Using Re-Sampling Methods in Mortality Studies

Traditional methods of computing standardized mortality ratios (SMR) in mortality studies rely upon a number of conventional statistical propositions to estimate confidence intervals for obtained values. Those propositions include a common but arbitrary choice of the confidence level and the assumption that observed number of deaths in the test sample is a purely random quantity. The latter assumption may not be fully justified for a series of periodic “overlapping” studies. We propose a new approach to evaluating the SMR, along with its confidence interval, based on a simple re-sampling technique. The proposed method is most straightforward and requires neither the use of above assumptions nor any rigorous technique, employed by modern re-sampling theory, for selection of a sample set. Instead, we include all possible samples that correspond to the specified time window of the study in the re-sampling analysis. As a result, directly obtained confidence intervals for repeated overlapping studies may be tighter than those yielded by conventional methods. The proposed method is illustrated by evaluating mortality due to a hypothetical risk factor in a life insurance cohort. With this method used, the SMR values can be forecast more precisely than when using the traditional approach. As a result, the appropriate risk assessment would have smaller uncertainties.     

Spatio-temporal dynamics of rabies and habitat suitability of the common marmoset Callithrix jacchus in Brazil

Rabies transmitted by wildlife is now the main source of human rabies in the Americas. The common marmoset, Callithrix jacchus, is considered a reservoir of rabies causing sporadic and unpredictable human deaths in Brazil, but the extent of the spillover risk to humans remains unknown. In this study, we described the spatiotemporal dynamics of rabies affecting C. jacchus reported to Brazil’s Ministry of Health passive surveillance system between 2008 and 2020, and combined ecological niche modelling with C. jacchus occurrence data to predict its suitable habitat. Our results show that 67 outbreaks (91 cases) of rabies affecting C. jacchus were reported by 41 municipalities between January 2008 and October 2020, with a mean of 5 outbreaks/year [range: 1–14]. The maximum number of outbreaks and municipalities reporting cases occurred in 2018, coinciding with higher surveillance of primate deaths due to Yellow Fever. A mean of 3 [1–9] new municipalities reported outbreaks yearly, suggesting potential spatial expansions of the C. jacchus variant in northeastern Brazil and emerging rabies spillover from vampire bat Desmodus rotundus to C. jacchus in the north and south. Outbreaks were concentrated in the states of Ceará (72%) and Pernambuco (16%) up to 2012, but are now reported in Piauí since 2013, in Bahia since 2017 (D. rotundus’ antigenic variant, AgV3) and in Rio de Janeiro since 2019 (AgV3). Besides confirming suitable habitat for this primate in the northeast and the east coast of Brazil, our Maximum Entropy model also predicted suitable habitat on the north and the west states of the country but predicted low habitat suitability among inland municipalities of the Caatinga biome reporting rabies. Our findings revealed new areas reporting rabies infecting C. jacchus, highlighting the need to implement strategies limiting spillover to humans and to better understand the drivers of C. jacchus rabies dynamics.     

Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data

When responding to infectious disease outbreaks, rapid and accurate estimation of the epidemic trajectory is critical. However, two common data collection problems affect the reliability of the epidemiological data in real time: missing information on the time of first symptoms, and retrospective revision of historical information, including right censoring. Here, we propose an approach to construct epidemic curves in near real time that addresses these two challenges by 1) imputation of dates of symptom onset for reported cases using a dynamically-estimated “backward” reporting delay conditional distribution, and 2) adjustment for right censoring using the NobBS software package to nowcast cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number (R_t) in real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between March and April 2020. We evaluate how these real-time estimates compare with more complete epidemiological data that became available later. We explore the impact of the different assumptions on the estimates, and compare our estimates with those obtained from commonly used surveillance approaches. Our framework can help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health systems in other locations.     

