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Introduction: There is a global effort to address the school dropout phenomenon. The urgency to act on it comes from the harmful evidence that school dropout has on societal and individual levels. Early Warning Systems (EWS) for school dropout at-risk student identification have been developed to anticipate and help schools have a better chance of acting on it. However, several studies point to a doubt that Correct EWS may come too late because they use only publicly available and general student and school information. We hypothesize that having a tool to assess more subjective and inter-relational factors would help anticipate where and when to act to prevent school dropout. This study aimed to develop a multidimensional measure for assessing relational factors for predicting school dropout (SD) risk in the Brazilian context. Methods: We performed several procedures, including (a) the specialized literature review, (b) the item development of the Relational Factors for the Risk of School Dropout Scale (IAFREE in Portuguese), (c) the content validity analysis, (d) a pilot study, and (e) the administration of the IAFREE to a large Brazilian sample of high school and middle school students (N = 15,924). Results: After the theoretical steps, we found content validity for five relational dimensions for SD (Student-School, Student-School Professionals, Student-Family, Student-Community, and Student-Student) that include 12 facets of risk factors. At the empirical stage, confirmatory analysis corroborated the proposed theoretical model with 12 first-order risk factors and 5 s-order dimensions (36 items). Further, through the Item Response Theory analysis, we assessed the individual item parameters of the items, providing a brief measure without losing psychometric quality (IAFREE-12). Discussion: We discuss how this model may fill gaps in Correct EWS models and how to advance it. The IAFREE is a good measure for scholars investigating the risk of SD. These results are important for implementing an early warning system for SD that looks into the complexity of the school dropout phenomenon.
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Leptospirosis is a zoonotic disease with a high burden in Latin America, including northeastern Argentina, where flooding events linked to El Niño are associated with leptospirosis outbreaks. The aim of this study was to evaluate the value of using hydrometeorological indicators to predict leptospirosis outbreaks in this region. We quantified the effects of El Niño, precipitation, and river height on leptospirosis risk in Santa Fe and Entre Ríos provinces between 2009 and 2020, using a Bayesian modelling framework. Based on several goodness of fit statistics, we selected candidate models using a long-lead El Niño 3.4 index and shorter lead local climate variables. We then tested predictive performance to detect leptospirosis outbreaks using a two-stage early warning approach. Three-month lagged Niño 3.4 index and one-month lagged precipitation and river height were positively associated with an increase in leptospirosis cases in both provinces. El Niño models correctly detected 89% of outbreaks, while short-lead local models gave similar detection rates with a lower number of false positives. Our results show that climatic events are strong drivers of leptospirosis incidence in northeastern Argentina. Therefore, a leptospirosis outbreak prediction tool driven by hydrometeorological indicators could form part of an early warning and response system in the region.
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Leptospirose , Leptospirose/epidemiologia , Argentina/epidemiologia , Surtos de Doenças , Humanos , Teorema de BayesRESUMO
The COVID-19 pandemic resulted in the collapse of healthcare systems and led to the development and application of several approaches of wastewater-based epidemiology to monitor infected populations. The main objective of this study was to carry out a SARS-CoV-2 wastewater based surveillance in Curitiba, Southern Brazil Sewage samples were collected weekly for 20 months at the entrance of five treatment plants representing the entire city and quantified by qPCR using the N1 marker. The viral loads were correlated with epidemiological data. The correlation by sampling points showed that the relationship between the viral loads and the number of reported cases was best described by a cross-correlation function, indicating a lag between 7 and 14 days amidst the variables, whereas the data for the entire city presented a higher correlation (0.84) with the number of positive tests at lag 0 (sampling day). The results also suggest that the Omicron VOC resulted in higher titers than the Delta VOC. Overall, our results showed that the approach used was robust as an early warning system, even with the use of different epidemiological indicators or changes in the virus variants in circulation. Therefore, it can contribute to public decision-makers and health interventions, especially in vulnerable and low-income regions with limited clinical testing capacity. Looking toward the future, this approach will contribute to a new look at environmental sanitation and should even induce an increase in sewage coverage rates in emerging countries.
