Basic statistical method in compiling health data

Basic statistical method in compiling health data.

Learning Objectives

• By the end of this session, students are expected to be able to:
• Explain methods of statistical data categorization
• Identify levels of statistical data measurement

Compile health data using appropriate statistical methods

Methods of statistical

data categorization

Data: The raw material of statistics. Data generally consists of numbers of measurement or counting of a population sample.

The methods of summarizing data (methods of descriptive statistics) vary with different types of data that are generated from different types of variables.

Definition of a Variable.

Variable: A term for a characteristic that is different in different members of a population or sample, such as height.

• This measurement is not constant, so therefore it is variable.

Variables can be qualitative or quantitative, continuous or discrete.

Types of Variables.

• There two types of variables:
• Qualitative (categorical) variables
• Quantitative (numerical) variables

Qualitative (Categorical) Variables

• Qualitative variables do not take numerical values (e.g. gender: male/female).
• Outcome of disease (recovery, chronic illness, death)
• Hair color (black, blonde)

Marital status (single, married, widowed, separate, divorced)

Quantitative (Numerical) Variables

• Quantitative variables take numerical values, for example:
• Age (years): 10, 19, 45, 60
• Height (cms):140, 50.6, 200

Parity: 0, 1, 2, 3, 4, 5, 6, 10.

Quantitative variables are of two types: continuous and discrete

Continuous variables take any value within meaningful extremes, can take decimals, for example:

• Height (cm): 159, 25, 160.35
• Weight (kg): 71.12, 80.56
• Exact age like 21 yrs 6 months and 4 days
• Discrete variables take only fixed values, in most cases whole numbers, for example:

Parity: 0, 1, 2, 3, 4, 5, 6, 10

Levels of Measurement

• Variables are measured on different levels/scales
• The term ‘measurement’ is used here in a broad sense
• These are nominal, ordinal, and ratio measurements

Nominal Measurement

The nominal scale classifies persons or things based on a qualitative assessment of the characteristic being assessed. It neither includes information on quantity or amount nor does it indicate ‘more than’ or ‘less than’

Example 1: Gender (male or female) is a common nominal variable used in epidemiologic studies.

Other examples: These used for identifying various categories that make up a given variable e.g. Religion: 1 = Muslim, 2 = Christian, 3 =

Ordinal Measurement

The ordinal scale also classifies persons or things based on the characteristic being assessed but does indicate ‘more than’ or ‘less than’. In this sense, it provides more information than the nominal scale.

Example: Rating students’ performance as being poor, average, good, or excellent indicates how well students perform and provides a basis for comparison. However, it does not indicate how much better an excellent performance is compared to a good one.

Interval Measurement

The interval scale has the same characteristics of the ordinal scale – classifying persons or things based on the characteristic assessed and indicating more than or less than – but the interval scale indicates how much more than or less than.

The interval scale does not indicate a true zero point, meaning that there cannot be an absence of a characteristic being measured.

Basic statistical method in compiling health data

Example: Temperature is an interval in that different values can tell you how much more or less. However, there is no true zero point. The value of zero in temperature does not indicate absence of temperature.

Ratio Measurement

The ratio scale includes all the characteristics of the interval scale but does indicate a true zero point.

Example: Height and weight measurements indicate how much more or less, but also have a true zero point. A weight of zero indicates an absence of weight.

Differences Between Nominal, Ordinal, Interval and Ratio Measurements

• Nominal
• Ordinal
• Interval
• Ratio
• Classifies persons or things based on a qualitative assessment
• Similar or dissimilar but not more or less
• Can be numeric but there is no implication of more or less
• Classifies persons or things based on a qualitative assessment
• More or less but not how much more or less
• Indicates how much more or less
• Does not contain a true zero point
• Cannot create meaningful ratios of these two numbers

Includes all the characteristics of the interval scale, but contains a true zero point.

Descriptive Methods for Qualitative Data

Frequency and Relative Frequency Distribution – 1

Frequency distribution

A presentation of the number of times (or the frequency) that each value (or group of values) occurs in the study population.

Frequency distribution helps to give a picture of the shape of the distribution of the data.

Frequency and Relative Frequency Distribution – 2

• Unimodal data: Data that only has one peak.
• Bimodal data: Data that has two peaks.

Multimodal data: Data that has more than two peaks.

Measures of dispersion help to form a clearer picture of the distribution of the data by describing the height, or the spread, of the data.

Frequency and Relative Frequency Distribution – 3

Multimodal Distribution

Frequency and Relative Frequency Distribution – 4

Relative frequency distribution: A frequency taken by a value relative to total frequency of a variable.

Cumulative relative frequency distribution: The accumulated relative frequency of distributions as the value of the variable increases.

Note: A frequency distribution can be displayed as a table, a bar chart, a histogram, or a frequency polygon.

• Each method should be clearly labeled with the frequency number.

The method usually depends on the type of variable being describe

Use of Tallies in Making Frequency Distribution – 1

A frequency distribution is normally formed (manually) by a process known as tallying.

Use of Tallies in Making Frequency Distribution – 2

• Steps involved in tallying:
• Scan the data and determine the categories

List the categories

Work through the data and allocate each observation to the category where it belongs using the tally marks to keep a count of the number in each category Add the tally marks to give the frequency

Use of Tallies in Making Frequency Distribution – 3

The following data show a qualitative variable; ‘Result of sputum examination for possible pulmonary tuberculosis infection’…

Sputum Examination for Possible TB Infection

• The results have been coded as follows:
• 1 represents Smear negative (–ve), culture negative (–ve).
• 2 represents Smear negative (–ve), not done.

