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Getting started with tables

Archives of Public HealthThe official journal of the Belgian Public Health Association201775:14

DOI: 10.1186/s13690-017-0180-1

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 hand-searched 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 non-obese). 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 Inter-quartile range Regression coefficients Correlation coefficients Ratios Relative measures

Background

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 [16].

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, socio-economic 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 main summary statistics provided within a table depend on the type of outcome under investigation in the study. If the variable is continuous (i.e. can take any numerical value, between a minimum and a maximum, such as blood pressure, height, birth weight), then means and standard deviations (SD) tend to be given when the distribution is symmetrical, and particularly when it follows the classical bell shaped curve known as a Normal or Gaussian distribution (see Fig. 1a). The mean is the usual arithmetic average and the SD is an indication of the spread of the values. Roughly speaking, the SD is about a quarter of the difference between the largest and the smallest value excluding 5% of values at the extreme ends. So, if the mean is 100 and the SD is 20 we would expect 95% of the values in our data to be between about 60 (i.e. 100–2×20) and 140 (100 + 2×40).
Fig. 1

Distribution of heights and weights of young women from the Southampton Women’s Survey [7]. a Shows the height distribution, which is symmetrical and generally follows a standard normal distribution, while b shows weight, which is skewed to the right

The median and inter-quartile 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 three-quarters 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 p-values 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 p-value 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 p-value 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 p-value 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 p-value the less likely the result is simply due to chance [8].

Frequently, data within tables are presented with 95% CIs but without p-values or vice versa. If the 95% CI includes 0 (for a continuous outcome measure) or 1 (for a binary outcome), then generally the p-value will be greater than 0.05, whereas if it does not include 0 or 1 respectively, then the p-value will be less than 0.05 [9]. Generally, 95% CIs are more informative than p-values; providing both may affect the readability of a table and so preference should generally be given to 95% CIs. Sometimes, rather than giving exact p-values, 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 table of characteristics in Table 1 is from a study assessing knowledge and practice in relation to tuberculosis control among in Ethiopian health workers [10]. The authors have presented the characteristics of the health workers who participated in the study. Summary statistics are based on categories of the characteristics, so numbers (frequencies) in each category and the percentages of the total study population within each category are presented for each characteristic. From this, the reader can see that:
  • 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

     

    Othersa

    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

     

    Othersb

    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

     

    3-6 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

     

    1-4 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

     

    4-6 days

    59

    45

     

    7-10 days

    35

    28.2

     

    >10 days

    12

    9.2

    OPD outpatient department; TB Tuberculosis.

    aMidwife, radiology, physiotherapy; bMCH, delivery,EPI, FP, physiotherapy

The table of characteristics in Table 2 is from a study of the relationship between distorted body image and lifestyle in adolescents in Japan [11]. Here the presentation is split into separate columns for boys and girls. The first four characteristics are continuous variables, not split into categories but, instead, presented as means, with the SDs given in brackets. The three characteristics in the lower part of the table are categorical variables and, similar to Table 1, the frequency/numbers and percentages in each category are presented. The p-values indicate that boys and girls differ on some of the characteristics, notably height, self-perceived weight status and body image perception.
Table 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

P-value

 

(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/m2)

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)

 

Self-perceived 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)

 

Data are expressed as numbers (%), values are means (standard deviation). The unpaired t-test and chi-squad test were used to compare characteristics between boys and girls

In Table 3, considerable detail is given for continuous variables in the table. This comes from an article describing the relationship between mid-upper-arm circumference (MUAC) and weight changes in young children admitted to hospital with severe acute malnutrition from three countries [12]. For each country, the categorical characteristic of sex is presented as in the previous two examples, but more detail is given for the continuous variables of age, MUAC and height. The mean is provided as in Table 2, though without a standard deviation, but we are also given the minimum value, the 25th percentile (labelled Q1 – for quartile 1), the median (the middle value), the 75th percentile (labelled Q2, here though correctly it should be Q3 – see above) and the maximum value. The table shows:
  • 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 mid-upper-arm 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

