl-31658267

RESUMO

BACKGROUND: Small island developing states (SIDS) in the Caribbean region are challenged with managing the health outcomes of a changing climate. Health and climate sectors have partnered to co-develop climate services to improve the management of emerging arboviral diseases such as dengue fever, for example, through the development of climate-driven early warning systems. The objective of this study was to identify health and climate stakeholder perceptions and needs in the Caribbean, with respect to the development of climate services for arboviruses. METHODS: Stakeholders included public decision makers and practitioners from the climate and health sectors at the regional (Caribbean) level and from the countries of Dominica and Barbados. From April to June 2017, we conducted interviews (n = 41), surveys (n = 32), and national workshops with stakeholders. Survey responses were tabulated, and audio recordings were transcribed and analyzed using qualitative coding to identify responses by research topic, country/region, and sector. RESULTS: Health practitioners indicated that their jurisdiction is currently experiencing an increased risk of arboviral diseases associated with climate variability, and most anticipated that this risk will increase in the future. National health sectors reported financial limitations and a lack of technical expertise in geographic information systems (GIS), statistics, and modeling, which constrained their ability to implement climate services for arboviruses. National climate sectors were constrained by a lack of personnel. Stakeholders highlighted the need to strengthen partnerships with the private sector, academia, and civil society. They identified a gap in local research on climate-arbovirus linkages, which constrained the ability of the health sector to make informed decisions. Strategies to strengthen the climate-health partnership included a top-down approach by engaging senior leadership, multi-lateral collaboration agreements, national committees on climate and health, and shared spaces of dialogue. Mechanisms for mainstreaming climate services for health operations to control arboviruses included climatic-health bulletins and an online GIS platform that would allow for regional data sharing and the generation of spatiotemporal epidemic forecasts. Stakeholders identified a 3-month forecast of arboviral illness as the optimal time frame for an epidemic forecast. CONCLUSIONS: These findings support the creation of interdisciplinary and intersectoral 'communities of practice' and the co-design of climate services for the Caribbean public health sector. By fostering the effective use of climate information within health policy, research and practice, nations will have greater capacity to adapt to a changing climate.

8.
Proc Biol Sci ; 286(1912): 20191867, 2019 Oct 09.
Artigo em Inglês | MEDLINE | ID: mdl-31594497

RESUMO

Dengue, an arboviral disease transmitted by Aedes mosquitoes, has been endemic in Brazil for decades. However, vector-control strategies have not led to a significant reduction in the disease burden and have not been sufficient to prevent chikungunya and Zika entry and establishment in the country. In Rio de Janeiro city, the first Zika and chikungunya epidemics were detected between 2015 and 2016, coinciding with a dengue epidemic. Understanding the behaviour of these diseases in a triple epidemic scenario is a necessary step for devising better interventions for prevention and outbreak response. We applied scan statistics analysis to detect spatio-temporal clustering for each disease separately and for all three simultaneously. In general, clusters were not detected in the same locations and time periods, possibly owing to competition between viruses for host resources, depletion of susceptible population, different introduction times and change in behaviour of the human population (e.g. intensified vector-control activities in response to increasing cases of a particular arbovirus). Simultaneous clusters of the three diseases usually included neighbourhoods with high population density and low socioeconomic status, particularly in the North region of the city. The use of space-time cluster detection can guide intensive interventions to high-risk locations in a timely manner, to improve clinical diagnosis and management, and pinpoint vector-control measures.

9.
BMC Med ; 17(1): 172, 2019 09 09.
Artigo em Inglês | MEDLINE | ID: mdl-31495336

RESUMO

BACKGROUND: Wolbachia-infected mosquitoes reduce dengue virus transmission, and city-wide releases in Yogyakarta city, Indonesia, are showing promising entomological results. Accurate estimates of the burden of dengue, its spatial distribution and the potential impact of Wolbachia are critical in guiding funder and government decisions on its future wider use. METHODS: Here, we combine multiple modelling methods for burden estimation to predict national case burden disaggregated by severity and map the distribution of burden across the country using three separate data sources. An ensemble of transmission models then predicts the estimated reduction in dengue transmission following a nationwide roll-out of wMel Wolbachia. RESULTS: We estimate that 7.8 million (95% uncertainty interval [UI] 1.8-17.7 million) symptomatic dengue cases occurred in Indonesia in 2015 and were associated with 332,865 (UI 94,175-754,203) lost disability-adjusted life years (DALYs). The majority of dengue's burden was due to non-severe cases that did not seek treatment or were challenging to diagnose in outpatient settings leading to substantial underreporting. Estimated burden was highly concentrated in a small number of large cities with 90% of dengue cases occurring in 15.3% of land area. Implementing a nationwide Wolbachia population replacement programme was estimated to avert 86.2% (UI 36.2-99.9%) of cases over a long-term average. CONCLUSIONS: These results suggest interventions targeted to the highest burden cities can have a disproportionate impact on dengue burden. Area-wide interventions, such as Wolbachia, that are deployed based on the area covered could protect people more efficiently than individual-based interventions, such as vaccines, in such dense environments.


