UNIT 1 Basic Health Statistics and survey.pptx

Blue Nile College
Department Of Nursing
Module Title: BASIC HEALTH STATISTICS
AND SURVEY For Level III Nursing
By: Tewodros Teshome
2024
04/05/2025 1

Course syllabus
 Module Title: BASIC HEALTH STATISTICS AND
SURVEY
 Credit hour: 4
 Course instructor: Tewodros T.
 Email address: teddyteshome173@gmail.com
04/05/2025 2

Course Description and Introduction to Basic Health
statistics
• Statistics is the process of data collection, organization,
Summarization, analysis and reporting.
• The word statistics can mean two things: the subject itself or data.
• Recently Statistics is defined as the science of uncertainty.
• The subject of Statistics is a wide discipline, ranging from ordinary
use such as collection of data and its description to methods used
in evaluation and research.
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•A statistic is a quantity computed from sample
observations for the purpose of making an inference
about the characteristic in the population.
•The characteristic may be any variable which is associated
with a member of the population, such as age, income,
employment status, etc. the quantity may be a total, an
average, a median, or other quantiles.
• It may also be a rate of change, a percentage, a standard
deviation, or it may be any other quantity whose value we
wish to estimate for the population.
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 Health care statistics deals with the collection, organization, management,
analysis and reporting of healthcare data in addition to using some of this
data to assist in making decisions about planning and resource allocation.
 Healthcare data comes from all facilities; hospitals, health centres, clinics and
health posts.
 Examples of how statistics (and collected data) can be used in a health care
setting include assisting in decision-making for medical treatment,
administrative decision-making, monitoring the incidence of disease and
conditions, measuring and reporting quality initiatives, improving
performance in clinical or administrative units, and reporting statistical data
both internally and externally to meet governmental and other agency
requirements.
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Teaching method and material
 Teaching method
 Interactive presentation
 Group discussion
 Group assignment and presentation
 Reading assignment
 Teaching Aids
 Printed materials
 LCD projectors
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Course Policy
• Attendance: this course will involve numerous
discussion and class activities students are
expected to attend all classes
• Assignments: students must do given
assignments on time
Late assignment submission will not be accepted
• Cheating/plagiarism: Students must do their
own work
Cheating or Plagiarism will result in
disqualification of the result
04/05/2025 7

Course Policy….
Assessment
• Continuous Institute Assessment Result (100%/LO)
• Test1…………………………..........………….100%
• Test 2…………………….………………………100%
• Test 3………………………………..…………..100%
• Test 4………………………………………………100%
• Test 5……………………………………………...100%
• Industry Assessment Result ………No?
• Average Total-----------------------------100%
• Grading system- Based on the college’s grading policy
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Module units
Prepare for the application of health survey
Undertake data collection
Compile, interpret and utilize health data
Prepare and submit reports
Take intervention measures accordingly
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Learning objectives of the Module
At the end of the module the learner will
be able to:
describe application of health survey
Undertake data collection
Compile, interpret and utilize health data
Prepare and submit reports
04/05/2025 10

Course contents
UNIT ONE: PLAN AND PREPARE FOR DATA
COLLECTION
•Definition of terms
•Characteristics of health statistics
•Scales of measurement
•Basic principles of health statistics
•Calculating rates and ratios
•Basic principles of health survey
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UNITTWO: UNDERTAKE DATA
COLLECTION
Types of questionnaires
Preparing questionnaire
Pre-testing, modifying and amending questionnaire
Training on data collection procedures
Equipment/materials for data collection
Informing members of community about data
collection
Inviting community leaders on data collection
process
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UNITTHREE: COMPILE, INTERPRET
AND UTILIZE HEALTH DATA
Collect health data
Analyze health data
Maintaining health data base system.
Diagrammatic presentation of data
Maintaining steps of confidentiality
according to prescribed procedures.
Collecting and updating vital events
Preparing and utilizing data
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UNIT FOUR: PREPARE AND
SUBMIT REPORTS
Preparing reports using standard
reporting formats
Report dissemination
Communicating Updates and reportable
diseases
 Preparing and utilizing data
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UNIT FIVE:TAKE INTERVENTION MEASURES
ACCORDINGLY
Discussion with key stakeholders regarding the
health problems
Identifying materials throughout the consultation
process
Providing feedback
Making contributions to the health problem of the
community Collecting information and data for
better intervention
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UNIT ONE: PLAN AND PREPARE FOR DATA
COLLECTION
upon completion of this Unit, you will be able
to:
•Identify characteristics of health statistics
•Explain scales of measurement
•Apply basic principles of health statistics
•Calculate rates and ratios
•Apply basic principles of health survey
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1.1. Definitions of terms
oHealth- world health organization defined health
as complete physical, social, psychological, and
spiritual well beingness and not merely the
absence of disease.
oStatistics- the term statistics is used to mean
either statistical data or statistical methods.
oHealth statistics- the application and utilization of
statistical data or statistical methods for health
oVariable:- a characteristic that can take on
different values in different situations.
04/05/2025 17