Many results tables are simple summaries and look similar to tables presenting characteristics, as described above. Sometimes the initial table of characteristics includes some basic comparisons that indicate the main results of the study. Table 4 shows part of a large table of characteristics for a study of risk factors for acute lower respiratory infections (ALRI) among young children in Rwanda [13]. In addition to presenting the numbers of children in each category of a variety of characteristics, it also shows the percentage in each category among those who suffered ALRI in the previous two weeks, and provides p-values for the differences between the categories among those who did and did not suffer from ALRI. Thus only 2.9% of older children (24–59 months) within the study suffered from ALRI, compared with about 5% in the two youngest categories. The p-value of 0.001, well below 0.05, indicates that this difference is statistically significant. The other finding of some interest is that children who took vitamin A supplements appeared to be less likely to suffer from ALRI than those who did not, but the p-value of 0.04 is close to 0.05 so not as remarkable a finding as for the difference between the age groups.
Table 4

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 (%)

Chi-squared

p-value

CHILD

  

0.001

Child age

 

82 (5.2)

 

 0-11 months

1,573

  

 12-23 months

1,615

82 (5.1)

 

 24-59 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 self-employed 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)

 
Table 5 shows a summary table of average life expectancy in British Columbia by socioeconomic status [14]. The average life expectancy at birth and the associated 95% CIs are given according to level of socio-economic status for the total population (column 1), followed by males and females separately. The study is large so the 95% CIs are quite narrow, and the table indicates that there are considerable differences in life expectancy between the three socioeconomic groups, with the lowest category having the poorest life expectancy. The gap in life expectancy between the lowest and highest category is more than three years, as shown in the final row.
Table 5

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 LE0

(95% CI)

Male LE0

(95% CI)

Female LE0

(95% CI)

Low

78.6 (78.0-79.3)

76.6 (75.7-77.5)

81.1 (80.4-81.8)

Medium

80.5 (79.8-81.1)

78.2 (77.5-78.9)

82.8 (82.0-83.5)

High

82.2 (81.6-82.8)

80.2 (79.5-81.0)

84.2 (83.7-84.8)

LE0 Gap between low and high SES

3.6

3.6

3.1

SES Socioeconomic status, LE 0 Life expectancy at birth, CI Confidence interval

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.

Table 6 shows an example of correlation coefficients indicating the degree of association between the exposure of interest (cognitive test scores) and the outcome measure (academic performance) [15]. No confidence intervals are presented, but the results show that almost all the particular cognitive test scores are statistically significantly associated (p-value < 0.05) with the two measures of academic performance. Note that this table is an example of where a footnote is used to give information about the p-values. Not surprisingly, all the correlations are positive; one would expect that as cognitive score increase so too would academic performance. The numbers labelled “N” give the number of children who contributed data to each correlation coefficient.
Table 6

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*

 

p-value

0.12

0.03

 

N

131

130

Rovers score

r

0.22*

0.22*

 

p-value

.013

0.01

 

N

131

130

Hand Movement score

r

0.16

0.20*

 

p-value

0.08

0.03

 

N

131

130

Pattern score

r

0.24**

0.27**

 

P-value

0.005

0.002

 

N

131

130

Word Order score

r

0.23**

0.19*

 

p-value

0.008

0.028

 

N

131

130

Triangles test score

r

0.33**

0.29**

 

p-value

0.001

0.001

 

N

131

130

Raven CPMtest score

r

0.38**

0.38**

 

p-value

0.001

<0.001

 

N

129

128

*Statistically significant at p<0.05, **Statistically significant a p>0.01

Table 7 is quite a complex table, but one that bears examination. It presents regression coefficients from an analysis of pregnancy exposure to nitrogen dioxide (NO2) and birth weight of the baby in a large study of four areas in Norway; more than 17,000 women-baby pairs contributed to the complete crude analysis [16]. Regression coefficients are presented and labelled “Beta”, the usual name for such coefficients, though the Greek letter β, B or b are sometimes used. They are interpreted as follows: for one unit increase in the exposure variable then the outcome measure increases by the amount of the regression coefficient. Regression coefficients of zero indicate no association. In this table, the Beta in the top left of the table indicates that as NO2 exposure of the mother increases by 1 unit (a ‘unit’ in this analysis is 10 μg/m3, see the footnote in the table, which gives the units of measurement used for the regression coefficients: grams per 10 μg/m3 NO2) then the birth weight of her baby decreases (because the Beta is negative) by 37.9 g. The 95% CI does not include zero and the p-value is small (<0.001) implying that the association is not due solely to chance.
Table 7

