Getting started with tables
 Hazel Inskip^{1}Email authorView ORCID ID profile,
 Georgia Ntani^{1},
 Leo Westbury^{1},
 Chiara Di Gravio^{1},
 Stefania D’Angelo^{1},
 Camille Parsons^{1} and
 Janis Baird^{1}
DOI: 10.1186/s1369001701801
© The Author(s). 2017
Received: 4 December 2016
Accepted: 20 January 2017
Published: 20 March 2017
Abstract
Background
Tables are often overlooked by many readers of papers who tend to focus on the text. Good tables tell much of the story of a paper and give a richer insight into the details of the study participants and the main research findings. Being confident in reading tables and constructing clear tables are important skills for researchers to master.
Method
Common forms of tables were considered, along with the standard statistics used in them. Papers in the Archives of Public Health published during 2015 and 2016 were handsearched for examples to illustrate the points being made. Presentation of graphs and figures were not considered as they are outside the scope of the paper.
Results
Basic statistical concepts are outlined to aid understanding of each of the tables presented. The first table in many papers gives an overview of the study population and its characteristics, usually giving numbers and percentages of the study population in different categories (e.g. by sex, educational attainment, smoking status) and summaries of measured characteristics (continuous variables) of the participants (e.g. age, height, body mass index). Tables giving the results of the analyses follow; these often include summaries of characteristics in different groups of participants, as well as relationships between the outcome under study and the exposure of interest. For continuous outcome data, results are often expressed as differences between means, or regression or correlation coefficients. Ratio/relative measures (e.g. relative risks, odds ratios) are usually used for binary outcome measures that take one of two values for each study participants (e.g. dead versus alive, obese versus nonobese). Tables come in many forms, but various standard types are described here.
Conclusion
Clear tables provide much of the important detail in a paper and researchers are encouraged to read and construct them with care.
Keywords
Tables Variables Characteristics Categories Mean Standard deviation Median Interquartile range Regression coefficients Correlation coefficients Ratios Relative measuresBackground
Tables are an important component of any research paper. Yet, anecdotally, many people say that they find tables difficult to understand so focus only on the text when reading a paper. However, tables provide a much richer sense of a study population and the results than can be described in the text. The tables and text complement each other in that the text outlines the main findings, while the detail is contained in the tables; the text should refer to each table at the appropriate place(s) in the paper. We aim to give some insights into reading tables for those who find them challenging, and to assist those preparing tables in deciding what they need to put into them. Producing clear, informative tables increases the likelihood of papers being published and read. Good graphs and figures can often provide a more accessible presentation of study findings than tables. They can add to the understanding of the findings considerably, but they can rarely contain as much detail as a table. Choosing when to present a graph or figure and when to present a table needs careful consideration but this article focuses only on the presentation of tables.
We provide a general description of tables and statistics commonly used when presenting data, followed by specific examples. No two papers will present the tables in the same way, so we can only give some general insights. The statistical approaches are described briefly but cannot be explained fully; the reader is referred to various books on the topic [1–6].
Presentation of tables
The title (or legend) of a table should enable the reader to understand its content, so a clear, concise description of the contents of the table is required. The specific details needed for the title will vary according to the type of table. For example, titles for tables of characteristics should give details of the study population being summarised and indicate whether separate columns are presented for particular characteristics, such as sex. For tables of main findings, the title should include the details of the type of statistics presented or the analytical method. Ideally the table title should enable the table to be examined and understood without reference to the rest of the article, and so information on study, time and place needs to be included. Footnotes may be required to amplify particular points, but should be kept to a minimum. Often they will be used to explain abbreviations or symbols used in the table or to list confounding factors for which adjustment has been made in the analysis.
Clear headings for rows and columns are also required and the format of the table needs careful consideration, not least in regard to the appropriateness and number of rows and columns included within the table. Generally it is better to present tables with more rows than columns; it is usually easier to read down a table than across it, and page sizes currently in use are longer than they are wide. Very large tables can be hard to absorb and make the reader’s work more onerous, but can be useful for those who require extra detail. Getting the balance right needs care.
Types of tables
Many research articles present a summary of the characteristics of the study population in the first table. The purpose of these tables is to provide information on the key characteristics of the study participants, and allow the reader to assess the generalisability of the findings. Typically, age and sex will be presented along with various characteristics pertinent to the study in question, for example smoking prevalence, socioeconomic position, educational attainment, height, and body mass index. A single summary column may be presented or perhaps more than one column split according to major characteristics such as sex (i.e. separate columns for males and females) or, for trials, the intervention and control groups.