Definitions of terms…
oPopulation: the largest collection of entities used in a study. For example,
the population could be hospital inpatients, all patients with a specific
diagnosis, all of the inhabitants of Addis Ababa, or the population of Ethiopia.
oSample: a small group or subset of a population. For example, when the
entire population of a city cannot be studied, a sample is used that would
represent the entire population. Methods of sampling will be explained later
in this module.
oParameter: - any numerical property, characteristics or facts that are
descriptive of a population. (A statistic applies to a sample).
oData:- is a set of facts expressed in quantitative form usually obtained from
a measurement, totals or from counting.
04/05/2025 18

Definitions of terms…
oData Sources: Data can also be data considered as primary or
secondary Primary data is data obtained directly from a source or
population. Secondary data is data that has been obtained and stored
and can be used by anyone with access to the data.
oDatabase: A database is an organized way to store data for easy access
oCoded data: data that have been translated into standard
nomenclature of classification so that they can be aggregated,
analyzed, and compared.
oQuantitative data can be expressed as a number, or quantified.
Examples of quantitative data are scores on achievement tests,
number of hours of study, numbers of patients with a specific disease,
or heights and weight of a subject. Quantitative data is a useful
method when you want to know how much or how many related to
the topic. Because quantitative data are reported in numbers be used
to manipulate and report this data. These data can also be
represented by ordinal, interval or ratios scales which will be
discussed below.
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oQualitative Data cannot be expressed as a number. Data that
represent nominal scales such as gender, socio-economic status,
and religious preference are usually considered to be qualitative
data.
•Data from qualitative studies often result in themes,
perceptions or categories of data such as nominal data.
Nominal data really means data that is named or assigned a
category.
• Both types of data are valid types of measurement but yield
different results.
•The data that results from quantitative studies are numbers or
scores (quantitative data) and the data resulting from qualitative
studies is more thematic or answers a why question.
―
•Only quantitative data can be analyzed statistically, and thus
more rigorous assessments of the data are possible.
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oData and information
•Terms like data, information and knowledge are often used
interchangeably in common speech.
•Each of these terms however, has a quite precise and distinct
definition in the information sciences.
•Data consists of facts. Facts are observations or measurements
about the world.
•For example- ‘today is Sunday, the patient ‘s blood pressure is
125/70mmHg or Aspirin is a NSAID ‘
• Information: Information is processed data of meaningful value,
enabling a decision to be taken.
•For example- 42 when it is realized as the temperature reading of a
patient in degree Celsius, we have some information about the
status of the patient‘s health showing it is much higher than the
average, which indicates danger and request for action.
•This information then enables a decision to be taken about the
patient.
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•Health information includes information gathered on
individuals from their birth to their death and can range
from the individual patient record to aggregate data on a
patient population that can span the whole world.
•Data typically collected and processed into health
information include:
Health care data
Health data is any data "related to health conditions,
reproductive outcomes, causes of death, and quality of
life" for an individual or population.
Health data includes clinical metrics along with
environmental, socio-economic, and behavior information
pertinent to health and wellness.
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Some typical types of health care data are grouped below
according to the stakeholders who typically create or use
the data, but it is important to note that there is wide
variation in whether or not these data are available in
one‘s local community, city, county, or state.
Some types of data may fall under more than one
category and may be available either at an individual or
aggregate level.
Each type of data can support multi-sector initiatives
Health care data can be expressed in different forms as
follow:
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A) Clinical data: most common type of health information –
signs, symptoms, diagnoses, impressions, treatments, and
outcome of the care process.
B) Epidemiological data: used to describe health related
issues – such as disease trends and events, used to inform the
public and to generate action.
C) Demographics data: In the health care sector,
demographic information can include personally identifiable
information such as name, date of birth, address, and account
or medical record numbers, and descriptive information such
as race, gender, income level, educational status, nativity,
immigration status, and housing status
D) Reference data: collected and maintained by health
institutions for use in the system, including formulary for
pharmacists, care-plan for nurses, protocols, clinical alerts and
reminders.
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E)Individual data
•Information that identifies an individual and their health
conditions and services is often protected by privacy laws at
the state and federal level and is called protected health
information (PHI).
•Technological innovations have made accurately collecting,
storing and sharing this type of data easier than ever.
•While individuals have some access to their individual
information, often there is a fee for medical records requests.
•Personal devices that automatically track blood pressure,
heartbeat, sleep, and physical activity levels, along with
programs that can store information about doctor visits,
prescriptions and other health information has created an
explosion of granular health data that exists outside of the
health care system and the associated protections.
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F)Provider data
Health care providers typically collect Protected Health
Information to help identify and track services and
outcomes of treatment offered to individuals.
This data may be privacy-protected, but often can be de-
identified, aggregated, and shared to respond to
population-level health trends. .
G)Medication prescriptions and adherence data
Information on prescribed medications including drug
name, dosage, if the prescription was filled and picked up
by the patient, and compliance with prescribed
medications over time
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1.2. Characteristics of health statistics
Health statistics are used to understand risk factors for
communities, track and monitor diseases, see the impact of
policy changes, and assess the quality and safety of health
care.
Health statistics are a form of evidence, or facts that can
support a conclusion.
Evidence-informed policy-making, "an approach to policy
decisions that is intended to ensure that decision making is
well-informed by the best available research evidence" and
evidence-based medicine (EBM), or "the conscientious,
explicit, judicious and reasonable use of modern, best
evidence in making decisions about the care of individual
patients" are essential to informing how best to provide
health care and promote population health.
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Not all evidence is, or should be, equally convincing in the
support of a conclusion.
Evidence varies in quality and whether it is applicable to a
given situation.
It is therefore essential that health researchers and policy
makers understand how to assess evidence in a systematic
way, including how to access transparent, high quality
health statistics and information.
04/05/2025 28