Assuntos
Aedes/microbiologia , Dengue/prevenção & controle , Modelos Teóricos , Controle Biológico de Vetores/métodos , Wolbachia , Animais , Efeitos Psicossociais da Doença , Dengue/epidemiologia , Dengue/transmissão , Vírus da Dengue , Humanos , Indonésia/epidemiologia
10.
N Engl J Med ; 381(2): 111-120, 2019 07 11.
Artigo em Inglês | MEDLINE | ID: mdl-31291514

RESUMO

BACKGROUND: Point-of-care testing of C-reactive protein (CRP) may be a way to reduce unnecessary use of antibiotics without harming patients who have acute exacerbations of chronic obstructive pulmonary disease (COPD). METHODS: We performed a multicenter, open-label, randomized, controlled trial involving patients with a diagnosis of COPD in their primary care clinical record who consulted a clinician at 1 of 86 general medical practices in England and Wales for an acute exacerbation of COPD. The patients were assigned to receive usual care guided by CRP point-of-care testing (CRP-guided group) or usual care alone (usual-care group). The primary outcomes were patient-reported use of antibiotics for acute exacerbations of COPD within 4 weeks after randomization (to show superiority) and COPD-related health status at 2 weeks after randomization, as measured by the Clinical COPD Questionnaire, a 10-item scale with scores ranging from 0 (very good COPD health status) to 6 (extremely poor COPD health status) (to show noninferiority). RESULTS: A total of 653 patients underwent randomization. Fewer patients in the CRP-guided group reported antibiotic use than in the usual-care group (57.0% vs. 77.4%; adjusted odds ratio, 0.31; 95% confidence interval [CI], 0.20 to 0.47). The adjusted mean difference in the total score on the Clinical COPD Questionnaire at 2 weeks was -0.19 points (two-sided 90% CI, -0.33 to -0.05) in favor of the CRP-guided group. The antibiotic prescribing decisions made by clinicians at the initial consultation were ascertained for all but 1 patient, and antibiotic prescriptions issued over the first 4 weeks of follow-up were ascertained for 96.9% of the patients. A lower percentage of patients in the CRP-guided group than in the usual-care group received an antibiotic prescription at the initial consultation (47.7% vs. 69.7%, for a difference of 22.0 percentage points; adjusted odds ratio, 0.31; 95% CI, 0.21 to 0.45) and during the first 4 weeks of follow-up (59.1% vs. 79.7%, for a difference of 20.6 percentage points; adjusted odds ratio, 0.30; 95% CI, 0.20 to 0.46). Two patients in the usual-care group died within 4 weeks after randomization from causes considered by the investigators to be unrelated to trial participation. CONCLUSIONS: CRP-guided prescribing of antibiotics for exacerbations of COPD in primary care clinics resulted in a lower percentage of patients who reported antibiotic use and who received antibiotic prescriptions from clinicians, with no evidence of harm. (Funded by the National Institute for Health Research Health Technology Assessment Program; PACE Current Controlled Trials number, ISRCTN24346473.).


Assuntos
Antibacterianos/uso terapêutico , Proteína C-Reativa/análise , Prescrição Inadequada/prevenção & controle , Testes Imediatos , Doença Pulmonar Obstrutiva Crônica/tratamento farmacológico , Idoso , Biomarcadores/sangue , Feminino , Nível de Saúde , Humanos , Masculino , Pessoa de Meia-Idade , Padrões de Prática Médica/normas , Padrões de Prática Médica/estatística & dados numéricos , Doença Pulmonar Obstrutiva Crônica/sangue
11.
BMJ Open ; 9(6): e027513, 2019 06 20.
Artigo em Inglês | MEDLINE | ID: mdl-31227535

