Public Heath System, Vital Statistics,

1. PUBLICHEALTHSYSTEM
Public HealthSystemData Analysis

2. Objectives
1. Discuss Vital Statistics of Health
 It’s Definition, importance and purpose in
Public Health
 Discuss the Different Points in Vital Statistics
2. Discuss Basic Concept of Data Analysis and how
to interpret it
3. Presentation of Data and Communication of
public health data

3. Vital Statistics of Health
Vital Statistics
 Accumulated data gathered on live births, deaths ,migration, fetal
death, marriages and divorces etc.
Conventionally numerical records of marriage, birth, sickness and
death by which the health and growth of community may be studied
A branch of biometry that deals with data and law of human
mortality, morbidity and demography

4. Aim of Vital Statistics
• Provide reliable ,relevant, up to date ,adequate ,timely and
reasonably complete information to the health authority
• Transformation of information through integration and processing
with perception and experience based on social and political value
• Health care providers are able to intervene the health status of the
population ,provided availability of appropriate tools for measuring
health ,illness andthhe well beig is there..

5. Indicators of Vital Statistics
1. Demography and vital events
2. Environment health statistics
3. Health resources, beds ,manpower
4. Utilization and non utilization of health
5. Health care indices
6. Financial statistics

6. Purpose
• Describe the level of community health, diagnose community illness
and solution of health problems
• Determine success or failure of specific health problem.
• Promote health legislation at local and national level
• Develop policies and procedure at state and center level

7. Importance of Vital Statistics
• To evaluate the impact of various national health program
• To plan for better future measures of disease control
• To explain hereditary nature of disease
• To evaluate economic and social development
• A tool of research activity

8. Sources of Vital information
• Census Surveillance
• Registration of vital events
• Hospital record
• Diseases register
• Population and surveys

9. Points of Vital statistics
1. Maternal Mortality Ratio
2. Maternal Morbidity Rate
3. Perinatal Mortality ,Morbidity Rate
4. Neonatal Mortality,morbidity rate
5. Post neonatal mortality,morbidity rate
6. Infant mortality,morbidity rate
7. 1-4 year child mortality,morbidity rate
8. Under 5 year.mortality ,morbidity rate

10. Maternal Mortality Ratio
• Total number of female death due to complication of pregnancy or
within 42 days of delivery from purpural causes divided by the total
number of live birth in same area in year multiply by 100,000.
Year2020
MMR= 750maternaldeaths 100,000
6,000livebirth
MMR=12,500maternaldeathper100,000populationinanareainanspecifiedperiod

11. Causes of Maternal Mortality
1.Hemorrhage
2.Pre Eclampsia
3.Septic Abortion
4.Sepsis

13. Factors of Mortality Rate
1.Age
2.Parity
3.Low socio economic Status
4.Antenatal Care

14. Maternal Morbidity ratio
• Measures the portion of people in a specific geographical location
who contracted a particular disease during a specific period of time.
• It indicates the frequency of the disease appearing in a population.
• Causes : Infection, poor service, hygiene, hemorrhage, anemia,
abortion,low socio economic status

15. PREVENTIVE MEASURES OF
MATERNAL MORTALITY AND
MORBIDITY

23. DATA ANALYSIS
• Turning Raw Data into useful Information
• Provide answers to questions being asked at a program or
research
• Even the greatest amount and best quality data mean noting
if not properly analyzed.
• Looking at the data in light of the questions you need to
answer.

24. Descriptive Analysis
• Describes the sample/target population(demographic
and clinic characteristics)
• Does not define causality-tells you what and not why.
• Example average clients seen per month

25. Basic Terminology and Concepts
• RATIO
• PROPORTION
• PERCENTAGE
• RATE
• MEAN
• MEDIAN

26. RATIO
• Comparison of two numbers expressed as: a to b, a per b, a:b
• Used to express such comparisons as clinicians to patients or beds to
clients
• Example : In Hospital X, there are 300 nurses and 30 wards.What is the
ratio of nurses to ward:
• 300/30=10 nurses per ward =10nurses :1 ward

27. PROPORTION
• A ratio in which all individuals in the numerator are also in the
denominator
• Used to compare part, share or number comparative of the whole.
• Example: if 20 of 100 clients on treatment are Senior citizens, what is
the proportion of senior citizens in the clinic.
• 20/100= 1/5

28. Percentage
• Way to express a proportion multiplied by 100.
• It expresses a number in relation to the whole.
• Example : 1/5 are senior citizens, we convert the fraction to decimal
(1/5=0.20) and multiply by 100. = 20%

29. Rate
• Measured with respect to another measured quantity during the
same time period.
• Used to express frequency of specific events in a certain time
period(fertility rate, mortality rate)
• Numerator and denominator must be from same time period and
often expressed as a ratio(per 1000).