Table of regression coefficients for the relationship between exposure to NO2 in pregnancy and birth weight [16]. Main and stratified analysis of association between pregnancy exposure to NO2 and birth weight

 

Crude

Model 1a

Model 2b

Model 3+c

 

N

Beta 95% CI

p-value

N

Beta 95% CI

p-value

N

Beta 95% CI

p-value

N

Beta 95% CI

p-value

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

Effect estimate in grams per 10μg/m3 NO2

GA gestational age, LMP last menstrual period

aModel 1 adjusted for: maternal education, birth season, sex of child, maternal age, maternal marital status, maternal smoking during pregnancy, maternal height

bModel 2 adjusted for: maternal education, birth season, sex of child, maternal age, maternal marital status, maternal smoking during pregnancy, maternal height, area

cModel 3 adjusted for: maternal education, birth season, sex of child, maternal age, maternal marital status, maternal smoking during pregnancy, maternal height, parity, maternal weight, in stratified analysis the corresponding stratification variable is not included in the adjusment

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 NO2 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 p-values >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 NO2 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

Table 8 presents results from a study assessing whether children’s eating styles are associated with having a waist-hip ratio greater or equal to 0.5 (the latter being the outcome variable expressed in binary form – ≥0.5 versus <0.5) [17]. Results for boys and girls are presented separately, along with the number of children in each of the eating style categories. The main results are presented as crude and adjusted odds ratios (ORs). The adjusted ORs take account of age, exercise, skipping breakfast and having a snack after dinner, all of these being variables thought to affect the association between eating style and waist-hip ratio. Looking at the crude OR column, the value of 2.04 in the first row indicates that, among boys, those who report eating quickly have around twice the odds of having a high waist-hip ratio than those who do not eat quickly (not eating quickly is the baseline category, with an odds ratio given as 1.00). The 95% CI for the crude OR for eating quickly is 1.31 – 3.18. This interval does not include 1, indicating that the elevated OR for eating quickly is unlikely to be a chance finding and that there is a 95% probability that the range of 1.31 – 3.18 includes the true OR. The p-value is 0.002, considerably smaller than 0.05, indicating that this finding is ‘statistically significant’. The other ORs can be considered in the same way, but note that, for both boys and girls, the ORs for eating until full are greater than 1 but their 95% CIs include 1 and the p-values are considerably greater than 0.05, so not ‘statistically significant’, indicating chance findings.
Table 8

Results table from a study assessing whether children’s eating styles are associated with having a waist-hip ratio ≥0.5 or not [17]. Crude and adjusted odds ratios of eating quickly or eating until full for waist-to-height ratio (WHtr) ≥ 0.5

Variables

Total

WHtR ≥ 0.5

Crude

Adjusted

 

N

n (%)

OR (95% CI)

P-value

OR (95% CI)

P-value

Boys

      

 Eating quickly

      

  Yes

255

37 (14.5)

2.04 (1.31-3.18)

0.002

2.05 (1.31-3.23)

0.002

  No

715

55 (7.7)

1.00

 

1.00

 

Eating until full

      

  Yes

515

54 (10.5)

1.29 (0.83-1.99)

0.259

1.25 (0.80-1.95)

0.321

  No

455

38 (8.4)

1.00

 

1.00

 

Girls

      

 Eating quickly

      

  Yes

126

16 (12.7)

2.02(1.12-3.64)

0.020

2.09(1.15-3.81)

0.016

  No

832

56 (6.7)

1.00

 

1.00

 

 Eating until full

      

  Yes

517

40 (7.7)

1.07 (0.66-1.74)

0.779

1.12 (068-1.82)

0.662

  No

441

32 (7.3)

1.00

 

1.00

 

OR odds ratio; CI confidence interval

Adjusted for age, exercise, skipping breakfast, and snack after dinner

The final columns present the ORs after adjustment for various additional factors, along with their 95% CIs and p-values. 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 waist-hip 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: 

Mid-upper-arm circumference

IQR: 

Inter-quartile range

NO2

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

(1)
MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital

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Copyright

© The Author(s). 2017

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