Health statistics measure four types of information.
The types are commonly referred to as the four Cs: Correlates,
Conditions, Care, and Costs.
Correlates: See how to measure the risk factors and protective
factors that impact our health.
Conditions: Learn to assess how often and how badly diseases
impact a community.
Care: Dig into how health care is delivered to the communities that
need it, to treat disease and illness.
Costs: Get more information on what health care costs, and why.
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Characteristics of statistical data
 In order that numerical descriptions may be called statistics they must possess
the following characteristics:
i) They must be in aggregates – This means that statistics are 'number of facts.’
• A single fact, even though numerically stated, cannot be called statistics.
ii) They must be affected to a marked extent by a multiplicity of causes.
• This means that statistics are aggregates of such facts only as grow out of a '
variety of circumstances’.
• Thus the explosion of outbreak is attributable to a number of factors, eg.
Human factors, parasite factors, mosquito and environmental factors.
• All these factors acting jointly determine the severity of the outbreak and it is
very difficult to assess the individual contribution of any one of these factors.
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iii) They must be enumerated or estimated according to a reasonable
standard of accuracy – Statistics must be enumerated or estimated
according to reasonable standards of accuracy.
• This means that if aggregates of numerical facts are to be called 'statistics'
they must be reasonably accurate.
• This is necessary because statistical data are to serve as a basis for
statistical investigations.
• If the basis happens to be incorrect the results are bound to be
misleading.
iv) They must have been collected in a systematic manner for a
predetermined purpose.
• Numerical data can be called statistics only if they have been compiled in
a properly planned manner and for a purpose about which the
enumerator had a definite idea.
• Facts collected in an unsystematic manner and without a complete
awareness of the object, will be confusing and cannot be made the basis
of valid conclusions.
04/05/2025 31

v)They must be placed in relation to each other.
•That is, they must be comparable.
•Numerical facts may be placed in relation to each
other either in point of time, space or condition.
•The phrase, ‘placed in relation to each other'
suggests that the facts should be comparable.
04/05/2025 32

Rationale of studying statistics
Statistics pervades a way of organizing information on a wider
and more formal basis than relying on the exchange of anecdotes
and personal experience.
More and more things are now measured quantitatively in
medicine and public health.
There is a great deal of intrinsic (inherent) variation in most
biological processes.
Public health and medicine are becoming increasingly quantitative.
As technology progresses, the physician encounters more and
more quantitative rather than descriptive information.
In one sense, statistics is the language of assembling and handling
quantitative material.
04/05/2025 33

Even if one’s concern is only with the results of
other people’s manipulation and assemblage of
data, it is important to achieve some
understanding of this language in order to
interpret their results properly.
The planning, conduct, and interpretation of much
of medical research are becoming increasingly
reliant on statistical technology.
For example it answers such the following
questions.
04/05/2025 34