30. Central Tendency
MEAN
• > the average of your dataset
• The value obtained by dividing the sum of a set of quantities by the number of quantities
in the set.
• Mean is sensitive to extreme numbers
• Example: average number of clients counseled per month.
• Jan- 30
• Feb-45
• March -38
• April-41
• May-37
• June -40
30+45+38+41+37+40=231/6 = 38.5
Mean or average =38.5

31. Median
• The middle of a distribution(when numbers are in order:half of the
numbers are above the median and half are below the median)
• Odd numbers of numbers, median= the middle number
• With median you have to rank (or order) the figures before you can
calculate it.
• Example :IN patients
• OB-9
• Pedia-11
• Surgery-4
• IM-6
4,6,9,11 = 6+9=7.5 middle number

32. Data Presentation and
Communication of Public Health of
Data

33. Summarizing Data
Tables
Simplest way to summarize data
Data are presented as absolute numbers or percentages
Charts
Visual presentation of data
Data are presented as absolute number or percentages

34. Basic Guidance when Summarizing
1. Ensure Graphic has a title
2. Label the components of your graphic
3. Indicate source of data with date
4. Provide number of observations as a reference point
5. Add Footnote if more information is needed

35. Tables: Frequency Distribution
Year Number of Covid Deaths in City of ABC
Feb 2020-December 2020 65
January 2021-December 2021 78
January 2022- October 2022 34
Total Covid Deaths in City of ABC
from Feb 2020- October 2022
Source: CDRRMO, City of ABC

36. Tables: Relative Frequency
Year Number of Covid Deaths in
City of ABC
Relative Frequency (%)
Feb 2020-December 2020 65 36.7%
January 2021-December 2021 78 44%
January 2022- October 2022 34 19.3%
Total 177
Number of values within an interval x 100
Total number of values in the table
Source: CDRRMO, City of ABC
Total Covid Deaths in City of ABC
from Feb 2020- October 2022

37. Charts and Graphs
• Used to portray Trends, relationshIps and comparisons
• Most informative are simple and self explanatory

38. Using the right Type of Graphic
• Bar Chart: Comparisons, Categories of Data
• Line Graph: Display trends over time
• Pie chart: Show percentages or proportional share

39. Bar Chart

40. Line Graph
• Displays trends over time
• Total number of Dengue
Cases in RHU, year 200-2008
Source: City of ABC, 2008

41. PIE Chart
• Displays the contribution to the total which is 100
Source: Philippine statistics
Aurrhority.

42. Interpreting Data
• Adding information by making connections and comparisons and
exploring causes of consequences.
Relevance
of finding
Reasons for
finding
Consider
other data
Conduct
further
research

43. Relevance
of finding
Reasons for
finding
Consider
other data
Conduct
further
research

44. Relevance
of finding
Reasons for
finding
Consider
other data
Conduct
further
research

45. Relevance
of finding
Reasons for
finding
Consider
other data
Conduct
further
research

46. DATA ANALYSIS
• With the latest data analytics, advancements in technology, wide use
of the internet, and connected gadgets, it is possible to collect both
medical and non-medical data of many populations. This data
includes medical data of diagnostic lab reports, or non-medical data
like search history, social media analysis, etc. Though predicting the
future through this method still has a margin of error evident in the
global handling of the current pandemic, scientists are trying fast to
significantly reduce it.

47. • Snehlata Parashar (2019) Vital Statistics, Retrieved from
https://www.slideshare.net/SnehlataParashar/vital-statistics-140140696
• Kara Tiernan (2014) Measures of Morbidity and Mortality Used in Epidemiology
Retrieved from: https://www.slideserve.com/kara/chapter-3-5175377
• Module 1: Data Analysis Key Concepts, Retrieved from
https://view.officeapps.live.com/op/view.aspx?src=https%3A%2F%2Fwww.measu
reevaluation.org%2Fresources%2Ftraining%2Fcapacity-building-
resources%2Fbasic-data-analysis-for-health-programs%2Fme-module-1-data-
analysis-key-concepts-may-2.ppt&wdOrigin=BROWSELINK
• Module 3 Data Presentation and Interpretation retrieved
fromhttps://view.officeapps.live.com/op/view.aspx?src=https%3A%2F%2Fwww.
measureevaluation.org%2Fresources%2Ftraining%2Fcapacity-building-
resources%2Fbasic-data-analysis-for-health-programs%2Fme-module-3-data-
presentation-and-interpretation-may-2.ppt&wdOrigin=BROWSELINK