A hierarchical model for analyzing multisite individual-level disease surveillance data from multiple systems

Passive surveillance systems are widely used to monitor diseases occurrence over wide spatial areas due to their cost-effectiveness and integration into broadly distributed healthcare systems. However, such systems are generally associated with imperfect ascertainment of disease cases and with heterogeneous capture probabilities arising from factors such as differential access to care. Augmenting passive surveillance systems with other surveillance efforts provides a way to estimate the true number of incident cases. We develop a hierarchical modeling framework for analyzing data from multiple surveillance systems that allows for individual-level covariate-dependent heterogeneous capture probabilities, and borrows information across surveillance sites to improve estimation of the true number of incident cases. Inference is carried out via a two-stage Bayesian procedure. Simulation studies illustrated superior performance of the proposed approach with respect to bias, root mean square error, and coverage compared to a model that does not borrow information across sites. We applied the proposed model to data from three surveillance systems reporting pulmonary tuberculosis (PTB) cases in a major center of ongoing transmission in China. The analysis yielded bias-corrected estimates of PTB cases from the passive system and led to the identification of risk factors associated with PTB rates, as well as factors influencing the operating characteristics of the implemented surveillance systems.     

The Effectiveness of U.S. Public Health Surveillance Systems for Situational Awareness during the 2009 H1N1 Pandemic: A Retrospective Analysis

Background

The 2009 H1N1 outbreak provides an opportunity to learn about the strengths and weaknesses of current U.S. public health surveillance systems and to identify implications for measuring public health emergency preparedness.

Methodology/Principal Findings

We adopted a “triangulation” approach in which multiple contemporary data sources, each with different expected biases, are compared to identify time patterns that are likely to reflect biases versus those that are more likely to be indicative of actual infection rates. This approach is grounded in the understanding that surveillance data are the result of a series of decisions made by patients, health care providers, and public health professionals about seeking and providing health care and about reporting cases to health authorities. Although limited by the lack of a gold standard, this analysis suggests that children and young adults are over-represented in many pH1N1 surveillance systems, especially in the spring wave. In addition, the nearly two-month delay between the Northeast and the South in the Fall peak in some surveillance data seems to at least partially reflect regional differences in concerns about pH1N1rather than real differences in pH1N1 infection rates.

Conclusions/Significance

Although the extent of the biases suggested by this analysis cannot be known precisely, the analysis identifies underlying problems with surveillance systems – in particular their dependence on patient and provider behavior, which is influenced by a changing information environment – that could limit situational awareness in future public health emergencies. To improve situational awareness in future health emergencies, population-based surveillance systems such as telephone surveys of representative population samples and seroprevalence surveys in well-defined population cohorts are needed.     

Systems biology approach to developing S2RM-based “systems therapeutics” and naturally induced pluripotent stem cells

The degree to, and the mechanisms through, which stem cells are able to build, maintain, and heal the body have only recently begun to be understood. Much of the stem cell’s power resides in the release of a multitude of molecules, called stem cell released molecules (SRM). A fundamentally new type of therapeutic, namely “systems therapeutic”, can be realized by reverse engineering the mechanisms of the SRM processes. Recent data demonstrates that the composition of the SRM is different for each type of stem cell, as well as for different states of each cell type. Although systems biology has been successfully used to analyze multiple pathways, the approach is often used to develop a small molecule interacting at only one pathway in the system. A new model is emerging in biology where systems biology is used to develop a new technology acting at multiple pathways called “systems therapeutics”. A natural set of healing pathways in the human that uses SRM is instructive and of practical use in developing systems therapeutics. Endogenous SRM processes in the human body use a combination of SRM from two or more stem cell types, designated as S²RM, doing so under various state dependent conditions for each cell type. Here we describe our approach in using state-dependent SRM from two or more stem cell types, S²RM technology, to develop a new class of therapeutics called “systems therapeutics.” Given the ubiquitous and powerful nature of innate S²RM-based healing in the human body, this “systems therapeutic” approach using S²RM technology will be important for the development of anti-cancer therapeutics, antimicrobials, wound care products and procedures, and a number of other therapeutics for many indications.     

