Statistical package for the social sciences spss for windows version 24

Countries were classified into LMICs, UMICs, and HICs on the basis of World Bank
criteria.⁸ The primary
objective was to describe oncologist workload across LMICs, UMICs, and HICs;
oncologist workload was defined as the annual number of consultations for new
patients with cancer seen per oncologist. Because of a relatively small number
of responses from low-middle–income African nations, we combined these
responses into a region called LMIC Africa. All data were initially collected in
Fluid Surveys and subsequently exported to IBM Statistical Package for the
Social Sciences (SPSS) for Windows version 24.0 (SPSS, Armonk, NY). Pearson
χ² tests were used to test for the difference in
proportions, and the Kruskal-Wallis test was used to compare ordinal and
continuous data by income stratification. Data consisted of categorical,
ordinal, and continuous formats, occasionally collected as ranges (eg, < 50,
51 to 100, 101 to 150, etc). In the latter case, medians were generated using
the midpoint of the categorical range (eg, a median value of 101 to 150 would be
reported as 125). Data were analyzed using IBM SPSS.

Data were analysed using the IBM Statistical Package for the Social Sciences (SPSS) for Windows version 24.0 (SPSS, Inc., Chicago, IL, USA). Frequencies, means and standard deviations were computed to describe the sample. Data were checked for normality distribution and outliers. Chi-square tests were used to test for differences in proportions. To identify the pattern of NCD multimorbidity, principal factor method was used with equimax rotation. Multinomial logistic regression was used by comparing NCD comorbidity and NCD multimorbidity prevalence with those not having any NCD (reference category). Linear regression was used to predict general physical and mental health, and logistic regression was used to predict psychological distress and PTSD symptoms. Probability below 0.05 was regarded as statistically significant.

Continuous variables are presented as mean ± standard deviation (SD) for normally distributed data. Non-normally distributed variables are shown as median (interquartile range [IQR]). Categorical variables are presented as frequency (%). A one-way ANOVA test was used to compare means between the groups and a chi-square test was used to analyze for categorical variables. The differences of non-normally distributed variable between groups are compared using Kruskal-Wallis. Independent sample T-test was used to figure out the correlation between chest radiography with lung function and 6MWD. Statistical significance is accepted at a two-sided p-value <0.050. Statistical analysis was conducted using the IBM Statistical Package for the Social Sciences (SPSS) for Windows, Version 24.0 (IBM Corp, Armonk, NY).