Subsequent tables generally present details of the associations identified in the main analyses. Sometimes these include results that are unadjusted or ‘crude’ (i.e. don’t take account of other variables that might influence the association) often followed by results from adjusted models taking account of other factors.
Other types of tables occur in some papers. For example, systematic review papers contain tables giving the inclusion and exclusion criteria for the review as well as tables that summarise the characteristics and results of each study included in the review; such tables can be extremely large if the review covers many studies. Qualitative studies often provide tables describing the characteristics of the study participants in a more narrative format than is used for quantitative studies. This paper however, focuses on tables that present numerical data.
Statistics commonly presented in tables
The median and interquartile range (IQR) are usually provided when the data are not symmetrical as in Fig. 1b, which gives an example of data that are skewed, such that if the values are plotted in a histogram there are many values at one end of the distribution but fewer at the other end [7]. If all the values of the variable were listed in order, the median would be the middle value and the IQR would be the values a quarter and threequarters of the way through the list. Sometimes the lower value of the IQR is labelled Q1 (quartile 1), the median is Q2, and the upper value is Q3. For categorical variables, frequencies and percentages are used.
Common statistics for associations between continuous outcomes include differences in means, regression coefficients and correlation coefficients. For these statistics, values of zero indicate no association between the exposure and outcome of interest. A correlation coefficient of 0 indicates no association, while a value of 1 or −1 would indicate perfect positive or negative correlation; values outside the range −1 to 1 are not possible. Regression coefficients can take any positive or negative value depending on the units of measurement of the exposure and outcome.
For binary outcome measures that only take two possible values (e.g. diseased versus not, dead versus alive, obese versus not obese) the results are commonly presented in the form of relative measures. These include any measure with the word ‘relative’ or ‘ratio’ in their name, such as odds ratios, relative risks, prevalence ratios, incidence rate ratios and hazard ratios. All are interpreted in much the same way: values above 1 indicate an elevated risk of the outcome associated with the exposure under study, whereas below 1 implies a protective effect. No association between the outcome and exposure is apparent if the ratio is 1.
Typically in results tables, 95% confidence intervals (95% CIs) and/or pvalues will be presented. A 95% CI around a result indicates that, in the absence of bias, there is a 95% probability that the interval includes the true value of the result in the wider population from which the study participants were drawn. It also gives an indication of how precisely the study team has been able to estimate the result (whether it is a regression coefficient, a ratio/relative measure or any of the summary measures mentioned above). The wider the 95% CI, the less precise is our estimate of the result. Wide 95% CIs tend to arise from small studies and hence the drive for larger studies to give greater precision and certainty about the findings.
If a 95% CI around a result for a continuous variable (difference in means, regression or correlation coefficient) includes 0 then it is unlikely that there is a real association between exposure and outcome whereas, for a binary outcome, a real association is unlikely if the 95% CI around a relative measure, such as a hazard or odds ratio, includes 1.
The pvalue is the probability that the finding we have observed could have occurred by chance, and therefore there is no identifiable association between the exposure of interest and the outcome measure in the wider population. If the pvalue is very small, then we are more convinced that we have found an association that is not explained by chance (though it may be due to bias or confounding in our study). Traditionally a pvalue of less than 0.05 (sometimes expressed as 5%) has been considered as ‘statistically significant’ but this is an arbitrary value and the smaller the pvalue the less likely the result is simply due to chance [8].
Frequently, data within tables are presented with 95% CIs but without pvalues or vice versa. If the 95% CI includes 0 (for a continuous outcome measure) or 1 (for a binary outcome), then generally the pvalue will be greater than 0.05, whereas if it does not include 0 or 1 respectively, then the pvalue will be less than 0.05 [9]. Generally, 95% CIs are more informative than pvalues; providing both may affect the readability of a table and so preference should generally be given to 95% CIs. Sometimes, rather than giving exact pvalues, they are indicated by symbols that are explained in a footnote; commonly one star (*) indicates p < 0.05, two stars (**) indicates p < 0.01.
Results in tables can only be interpreted if the units of measurement are clearly given. For example, mean or median age could be in days, weeks, months or years if infants and children are being considered, and 365, 52, 12 or 1 for a mean age of 1 year could all be presented, as long the unit of measurement is provided. Standard deviations should be quoted in the same units as the mean to which they refer. Relative measures, such as odds ratios, and correlation coefficients do not have units of measurement, but for regression coefficients the unit of measurement of the outcome variable is required, and also of the exposure variable if it is continuous.
Examples
The examples are all drawn from recent articles in Archives of Public Health. They were chosen to represent a variety of types of tables seen in research publications.
Tables of characteristics

