Risk factors for death due to COVID-19, in the state of Acre, Brazil, 2020: a retrospective cohort study

OBJECTIVE

To analyze risk factors for death in individuals with severe acute respiratory syndrome due to COVID-19.

METHODS

This was a retrospective cohort study, comprised of adult individuals with COVID-19, from March to September 2020, notified by the Epidemiological Surveillance System in the state of Acre, Brazil. Cox regression was used.

RESULTS

Among 57,700 individuals analyzed, the incidence was 2,765.4/100,000 inhabitants, and mortality was, 61.8/100,000 inhabitants. The risk factors for death were: being male (HR=1.48 -95% CI 1.25;1.76), age ≥60 years (HR=10.64 -95% CI 8.84;12.81), symptom of dyspnea (HR=4.20 -95% CI 3.44;5.12) and multimorbidity (HR=2.23 -95% CI 1.77;2.81), with emphasis on heart disease and diabetes mellitus. 'Sore throat' and 'headache' were symptoms present in mild cases of COVID-19.

CONCLUSION

Being male, elderly, having heart disease, diabetes mellitus and dyspnea were characteristics associated with death due to COVID-19.

Evaluation of prediction models for the occurrence of malaria in the state of Amapá, Brazil, 1997-2016: an ecological study

OBJECTIVE

To evaluate the predictive power of different malaria case time-series models in the state of Amapá, Brazil, for the period 1997-2016.

METHODS

This is an ecological time series study with malaria cases recorded in the state of Amapá. Ten deterministic or stochastic statistical models were used for simulation and testing in 3, 6, and 12 month forecast horizons.

RESULTS

The initial test showed that the series is stationary. Deterministic models performed better than stochastic models. The ARIMA model showed absolute errors of less than 2% on the logarithmic scale and relative errors 3.4-5.8 times less than the null model. It was possible to predict future malaria cases 6 and 12 months in advance.

CONCLUSION

The ARIMA model is recommended for predicting future scenarios and for earlier planning in state health services in the Amazon Region.

Heart rate variability helps to distinguish the intensity of menopausal symptoms: A prospective, observational and transversal study

Heart Rate Variability (HRV) represents cardiac autonomic behavior and have been related to menopausal symptoms, mainly vasomotor symptoms and their imbalance to cardiovascular risk. It is not clear in the literature which index represents this imbalance and what is their involvement with the menopausal state. The aim of this study was to evaluate HRV in menopausal transition and post-menopausal symptoms with different intensities. This cross-sectional study was conducted in Rio Branco, State of Acre, Brazil from October 2016 to July 2017. We used Kupperman-Blatt Menopausal Index (KMI) to measure menopausal symptoms intensity. HRV analysis was performed based on the guidelines of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. HRV is lower in the group with moderate/intense menopausal symptoms compared to mild symptoms. (RMSSD: p = 0.005, Cohen’s d = 0.53, pNN50: p = 0.0004, Cohen’s d = 0.68; HF: p = 0.024, Cohen’s d = 0.44). There was association between HRV and KMI (RMSSD: r = -1.248, p = 0.004; and pNN50: r = -0.615, p: 0.029) in inverse relation to the intensity of vasomotor symptoms in women in TM. In conclusion, HRV was able to distinguish menopausal symptoms, indicating reduced vagal control in women with more intense symptoms.