Age and socio-economic status affect dengue and COVID-19 incidence: Spatio-temporal analysis of the 2020 syndemic in Buenos Aires City

In early 2020, Argentina experienced the worst dengue outbreak in its history, concomitant with first-to-date increasing COVID-19 cases. Dengue epidemics in temperate Argentina have already been described as spatially heterogeneous; in the previous 2016 outbreak, transmission occurred 7.3 times more frequently in slums compared to the rest of Buenos Aires City (CABA). These informal settlements have deficient sanitary conditions, precarious housing and high incidence of social vulnerabilities. The purpose of this work was to study the spatio-temporal patterns of the 2020 dengue epidemic in CABA in relation to socio-economic living conditions of its inhabitants and its interaction with the onset of COVID-19. The study considered the period between Jan 1st and May 30th 2020. Dengue and COVID-19 databases were obtained from the National Health Surveillance System; each record was anonymized and geo-localized. The city was divided according to census tracts and grouped in four socio-economic strata: slums, high, mid and low residential. An aligned-rank transform ANOVA was performed to test for differences in the incidence of dengue and COVID-19, and age at death due to COVID-19, among socio-economic strata, four age categories and their interaction. The incidence by cluster was calculated with a distance matrix up to 600 m from the centroid. Spatial joint dengue and COVID-19 risk was estimated by multiplying the nominal risk for each disease, defined from 1 (low) to 5 (high) according to their quantiles. During the study period, 7,175 dengue cases were registered in CABA (incidence rate 23.3 cases per 10,000 inh), 29.2% of which occurred in slums. During the same period, 8,809 cases of COVID-19 were registered (28.6 cases per 10,000 inh); over half (51.4%) occurred in slums, where the median age of cases (29 years old) was lower than in residential areas (42 years old). The mean age of the deceased was 58 years old in slums compared to 79 years old outside. The percentage of deaths in patients under 60 years old was 56% in slums compared to 8% in the rest of the city. The incidence of both diseases was higher in slums than in residential areas for most age categories. Spatial patterns were heterogeneous: dengue presented higher incidence values in the southern sector of the city and the west, and low values in highly urbanized quarters, whereas COVID-19 presented higher values in the east, south, high populated areas and slums. The lowest joint risk clusters were located mainly in high residential areas, whereas high joint risk was observed mainly in the south, some western clusters, the historical part of the city and center north. The social epidemiological perspective of dengue and COVID-19 differed, given that socio environmental heterogeneity influenced the burden of both viruses in a different manner. Despite the overwhelming effect of the COVID-19 pandemic, health care towards other diseases, especially in territories with pre-existing vulnerabilities, should not be unattended.Fil: Carbajo, Anibal Eduardo. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación e Ingeniería Ambiental; ArgentinaFil: Cardo, María Victoria. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación e Ingeniería Ambiental; ArgentinaFil: Pesce, Martina. Ministerio de Salud de la Nación; ArgentinaFil: Iummato, Luciana E.. Ministerio de Salud de la Nación; ArgentinaFil: Bárcena Barbeira, Pilar. Ministerio de Salud de la Nación; ArgentinaFil: Santini, Maria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Dirección Nacional de Instituto de Investigación. Administración Nacional de Laboratorio e Instituto de Salud "Dr. C. G. Malbrán". Instituto Nacional de Parasitología "Dr. Mario Fatala Chaben"; ArgentinaFil: Utgés, María Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Dirección Nacional de Institutos de Investigación. Administración Nacional de Laboratorios e Institutos de Salud. Centro Nacional de Diagnóstico e Investigaciones Endemo-epidémicas; Argentin

Is autumn the key for dengue epidemics in non endemic regions? The case of Argentina

Background. Dengue is a major and rapidly increasing public health problem. In Argentina, the southern extreme of its distribution in the Americas, epidemic transmission takes place during the warm season. Since its re-emergence in 1998 two major outbreaks have occurred, the biggest during 2016. To identify the environmental factors that trigger epidemic events, we analyzed the occurrence and magnitude of dengue outbreaks in time and space at different scales in association with climatic, geographic and demographic variables and number of cases in endemic neighboring countries. Methods. Information on dengue cases was obtained from dengue notifications reported in the National Health Surveillance System. The resulting database was analyzed by Generalized Linear Mixed Models (GLMM) under three methodological approaches to: identify in which years the most important outbreaks occurred in association with environmental variables and propose a risk estimation for future epidemics (temporal approach); characterize which variables explain the occurrence of local outbreaks through time (spatio-temporal approach); and select the environmental drivers of the geographical distribution of dengue positive districts during 2016 (spatial approach). Results. Within the temporal approach, the number of dengue cases country-wide between 2009 and 2016 was positively associated with the number of dengue cases in bordering endemic countries and negatively with the days necessary for transmission (DNT) during the previous autumn in the central region of the country. Annual epidemic intensity in the period between 1999-2016 was associated with DNT during previous autumn and winter. Regarding the spatio-temporal approach, dengue cases within a district were also associated with mild conditions in the previous autumn along with the number of dengue cases in neighboring countries. As for the spatial approach, the best model for the occurrence of two or more dengue cases per district included autumn minimum temperature and human population as fixed factors, and the province as a grouping variable. Explanatory power of all models was high, in the range 57-95%. Discussion. Given the epidemic nature of dengue in Argentina, virus pressure from endemic neighboring countries along with climatic conditions are crucial to explain disease dynamics. In the three methodological approaches, temperature conditions during autumn were best associated with dengue patterns. We propose that mild autumns represent an advantage for mosquito vector populations and that, in temperate regions, this advantage manifests as a larger egg bank from which the adult population will re-emerge in spring. This may constitute a valuable anticipating tool for high transmission risk events.Fil: Carbajo, Anibal Eduardo. Universidad Nacional de San Martín. Instituto de Investigación en Ingeniería Ambiental; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cardo, María Victoria. Universidad Nacional de San Martín. Instituto de Investigación en Ingeniería Ambiental; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Guimarey, Pilar Consuelo. Dirección Nacional de Instituto de Investigación.Administración Nacional de Laboratorios e Institutos de Salud "Dr. Carlos G. Malbrán"; ArgentinaFil: Lizuain, Arturo Andrés. Dirección Nacional de Instituto de Investigación.Administración Nacional de Laboratorios e Institutos de Salud "Dr. Carlos G. Malbrán"; ArgentinaFil: Buyayisqui, María Pía. Ministerio de Salud de la Nación; ArgentinaFil: Varela, Teresa. Ministerio de Salud de la Nación; ArgentinaFil: Utgés, Maria E.. Dirección Nacional de Instituto de Investigación.Administración Nacional de Laboratorios e Institutos de Salud "Dr. Carlos G. Malbrán"; ArgentinaFil: Giovacchini, Carlos. Ministerio de Salud de la Nación; ArgentinaFil: Santini, Maria Soledad. Dirección Nacional de Instituto de Investigación.Administración Nacional de Laboratorios e Institutos de Salud "Dr. Carlos G. Malbrán"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Prevalence and risk factors for shedding of Cryptosporidium spp. oocysts in dairy calves of Buenos Aires Province, Argentina