Quantifying the Potential Bias when Directly Comparing Standardised Mortality Ratios for In-Unit Neonatal Mortality

Introduction

The Standardised Mortality Ratio (SMR) is increasingly used to compare the performance of different healthcare providers. However, it has long been known that differences in the populations of the providers can cause biased results when directly comparing two SMRs. This is potentially a particular problem in neonatal medicine where units provide different levels of care.

Methods

Using data from The Neonatal Survey (TNS), babies born at 24 to 31 weeks gestational age from 2002 to 2011 and admitted to one of 11 UK neonatal units were identified. Risk-adjusted SMRs were calculated for each unit using a previously published model to estimate the expected number of deaths. The model parameters were then re-estimated based on data from each individual neonatal unit (“reference” unit) and these then applied to each of the other units to estimate the number of deaths each unit would have observed if they had the same underlying mortality rates as each of the “reference” hospitals. The ratios of the SMRs were then calculated under the assumption of identical risk-specific probabilities of death.

Results

7243 babies were included in all analyses. When comparing between Network Neonatal Units (Level 3) the ratio of SMRs ranged from 0.92 to 1.00 and for the comparisons within Local Neonatal Units (Level 2) ranged from 0.79 to 1.56. However when comparing between neonatal units providing different levels of care ratios up to 1.68 were observed.

Conclusions

If the populations of healthcare providers differ considerably then it is likely that bias will be an issue when directly comparing SMRs. In neonatal care, the comparison of Network Neonatal Units is likely to be useful but caution is required when comparing Local Neonatal Units or between units of different types. Tools to quantify the likely bias are required.     

The Impact of Symbolic and Non-Symbolic Quantity on Spatial Learning

An implicit mapping of number to space via a “mental number line” occurs automatically in adulthood. Here, we systematically explore the influence of differing representations of quantity (no quantity, non-symbolic magnitudes, and symbolic numbers) and directional flow of stimuli (random flow, left-to-right, or right-to-left) on learning and attention via a match-to-sample working memory task. When recalling a cognitively demanding string of spatial locations, subjects performed best when information was presented right-to-left. When non-symbolic or symbolic numerical arrays were embedded in these spatial locations, and mental number line congruency prompted, this effect was attenuated and in some cases reversed. In particular, low-performing female participants who viewed increasing non-symbolic number arrays paired with the spatial locations exhibited better recall for left-to-right directional flow information relative to right-to-left, and better processing for the left side of space relative to the right side of space. The presence of symbolic number during spatial learning enhanced recall to a greater degree than non-symbolic number—especially for female participants, and especially when cognitive load is high—and this difference was independent of directional flow of information. We conclude that quantity representations have the potential to scaffold spatial memory, but this potential is subtle, and mediated by the nature of the quantity and the gender and performance level of the learner.     

Using Bayes' Rule to Define the Value of Evidence from Syndromic Surveillance

In this work we propose the adoption of a statistical framework used in the evaluation of forensic evidence as a tool for evaluating and presenting circumstantial “evidence” of a disease outbreak from syndromic surveillance. The basic idea is to exploit the predicted distributions of reported cases to calculate the ratio of the likelihood of observing n cases given an ongoing outbreak over the likelihood of observing n cases given no outbreak. The likelihood ratio defines the Value of Evidence (V). Using Bayes'' rule, the prior odds for an ongoing outbreak are multiplied by V to obtain the posterior odds. This approach was applied to time series on the number of horses showing clinical respiratory symptoms or neurological symptoms. The separation between prior beliefs about the probability of an outbreak and the strength of evidence from syndromic surveillance offers a transparent reasoning process suitable for supporting decision makers. The value of evidence can be translated into a verbal statement, as often done in forensics or used for the production of risk maps. Furthermore, a Bayesian approach offers seamless integration of data from syndromic surveillance with results from predictive modeling and with information from other sources such as disease introduction risk assessments.     