the study population is quite young, as only around 10% are more than 40 years old;

the majority are female;

more than half are nurses;

about half were educated to degree level or above.Table 1
Table of study population characteristics from a paper on the assessment of knowledge and practice in relation to tuberculosis control in health workers in Ethiopia [10]. Socio demographic characteristics of the study population in public health facilities, Addis Ababa, 2014
Variable
Characteristics
Frequency
Percent
(N=582)
Age
18–29
383
65.8
30–39
136
23.4
>40
63
10.4
Sex
Male
228
39.2
Female
352
60.5
Marital status
Single
308
52.9
Married
260
44.7
Divorced and Widowed
14
2.4
Profession
Physician
35
6
Nurse
66
56.4
Health Officer
328
11.3
Lab personnsel
49
8.4
Pharmacy personnsel
45
7.7
Others^{a}
59
10.1
Currently working unit
OPD
181
31.1
TB clinic and TB ward
30
5.2
Laboratory
43
7.4
Pharmacy
46
7.9
Triage
24
4.1
Medical ward
32
5.5
Others^{b}
226
38.8
Educational status
Diploma
280
48.1
First degree
289
49.7
Second degree and above
13
2.2
Service year in health facility
<3 years
341
58.6
36 year
150
25.8
>6 years
91
15.6
Experience in TB clinics
Yes
134
23
No
444
76.3
Year of experience in TB clinic
<1 year
57
57
14 years
37
37
>4 years
6
6
Have TB training
Yes
134
23
No
444
76.3
Duration of training
<3 days
23
17.6
46 days
59
45
710 days
35
28.2
>10 days
12
9.2
Table of study population characteristics from a paper on the relationship between distorted body image and lifestyle in adolescents in Japan [11]. Characteristics of study participants by sex (Japan; 2005–2009)
Variable  Boys  Girls  Pvalue 

(n=885)  (n=846)  
Age (years)  12.3 (0.4)  12.3 (0.4)  0.631 
Height (cm)  154.4 (8.1)  152.5 (6.0)  <0.001 
Weight (kg)  44.5(9.7)  43.6 (7.9)  0.040 
Body mass index (kg/m^{2)}  18.5 (3.0)  1837 (2.7)  0.276 
Actual weight (%)  
Underweight  73 (8.2)  88 (10.4)  0.116 
Normal weight  694 (78.4)  666 (78.7)  
Overweight  118 (13.3)  92 (10.9)  
Selfperceived weight status (%)  
Thin  268 (30.3)  139 (16.4)  <0.001 
Normal  484 (54.7)  560 (59.8)  
Heavy  133 (15.0)  201 (23.8)  
Body image perception (%)  
Underestimated  230 (26.0)  99 (11.7)  <0.001 
Correct  605 (68.4)  591 (69.9)  
Overestimated  50 (5.6)  156 (18.4) 