Is this new drug or procedure better than the one
commonly in use?
How much better?
What, if any, are the risks of side effects associated with
its use?
In testing a new drug how many patients must be treated,
and in what manner, in order to demonstrate its worth?
What is the normal variation in some clinical
measurement?
How reliable and valid is the measurement?
What is the magnitude and effect of laboratory and
technical error?
04/05/2025 35

Statistics pervades the medical literature.
As a consequence of the increasingly quantitative nature of
public health and medicine and its reliance on statistical
methodology, the medical literature is replete with reports
in which statistical techniques are used extensively.
"It is the interpretation of data in the presence of such
variability that lays at the heart of statistics."
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Limitations of statistics:
It deals with only those subjects of inquiry that are
capable of being quantitatively measured and
numerically expressed.
1. It deals on aggregates of facts and no importance is
attached to individual items–suited only if their group
characteristics are desired to be studied.
2. Statistical data are only approximately and not
mathematically correct.
04/05/2025 37

1.3. Scales of measurement
Any aspect of an individual that is measured and take any value for
different individuals or cases, like blood pressure, or records, like
age, sex is called a variable.
It is helpful to divide variables into different types, as different
statistical methods are applicable to each.
The main division is into qualitative (or categorical) or quantitative
(or numerical variables).
Qualitative variable: a variable or characteristic which cannot be
measured in quantitative form but can only be identified by name or
categories, for instance place of birth, ethnic group, type of drug,
stages of breast cancer (I, II, III, or IV), degree of pain (minimal,
moderate, severe or unbearable).
04/05/2025 38

Quantitative variable:A quantitative variable is one
that can be measured and expressed numerically
and they can be of two types (discrete or
continuous).
The values of a discrete variable are usually whole
numbers, such as the number of episodes of
diarrhea in the first five years of life.
A continuous variable is a measurement on a
continuous scale.
Examples include weight, height, blood pressure,
age, etc.
04/05/2025 39

Although these types of variables could be broadly divided into
categorical (qualitative) and quantitative, it has been a common
practice to see four basic types of data (scales of measurement).
Nominal data:- Data that represent categories or names.
• There is no implied order to the categories of nominal data.
• In these types of data, individuals are simply placed in the proper
category or group, and the number in each category is counted.
• Each item must fit into exactly one category.
• The simplest data consist of unordered, dichotomous, or "either -
or" types of observations, i.e., either the patient lives or the patient
dies, either he has some particular attribute or he does not.
04/05/2025 40

Some other examples of nominal data:
Eye color - brown, black, etc.
Religion - Christianity, Islam, Hinduism, etc.
Sex - male, female
04/05/2025 41

Ordinal Data:- have order among the response
classifications (categories).
•The spaces or intervals between the categories are not
necessarily equal.
Interval Data:- In interval data the intervals between
values are the same.
•For example, in the Fahrenheit temperature scale, the
difference between 70 degrees and 71 degrees is the same
as the difference between 32 and 33 degrees.
•But the scale is not a RATIO Scale.
•40 degrees Fahrenheit is not twice as much as 20 degrees
Fahrenheit.
04/05/2025 42

Ratio Data:- The data values in ratio data do have meaningful ratios,
for example, age is a ratio data, some one who is 40 is twice as old
as someone who is 20.
• Both interval and ratio data involve measurement.
• Most data analysis techniques that apply to ratio data also apply to
interval data.
• Therefore, in most practical aspects, these types of data (interval
and ratio) are grouped under metric data.
• In some other instances, these type of data are also known as
numerical discrete and numerical continuous.
04/05/2025 43

Numerical discrete
Numerical discrete data occur when the
observations are integers that correspond with a
count of some sort.
Some common examples are: the number of
bacteria colonies on a plate, the number of cells
within a prescribed area upon microscopic
examination, the number of heart beats within a
specified time interval, a mother’s history of
number of births ( parity) and pregnancies
(gravidity), the number of episodes of illness a
patient experiences during some time period, etc.
04/05/2025 44

Numerical continuous
•The scale with the greatest degree of
quantification is a numerical continuous scale.
•Each observation theoretically falls somewhere
along a continuum.
•One is not restricted, in principle, to particular
values such as the integers of the discrete scale.
•The restricting factor is the degree of accuracy of
the measuring instrument most clinical
measurements, such as blood pressure, serum
cholesterol level, height, weight, age etc. are on a
numerical continuous scale.
04/05/2025 45

1.4. Basic principles of health statistics
Descriptive Statistics
•Concept: The branch of statistics that focuses on
collecting, summarizing, and presenting a set of
data.
E.g.
The average age of citizens who voted for the
winning candidate in the last presidential election,
the average length of all books about statistics,
The variation in the weight of 100 boxes of cereal
selected from a factory’s production line.
04/05/2025 46