Identifying the Threshold of Dominant Controls on Fire Spread in a Boreal Forest Landscape of Northeast China

The relative importance of fuel, topography, and weather on fire spread varies at different spatial scales, but how the relative importance of these controls respond to changing spatial scales is poorly understood. We designed a “moving window” resampling technique that allowed us to quantify the relative importance of controls on fire spread at continuous spatial scales using boosted regression trees methods. This quantification allowed us to identify the threshold value for fire size at which the dominant control switches from fuel at small sizes to weather at large sizes. Topography had a fluctuating effect on fire spread across the spatial scales, explaining 20–30% of relative importance. With increasing fire size, the dominant control switched from bottom-up controls (fuel and topography) to top-down controls (weather). Our analysis suggested that there is a threshold for fire size, above which fires are driven primarily by weather and more likely lead to larger fire size. We suggest that this threshold, which may be ecosystem-specific, can be identified using our “moving window” resampling technique. Although the threshold derived from this analytical method may rely heavily on the sampling technique, our study introduced an easily implemented approach to identify scale thresholds in wildfire regimes.     

Assessing the reporting of Dengue,Chikungunya and Zika to the National Surveillance System in Colombia from 2014–2017: A Capture-recapture analysis accounting for misclassification of arboviral diagnostics

BackgroundChikungunya, dengue, and Zika are three different arboviruses which have similar symptoms and are a major public health issue in Colombia. Despite the mandatory reporting of these arboviruses to the National Surveillance System in Colombia (SIVIGILA), it has been reported that the system captures less than 10% of diagnosed cases in some cities.Methodology/Principal findingsTo assess the scope and degree of arboviruses reporting in Colombia between 2014–2017, we conducted an observational study of surveillance data using the capture-recapture approach in three Colombian cities. Using healthcare facility registries (capture data) and surveillance-notified cases (recapture data), we estimated the degree of reporting by clinical diagnosis. We fit robust Poisson regressions to identify predictors of reporting and estimated the predicted probability of reporting by disease and year. To account for the potential misclassification of the clinical diagnosis, we used the simulation extrapolation for misclassification (MC-SIMEX) method. A total of 266,549 registries were examined. Overall arboviruses’ reporting ranged from 5.3% to 14.7% and varied in magnitude according to age and year of diagnosis. Dengue was the most notified disease (21–70%) followed by Zika (6–45%). The highest reporting rate was seen in 2016, an epidemic year. The MC-SIMEX corrected rates indicated underestimation of the reporting due to the potential misclassification bias.ConclusionsThese findings reflect challenges on arboviruses’ reporting, and therefore, potential challenges on the estimation of arboviral burden in Colombia and other endemic settings with similar surveillance systems.     

Re-Examination of Globally Flat Space-Time

In the following, we offer a novel approach to modeling the observed effects currently attributed to the theoretical concepts of “dark energy,” “dark matter,” and “dark flow.” Instead of assuming the existence of these theoretical concepts, we take an alternative route and choose to redefine what we consider to be inertial motion as well as what constitutes an inertial frame of reference in flat space-time. We adopt none of the features of our current cosmological models except for the requirement that special and general relativity be local approximations within our revised definition of inertial systems. Implicit in our ideas is the assumption that at “large enough” scales one can treat objects within these inertial systems as point-particles having an insignificant effect on the curvature of space-time. We then proceed under the assumption that time and space are fundamentally intertwined such that time- and spatial-translational invariance are not inherent symmetries of flat space-time (i.e., observable clock rates depend upon both relative velocity and spatial position within these inertial systems) and take the geodesics of this theory in the radial Rindler chart as the proper characterization of inertial motion. With this commitment, we are able to model solely with inertial motion the observed effects expected to be the result of “dark energy,” “dark matter,” and “dark flow.” In addition, we examine the potential observable implications of our theory in a gravitational system located within a confined region of an inertial reference frame, subsequently interpreting the Pioneer anomaly as support for our redefinition of inertial motion. As well, we extend our analysis into quantum mechanics by quantizing for a real scalar field and find a possible explanation for the asymmetry between matter and antimatter within the framework of these redefined inertial systems.     

