[Numerical expression of the clinical course of the disease. Data management]

Data management "behind the scenes" refers to collection, cleaning, imputation, and demarcation; and despite of being indispensable processes, they are usually neglected and thus, generate erroneous information. During the collection are errors: omission of covariates, deviation from the objective, and insufficient quality. The omission of covariates distorts the result attributed to the main manoeuvre. Deviation from the primary objective commonly occurs when the outcome is rare, delayed, or subjective and promotes substitution by non-equivalent surrogate variables. Moreover, insufficient quality occurs due to inadequate instruments, omission of the measurement procedure, or measurements out of context, such as attribution at the wrong time or equivalent. Furthermore, cleaning implies identifying erroneous, extreme, and missing values, which may or may not be imputed, depending on the percentage. The values of the manoeuvre or the outcome are never imputed, nor are patients eliminated due to a lack of values. Finally, the demarcation of each variable seeks to give it a clinical meaning about the outcome, for which a hierarchical sequence of criteria is followed: 1) previous clinical study, 2) expert agreement, 3) clinical judgment of the investigator/investigators, and 4) statistics. Acting without quality controls in data management frequently causes involuntary lies and confuses instead of clarifying.

Risk Assessment of Dyslipidemias, Hyperglycemia, Hyperuricemia, and Hypertension Utilizing Self-Reported Body Silhouettes

OBJECTIVE

Self-reported body silhouette is an anthropometric instrument that has been utilized as a screening tool for underweight, overweight, obesity, and other abnormal anthropometric variables. Herein, we analyzed the risk associated with the self-reported body silhouette in the scope of dyslipidemias, hyperglycemia, hyperuricemia, and hypertension.

METHODS

Adult participants of the Health Workers Cohort Study enrolled between March 2004 and April 2006 were included. Then, risk analysis was performed considering dyslipidemias as serum triglycerides, high total cholesterol, high LDL-C, low HDL-C, hyperglycemia, hyperuricemia, and hypertension.

RESULTS

A total of 2,297 males and 5,003 females were analyzed. The median ages of the studied population was 39 (30-49) and 41 (31-50) years for males and females, respectively. Overall, there is a stepwise increase in the risk of presenting dyslipidemias, hyperglycemia, hyperuricemia, and hypertension as the self-reported body silhouette number increases, this tendency was observed in both males and females.

CONCLUSION

Self-reported body silhouette is a useful risk assessment tool for dyslipidemias, hyperglycemia, hyperuricemia, and hypertension in Mexican adults. Applications of questioners containing this silhouette might be considered a valuable public health instrument due to their low cost, relative simplicity, and absence of specialized equipment, training, or respondent knowledge.

Self-reported body silhouettes: a diagnostic instrument for anthropometric parameters

OBJECTIVE

Routine body size measurement of anthropometric values requires professionals, standardized techniques, and calibrated tools. Therefore, there is a need for easier screening tools such as the self-reported body silhouette (Self-bosi). The aim of this study was to analyze the performance of Self-bosi as a proxy of anthropometric values.

STUDY DESIGN

Prospective analytic study of the Health Workers Cohort Study.

METHODS

Adult participants of the Health Workers Cohort Study were included. Then, through the calculation sensitivity and specificity of Self-bosi to detect abnormal waist circumference (WC) (≥90 cm for male and ≥80 cm for female participants), elevated body fat percentage (BF%) (≥25% for male and ≥35% for female participants), as well as overweight and obesity (≥25 kg/m²) and obesity (≥30 kg/m²).

RESULTS

A total of 2471 male and 5940 female participants were analyzed. Overall, Self-bosi discriminate high WC values (area under the curve [AUC]; male participants: 0.80, female participants: 0.82); increased BF% (AUC: male participants: 0.78, female participants: 0.83); overweight and obesity (AUC: male participants: 0.81, female participants: 0.86); and obesity (AUC: male participants: 0.83, female participants: 0.89).

CONCLUSION

Self-bosi is an accurate method to assess increased WC, BF%, obesity, and overweight-obesity in Mexican adults. Given its simplicity and low-cost of the self-reported body silhouette, it might be considered a useful anthropometric screening instrument in large scale epidemiological research.

