Health Vigilance and Risk Factors for Cervical Cancer: A Study in the State of Maranhão, Brazil

The purpose of this cross-sectional study was to investigate the importance of health vigilance in the prevention of cervical cancer. The sample consisted of 536 women. To identify which risk factors were associated with cervical cancer or with an increase in the mortality rate of this kind of cancer, a logistic regression model was used. The Odds Ratios (OR) and their respective 95% confidence intervals were also calculated. Most of them were at the age of highest incidence of cervical cancer (21 - 59 years old), had an unstable conjugal situation, minimal educational level (78.7%), low income (58.3%), consumed alcoholic beverages (56.4%), had a history of smoking (57.0%) and possessed limited knowledge about the prevention. These results are discussed in terms of a predictive model for cervical cancer that considers the importance of public health vigilance in the screening and precocious diagnosis.

Modelling the utility of group testing for public health surveillance

In epidemic or pandemic situations,resources for testing the infection status of individuals may be scarce.Although group testing can help to significantly increase testing capabilities,the(repeated)testing of entire populations can exceed the resources of any country.We thus propose an extension of the theory of group testing that takes into account the fact that definitely specifying the infection status of each individual is impossible.Our theory builds on assigning to each individual an infection status(healthy/infected),as well as an associated cost function for erroneous assignments.This cost function is versatile,e.g.,it could take into account that false negative assignments are worse than false positive assignments and that false assignments in critical areas,such as health care workers,are more severe than in the general population.Based on this model,we study the optimal use of a limited number of tests to minimize the expected cost.More specifically,we utilize information-theoretic methods to give a lower bound on the expected cost and describe simple strategies that can significantly reduce the expected cost over currently known strategies.A detailed example is provided to illustrate our theory.

Analysis of temporal trends of human brucellosis between 2013 and 2018 in Yazd Province, Iran to predict future trends in incidence: A time-series study using ARIMA model

Objective:To determine the temporal patterns of cumulative incidence of brucellosis using autoregressive integrated moving average models.Methods:This cross-sectional study employed yearly and monthly data of 1117 laboratory-confirmed human brucellosis cases from January 2013 to December 2018 using the Yazd brucellosis national surveillance system.The monthly incidences constructed a timeseries model.The trend of cumulative incidence was perceived by tracing a line plot,which displayed a seasonal trend with periodicity.Thus,the ARIMA models were selected.Thereafter,Akaike information criteria(AIC)and Bayesian information criterion(BIC)values among different models indicated a preferable model from models which were expanded by diverse lags[(3,0,3),(2,0,3),(3,0,2),(4,0,3)and(3,0,4)].Then,the achieved ARIMA model was applied to the forecasting cumulative incidence of monthly brucellosis incidences.All analyses were performed using Stata,version 11.2.Results:For the ARIMA(3,0,4)model,MAPE value was 56.20%with standard error 0.009–0.016,and white noise diagnostic check(Q=19.79,P=0.975)for the residuals of the selected model showed that the data were completely modelled.The monthly incidences that were fitted by the ARIMA(3,0,4)model,with AIC(25.7)and BIC(43.35)with a similar pattern of actual cases from 2013 to 2018 and forecasting incidences from January 2019 to December 2019 were,respectively,0.50,0.44,0.45,0.49,0.55,0.58,0.56,0.51,0.46,0.44,0.45 and 0.49 per 100000 people.Conclusions:In summary,the study showed that the ARIMA(3,0,4)model can be applied to forecast human brucellosis patterns in Yazd province,supplementing present surveillance systems,and may be better for health policy-makers and planners.

Injury surveillance information system: A review of the system requirements

Purpose: An injury surveillance information system (ISIS) collects, analyzes, and distributes data on injuries to promote health care delivery. The present study aimed to review the data elements and functional requirements of this system.Method: This study was conducted in 2019. Studies related to injury surveillance system were searched from January 2000 to September 2019 via the databases of PubMed, Web of Knowledge, ScienceDirect, and Scopus. Articles related to the epidemiology of injury, population survey, and letters to the editor were excluded, while the review and research articles related to ISISs were included in the study. Initially 324 articles were identified, and finally 22 studies were selected for review. Having reviewed the articles, the data needed were extracted and the results were synthesized narratively.Results: The results showed that most of the systems reviewed in this study used the minimum data set suggested by the World Health Organization injury surveillance guidelines along with supplementary data. The main functions considered for the system were injury track, data analysis, report, data linkage, electronic monitoring and data dissemination.Conclusion: ISISs can help to improve healthcare planning and injury prevention. Since different countries have various technical and organizational infrastructures, it is essential to identify system requirements in different settings.