Characteristics of Hospitalized Cases with Influenza A (H1N1)pdm09 Infection during First Winter Season of Post-Pandemic in China

Background

Influenza A (H1N1)pdm09 (2009 H1N1) re-circulated as the predominant virus from January through February 2011 in China. National surveillance of 2009 H1N1 as a notifiable disease was maintained to monitor potential changes in disease severity from the previous season.

Methodology/Principal Findings

To describe the characteristics of hospitalized cases with 2009 H1N1 infection and analyze risk factors for severe illness during the 2010–2011winter season in China, we obtained surveillance data from hospitalized cases with 2009 H1N1 infection from November 2010 through May 2011, and reviewed medical records from 701 hospitalized cases. Age-standardized risk ratios were used to compare the age distribution of patients that were hospitalized and died due to 2009 H1N1 between the 2010–2011winter season to those during the 2009–2010 pandemic period. During the 2010–2011 winter season, children less than 5 years of age had the highest relative risk of hospitalization and death, followed by adults aged 65 years or older. Additionally, the relative risk of hospitalized cases aged 5–14 and 15–24 years was lower compared to children less than 5 years of age. During the winter season of 2010–2011, the proportions of adults aged 25 years or older for hospitalization and death were significantly higher than those during the 2009–2010 pandemic period. Being male, having a chronic medical condition, delayed hospital admission (≥3 days from onset) or delayed initiation of antiviral treatment (≥5 days from onset) were associated with severe illness among non-pregnant patients ≥2 years of age.

Conclusions/Significance

We observed a change in high risk groups for hospitalization for 2009 H1N1 during the winter months immediately following the pandemic period compared to the high risk groups identified during the pandemic period. Our nationally notifiable disease surveillance system enabled us to understand the evolving epidemiology of 2009 H1N1 infection after the pandemic period.     

Changing Patterns of Human Anthrax in Azerbaijan during the Post-Soviet and Preemptive Livestock Vaccination Eras

We assessed spatial and temporal changes in the occurrence of human anthrax in Azerbaijan during 1984 through 2010. Data on livestock outbreaks, vaccination efforts, and human anthrax incidence during Soviet governance, post-Soviet governance, preemptive livestock vaccination were analyzed. To evaluate changes in the spatio-temporal distribution of anthrax, we used a combination of spatial analysis, cluster detection, and weighted least squares segmented regression. Results indicated an annual percent change in incidence of +11.95% from 1984 to 1995 followed by declining rate of −35.24% after the initiation of livestock vaccination in 1996. Our findings also revealed geographic variation in the spatial distribution of reporting; cases were primarily concentrated in the west early in the study period and shifted eastward as time progressed. Over twenty years after the dissolution of the Soviet Union, the distribution of human anthrax in Azerbaijan has undergone marked changes. Despite decreases in the incidence of human anthrax, continued control measures in livestock are needed to mitigate its occurrence. The shifting patterns of human anthrax highlight the need for an integrated “One Health” approach that takes into account the changing geographic distribution of the disease.     

Effects of Biogents Sentinel Trap Field Placement on Capture Rates of Adult Asian Tiger Mosquitoes,Aedes albopictus

The Biogents® Sentinel (BGS) trap is the standard tool to monitor adult Aedes (Stegomyia) albopictus (Skuse) (Diptera: Culicidae), the Asian tiger mosquito. BGS traps are commonly placed in residential properties during surveillance operations, but locations within properties may have significant differences in ambient light, temperature, and humidity (e.g. between a sunlit lawn and shady underbrush). We examined the effect of BGS trap placement on Ae. albopictus capture rates in three residential properties in Monmouth County, New Jersey, USA. In each property we visually selected locations as shade, partial shade, and sun. Traps in “partial shade” locations were under vegetation and were exposed to filtered sunlight during some parts of the day while “shaded” locations were never exposed to direct sunlight. Locations defined as “sun” were exposed to direct sunlight for large parts of the day. We placed a BGS trap in each of the three location types and used small data loggers to measure temperature, relative humidity, and light exposure at each trap during a 24-hour deployment. To address temporal variability, we made seven separate measurements from 31 August to 22 September 2010. We found that “partial shade” and “full shade” locations did not differ but that “full sun” locations had significantly higher light exposure, higher temperature, and lower humidity. Importantly, Ae. albopictus catches (males, females, or both) were consistently and significantly over 3 times higher in traps located in shaded locations. To further investigate the effects of local temperature and humidity on surveillance we examined Ae. albopictus collections from 37 BGS traps fitted with data loggers and deployed weekly from August through mid October, during the 2009 season, in three urban sites in Mercer County, NJ. We confirmed that local climate influences capture rates and that Ae. albopictus surveillance projects need to monitor trap placement carefully for maximum efficiency.     