RESUMO

INTRODUCTION: Care home residents are at increased risk of infections and antibiotic prescription. Reduced antibiotic use from fewer infections would improve quality of life. The Probiotics to Reduce Infections iN CarE home reSidentS (PRINCESS) trial aims to determine the efficacy and investigate mechanisms of daily probiotics on antibiotic use and incidence of infections in care home residents. METHODS AND ANALYSIS: PRINCESS is a double-blind, individually randomised, placebo-controlled trial that will assess the effect of a daily oral probiotic combination of Lactobacillus rhamnosus, GG (LGG) and Bifidobacterium animalis subsp. lactis, BB-12 (BB-12) on cumulative antibiotic administration days (CAADs) (primary outcome) for infection in up to 330 care home residents aged ≥65 years over up to 12 months. Secondary outcomes include: Infection: Total number of days of antibiotic administration for each infection type (respiratory tract infection, urinary tract infection, gastrointestinal infection, unexplained fever and other); number, site, duration of infection; estimation of incidence and duration of diarrhoea and antibiotic-associated diarrhoea; Stool microbiology: Clostridium difficile infection; Gram-negative Enterobacteriaceae and vancomycin-resistant enterococci; LGG and BB-12. Oral microbiology: Candida spp. Health and well-being: Self and/or proxy health-related quality of life EQ5D (5 L); self-and/or proxy-reported ICEpop CAPability measure for older people. Hospitalisations: number and duration of all-cause hospital stays. Mortality: deaths. Mechanistic immunology outcomes: influenza vaccine efficacy (haemagglutination inhibition assay and antibody titres); full blood count and immune cell phenotypes, plasma cytokines and chemokines; cytokine and chemokine response in whole blood stimulated ex vivo by toll-like receptor 2 and 4 agonists; monocyte and neutrophil phagocytosis of Escherichia coli; serum vitamin D. ETHICS AND DISSEMINATION: Ethics approval is from the Wales Research Ethics Committee 3. Findings will be disseminated through peer-reviewed journals and conferences; results will be of interest to patient and policy stakeholders. TRIAL REGISTRATION NUMBER: ISRCTN16392920; Pre-results.

12.
PLoS Comput Biol ; 15(2): e1006785, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-30742608

RESUMO

Real-time forecasts based on mathematical models can inform critical decision-making during infectious disease outbreaks. Yet, epidemic forecasts are rarely evaluated during or after the event, and there is little guidance on the best metrics for assessment. Here, we propose an evaluation approach that disentangles different components of forecasting ability using metrics that separately assess the calibration, sharpness and bias of forecasts. This makes it possible to assess not just how close a forecast was to reality but also how well uncertainty has been quantified. We used this approach to analyse the performance of weekly forecasts we generated in real time for Western Area, Sierra Leone, during the 2013-16 Ebola epidemic in West Africa. We investigated a range of forecast model variants based on the model fits generated at the time with a semi-mechanistic model, and found that good probabilistic calibration was achievable at short time horizons of one or two weeks ahead but model predictions were increasingly unreliable at longer forecasting horizons. This suggests that forecasts may have been of good enough quality to inform decision making based on predictions a few weeks ahead of time but not longer, reflecting the high level of uncertainty in the processes driving the trajectory of the epidemic. Comparing forecasts based on the semi-mechanistic model to simpler null models showed that the best semi-mechanistic model variant performed better than the null models with respect to probabilistic calibration, and that this would have been identified from the earliest stages of the outbreak. As forecasts become a routine part of the toolkit in public health, standards for evaluation of performance will be important for assessing quality and improving credibility of mathematical models, and for elucidating difficulties and trade-offs when aiming to make the most useful and reliable forecasts.


Assuntos
Previsões/métodos , Doença pelo Vírus Ebola/epidemiologia , Tomada de Decisões , Surtos de Doenças , Epidemias , Métodos Epidemiológicos , Humanos , Modelos Estatísticos , Modelos Teóricos , Serra Leoa , Tempo
14.
BMC Med ; 16(1): 180, 2018 10 03.
Artigo em Inglês | MEDLINE | ID: mdl-30285863