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COVID-19 , Myrtaceae , Humanos , Águas Residuárias , SARS-CoV-2 , Esgotos , COVID-19/epidemiologia , Brasil/epidemiologia , PandemiasRESUMO
Arboviruses transmitted by Aedes aegypti in urban environments have spread rapidly worldwide, causing great impacts on public health. The development of reliable and timely alert signals is among the most important steps in designing accurate surveillance systems for vector-borne diseases. In July and September 2017, we conducted a pilot study to improve an existing integrated surveillance system by using entomo-virological surveillance to prioritize areas to conduct active searches for individuals with arbovirus infection symptoms. Foz do Iguaçu City has a permanent entomo-virological surveillance system with approximately 3,500 traps to capture Aedes sp. in the adult stage. The Aedes aegypti females are captured alive and human samples are submitted to RT-qPCR (real-time qPCR) screening for DENV, ZIKV, and CHIKV diagnosis. Of the 55 Ae. aegypti mosquitoes tested in July 2017, seven (12.7%) were considered positive for DENV-2 and three (5.4%) for CHIKV. In September, we tested a sample of 54 mosquitoes, and 15 (27.7%) were considered infected by DENV-2. We created 25 circumferences with 150-m radius each to perform an active survey to identify symptomatic householders. In July, we selected one circumference, and five (35.7%) patients were positive for DENV, whereas two (14.3%) for CHIKV. In September, we selected four circumferences, and, from the 21 individuals sampled, nine (42.8%) were positive for DENV-2. A statistical model with a binomial response was used to estimate the number of cases in areas without active surveys, i.e., 20 circumferences. We estimated an additional 83 symptomatic patients (95% CI: 45-145) to be found in active searches, with 38 (95% CI: 18-72) of them confirming arbovirus infection. Arbovirus detection and serotyping in mosquitoes, but also in symptomatic individuals during active surveys, can provide an alert signal of early arbovirus transmission.
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Aedes , Arbovírus , Vírus da Dengue , Infecção por Zika virus , Zika virus , Adulto , Animais , Feminino , Humanos , Vírus da Dengue/genética , Mosquitos Vetores , Projetos Piloto , Zika virus/genética , Infecção por Zika virus/epidemiologia , Vigilância de Evento SentinelaRESUMO
Arboviruses remain a significant cause of morbidity, mortality and economic cost across the global human population. Epidemics of arboviral disease, such as Zika and dengue, also cause significant disruption to health services at local and national levels. This study examined 2014-2016 Zika and dengue epidemic data at the sub-national level to characterise transmission across the Dominican Republic. For each municipality, spatio-temporal mapping was used to characterise disease burden, while data were age and sex standardised to quantify burden distributions among the population. In separate analyses, time-ordered data were combined with the underlying disease migration interval distribution to produce a network of likely transmission chain events, displayed using transmission chain likelihood matrices. Finally, municipal-specific reproduction numbers (Rm) were established using a Wallinga-Teunis matrix. Dengue and Zika epidemics peaked during weeks 39-52 of 2015 and weeks 14-27 of 2016, respectively. At the provincial level, dengue attack rates were high in Hermanas Mirabal and San José de Ocoa (58.1 and 49.2 cases per 10,000 population, respectively), compared with the Zika burden, which was highest in Independencia and San José de Ocoa (21.2 and 13.4 cases per 10,000 population, respectively). Across municipalities, high disease burden was observed in Cotuí (622 dengue cases per 10,000 population) and Jimani (32 Zika cases per 10,000 population). Municipal infector-infectee transmission likelihood matrices identified seven 0% likelihood transmission events throughout the dengue epidemic and two 0% likelihood transmission events during the Zika epidemic. Municipality reproduction numbers (Rm) were consistently higher, and persisted for a greater duration, during the Zika epidemic (Rm = 1.0) than during the dengue epidemic (Rm < 1.0). This research highlights the importance of disease surveillance in land border municipalities as an early warning for infectious disease transmission. It also demonstrates that a high number of importation events are required to sustain transmission in endemic settings, and vice versa for newly emerged diseases. The inception of a novel epidemiological metric, Rm, reports transmission risk using standardised spatial units, and can be used to identify high transmission risk municipalities to better focus public health interventions for dengue, Zika and other infectious diseases.