[The relevance and irrelevance of charts in clinical research]

Charts are a visual aid that is used in articles in order to highlight the results of an investigation. They allow illustrating the results with the purpose of making them clearer. Charts, just like statistical tests, are selected based on the objective of the study, the types of variable, and the statistical analyzes to be illustrated. Some of the most commonly used charts in clinical practice are frequency histograms, which illustrate qualitative variables or frequencies; also error charts, that are used for normally distributed quantitative variables; box plots or violin plots are used for distribution-free quantitative variables, and survival curves are for variables that include the person-time variable. The aforementioned charts can be used to illustrate the comparisons between maneuvers and outcome depending on the type of variable that is being analyzed. When two groups are compared and the dependent variable is dichotomous, forest plots are used; for multivariate models, the chart depends on the type of analysis. As for logistic regression and linear regression, tree diagrams are used; and scatter plots are used for linear regression. Survival plots are used for Cox proportional hazards. Although charts can be very useful, if they are misused, they can show differences where there are none, which leads to a misinterpretation of the studies. In this article, we will use examples to complement the topics that were previously addressed in the articles of this series.

Height and weight progression patterns in Mexican children aged between 6 and 12 years and differences with Ramos-Galván growth charts 40 years later

Introduction

Children and adolescents weight and height are a reflection of the health status and socioeconomic development of a population.

Objective

To evaluate height and weight progression patterns of Mexican children and compare them with Dr. Ramos-Galván growth charts 40 years later.

Method

Cross-sectional survey conducted on the population of the National Physical Activation Program "Ponte al 100", which includes boys and girls aged six to 12 years.

Results

43,670 boys and 44,103 girls were assessed, stratified by gender and age. The height progression pattern between six and 12 years was 21 cm in males and 22 cm in females, whereas the weight progression pattern was 9.86 and 10.05 kg, respectively, for males and females. The proportion of six- and 12-year-old boys who were overweight was 11.2 and 9 %, while 14.7 and 15 % were obese. The proportion of six- and 12-year-old girls who were overweight was 8.2 and 9.1 %, whereas 21.7 and 13.3 %, respectively, were obese. When the obtained values were compared with those of Dr. Ramos Galván growth charts for boys and girls, the average difference was 2 cm.

Conclusions

No secular height or weight increase within the last 40 years was documented.

[Correlation: not all correlation entails causality]

The concept of correlation entails having a couple of observations (X and Y), that is to say, the value that Y acquires for a determined value of X; the correlation makes it possible to examine the trend of two variables to be grouped together. We know that, with increasing age, blood pressure figures also increase, therefore, if we want to answer a research question like "what is the connection between age and blood pressure?" the relevant statistical test is a correlation test. This test makes it possible to quantify the magnitude of the correlation between two variables, but it is also helpful for predicting values. If these variables had a perfect correlation, the value of the variable Y could be deduced by knowing the value of X. Because of these advantages, the correlation is one of the most frequently used tests in the clinical setting since, in addition to measuring the direction and magnitude of the association of two variables, it is one of the foundations for prediction models, such as linear regression model, logistic regression model and Cox proportional hazards model.

De vuelta a la clínica. Métodos I. Diseños de investigación. Mayor calidad de información, mayor certeza a la respuesta

Research designs refer to the way information is obtained and are limited by ethical, economic and temporal viability. Research designs are standardized strategies to reduce biases, which in the architectural model of research are identified in the baseline state, the maneuver and the outcome; hence, there are no specific designs for each question. The design with the lowest probability of bias is the clinical trial, followed by cohort and case-control studies and, finally, by cross-sectional surveys. Among the main characteristics that give merit to research designs are the following: population inquiry, which refers to the situation of the population in relation to the clinical course/natural history of the disease; the maneuver, or action that is expected to modify the baseline state, which can be observational or experimental; follow-up, or documented monitoring that is given to each subject, which can be longitudinal or cross-sectional; and directionality, which can prolective or retrolective and refers to the timing of data collection for research purposes. It will always be better having a valuable question, even when answered with a design with higher risk of bias, than a question that is irrelevant or has no applicability.