1. Prepare for the application of health survey.pptx

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Applying Basic Health Statistics
and survey

LEARNING OUTCOMES
At the end of the module the learner will be able to:
 LO1 Prepare for the application of health survey
 LO2 Undertake data collection
 LO3 Compile, interpret and utilize health data
 LO4 Prepare and submit reports
 LO5 Take intervention measures accordingly

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Introduction to Biostatistics
After completing this chapter, the student will be able to:
 Define Statistics and Biostatistics.
 Differentiate b/n descriptive and inferential statistics.
 Classify variables.
 Define and Identify different types of data
 Calculate ratio, proportion & rate

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Definition of Statistics
 Is a field of study concerned with the collection, organization,
summarization, analysis and interpretation of data, and
the drawing of inferences about a body of data when only a
part of the data is observed.
 Statistics is a group of methods used to collect, analyze,
present, and interpret data and to make decisions.

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Cont…
 Biostatistics is the segment of statistics that deals
with data related to living organisms, medicine or
health data.
 It is a scientific methods of collecting, organizing,
analyzing and interpreting biological or medical data.

What is basic health statistics?
 Health statistics are numbers that summarize
information related to health.
 Researchers and experts from government, private, and
non-profit agencies and organizations collect health
statistics.
 They use the statistics to learn about public health and
health care.

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Types of Statistics
1. Descriptive statistics: is consists of procedures used to summarize
and describe the important characteristics of data.
 It merely describes, organizes or summarizes the actual data
available.
 Tables
 Graphs and
 Measures of central tendency and variation
 Vital statistics(birth, death, marriage and divorce)

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2. Inferential Statistics
 Statistical inference is reaching on a conclusion about a population
on the basis of the information contained in a sample.
 Inferential statistics: consists of procedures used to make
inferences about population characteristics from information in a
sample.
 Is most relevant to public health and clinical medicine.
 Builds upon descriptive statistics.
 Eg: Estimation and hypothesis testing

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Five stages of statistical investigation
I. Collection of data
II. Organization of data: data collected from published sources are
generally in organized form.
III. Presentation of data: after the data has been collected and
organized, they are ready for presentation.
IV. Analysis: after collection, organization and presentation, the next
step is analysis.
V. Interpretation: drawing conclusion from the statistical results.

RATIONALE OF STUDYING STATISTICS FOR HEALTH
PROFESSIONALS
I. It provides a way of organizing information on a more formal
basis than relying on the exchange of personal experience.
II. Public health and medicine are becoming increasingly
quantitative rather than qualitative.
III. The planning, conducting and interpretation of medical
research are becoming increasingly reliant on statistical
technology.
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Uses of statistics/biostatistics
 It presents facts in a precise form.
 Data reduction(summarizes data with few values).
 Measuring the magnitude of variations in data.
 Furnishes a technique of comparison.
 Estimating unknown population characteristics.
 Formulating and Testing hypothesis.
 Studying the relationship between two or more variable.
 For planning, conducting and interpretation of medical
research. 15

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Variable
 A variable is a characteristic that changes or varies over time
and/or for different individuals or objects.
 It is a characteristic that takes on different values for different
persons, places or things.
 Is a quality or quantity which varies from one member of a
sample or population to another.
For example: sex of the 1st
year MPH students
 Disease Dx in OPD
 heart rate, the weights of preschool children, BP, RR.

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Types of variable
A. Qualitative: a variable which can not be
measured in quantitative form.
 Takes categories/ names as their values.
 Measurements made on qualitative variables convey
information regarding attribute.
Eg: type of drug, place of birth, ethnic group, stages of
breast cancer, degree of pain, type of diagnosis.

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B. Quantitative variable
 is one that can be measured and expressed numerically.
 Take numbers as their values.
 Measurements made on quantitative variables convey
information regarding amount.
Eg: the weights of children
 Drug dose
 the heights of adult females.
 Blood count

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Types of quantitative variables
1. Discrete variable
 can assume only a finite or countable number of values b/n any two values.
 characterized by gaps or interruptions in the values that it can assume.
 These gaps or interruptions indicate the absence of values between particular
values that the variable can assume.
 values of a discrete variable are usually whole numbers.
Eg. The number of daily admissions to a general hospital
 Number of malaria cases/day
 Episodes of diarrhea/day.

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B. Continuous variable
2. Continuous variable
 Does not possess the gaps or interruptions in values it can
assumes.
 May take on any possible value between any two values.
 For any two values you pick, a third value can always be found
between them!
Eg: Hemoglobin level, height, weight and skull circumference.
 No matter how close together the observed heights of two people,
we can find another person whose height falls somewhere in
between

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Scales of Measurement
 Scales are all possible values (numbers) assigned for a
given variables.
 Based on their scales of measurement(values assigned to
them), variables are classified as:
 Nominal
 Ordinal
 Interval
 Ratio

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1. Nominal variable
 Have unordered categories and no magnitude.
 Numbers used to represent categories.
 Numbers help to decide whether the categories are the same or different
 Comparisons are = or ≠ .
 For nominal variable, the descriptive summary measure is the proportion of
subjects who posses the attribute.
Examples: : Sex – 1. male 2. female.
 Religion – 1. Christian 2.Islam 3.Hinduism, etc,
Marital status-1.Single 2. married 3. divorced 4.widowed.