Ethiopian children in this study were older and taller than those from the other two countries but their MUAC measurements tended to be smaller;

in Bangladesh, disproportionally more females than males were admitted for treatment compared with the other two countries.Table 3
Table of study population characteristics from a paper describing the relationship between midupperarm circumference (MUAC) and weight changes in young children [12]. Characteristics of study population at admission
Ethiopia
n
%
Males
199
46.2%
Females
232
53.8%
Min.
Q1
Median
Mean
Q2
Max.
Age at admission (months)
7.0
25.1
37.0
39.5
48.0
66.0
MUAC at admission (cm)
7.5
10.2
10.5
10.4
10.8
10.9
Height at admission (cm)
61.5
73.5
80.4
81.0
88.0
109.2
Malawi
n
%
Males
105
44.7%
Females
130
55.3%
Min.
Q1
Median
Mean
Q2
Max
Age at admission (months)
6.0
10.0
14.0
16.4
21.0
51.0
MUAC at admission (cm)
8.2
10.5
11.0
10.8
11.4
11.5
Height at admission (cm)
53.3
63.0
67.2
67.5
72.2
92.5
Bangladesh
n
%
Males
88
33.3%
Females
176
66.7%
Min.
Q1
Median
Mean
Q2
Max.
Age at admission (months)
6.0
7.0
10.0
12.9
17.0
56.0
MUAC at admission (cm)
8.5
11.1
11.3
11.2
11.4
11.4
Height at admission (cm)
51.6
62.3
65.6
67.4
71.8
99.0
It is unusual to present as much detail on continuous characteristics as is given in Table 3 . Usually, for each characteristic, either (a) mean and SD or (b) median and IQR would be given, but not both.
Tables of results – summary findings
Part of a table of basic results from a study of risk factors for acute lower respiratory infections (ALRI) among young children in Rwanda [13]. Bivariate analysis of factors associated with acute lower respiratory infection among children under five in Rwanda, RDHS 2010
Name of Variable  Children in study Number  Children suffering fronALRI in last two weeks Number (%)  Chisquared pvalue 

CHILD  0.001  
Child age  82 (5.2)  
011 months  1,573  
1223 months  1,615  82 (5.1)  
2459 months  5,411  157 (2.9)  
Child sex  0.104  
Boy  4,361  179 (4.1)  
Girl  4,238  144 (3.4)  
Child underweight  0.991  
No  3,648  139 (3.8)  
Yes  467  18 (3.8)  
Not measured  4,424  164 (3.7)  
Child received BCG  0.109  
No  94  1 (0.9)  
Yes  8,503  323 (3.8)  
Child received intestinal drugs in last 6 months  0.119  
No  94  4 (4.4)  
Yes  8,503  306 (3.6)  
Anemia level  0.083  
Not anemic  2,316  74 (3.2)  
Mild or moderate  1,441  60 (4.2)  
Severe  17  2 (14.6)  
Not measured  4,424  164 (3.7)  
Child received vitamin A in last 6 months  0.040  
No  1,109  54 (4.9)  
Yes  7,484  269 (3.6)  
Child delivered at a health facility  0.326  
No  2,625  89 (3.4)  
Yes  5,969  233 (3.9)  
PARENT  
Mother current age  0.178  
<21 years  273  14 (5.3)  
21+ years  8,326  308 (3.7)  
Mother employment status  0.225  
Not working or selfemployed agriculture  7,488  269 (3.6)  
Working  1,100  50 (4.6)  
Mother education level  0.210  
Less than secondary  7,837  282 (3.6)  
Secondary or high  762  37 (4.9)  
Partner education level  0.406  
Less than secondary  7,155  257 (3.6)  
Secondary or higher  882  40 (4.4) 
Summary table of average life expectancy in British Columbia by socioeconomic status [14]. British Columbia regional average life expectancy at birth by regional socioeconomic status, 2007–2011
SES category  Total LE_{0} (95% CI)  Male LE_{0} (95% CI)  Female LE_{0} (95% CI) 