•Interpretation:You are most likely to be familiar with this
branch of statistics, because many examples arise in
everyday life.
•Descriptive statistics forms the basis for analysis and
discussion in such diverse fields as securities trading, the
social sciences, government, the health sciences, and
professional sports.
•A general familiarity and widespread availability of
descriptive methods in many calculating devices and
business software can often make using this branch of
statistics seem deceptively easy.
04/05/2025 47

Inferential Statistics
Concept: The branch of statistics that analyses
sample data to draw conclusions about a
population.
Interpretation: When you use inferential statistics,
you start with a hypothesis and look to see
whether the data are consistent with that
hypothesis.
Inferential statistical methods can be easily
misapplied or misconstrued, and many inferential
methods require the use of a calculator or
computer.
04/05/2025 48

1.5. Measurement of health
Health status of a community is assessed by the collection,
compilation, analysis and interpretation of data on illness
(morbidity), death (mortality), disability and utilization of
health services.
The most basic measure of disease frequency is a simple
count of affected individuals.
Such information is useful for public health planners and
administrators for proper allocation of health care
resources in a particular community.
However, to investigate distributions and determinants of
disease, it is also necessary to know the size of the source
population from which affected individuals were counted.
04/05/2025 49

1.5.1. Ratios, proportions, and rates
Ratio
•A ratio quantifies the magnitude of one
occurrence or condition to another.
•It expresses the relationship between two
numbers in the form of x: y or x/y X k
Example:
•The ratio of males to females (M:F) in Ethiopia.
•The ratio of male malaria patients to female
malaria patients
04/05/2025 50

Proportion
•A proportion quantifies occurrences in relation to
the populations in which these occurrences take
place.
• It is a specific type of ratio in which the
numerator is included in the denominator and the
result is expressed as a percentage.
Example:
•The proportion of all births that was male
•Male births x 100 divided to Male + Female births
04/05/2025 51

Rate
•Rate is the most important epidemiological tool used for
measuring diseases.
•Rate is a special form of proportion that includes time.
•It is the measure that most clearly expresses probability or
risk of disease in a defined population over a specified
period of time, hence, it is considered to be a basic
measure of disease occurrence.
• Accurate count of all events of interest that occur in a
defined population during a specified period is essential for
the calculation of rate.
•Rate = Number of events in a specific period x k divided to
Population at risk of these events in a specified Period
Example: The number of newly diagnosed pneumonia cases
in 1999 per 1000 under five children.
04/05/2025 52

1.5.2. Measurements of morbidity
•Morbidity rates are rates used to quantify the occurrence
of disease.
•Measures of morbidity include incidence, period
prevalence, and point prevalence rates.
Incidence rate
•The incidence of a disease is defined as the number of new
cases of a disease that occur during a specified period of
time in a population at risk for developing the disease.
•Incidence rate = Number of new cases of a disease over a
period of time X K
04/05/2025 53

1.6. Basic principles of health survey
•A health survey is a tool used to gather information on
the behavior of a specific group of people from a
determined area.
•This kind of survey allows health care experts to
understand better how a community acts towards health.
•Health surveys are a necessary and helpful instrument for
decision-making when crafting a health plan.
•Health surveys provide specific information about the
epidemiological situation, health trends, life habits, and the
use of health services from the patients’ point of view.
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• This type of survey allows physicians to locate risk factors
in the community around the hospital or health care
centers, such as tobacco use, alcohol use, poor diet habits,
and lack of physical exercise, which are common health
behavior.
Example:The most important part of a health survey is the
correct implementation.
• Patients need to have a specific time to answer the survey
question without intervention during the hospital
experience, which usually is when they feel least prepared
to answer questions; instead, they should answer them at
the end of their visit.
• Having the right health survey questions in a survey will
allow you to collect valuable data about your respondents’
health and well-being and adequately meet your research
objectives.
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How to begin to conduct your survey
Step 1: Define who will do your survey.
•The ideal situation is to identify an outside party to
interview your community such as a church group,
graduate or undergraduate students, United Way, or
another community organization.
•Having an outside group will reduce the “conflict of
interest” concern the authorities always use when a
survey is conducted by the community itself.
Step 2: Define how you to conduct the interviews.
Step 3:Train your interviewers.
Step 4: Learn how to fill out the questionnaire. It is
essential to teach your interviewers how to fill out the
questionnaire.
04/05/2025 56

UNIT 1 Basic Health Statistics and survey.pptx

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