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Dengue/epidemiologia , Epidemias/estatística & dados numéricos , Saúde Pública/métodos , Infecção por Zika virus/epidemiologia , Cidades/estatística & dados numéricos , Conjuntos de Dados como Assunto , Dengue/prevenção & controle , Vírus da Dengue/patogenicidade , República Dominicana/epidemiologia , Epidemias/prevenção & controle , Humanos , Modelos Estatísticos , Zika virus/patogenicidade , Infecção por Zika virus/prevenção & controleRESUMO
The transmission of dengue (DENV) and Zika (ZIKV) has been continuously increasing worldwide. An efficient arbovirus surveillance system is critical to designing early-warning systems to increase preparedness of future outbreaks in endemic countries. The Near Infrared Spectroscopy (NIRS) is a promising high throughput technique to detect arbovirus infection in Ae. aegypti with remarkable advantages such as cost and time effectiveness, reagent-free, and non-invasive nature over existing molecular tools for similar purposes, enabling timely decision making through rapid detection of potential disease. Our aim was to determine whether NIRS can differentiate Ae. aegypti females infected with either ZIKV or DENV single infection, and those coinfected with ZIKV/DENV from uninfected ones. Using 200 Ae. aegypti females reared and infected in laboratory conditions, the training model differentiated mosquitoes into the four treatments with 100% accuracy. DENV-, ZIKV-, and ZIKV/DENV-coinfected mosquitoes that were used to validate the model could be correctly classified into their actual infection group with a predictive accuracy of 100%, 84%, and 80%, respectively. When compared with mosquitoes from the uninfected group, the three infected groups were predicted as belonging to the infected group with 100%, 97%, and 100% accuracy for DENV-infected, ZIKV-infected, and the co-infected group, respectively. Preliminary lab-based results are encouraging and indicate that NIRS should be tested in field settings to evaluate its potential role to monitor natural infection in field-caught mosquitoes.
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Aedes , Vírus da Dengue , Dengue , Infecção por Zika virus , Zika virus , Animais , Feminino , Infecção por Zika virus/epidemiologia , Espectroscopia de Luz Próxima ao InfravermelhoRESUMO
OBJECTIVES: Quantitative analysis of the implementation of the bedside paediatric early warning system (B-PEWS) in a resource-limited setting. The B-PEWS serves to pre-emptively identify hospitalised children who are at risk for cardiopulmonary arrest and subsequently to provide critical care in time. METHODS: We performed a retrospective review through the medical data records of patients after discharge from the paediatric ward of a philanthropic hospital in Brazil. Nurses' performance using the system was measured with various parameters. RESULTS: A total of 499 patients were included, and a total of 8024 scores were checked. During the 21-week research period, the implementation rate increased significantly from 66.5% (SD 26.0) in Period 1 to 93.1% (SD 16.6) in Period 2. The number of scores that resulted in a correct total score went from 7.5% in Period 1 to 32.2% in Period 2, p < 0.001. There was an improvement in the correct choice of age group between the two periods (from 32.2% to 53.4%). There was no difference in the mean admission time of patients in the two periods: in the first period 4.8 days (SD 2.9) and in the second period 4.8 days (SD 4.1). CONCLUSIONS: It is possible to implement a PEWS in resource-limited settings while achieving high implementation rates. However, this is a time- and energy-consuming process. Having an active and involved team that is responsible for implementation is key for a successful implementation. Factors that likely hindered implementation were a large change in workflow for the nursing staff, non-native speakers as main investigators.