Low  78.6 (78.079.3)  76.6 (75.777.5)  81.1 (80.481.8) 
Medium  80.5 (79.881.1)  78.2 (77.578.9)  82.8 (82.083.5) 
High  82.2 (81.682.8)  80.2 (79.581.0)  84.2 (83.784.8) 
LE_{0} Gap between low and high SES  3.6  3.6  3.1 
Tables of results – continuous outcomes
Continuous outcome measures can be analysed in a variety of ways, depending on the purpose of the study and whether the measure of the exposure is continuous, categorical or binary.
Correlation coefficients from a study assessing the association between cognitive function and academic performance in Ethiopia [15]. Correlation between cognitive fuinction test and academic performance among school aged children in Goba Town, South east Ethiopia, May 2014
Cognitive test scores  Academic performance  

Average semester result  Mathematics  
Number Recall score  r  0.14  0.19* 
pvalue  0.12  0.03  
N  131  130  
Rovers score  r  0.22*  0.22* 
pvalue  .013  0.01  
N  131  130  
Hand Movement score  r  0.16  0.20* 
pvalue  0.08  0.03  
N  131  130  
Pattern score  r  0.24**  0.27** 
Pvalue  0.005  0.002  
N  131  130  
Word Order score  r  0.23**  0.19* 
pvalue  0.008  0.028  
N  131  130  
Triangles test score  r  0.33**  0.29** 
pvalue  0.001  0.001  
N  131  130  
Raven CPMtest score  r  0.38**  0.38** 
pvalue  0.001  <0.001  
N  129  128 
Table of regression coefficients for the relationship between exposure to NO_{2} in pregnancy and birth weight [16]. Main and stratified analysis of association between pregnancy exposure to NO_{2} and birth weight
Crude  Model 1^{a}  Model 2^{b}  Model 3+c  