RESUMO

BACKGROUND: Zika virus (ZIKV) emerged in Latin America and the Caribbean (LAC) region in 2013, with serious implications for population health in the region. In 2016, the World Health Organization declared the ZIKV outbreak a Public Health Emergency of International Concern following a cluster of associated neurological disorders and neonatal malformations. In 2017, Zika cases declined, but future incidence in LAC remains uncertain due to gaps in our understanding, considerable variation in surveillance and the lack of a comprehensive collation of data from affected countries. METHODS: Our analysis combines information on confirmed and suspected Zika cases across LAC countries and a spatio-temporal dynamic transmission model for ZIKV infection to determine key transmission parameters and projected incidence in 90 major cities within 35 countries. Seasonality was determined by spatio-temporal estimates of Aedes aegypti vectorial capacity. We used country and state-level data from 2015 to mid-2017 to infer key model parameters, country-specific disease reporting rates, and the 2018 projected incidence. A 10-fold cross-validation approach was used to validate parameter estimates to out-of-sample epidemic trajectories. RESULTS: There was limited transmission in 2015, but in 2016 and 2017 there was sufficient opportunity for wide-spread ZIKV transmission in most cities, resulting in the depletion of susceptible individuals. We predict that the highest number of cases in 2018 would present within some Brazilian States (Sao Paulo and Rio de Janeiro), Colombia and French Guiana, but the estimated number of cases were no more than a few hundred. Model estimates of the timing of the peak in incidence were correlated (p < 0.05) with the reported peak in incidence. The reporting rate varied across countries, with lower reporting rates for those with only confirmed cases compared to those who reported both confirmed and suspected cases. CONCLUSIONS: The findings suggest that the ZIKV epidemic is by and large over within LAC, with incidence projected to be low in most cities in 2018. Local low levels of transmission are probable, but the estimated rate of infection suggests that most cities have a population with high levels of herd immunity.


Assuntos
Epidemias , Modelos Teóricos , Infecção por Zika virus/epidemiologia , Animais , Humanos , Incidência , América Latina/epidemiologia , Organização Mundial da Saúde , Zika virus , Infecção por Zika virus/transmissão
15.
PLoS Med ; 15(7): e1002613, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-30016319

RESUMO

BACKGROUND: Over the last 5 years (2013-2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016. METHODS AND FINDINGS: Distributed lag nonlinear models (DLNMs) coupled with a hierarchal mixed-model framework were used to understand the exposure-lag-response association between dengue relative risk and key climate indicators, including the standardised precipitation index (SPI) and minimum temperature (Tmin). The model parameters were estimated in a Bayesian framework to produce probabilistic predictions of exceeding an island-specific outbreak threshold. The ability of the model to successfully detect outbreaks was assessed and compared to a baseline model, representative of standard dengue surveillance practice. Drought conditions were found to positively influence dengue relative risk at long lead times of up to 5 months, while excess rainfall increased the risk at shorter lead times between 1 and 2 months. The SPI averaged over a 6-month period (SPI-6), designed to monitor drought and extreme rainfall, better explained variations in dengue risk than monthly precipitation data measured in millimetres. Tmin was found to be a better predictor than mean and maximum temperature. Furthermore, including bidimensional exposure-lag-response functions of these indicators-rather than linear effects for individual lags-more appropriately described the climate-disease associations than traditional modelling approaches. In prediction mode, the model was successfully able to distinguish outbreaks from nonoutbreaks for most years, with an overall proportion of correct predictions (hits and correct rejections) of 86% (81%:91%) compared with 64% (58%:71%) for the baseline model. The ability of the model to predict dengue outbreaks in recent years was complicated by the lack of data on the emergence of new arboviruses, including chikungunya and Zika. CONCLUSION: We present a modelling approach to infer the risk of dengue outbreaks given the cumulative effect of climate variations in the months leading up to an outbreak. By combining the dengue prediction model with climate indicators, which are routinely monitored and forecasted by the Regional Climate Centre (RCC) at the Caribbean Institute for Meteorology and Hydrology (CIMH), probabilistic dengue outlooks could be included in the Caribbean Health-Climatic Bulletin, issued on a quarterly basis to provide climate-smart decision-making guidance for Caribbean health practitioners. This flexible modelling approach could be extended to model the risk of dengue and other arboviruses in the Caribbean region.