In order to determine the prevalence and risk factors for shedding of Cryptosporidium spp. in dairy calves, a cross-sectional study was carried out in the northeastern region of Buenos Aires Province, Argentina. Fecal samples from a total of 552 calves from 27 dairy herds were collected, along with a questionnaire about management factors. Cryptosporidium spp. oocysts were detected by light microscopy using Kinyoun staining. Putative risk factors were tested for association using generalized linear mixed models (GLMMs). Oocyst shedding calves were found in 67% (CI95% = 49–84) of herds (corresponding to a true herd prevalence of 98%) and 16% (CI95% = 13–19) of calves (corresponding to a true calve prevalence of 8%). Within-herd prevalence ranged from 0 to 60%, with a median of 8%. Cryptosporidium spp. excretion was not associated with the type of liquid diet, gender, time the calf stayed with the dam after birth, use of antibiotics, blood presence in feces, and calving season. However, important highly significant risk factors of oocyst shedding of calves was an age of less or equal than 20 days (OR = 7.4; 95% CI95% = 3–16; P < 0.0001) and occurrence of diarrhea (OR = 5.5; 95% CI95% = 2–11; P < 0.0001). The observed association with young age strongly suggests an early exposure of neonatal calves to Cryptosporidium spp. oocysts in maternity pens and/or an age-related susceptibility. Association with diarrhea suggests that Cryptosporidium spp. is an important enteropathogen primarily responsible for the cause of the observed diarrheal syndrome. Results demonstrate that Cryptosporidium spp. infection is widespread in the study region. Monitoring and control of this parasitic protozoan infection in dairy herds is recommended

Dengue cases in Argentina at different spatial at temporal spans

All years are considered from 1 July of the previous year to 30 June of the current year (for example, the year referred to as 2004 includes the period 07/01/2003 - 06/30/2004). <br><br>Database 1. Annual cumulative number of dengue cases reported in Argentina between 1999 and 2016, along with temperature dependent and El Niño explanatory variables used for modelling the temporal occurrence of dengue epidemics in the country. Epidemic intensity is a categorical variable with the following breaks: 0= 0 cases, 1= 1-1,000 cases, 2= 1,001 - 5,000 cases, 3= >5,000 cases. Abbreviations: ApSe= sum of monthy Oceanic Niño index 3.4 from April-Sept of the previous year, JnDe = idem for January-December; JlDe= idem for July-December; JnJn= idem for January-June; DNT= days necessary for transmission per month and season.<br> <br>Database 2. Annual cumulative number of dengue cases reported in Argentina between 2009 and 2016, along with explanatory variables (temperature dependent, El Niño and dengue in neighbouring endemic countries). Abbrev as in database 1 along with DenBra= Number of dengue cases (in thousands) in southern Brazil; DenBol= Number of dengue cases (in thousands) in Bolivia; DenPar= Number of dengue cases (in thousands) in Paraguay.<br><br>Database 3. Annual cumulative number of dengue cases per district in Argentina between 2009 and 2016 for 15 selected districts located contiguous to meteorological stations, along with explanatory variables. Abbrev as in databases 1 and 2 along with denbi= binomial (0/1) variables denoting occurrence of dengue cases; cases20 and 100= binomial (0/1) variables classifying each locality in occurrence of epidemics according to two criteria (>20 or >100 cases, respectively); pop= population number, in thousands. In Tartagal, year 2011 is a missing point. <br><br>Database 4. Number of dengue cases per district during the 2016 epidemics for all districts in the country. Abbrev Tme= mean annual temperature (in ºC); PP= cumulative precipitation (in mm); DE= mean annual dew point (in ºC); WI= mean annual windspeed (in m/s); Ar= area (in m2); Al= mean district elevation above sea level (in m); AlSd= standard deviation of altitude of all pixels within a district (in m); DiWa= distance to the nearest water body or course (excluding the sea) (in km); DiBol= distance to nearest border crossing to Bolivia (in km); DiNea= distance to nearest border crossing to Brazil/Paraguay (in km); prc= percentage of population change between per district 2001 and 2010