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Indicadores Básicos de Saúde , Hospitais , Monitorização Fisiológica/normas , Brasil , Criança , Pré-Escolar , Protocolos Clínicos , Cuidados Críticos , Estado Terminal , Progressão da Doença , Humanos , Lactente , Estudos RetrospectivosRESUMO
Pediatric oncology patients hospitalized in resource-limited settings are at high risk for clinical deterioration resulting in mortality. Intermediate care units (IMCUs) provide a cost-effective alternative to pediatric intensive care units (PICUs). Inappropriate IMCU triage, however, can lead to poor outcomes and suboptimal resource utilization. In this study, we sought to characterize patients with clinical deterioration requiring unplanned transfer to the IMCU in a resource-limited pediatric oncology hospital. Patients requiring subsequent early PICU transfer had longer PICU length of stay. PEWS results prior to IMCU transfer were higher in patients requiring early PICU transfer, suggesting PEWS can aid in triage between IMCU and PICU care.
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Técnicas de Apoio para a Decisão , Neoplasias , Transferência de Pacientes , Triagem/métodos , Algoritmos , Criança , Pré-Escolar , Países em Desenvolvimento , Feminino , Guatemala , Hospitais Pediátricos , Humanos , Unidades de Terapia Intensiva Pediátrica , MasculinoRESUMO
BACKGROUND: Pediatric oncology patients are at high risk of clinical deterioration, particularly in hospitals with resource limitations. The performance of pediatric early warning systems (PEWS) to identify deterioration has not been assessed in these settings. This study evaluates the validity of PEWS to predict the need for unplanned transfer to the pediatric intensive care unit (PICU) among pediatric oncology patients in a resource-limited hospital. METHODS: A retrospective case-control study comparing the highest documented and corrected PEWS score before unplanned PICU transfer in pediatric oncology patients (129 cases) with matched controls (those not requiring PICU care) was performed. RESULTS: Documented and corrected PEWS scores were found to be highly correlated with the need for PICU transfer (area under the receiver operating characteristic, 0.940 and 0.930, respectively). PEWS scores increased 24 hours prior to unplanned transfer (P = .0006). In cases, organ dysfunction at the time of PICU admission correlated with maximum PEWS score (correlation coefficient, 0.26; P = .003), patients with PEWS results ≥4 had a higher Pediatric Index of Mortality 2 (PIM2) (P = .028), and PEWS results were higher in patients with septic shock (P = .01). The PICU mortality rate was 17.1%; nonsurvivors had higher mean PEWS scores before PICU transfer (P = .0009). A single-point increase in the PEWS score increased the odds of mechanical ventilation or vasopressors within the first 24 hours and during PICU admission (odds ratio 1.3-1.4). CONCLUSIONS: PEWS accurately predicted the need for unplanned PICU transfer in pediatric oncology patients in this resource-limited setting, with abnormal results beginning 24 hours before PICU admission and higher scores predicting the severity of illness at the time of PICU admission, need for PICU interventions, and mortality. These results demonstrate that PEWS aid in the identification of clinical deterioration in this high-risk population, regardless of a hospital's resource-level. Cancer 2017;123:4903-13. © 2017 American Cancer Society.
