Objective: To estimate the incubation period and the serial interval of Covid-19 from a sample of symptomatic patients in Bahia Blanca city. Methods: We collected dates of illness onset for primary cases (infectors) and secondary cases (infectees) for the first 18 secondary patients infected with SARS-Cov-2 in Bahia Blanca (Argentina). We ranked the fiability of the data depending upon certainty about the identification of the infector and the date of exposition to infector. The sample has some missing values. In the case of incubation, as 3 patients were infected by other household members, we only have 15 observations with an observed date of exposition. For the estimation of serial interval, one patient became ill from close contact with an asymptomatic infectious. Also, estimations of both the incubation period and the serial interval were carried using the full sample and a subsample with higher certainty about the transmissor and date of exposition. By the time the dataset was prepared all infectors were recovered so estimations do need to take into account right censoring. Results: The mean incubation period for symptomatic patients is 7.9 days (95% CI: 4.6, 11.1) considering the sample of 15 cases patients and 7.5 days (95% CI: 4.1, 10.9) if the sample is restricted to the most certain cases (n=12). The median is 6.1 (95% CI: 4.1, 9.2) and 5.8 (95% CI: 3.6, 9.3) respectively. Moreover, 97.5% of symptomatic cases will develop symptoms after 13.6 days from exposition (95% CI 10.7, 16.5). The point estimation for the mean serial interval is 6.8 days (95% CI: 4.0-9.6). Considering only the most certain pairs, the mean serial interval is estimated at 5.5 days (95% CI: 2.8, 8.1). The estimated median serial intervals were 5.2 (95% CI: 3.0, 8.1) and 4.1 (95% CI: 2.0, 6.9) days respectively. Conclusions: Evidence from Bahia Blanca (Argentina) suggests that the median and mean serial interval of Covid-19 is shorter than the incubation period. This suggests that a pre-symptomatic transmission is not negligible. Comparisons with foreign estimates show that incubation period and serial interval could be longer in Bahia Blanca city than in other regions. That poses a signal of opportunity to attain more timely contact tracing and effective isolation.

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RATIONALE: There are few studies of cutaneous sensitivity to gramineae in our region. Mostly of them use allergens of foreign species. The study aims to estimate the prevalence of skin sensitivity to widespread grasses in our region. METHODS: This is a retrospective observational study of 894 patients with seasonal allergic rhinitis. Patients were studied using skin tests with pollens extracts from Pooideae, Chloridoideae and Panicoideae grass species. RESULTS: The prevalence of positive reaction to pollen from Pooideae subfamily was 86.8% (IC: 84.4%-88.9%). In turn, prevalence of allergy to Panicoideae subfamily pollens was 69.6% (IC: 66.5%-72.5%) and positive reaction to Chloridoideae subfamily reach 48.1% (IC: 44.8%-54.1%). Cochran test suggests that prevalence in those three groups is different (x25319.11, p<0.01). When comparing just the groups of allergens from Pooideae and Panicoideae differences are also significant (x2571.43, p<0.01). In particular, 52.5% (IC: 49.2%-55.7%) of patients were allergic to Paspalum notatum. Regarding cross-reactivity between subfamilies, we find a no cross-correlation between Pooideae and Panicoideae (x252.197, p50.138). CONCLUSIONS: In Bahia Blanca, patients with seasonal rhinitis are sensitive to Pooideae, Chloridoideae and Panicoideae. Paspalum notatum, belonging to Panicoideae, has a significant prevalence, high reactivity and low cross-reactivity within the group of species studied. This last species is relevant because it is a native grass from the Northwest region of our country, Paraguay and the South of Brazil.

BackgroundGrass pollinosis is an important contributor to allergic diseases, with varying patterns and frequency of allergens according to the geographical location studied. Our study aims to provide a better understanding of subtropical grass pollinosis in Argentinian patients with seasonal allergic rhinitis.MethodsWe conducted a retrospective cross-sectional study involving 894 patients with seasonal allergic rhinitis from Bahía Blanca, Argentina. Skin prick tests were performed to selected pollen species belonging to three subfamilies of the Poaceae genera. Frequency of sensitization to specific grass pollen extracts, cross-reactivity of allergens assessed by skin prick test, and possible associations between allergen extracts and asthma or allergic conjunctivitis were analyzed.ResultsSensitization to the Pooideae subfamily was the most frequent, encompassing 86.8% (CI: 84.4%–88.9%) of the studied population. Positive reactions to allergen extracts from the Chloridoideae and the Panicoideae subfamilies showed smaller papule size than allergen extracts from the Pooideae subfamily (χ2(5) = 83.75, p < 0.001). Patients with a positive skin prick test (SPT) to a specific extract were more likely to present some degree of cross-reactivity to the remaining pollens when compared to patients with negative SPT to the same specific extract. Even though the proportion of patients presenting with asthma (46.9%) was higher than those with conjunctivitis (22.6%), there was only a statistically significant association between sensitization to Festuca arundinacea (φ = 0.089, p = .009), Phalaris arundinacea (φ = 0.074, p = .032) and Paspalum notatum (φ = 0.070, p = .038) and the presence of conjunctivitis.ConclusionsOur results suggest a high frequency of sensitization to grass pollen extracts from the Poaceae family among patients with seasonal allergic rhinitis. Overall, sensitization to the Pooidae subfamily was the most common, where Phalaris arundinacea presented the highest frequency.

BackgroundHypertension, diabetes and hypercholesterolemia are the most frequent and diagnosed chronic diseases in Argentina. They contribute largely to the burden of chronic disease and they are strongly influenced by a small number of risk factors. These risk factors are all modifiable at the population and individual level and offer major prospects for their prevention. We are interested in socioeconomic determinants of prevalence of those 3 specific diseases.Design and methodsWe estimate 3-equation probit model, combined with 3 separate probit estimations and a probit-based Heckman correction considering possible sample selection bias. Estimations were carried out using secondary self-reported data coming from the 2013 Risk Factor National Survey.ResultsWe find a negative association between socioeconomic status and prevalence of hypertension, cholesterolemia and diabetes; main increases concentrate in the transition from low to high SES in hypertension and diabetes. In cholesterol, the major effect takes place when individual crosses from low to middle SES and then vanishes. Anyway, in Argentina SES exhibit and independent effect on chronic diseases apart from those based on habits and body weight.ConclusionsPublic strategies to prevent chronic diseases must be specially targeted at women, poorest households and the least educated individuals in order to achieve efficacy. Also, as the probability of having a condition related to excessive blood pressure, high levels of cholesterol or glucose in the blood do not increase proportionally with age, so public campaigns promoting healthy diets, physical activity and medical checkups should be focused on young individuals to facilitate prophylaxis.Significance for public healthLatin American countries are going through an epidemiological transition where infectious illnesses are being superseded by chronic diseases which, in turn, are related to lifestyles and socioeconomic factors. Specificities in the relationship between chronic diseases and socioeconomic status have been recorded in high income countries, but has not been sufficiently studied in low and middle income countries. We believe that analysis grounded on large scale datasets, recently available in Argentina, and based on proper statistical tools can provide useful guidance for decision making in public health policies as they highlight where population needs and risks do concentrate.