When designing any research project, definition is required of the sample size needed in order to carry out the study. This sample size is an estimate of the number of patients required in accordance with the pursued study objective. In this context, it is more efficient in terms of both cost and time to use samples than to work with the entire population. The present article describes the way to establish sample size in the kinds of studies most frequently found in health research, and how to calculate it using the epicalc package included in the shareware R program. A description is provided of the formulae used to calculate sample sizes for the estimation of a mean and percentage (referring to both finite and infinite populations) and for the comparison of two proportions and two means. Likewise, examples of the application of the mentioned statistical package are provided.

Any form of data analysis requires the prior creation of a database to house the study information collected in one format or other (questionnaire, clinical history, etc.). The design of such databases should be optimised to allow adequate statistical analysis without the drawing of wrong conclusions. In addition, prior to analysis, debugging or filtering of the variables is required in order to avoid doubling the effort made in extracting the results. The present study offers a series of suggestions for database design and debugging, to ensure that the later statistical analyses are based on the revised data.

Objective. To determine the epidemiological characteristics of significative bacteriuria (SB) and their relationship with sociodemographic factors and to analyze risk factors in inpatients. Material and methods. Cross-sectional descriptive study carried out on urine culture samples received between 20162020 in the Microbiology laboratory, differentiating between minors and adults. The dependent variable was the presence of SB and the independent variables were age, sex, year, type of sample and source of the sample. In urine cultures of inpatients, risk factors were evaluated from the Minimum Basic Data Set. Results. A total of 68,587 valid records (96.3% of the total) were analyzed. 40.8% (95% CI: 40.4%-41.2%) of urine cultures in adults and 33.8% (95% CI: 32.9%-34.7%) in children were positive, with an incidence that ranged in adults between 18.2 cases/1,000 inhabitants in 2016 and 14.6 cases/1,000 inhabitants in 2020 and 21.1 and 8.4 cases/1,000 inhabitants respectively in minors. Positive urine cultures were more frequent in children from urban areas compared to rural areas (OR=1.37; p<0.01). In hospitalized adults, for each year of age the risk of SB increased by 2%, it was 36% higher in women, 18% higher in obese patients and 17% more frequent in patients with kidney disease, (p<0.01). No relationship was observed between SB and diagnosis of COVID-19. Conclusion. The sociodemographic characteristics of the population with SB in our health area are similar to those found in other geographical areas worldwide, observing a decreasing trend in incidence in the years studied. The frequency of SB in children is higher in urban areas.