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2. Ordinal variable
 Observations can be ranked according to some criterion.
 Categories can be compared as to whether they are the same or not and
put in order.
 the members of one category are considered lower, worse, or smaller
than those in another category(possible comparisons are: = or ≠, < or >).
 Differences between categories are meaningless.
 Example: patients may be characterized as:
1. unimproved 2.improved 3. much improved.
 Level of pain(1. mild 2. moderate 3. severe)
 Anemia status: 1. mild anemia 2. moderate anemia 3. severe anemia

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3. Interval variable
 In interval data, difference between any two measurements can be quantified.
 The difference between two consecutive values are the same.
 For example, in the Fahrenheit temperature scale, the difference between 70 and 71
degrees is the same as the difference between 32 and 33 degrees.
 But the scale is not a Ratio Scale(40 degree Fahrenheit is not twice as much as 20
degree Fahrenheit).
 No natural Zero value.
 Comparisons are: = or ≠, < or >, + or -).
 The interval scale unlike the nominal and ordinal scales is a truly quantitative scale.
 Eg: Temperature

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E.g. years:
The difference between 1993-1994 is the same as 1995-
1996, but year 0 was not the beginning of time.
2000 E.C ≠2×1000

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4. Ratio data
 Equality of ratios as well as equality of intervals may be
determined.
 All operations are possible(= or ≠, < or >, + or -, * or ÷)
 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.
 Has natural zero.
 Eg. Amount of money, Age, height, weight

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Data
 Are measurements or observations obtained from the different
members of a sample or a population for a certain variable.
 Are the quantities (numbers) or qualities (attributes) measured or
observed that are to be collected or analyzed.
 The raw materials of Statistics are data.
 Raw facts or figures resulting from the process of counting or from
taking a measurement.
 The word data is plural, datum is singular.

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Exercises
 For each of the following variable indicate whether it is
quantitative or qualitative and specify the measurement scale
for each variable :
a) Blood Pressure (mmHg)
b) Cholesterol (mmol/l)
c) Diabetes (Yes/No)
d) Body Mass Index (Kg/m2)
e) Age (years)
f) Sex (female/male)
g) Employment (paid work/retired/housewife)
h) Smoking Status (smokers/non-smokers, ex-smokers)
i) Level of pain (mild/moderate/severe)

By Muhaba Y. 36
VITAL STATISTICS
 Are quantitative information about a
population’s vital events such as number of
births(natality),deaths(mortality),marriages(nu
ptiality)and divorces
 The most common way of collecting
information on these events is through civil
registration.
 Civil registration is an administrative system
used by government to record vital events
which occur in their population.

By Muhaba Y. 37
Ratio:
 A ratio quantifies the magnitude of one
occurrence or condition in relation to another
1) Sex Ratio (SR): is the total number of male
population per 100 female population,
 SR=M/F*100, where M and F are total number
of male and female populations, respectively.
Sex ratios are used for purposes of comparison.
 a) The balance between the two sexes
 b) The variation in the sex balance at different
ages
 c) It is also used for detecting errors in
demographic data

By Muhaba Y. 38
2) Child-Woman-Ratio (CWR): the ratio of the
number of children under 5 years of age to the
number of women in the child bearing age
group (usually 15-49).
CWR = P0-4 / Pf 15-49 x 1000 = Number of children
under 5 years of age per 1000 women in the
child bearing age.
 The child woman ratio is also known as
measure of effective fertility

By Muhaba Y. 39
Proportion
 It is defined as the percentage of the total
number of events which occur in a data set,
usually expressed as a percentage.
 The formula is (x/y)k, where x is the number
of individuals or events in a category and y is
the total number of events or individuals in
the data set and k is a constant, in this case
100.
 Example: Of the 120 cases of malaria admitted
to hospital X last year, 80 were children. The
proportion (percentage) of children among
cases is (80/120) x 100 or 66.7%.

By Muhaba Y. 40
Rate:
 A rate is a proportion with a time element, i.e., in
which Occurrences are quantified over a period
of time.
 The term rate appropriately refers to the ratio of
demographic events to the population at risk in a
specified period.
 Rate =Number of demographic events of interests in certain time x k
Population at risk at certain time
where K is a constant mainly a multiple of 10 (100,
1000, 10000, etc.).

By Muhaba Y. 41
 Population at risk: This could be the mid-year population
(population at the first of July 1),population at the
beginning of the year or a more complex definition.
 Period for a rate is usually a year.
Rate could be crude or specific:
 It is considered as crude when it shows the frequency of a
class of events through out the entire population without
regarding to any of the smaller groupings.
 Crude rates are highly sensitive to the structure (age) of
the population and are not directly used for comparison
purposes.
 Where as specific rate implies the events in a particular
category of age, sex, race, particular disease, or other
classification variables are used.

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