3 represents Smear positive (+ve) , culture positive (+ve).

Sample Raw Data

1 2 1 1 3 1 1 3 3 2 1 3 1 1 2 3 1 1 3 1 2 3 1 1 3 1 1 3 1 3 1 3 2 1 1 3 1 1 2 1 1 2 3 1 1 1 2 1 2 2 3 1 1 2 1 3 1 1 1 1 1 2 1 3 1 1 3 1 1 1 2 1 1 1 3 2 3 3 3 1 1 1 2 1 1 1

From the above data we can summarize as follows

• Note: IIII indicates 5 observations.
• Values
• Tally
• Frequency
• Smear –ve, culture -ve

IIII IIII IIII … IIII

• 144
• Smear –ve, culture not done

IIII IIII IIII … IIII

• 40
• Smear +ve, culture +ve

IIII IIII IIII … IIII

45

Can We Summarize it Further?

• The previous data can be summarized further into a table with the following:
• Frequency,

Relative Frequency

Cumulative Relative Frequency for Sputum Examination.

A better summarization!

• Values
• Frequency
• Relative

Cumulative Relative Frequency

• Smear –ve, culture –ve
• 144
• 62.9
• Smear –ve, culture not done
• 40
• 17.5
• 80.4
• Smear +ve, culture +ve
• 45
• 19.6
• 100.0
• Total
• 229

100.0

Use of Diagrams

• Frequency distributions can be illustrated visually by means of statistical diagrams.
• Diagrams serve two main purposes:
• Presentation of information/data (e.g. report) in articles for ease of appreciation

To serve as a private aid for further statistical analysis.

Pie Charts

These are used to express the distribution of individual observation into different categories.

• Frequencies must be converted into percentages totaling 100 for pie chart to be used.

As an example we will use 2017 TTCIH first year students’ enrollment data.

Example: 2017 TTCIH Students’ Enrollment Data

• Course
• Number of students
• Advanced Diploma in Clinical Medicine
• 20
• Ordinary Diploma in Clinical Medicine (in-service)
• 13
• Ordinary Diploma in Clinical Medicine (pre-service)

37

Bar Chart – 1

The bar chart is the simples and most effective means of illustrating qualitative data.

The various categories of a variable are represented on the horizontal axis and the frequency or relative frequency is represented on the vertical axis.

Bar Chart – 2

The length of each bar represents the number of observations ( frequencies) in each categories or the relative frequencies in percentage.

For example, consider the following birth control method mix in a certain population.

Example:

• Birth Control Methods Used in a Certain Population

Birth Control Method

• Percentage
• Abstinence
• 3
• Oral contraceptive
• 32

Depo-Provera

• 9
• Loop
• 17
• Spermicides
• 7
• Condoms
• 26
• Vasectomy
• 3
• Hysterectomy
• 2
• Norplant
• 1
• Total

100

Why not use a pie chart in the previous table?

A pie chart for this variable would not be suitable because the diagram will be too congested.

Hence a bar chart is more appropriate.

Two-Way Tables – 1

Statistical information on two variables can be presented simultaneously in a form of a two-way table.

It makes information easier to assimilate by showing many of the properties of the data at a glance.

Two-Way Tables – 2

Data are presented in rows and columns.

The format for a table depends upon the data and the aspects of the data which are important to portray.

Two-Way Tables – 2

• A two-way table should include the following:
• A clear title.

A caption for the rows and columns with units of measurement of the variable.

Labels for each individual row or column, i.e. the values taken by the variable concerned.

Marginal and grand totals.

Example

In a study to investigate whether or not HIV infection is a risk factor to pulmonary tuberculosis (PTB), a total of 2165 individuals were examined.

Blood samples were also collected from these individuals for laboratory diagnosis of HIV infection.

• Of the 2165 individuals examined, 651 were found to be negative for HIV infection.
• Of those who were negative, 57 were found to have PTB.

1514 of the HIV positive, 875 were found to have PTB.

This information can be summarized in a two by two table as shown in Figure in the following slides.

Pulmonary Tuberculosis by HIV Status

• (in a two-way table)
• HIV status
• PTB Status
• Positive
• Negative
• Total
• Positive
• 875 (57.8%)
• 639 (42.2%)
• 1514 (100.0%)
• Negative
• 57 (8.9%)
• 594 (91.1%)
• 651 (100.0%)
• Total
• 932 (43.0%)
• 1233 (57.0%)
• 2165 (100.0%)

Note: Numbers in brackets show the row percentages.

Two-Way Table – 3

The cells of a two way table may contain percentages instead of the real counts.

Calculation of percentages may be row-wise or column-wise depending on the purpose of the table.

In the previous table, the interest is to investigate whether HIV infection is a risk factor to PTB.

• The aim is to see whether PTB is higher in HIV positives than in HIV negatives.

The row percentages are more appropriate in this case.

Take Home!

The term biostatics means the application of statistics to biological health problems.

There is a need for studying biostatistics in medical science for some standardized techniques to cope with the inevitable biological variability.

Biostatics is applied in statistical methods which have a role to play in official health statistics, epidemiology, clinical studies, human biology, laboratory studies, health service administration, and there may be need to prioritize target groups for necessary interventions.

Take Home!

The descriptive statistics, also known as methods of descriptive statistics, vary with different types of data that are generated from different types of variables.

Frequency distribution is a descriptive data method for qualitative data.

This means that the number of times (or the frequency) that each value (or group of values) occurs in the study population is tallied and summarized by using a variety of methods (pie graphs, bar charts, etc.) depending on the type data and purpose.