Assuntos
Aedes/virologia , Clima , Vírus da Dengue/patogenicidade , Dengue/epidemiologia , Surtos de Doenças , Vetores de Doenças , Tempo (Meteorologia) , Animais , Barbados/epidemiologia , Teorema de Bayes , Dengue/diagnóstico , Dengue/transmissão , Dengue/virologia , Secas , Inundações , Temperatura Alta/efeitos adversos , Humanos , Dinâmica não Linear , Chuva , Medição de Risco , Fatores de Risco , Fatores de Tempo
16.
Artigo em Inglês | MEDLINE | ID: mdl-29315224

RESUMO

The first confirmed case of Zika virus infection in the Americas was reported in Northeast Brazil in May 2015, although phylogenetic studies indicate virus introduction as early as 2013. Zika rapidly spread across Brazil and to more than 50 other countries and territories on the American continent. The Aedesaegypti mosquito is thought to be the principal vector responsible for the widespread transmission of the virus. However, sexual transmission has also been reported. The explosively emerging epidemic has had diverse impacts on population health, coinciding with cases of Guillain-Barré Syndrome and an unexpected epidemic of newborns with microcephaly and other neurological impairments. This led to Brazil declaring a national public health emergency in November 2015, followed by a similar decision by the World Health Organization three months later. While dengue virus serotypes took several decades to spread across Brazil, the Zika virus epidemic diffused within months, extending beyond the area of permanent dengue transmission, which is bound by a climatic barrier in the south and low population density areas in the north. This rapid spread was probably due to a combination of factors, including a massive susceptible population, climatic conditions conducive for the mosquito vector, alternative non-vector transmission, and a highly mobile population. The epidemic has since subsided, but many unanswered questions remain. In this article, we provide an overview of the discovery of Zika virus in Brazil, including its emergence and spread, epidemiological surveillance, vector and non-vector transmission routes, clinical complications, and socio-economic impacts. We discuss gaps in the knowledge and the challenges ahead to anticipate, prevent, and control emerging and re-emerging epidemics of arboviruses in Brazil and worldwide.


Assuntos
Epidemias/estatística & dados numéricos , Saúde Pública/estatística & dados numéricos , Saúde Pública/tendências , Infecção por Zika virus/diagnóstico , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Animais , Brasil/epidemiologia , Criança , Pré-Escolar , Feminino , Humanos , Incidência , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Vigilância da População , Adulto Jovem , Infecção por Zika virus/epidemiologia
17.
PLoS Negl Trop Dis ; 11(11): e0006088, 2017 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-29176840

RESUMO

Human excreta is a low cost source of nutrients vital to plant growth, but also a source of pathogens transmissible to people and animals. We investigated the cost-savings and infection risk of soil transmitted helminths (STHs) in four scenarios where farmers used either inorganic fertilizer or fresh/composted human excreta supplemented by inorganic fertilizer to meet the nutrient requirements of rice paddies in the Red River Delta, Vietnam. Our study included two main components: 1) a risk estimate of STH infection for farmers who handle fresh excreta, determined by systematic review and meta-analysis; and 2) a cost estimate of fertilizing rice paddies, determined by nutrient assessment of excreta, a retailer survey of inorganic fertilizer costs, and a literature review to identify region-specific inputs. Our findings suggest that farmers who reuse fresh excreta are 1.24 (95% CI: 1.13-1.37, p-value<0.001) times more likely to be infected with any STH than those who do not handle excreta or who compost appropriately, and that risk varies by STH type (Ascaris lumbricoides RR = 1.17, 95% CI = 0.87-1.58, p-value = 0.29; Hookworm RR = 1.02, 95% CI = 0.50-2.06, p-value = 0.96; Trichuris trichiura RR = 1.38, 95% CI = 0.79-2.42, p-value = 0.26). Average cost-savings were highest for farmers using fresh excreta (847,000 VND) followed by those who composted for 6 months as recommended by the WHO (312,000 VND) and those who composted for a shorter time (5 months) with lime supplementation (37,000 VND/yr); however, this study did not assess healthcare costs of treating acute or chronic STH infections in the target group. Our study provides evidence that farmers in the Red River Delta are able to use a renewable and locally available resource to their economic advantage, while minimizing the risk of STH infection.