Surveillance for Neisseria meningitidis Disease Activity and Transmission Using Information Technology

BackgroundWhile formal reporting, surveillance, and response structures remain essential to protecting public health, a new generation of freely accessible, online, and real-time informatics tools for disease tracking are expanding the ability to raise earlier public awareness of emerging disease threats. The rationale for this study is to test the hypothesis that the HealthMap informatics tools can complement epidemiological data captured by traditional surveillance monitoring systems for meningitis due to Neisseria meningitides (N. meningitides) by highlighting severe transmissible disease activity and outbreaks in the United States.MethodsAnnual analyses of N. meningitides disease alerts captured by HealthMap were compared to epidemiological data captured by the Centers for Disease Control’s Active Bacterial Core surveillance (ABCs) for N. meningitides. Morbidity and mortality case reports were measured annually from 2010 to 2013 (HealthMap) and 2005 to 2012 (ABCs).FindingsHealthMap N. meningitides monitoring captured 80-90% of alerts as diagnosed N. meningitides, 5-20% of alerts as suspected cases, and 5-10% of alerts as related news articles. HealthMap disease alert activity for emerging disease threats related to N. meningitides were in agreement with patterns identified historically using traditional surveillance systems. HealthMap’s strength lies in its ability to provide a cumulative “snapshot” of weak signals that allows for rapid dissemination of knowledge and earlier public awareness of potential outbreak status while formal testing and confirmation for specific serotypes is ongoing by public health authorities.ConclusionsThe underreporting of disease cases in internet-based data streaming makes inadequate any comparison to epidemiological trends illustrated by the more comprehensive ABCs network published by the Centers for Disease Control. However, the expected delays in compiling confirmatory reports by traditional surveillance systems (at the time of writing, ABCs data for 2013 is listed as being provisional) emphasize the helpfulness of real-time internet-based data streaming to quickly fill gaps including the visualization of modes of disease transmission in outbreaks for better resource and action planning. HealthMap can also contribute as an internet-based monitoring system to provide real-time channel for patients to report intervention-related failures.     

Statistical Analysis of 3D Images Detects Regular Spatial Distributions of Centromeres and Chromocenters in Animal and Plant Nuclei

In eukaryotes, the interphase nucleus is organized in morphologically and/or functionally distinct nuclear “compartments”. Numerous studies highlight functional relationships between the spatial organization of the nucleus and gene regulation. This raises the question of whether nuclear organization principles exist and, if so, whether they are identical in the animal and plant kingdoms. We addressed this issue through the investigation of the three-dimensional distribution of the centromeres and chromocenters. We investigated five very diverse populations of interphase nuclei at different differentiation stages in their physiological environment, belonging to rabbit embryos at the 8-cell and blastocyst stages, differentiated rabbit mammary epithelial cells during lactation, and differentiated cells of Arabidopsis thaliana plantlets. We developed new tools based on the processing of confocal images and a new statistical approach based on G- and F- distance functions used in spatial statistics. Our original computational scheme takes into account both size and shape variability by comparing, for each nucleus, the observed distribution against a reference distribution estimated by Monte-Carlo sampling over the same nucleus. This implicit normalization allowed similar data processing and extraction of rules in the five differentiated nuclei populations of the three studied biological systems, despite differences in chromosome number, genome organization and heterochromatin content. We showed that centromeres/chromocenters form significantly more regularly spaced patterns than expected under a completely random situation, suggesting that repulsive constraints or spatial inhomogeneities underlay the spatial organization of heterochromatic compartments. The proposed technique should be useful for identifying further spatial features in a wide range of cell types.     