N  Beta 95% CI  pvalue  N  Beta 95% CI  pvalue  N  Beta 95% CI  pvalue  N  Beta 95% CI  pvalue  
Main analysis  
Entire study population  17523  37.9 (49.7 to 26.0)  <0.001  16273  43.6 (55.8 to 31.5)  <0.001  16273  5.6 (23.6 to 12.4)  0.54  15829  7.4 (19.6 to 4.8)  0.24 
Women who did not change address  15191  37.4 (50.2 to 24.7)  <0.001  14196  42.7 (55.7 to 29.6)  <0.001  14196  7.0(26.3 to 12.3)  0.48  13818  4.7 (17.8 yo 8.4)  0.48 
LMP based GA only  16805  35.4 (47.5 to 23.2)  <0.001  15618  408 (53.3 to 28.4)  <0.001  15618  3.2 (21.6 to 15.1)  0.73  15195  5.8 (18.3 to 6.7)  0.36 
Stratified analysis  
Oslo  4669  75 (27.7 to 42.7)  0.68  4380  5.9 (42.8 to 31.0)  0.75  4285  12.5 (24.3 to 49.3)  0.51  
Akerhus  7547  10.5 (22.8 to 43.9)  0.54  6982  8.9 (25.4 to 43.1)  0.61  6759  29.2 (4.8 to 63.1)  0.09  
Bergen  3866  15.6 (43.7 to 12.4)  0.28  3577  4.8 (33.0 to 23.4)  0.74  3490  19.8 (7.7 to 47.2)  0.16  
Hordaland  1441  37.6 (104.6 to 29.4)  0.27  1334  36.0 (103.5 to 31.5)  0.30  1295  26.7 (92.7 to 39.2)  0.43  
Not smoking  15440  41.3 (53.8 to 28.8)  <0.001  15229  43.3 (55.8 to 30.8)  <0.001  15229  6.6 (25.1 to 12.0)  0.49  14835  5.6 (18.2 to 6.9)  0.38 
Smoking  1083  28.3 (80.0 to 23.3)  0.28  1044  45.5 (97.7 to 6.8)  0.09  1044  22.1 (51.8 to 96.1)  0.56  994  27.3 (80.1 to 25.5)  0.31 
Parity 0  8304  16.8 (33.3 to 0.4)  0.045  7803  17.8 (34.7 to 10)  0.04  7803  4.3 (20.5 to 29.0)  0.74  7594  8.3 (25.2 to 8.5)  0.33 
Parity 1  6326  0.6 (20.6 to 19.4)  0.95  5858  6.9 (27.4 to 13.5)  0.51  5858  21.8 (8.2 to 51.8)  0.15  5695  2.0(18.3 to 22.4)  0.85 
Parity ≥2  2893  26.5 (60.3 to 7.4)  0.13  2612  31.0 (66.4 to 4.4)  0.09  2612  17.8 (31.7 to 67.4)  0.48  2540  24.8 (59.9 to 10.4)  0.17 
Boys  8921  30.7 (47.5 to 13.8)  <0.001  8290  39.6 (57.0 to 22.2)  <0.001  <8290  7.5 (33.0 to 18.1)  0.57  8040  5.4 (22.8 to 12.1)  0.55 
Girls  8602  45.5 (62. 0 to 29.1)  <0.001  7983  47.8 (64.8 to 30.8)  <0.001  7983  3.6 (28.9 to 21.8)  0.78  7789  9.4(26.4 to 7.6)  0.28 
Education less than high school  985  35.4 (95.3 to 24.5)  0.25  968  24.5 (83.4 to 34.5)  0.42  968  18.4 (96 to 60.0)  0.65  905  27.8 (87.2 to 31.5)  0.36 
Education high school  4173  31.9 (58.5 to 5.3)  0.02  4098  36.0 (62.3 to 9.7)  0.007  4098  10.4 (27.3 to 48.1)  0.59  3948  4.8 (21.7 to 31.3)  0.72 
Education up to 4 years of college  6474  41.4 (61.5 to 23.3)  <0.001  6403  44.0 (62.8 to 25.3)  <0.001  6403  1.5 (30.2 to 27.1)  092  6262  4.9 (23.7 to 13.9)  0.61 
Educatiom more than 4 years of college (master of professional degree)  4866  48.2 (69.6 to 26.9)  <0.001  4804  50.2 (71.4 to 29.0)  <01001  4804  17.8 (49.4 to 13.8)  0.27  4714  13.3 (34.5 to 8.0)  0.22 
Born in winter  4097  20.2 (46.6 to 6.2)  0.13  3797  35.3 (62.5 to 8.2)  0.01  3797  7.8 (31.1 to 46.7)  0.69  3677  4.9 (22.4 to 32.1)  0.73 
Born in winter  4097  20.2 (46.6 to 6.2)  0.13  3797  35.5 (62.5 to 8.2)  0.01  3797  7.8 (31.1 to 46.7)  0.69  3677  4.9 (22.4 to 32.1)  0.73 
Born in spring  4684  60.6 (82.2 to 39.0)  <0.001  4355  60.2 (82.2 to 38.3)  <0.001  4355  46.7 ((79.5 to 13.8)  0.005  4226  28.5(50.6 to 6.4)  0.01 
Born in summer  4626  35.1 (57.4 to 12.8)  0.002  4272  40.5 (63.3 to 17.6)  0.001  4272  14.2 (20.7 to 49.1)  0.43  4167  2.7 (25.7 to 20.3)  0.82 
Born in autumn  4116  28.8 (54.9 to 2.7)  0.03  3849  31.9 (58.6 to 5.3)  0.03  3849  16.1 (23.0 to 55.1)  0.42  3759  5.1 (21.4 to 31.7)  0.70 
However, reading across the columns of the table gives a different story. The successive sets of columns include adjustment for increasing numbers of factors that might affect the association. While model 1 still indicates a negative association between NO_{2} and birth weight that is highly significant (p < 0.001), models 2 and 3 do not. Inclusion of adjustment for parity or area and maternal weight has reduced the association such that the Betas have shrunk in magnitude to be closer to 0, with 95% CIs including 0 and pvalues >0.05.
The table has multiple rows, with each one providing information on a different subset of the data, so the numbers in the analyses are all smaller than in the first row. The second row restricts the analysis to women who did not move address during pregnancy, an important consideration in estimating NO_{2} exposure from home addresses. The third row restricts the analysis to those whose gestational age was based on the last menstrual period. These second two rows present ‘sensitivity analyses’, performed to check that the results were not due to potential biases resulting from women moving house or having uncertain gestational ages. The remaining rows in the table present stratified analyses, with results given for each category of various variables of interest, namely geographical area, maternal smoking, parity, baby’s sex, mother’s educational level and season of birth. Only one row of this table has a statistically significant result for models 2 and 3, namely babies born in spring, but this finding is not discussed in the paper. Note the gap in the table in the model 2 column as it is not possible to adjust for area (one of the adjustment factors in model 2) when the analysis is being presented for each area separately.
Tables of results – binary outcomes
Results table from a study assessing whether children’s eating styles are associated with having a waisthip ratio ≥0.5 or not [17]. Crude and adjusted odds ratios of eating quickly or eating until full for waisttoheight ratio (WHtr) ≥ 0.5
Variables  Total  WHtR ≥ 0.5  Crude  Adjusted  