Assuntos
Agricultura/métodos , Ascaríase/transmissão , Fezes/parasitologia , Infecções por Uncinaria/transmissão , Solo/parasitologia , Tricuríase/transmissão , Ancylostomatoidea/isolamento & purificação , Animais , Ascaríase/parasitologia , Ascaris lumbricoides/isolamento & purificação , Líquidos Corporais/parasitologia , Análise Custo-Benefício , Infecções por Uncinaria/parasitologia , Humanos , Modelos Lineares , Tricuríase/parasitologia , Trichuris/isolamento & purificação , Vietnã
18.
J Pharmacol Pharmacother ; 8(3): 140-144, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29081626

RESUMO

Methicillin-susceptible Staphylococcus aureus (MSSA) causes 45% of S. aureus bloodstream infections (BSI) and is the most important cause of BSI-associated death. The standard of care therapy is an anti-staphylococcal penicillin or cefazolin, but dosing frequencies for these agents are often infeasible; multiple daily doses tie up infusion lines and are impractical for outpatient antibiotic infusion. Ceftriaxone represents a promising alternative, with once daily dosing and a short infusion time. Currently, treatment with ceftriaxone for invasive MSSA infections is infrequent, with minimal data supporting the clinical utility of ceftriaxone for MSSA BSI. In this case series, we identified 15 patients receiving ceftriaxone for treatment of MSSA BSI, either following standard of care therapy or as initial therapy. Patients were evaluated for clinical cure (CC)(clearance of BSI and normalization of white blood cell count) and microbiological cure (MC)(clearance of blood cultures and no recurrence of organism within 60 days). CC was observed in seven patients, with MC observed in all patients. Only one patient was readmitted to the hospital. This case series provides vital data to support ceftriaxone for treatment of MSSA BSI. With few readmissions and recurrences of infection, ceftriaxone was an effective option for maintenance therapy after resolution of the BSI. Ceftriaxone appears to be a viable alternative for the treatment of MSSA BSI.

19.
Lancet Planet Health ; 1(4): e142-e151, 2017 07.
Artigo em Inglês | MEDLINE | ID: mdl-29851600

RESUMO

BACKGROUND: El Niño and its effect on local meteorological conditions potentially influences interannual variability in dengue transmission in southern coastal Ecuador. El Oro province is a key dengue surveillance site, due to the high burden of dengue, seasonal transmission, co-circulation of all four dengue serotypes, and the recent introduction of chikungunya and Zika. In this study, we used climate forecasts to predict the evolution of the 2016 dengue season in the city of Machala, following one of the strongest El Niño events on record. METHODS: We incorporated precipitation, minimum temperature, and Niño3·4 index forecasts in a Bayesian hierarchical mixed model to predict dengue incidence. The model was initiated on Jan 1, 2016, producing monthly dengue forecasts until November, 2016. We accounted for misreporting of dengue due to the introduction of chikungunya in 2015, by using active surveillance data to correct reported dengue case data from passive surveillance records. We then evaluated the forecast retrospectively with available epidemiological information. FINDINGS: The predictions correctly forecast an early peak in dengue incidence in March, 2016, with a 90% chance of exceeding the mean dengue incidence for the previous 5 years. Accounting for the proportion of chikungunya cases that had been incorrectly recorded as dengue in 2015 improved the prediction of the magnitude of dengue incidence in 2016. INTERPRETATION: This dengue prediction framework, which uses seasonal climate and El Niño forecasts, allows a prediction to be made at the start of the year for the entire dengue season. Combining active surveillance data with routine dengue reports improved not only model fit and performance, but also the accuracy of benchmark estimates based on historical seasonal averages. This study advances the state-of-the-art of climate services for the health sector, by showing the potential value of incorporating climate information in the public health decision-making process in Ecuador. FUNDING: European Union FP7, Royal Society, and National Science Foundation.


Assuntos
Dengue/epidemiologia , El Niño Oscilação Sul , Tempo (Meteorologia) , Cidades/epidemiologia , Clima , Dengue/virologia , Equador/epidemiologia , Previsões , Incidência , Modelos Teóricos , Estudos Retrospectivos
20.
Ann N Y Acad Sci ; 1382(1): 8-20, 2016 10.
Artigo em Inglês | MEDLINE | ID: mdl-27428726

RESUMO

After several decades of intensive research, steady improvements in understanding and modeling the climate system have led to the development of the first generation of operational health early warning systems in the era of climate services. These schemes are based on collaborations across scientific disciplines, bringing together real-time climate and health data collection, state-of-the-art seasonal climate predictions, epidemiological impact models based on historical data, and an understanding of end user and stakeholder needs. In this review, we discuss the challenges and opportunities of this complex, multidisciplinary collaboration, with a focus on the factors limiting seasonal forecasting as a source of predictability for climate impact models.


Assuntos
Mudança Climática , Avaliação do Impacto na Saúde/métodos , Modelos Teóricos , Estações do Ano , Tempo (Meteorologia) , Previsões , Avaliação do Impacto na Saúde/tendências , Humanos
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