Dengue,Zika, and Chikungunya viral circulation and hospitalization rates in Brazil from 2014 to 2019: An ecological study

BackgroundIn addition to their direct pathogenic effects, arthropod-borne (arboviruses) have been hypothesized to indirectly contribute to hospitalizations and death through decompensation of pre-existing comorbidities. Using nationwide data routinely collected from 1 January 2014 to 31 December 2019 in Brazil, we investigated whether local increases in arbovirus notifications were associated with excess hospitalization.MethodsWe estimated the relative risks for the association between municipality- and state-level increases in arboviral case notifications and age-standardized hospitalization rates (i.e., classified as direct or indirect based on ICD-10 codes) using Bayesian multilevel models with random effects accounting for temporal and geographic correlations. For municipality-level analyses, we excluded municipalities with <200 notifications of a given arbovirus and further adjusted the models for the local Gini Index, Human Development Index, and Family Healthcare Strategy (Estratégia de Saúde da Família) coverage. Models for dengue, Zika, and chikungunya were performed separately.ResultsFrom 2014 to 2019, Brazil registered 7,566,330 confirmed dengue cases, 159,029 confirmed ZIKV cases, and 433,887 confirmed CHIKV cases. Dengue notifications have an endemic and seasonal pattern, with cases present in 5334 of the 5570 (95.8%) Brazilian municipalities and most (69.5%) registered between February and May. Chikungunya notifications followed a similar seasonal pattern to DENV but with a smaller incidence and were restricted to 4390 (78.8%) municipalities. ZIKV was only notified in 2581 (46.3%) municipalities. Increases in dengue and chikungunya notifications were associated with small increases in age-standardized arbovirus-related hospitalizations, but no consistent association was found with all-cause or other specific indirect causes of hospitalization. Zika was associated to increases in hospitalizations by neurological diseases.ConclusionsAlthough we found no clear association between increased incidence of the three arboviruses and excess risks of all-cause or indirect hospitalizations at the municipality- and state-levels, follow-up investigations at the individual-level are warranted to define any potential role of acute arbovirus infection in exacerbating risks of hospitalization from underlying conditions.     

What Is a “Community Perception” of REDD+? A Systematic Review of How Perceptions of REDD+ Have Been Elicited and Reported in the Literature

Reducing emissions from deforestation and forest degradation (REDD+) is expected to generate co-benefits and safeguard the interests of people who live in the forested regions where emissions are reduced. Participatory measurement, reporting and verification (PMRV) is one way to ensure that the interests of local people are represented in REDD+. In order to design and use PMRV systems to monitor co-benefits and safeguards, we need to obtain input on how local people perceive REDD+. In the literature, this is widely discussed as “community perceptions of REDD+.” We systematically reviewed this literature to understand how these perceptions have been assessed, focusing specifically on how individual perceptions have been sampled and aggregated into “community perceptions.” Using Google Scholar, we identified 19 publications that reported community perceptions of REDD+, including perceptions of its design, implementation, impacts, relationship with land tenure, and both interest and actual participation by local people. These perceptions were elicited through surveys of probability samples of the local population and interviews with purposively selected community representatives. Many authors did not provide sufficient information on their methods to interpret the reported community perceptions. For example, there was often insufficient detail on the selection of respondents or sampling methods. Authors also reported perceptions by unquantified magnitudes (e.g., “most people”, “the majority”) that were difficult to assess or compare across cases. Given this situation in the scholarly literature, we expect that there are even more severe problems in the voluminous gray literature on REDD+ not indexed by Google Scholar. We suggest that readers need to be cognizant of these issues and that publication outlets should establish guidelines for better reporting, requiring information on the reference population, sampling methods, and methods used to aggregate individual responses into “community perceptions.”