N  n (%)  OR (95% CI)  Pvalue  OR (95% CI)  Pvalue  
Boys  
Eating quickly  
Yes  255  37 (14.5)  2.04 (1.313.18)  0.002  2.05 (1.313.23)  0.002 
No  715  55 (7.7)  1.00  1.00  
Eating until full  
Yes  515  54 (10.5)  1.29 (0.831.99)  0.259  1.25 (0.801.95)  0.321 
No  455  38 (8.4)  1.00  1.00  
Girls  
Eating quickly  
Yes  126  16 (12.7)  2.02(1.123.64)  0.020  2.09(1.153.81)  0.016 
No  832  56 (6.7)  1.00  1.00  
Eating until full  
Yes  517  40 (7.7)  1.07 (0.661.74)  0.779  1.12 (0681.82)  0.662 
No  441  32 (7.3)  1.00  1.00 
The final columns present the ORs after adjustment for various additional factors, along with their 95% CIs and pvalues. The ORs given here differ little from the crude ORs in the table, indicating that the adjustment has not had much effect, so the conclusions from examining the crude ORs are unaltered. It thus appears that eating quickly is strongly associated with a greater waisthip ratio, but that eating until full is not.
Conclusion
Summary tables of characteristics describe the study population and set the study in context. The main findings can be presented in different ways and choice of presentation is determined by the nature of the variables under study. Scrutiny of tables allows the reader to acquire much more information about the study and a richer insight than if the text only is examined. Constructing clear tables that communicate the nature of the study population and the key results is important in the preparation of papers; good tables can assist the reader enormously as well as increasing the chance of the paper being published.
Abbreviations
 ALRI:

Acute lower respiratory infections
 CI:

Confidence interval
 MUAC:

Midupperarm circumference
 IQR:

Interquartile range
 NO_{2} :

Nitrogen dioxide
 OR:

Odds ratio
 Q1:

Quartile 1 (25th percentile)
 Q2:

Quartile 2 (50th percentile = median)
 Q3:

Quartile 3 (75th percentile)
 SD:

Standard deviation
Declarations
Acknowledgement
Not applicable.
Funding
The work was funded by the UK Medical Research Council which funds the work of the MRC Lifecourse Epidemiology Unit where the authors work. The funding body had no role in the design and conduct of the work, or in the writing the manuscript.
Availability of data and materials
Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.
Authors’ contributions
HI conceived the idea for the paper in discussion with JB. HI wrote the first draft and all other authors commented on successive versions and contributed ideas to improve content, clarity and flow of the paper. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Not applicable.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
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