Descriptive statistics are an important part of biomedical research which is used to describe the basic features of the data in the study. They provide simple summaries about the sample and the measures. Measures of the central tendency and dispersion are used to describe the quantitative data. For the continuous data, test of the normality is an important step for deciding the measures of central tendency and statistical methods for data analysis. When our data follow normal distribution, parametric tests otherwise nonparametric methods are used to compare the groups. There are different methods used to test the normality of data, including numerical and visual methods, and each method has its own advantages and disadvantages. In the present study, we have discussed the summary measures and methods used to test the normality of the data.

Student's t test (t test), analysis of variance (ANOVA), and analysis of covariance (ANCOVA) are statistical methods used in the testing of hypothesis for comparison of means between the groups. The Student's t test is used to compare the means between two groups, whereas ANOVA is used to compare the means among three or more groups. In ANOVA, first gets a common P value. A significant P value of the ANOVA test indicates for at least one pair, between which the mean difference was statistically significant. To identify that significant pair(s), we use multiple comparisons. In ANOVA, when using one categorical independent variable, it is called one-way ANOVA, whereas for two categorical independent variables, it is called two-way ANOVA. When using at least one covariate to adjust with dependent variable, ANOVA becomes ANCOVA. When the size of the sample is small, mean is very much affected by the outliers, so it is necessary to keep sufficient sample size while using these methods.

In biostatistics, for each of the specific situation, statistical methods are available for analysis and interpretation of the data. To select the appropriate statistical method, one need to know the assumption and conditions of the statistical methods, so that proper statistical method can be selected for data analysis. Two main statistical methods are used in data analysis: descriptive statistics, which summarizes data using indexes such as mean and median and another is inferential statistics, which draw conclusions from data using statistical tests such as student's t -test. Selection of appropriate statistical method depends on the following three things: Aim and objective of the study, Type and distribution of the data used, and Nature of the observations (paired/unpaired). All type of statistical methods that are used to compare the means are called parametric while statistical methods used to compare other than means (ex-median/mean ranks/proportions) are called nonparametric methods. In the present article, we have discussed the parametric and non-parametric methods, their assumptions, and how to select appropriate statistical methods for analysis and interpretation of the biomedical data.

We found that almost half of our under five children are underweight, girl child being affected more. For attainment of best possible nutrition and growth in children, targeted short-term strategies addressing underlying risk factors and more long-term poverty alleviation strategies may be needed.

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Objective: To evaluate the diagnostic performance of a rapid bedside 6-point lung ultrasonography (LUS) performed by an intensive care unit (ICU) physician for detection of four common pathological conditions of the lung, such as alveolar consolidation, pleural effusion, interstitial syndrome and pneumothorax, in critically ill patients and its comparison with bedside chest X-ray (CXR) and high-resolution computed tomography (CT) scan of the thorax. Volume of pleural effusion measured by LUS and CT thorax was also compared. Methods: This was a cross-sectional, observational study of 90 adult patients with an acute lung injury score of ≥1 admitted to the medical-surgical ICU. They were examined by CXR and 6-point LUS as per BLUE protocol at bedside, followed by CT thorax in the radiology department. Results: The sensitivity of 6-point LUS for detecting alveolar consolidation, pleural effusion, interstitial syndrome and pneumothorax was 76%, 88%, 83% and 89%, respectively, which was remarkably higher than that of CXR. The specificity of LUS was 100% for all pathologies, which was again notably higher than that of CXR except for interstitial syndrome for which it was 88.5%. Measurement of volume of pleural effusion by LUS was comparable and had a strong absolute agreement with CT thorax. Conclusion: 6-Point LUS can be a useful diagnostic tool and is better than CXR in diagnosing respiratory pathologies in critically ill patients. Owing to the comparable diagnostic performance of LUS and CT scan and with increasing evidence in favour of LUS, the requirement of CT thorax can be reduced. Radiation hazards associated with CXR and CT, as well as potentially risky transfer of patients to CT room, can also be minimised.

Background Coronavirus disease 2019 (COVID‐19) has impacted cancer care globally. The aim of this study is to analyze the impact of COVID‐19 on cancer healthcare from the perspective of patients with cancer. Methods A cross‐sectional survey was conducted between June 19, 2020, to August 7, 2020, using a questionnaire designed by patients awaiting cancer surgery. We examined the impact of COVID‐19 on five domains (financial status, healthcare access, stress, anxiety, and depression) and their relationship with various patient‐related variables. Factors likely to determine the influence of COVID‐19 on patient care were analyzed. Results A significant adverse impact was noted in all five domains (p =  < 0.05), with the maximal impact felt in the domain of financial status followed by healthcare access. Patients with income levels of INR < 35 K (adjusted odds ratio [AOR] = 1.61, p < 0.05), and 35K‐ 100 K (AOR = 1.96, p < 0.05), married patients (AOR = 3.30, p < 0.05), and rural patients (AOR = 2.82, p < 0.05) experienced the most adverse COVID‐19‐related impact. Conclusion Delivering quality cancer care in low to middle‐income countries is a challenge even in normal times. During this pandemic, deficiencies in this fragile healthcare delivery system were exacerbated. Identification of vulnerable groups of patients and strategic utilization of available resources becomes even more important during global catastrophes, such as the current COVID‐19 pandemic. Further work is required in these avenues to not only address the current pandemic but also any potential future crises.

Backgroud Coronavirus Disease 2019 (COVID-19) has spread globally affecting all strata of people including the orthopaedic surgeons of India. We have witnessed a drastic fall in the number of patients. The aim of study was to assess the extent to which the Indian orthopaedic practice has been affected by the pandemic. Methods We conducted an online survey amongst currently practicing Indian orthopaedic surgeons. Those currently not in practice or under training or having left clinical practice before the onset of pandemic were excluded. A total of 533 orthopaedic surgeons took part in the study amongst which, complete responses were obtained from 407 individuals. Statistical analysis was done to see the association between demographic profile of study participants and various variables of orthopaedic practice. Results There was drastic fall in all the parameters of orthopaedic practice. Over half of the practicing surgeons witnessed fall in outpatients over 90%. Most had stopped elective surgeries (64%) and even emergency ones (21%) altogether. More than 50% of doctors had their earnings reduced by > 75%. We found a statistically highly significant association of reduction in earnings with the sector, type of setup and duration of practice. (p-value < 0.001). Conclusion This study suggests that orthopaedic surgeons across all sectors in different kinds of setups have been affected in their outpatient and operative numbers. With regards to earnings, those working in private and running their own (individual) hospitals & clinics have been most severely affected while those in government sector and medical colleges have been affected the least.