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Recursos em Saúde/economia , Mortalidade Hospitalar/tendências , Unidades de Terapia Intensiva Pediátrica/economia , Neoplasias/economia , Neoplasias/terapia , Estudos de Casos e Controles , Criança , Pré-Escolar , Países em Desenvolvimento , Feminino , Guatemala , Hospitalização/economia , Hospitalização/estatística & dados numéricos , Humanos , Lactente , Unidades de Terapia Intensiva Pediátrica/estatística & dados numéricos , Masculino , Neoplasias/patologia , Pediatria/economia , Curva ROC , Estudos Retrospectivos , Fatores SocioeconômicosRESUMO
BACKGROUND: Dengue has been prevalent in Colombia with high risk of outbreaks in various locations. While the prediction of dengue epidemics will bring significant benefits to the society, accurate forecasts have been a challenge. Given competing health demands in Colombia, it is critical to consider the effective use of the limited healthcare resources by identifying high risk areas for dengue fever. METHODS: The Climate Risk Factor (CRF) index was constructed based upon temperature, precipitation, and humidity. Considering the conditions necessary for vector survival and transmission behavior, elevation and population density were taken into account. An Early Warning Signal (EWS) model was developed by estimating the elasticity of the climate risk factor function to detect dengue epidemics. The climate risk factor index was further estimated at the smaller geographical unit (5 km by 5 km resolution) to identify populations at high risk. RESULTS: From January 2007 to December 2015, the Early Warning Signal model successfully detected 75% of the total number of outbreaks 1 ~ 5 months ahead of time, 12.5% in the same month, and missed 12.5% of all outbreaks. The climate risk factors showed that populations at high risk are concentrated in the Western part of Colombia where more suitable climate conditions for vector mosquitoes and the high population level were observed compared to the East. CONCLUSIONS: This study concludes that it is possible to detect dengue outbreaks ahead of time and identify populations at high risk for various disease prevention activities based upon observed climate and non-climate information. The study outcomes can be used to minimize potential societal losses by prioritizing limited healthcare services and resources, as well as by conducting vector control activities prior to experiencing epidemics.
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Dengue/epidemiologia , Animais , Clima , Colômbia/epidemiologia , Culicidae , Surtos de Doenças , Humanos , Umidade , Densidade Demográfica , Fatores de Risco , Temperatura , Tempo (Meteorologia)RESUMO
BACKGROUND: Hospitalized pediatric oncology patients are at high risk of clinical decline and mortality, particularly in resource-limited settings. Pediatric early warning systems (PEWS) aid in the early identification of clinical deterioration; however, there are limited data regarding their feasibility or impact in low-resource settings. This study describes the successful implementation of PEWS at the Unidad Nacional de Oncología Pediátrica (UNOP), a pediatric oncology hospital in Guatemala, resulting in improved inpatient outcomes. METHODS: A modified PEWS was implemented at UNOP with systems to track errors, transfers to a higher level of care, and high scores. A retrospective cohort study was used to evaluate clinical deterioration events in the year before and after PEWS implementation. RESULTS: After PEWS implementation at UNOP, there was 100% compliance with PEWS documentation and an error rate of <10%. Implementation resulted in 5 high PEWS per week, with 30% of patients transferring to a higher level of care. Among patients requiring transfer to the pediatric intensive care unit (PICU), 93% had an abnormal PEWS before transfer. The rate of clinical deterioration events decreased after PEWS implementation (9.3 vs 6.5 per 1000-hospitalpatient-days, p = .003). Despite an 18% increase in total hospital patient-days, PICU utilization for inpatient transfers decreased from 1376 to 1088 PICU patient-days per year (21% decrease; P<.001). CONCLUSIONS: This study describes the successful implementation of PEWS in a pediatric oncology hospital in Guatemala, resulting in decreased inpatient clinical deterioration events and PICU utilization. This work demonstrates that PEWS is a feasible and effective quality improvement measure to improve hospital care for children with cancer in hospitals with limited resources. Cancer 2017;123:2965-74. © 2017 American Cancer Society.
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Algoritmos , Institutos de Câncer , Recursos em Saúde , Hospitais Pediátricos , Neoplasias/terapia , Avaliação em Enfermagem , Pneumonia/diagnóstico , Sepse/diagnóstico , Adolescente , Criança , Pré-Escolar , Estudos de Coortes , Progressão da Doença , Diagnóstico Precoce , Intervenção Médica Precoce , Feminino , Guatemala , Humanos , Lactente , Unidades de Terapia Intensiva Pediátrica , Tempo de Internação , Masculino , Pneumonia/terapia , Melhoria de Qualidade , Estudos Retrospectivos , Sepse/terapia , Choque Séptico/diagnóstico , Choque Séptico/terapia , Sinais VitaisRESUMO
Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.