An Analysis of maternal mortality ratio across the world. Actions: Data Collection from WHO and UNESCO sites Import Data in SQL Server Management Studio Using SQL Queries to JOIN data from different tables, using UNPIVOT to view data in a better and comparable form Summary Statistics Analysis of Data Visualization and Spotting Trends Using Tableau Link: https://public.tableau.com/profile/rutuja.gangane#!/vizhome/ACountry-wiseAnalysisofMaternalMortalityRatio/AnalysisofMMR

1. Analysis of Maternal Mortality Ratio
Data Management IS 6030 Final Project
Rutuja Gangane (M08655415)
Definition and Concept:
“The maternal mortality ratio (MMRatio) is obtained by dividing the number of maternal deaths in a
population during some time interval by the number of live births occurring in the same period. Thus,
the MMRatio depicts the risk of maternal death relative to the frequency of childbearing. A related measure,
the maternal mortality rate (MMRate), is found by dividing the average annual number of maternal deaths in
a population by the average number of women of reproductive age (typically those aged 15 to 49 years) who
are alive during the observation period. Thus,the MMRate reflects not only the risk of maternal death per
pregnancy or per birth, but also the level of fertility in a population.
In addition to the MMRatio and the MMRate, the lifetime risk, or probability, of maternal death in a
population is anotherpossible measure. Whereas the MMRatio and the MMRate are measures of the
frequency of maternal death in relation to the number of live births or to the female population of
reproductive age, the lifetime risk of maternal mortality describes the cumulative loss of human life due to
maternal death over the female life course.” – W.H.O.
Data Sources:
World Bank, WHO- World Development Indicators
UN data: http://data.un.org/ - Poverty, Life expectancy in adults, Mortality rate in adults
11_Topic_en_excel_v2.
xls
Rut_final.xlsx UNdata_Export_Adult
Mortality.xlsx
Maternal mortality
ratio 2013_7037a7.xlsx
UNdata_Export_lifeex
pectancy.xlsx
UNdata_Export_pove
rty.xlsx
Data was initially taken from World Bank for MMR. After going through the data, many of
the other indicators were added and data was downloaded again. After doing some analysis
on Tableau, I realised the factors I had chosen did not seem to have a significant relation to
MMR. So, I searched some more and added the Poverty, Adult Mortality and Life
Expectancy from UN data site.
Earlier data had about 5000 rows and after choosing the indicators that seemed right, I saved
that data in a new excel file and imported the same.
Data Dictionary:
Tables:
[dbo].[MMR]: In the beginning all columns were nvarchar or float by default which were then
corrected to following:
[Country Name]: [varchar](255)
[Indicator Name]: [varchar](255)

2. For all the Years: [decimal](38,20)
Initial Import table
Columns:
Country - Country or Region
Indicator - This is the Development Indicator defined and data collection by WorldBank,
WHO, UN, etc.
Years 1960-2015- These are all the years from 1960 to 2015 as columns.
[dbo].[MMR1]: Table with correct data types.
[dbo].[MMR2]: UNPIVOT view of data
[dbo].[MMRYrAvg] : Table with each country and its Average Indicator Value over the years.
[dbo].[MMR_byindicator] : Table with PIVOT view of Indicator Names as Columns. (BUT,
only for Top 20 Countries with highest MMR Rate)
MMR:
Total Records: 1902
Indicator: Maternal mortality ratio (modeled estimate, per 100,000 live births)
Unit: Modeled estimate, per 100,000 live births
Country AvgValue
Sierra Leone 2233.462
Chad 1227.038
South Sudan 1213.077
Female Mortality:
[dbo].[UN_AdultMortality] :
Country [varchar] (255), Year [INT], Gender [varchar] (10), Value [decimal] (38, 20)
[dbo].[AdultMortalityFemale] :
Country [varchar] (255), Year [INT], Value [decimal] (38, 20)
Total Records: 582
Maximum 3 Values
Country Year Value
Zimbabwe 2000 764
Botswana 2000 674
Zambia 2000 633
Life Expectancy:
[dbo].[UN_lifeexpectancy]
Country [varchar] (255), Year [INT], Value [decimal] (38, 20)
Total Records: 12196
Maximum 3 Values

3. Country Year Value
Cambodia 1977 19.50493
Cambodia 1976 20.61488
Cambodia 1978 20.75046
Poverty:
[dbo].[UN_poverty]
Country [varchar] (255), Year [INT], Value [decimal] (38, 20)
Total Records: 291
Maximum 3 Values
Country Year Value
Madagascar 2010 75.3
Zimbabwe 2011 72.3
Guinea-Bissau 2010 69.3
Normalization:
The data was not normalized.
One way tostore these valuesishave one mainfacttable,
One normalizedtable forIndicatorValues,andtheirCodesandID.
One NormalizedTable forCountry,CountryCode andID
One NormalizedTable forYear- Optional,Itcouldbe easiertoaccesswithYears beingusedinstead
of IDs.However,forupdating,itisbettertohave thisnormalizedandina differenttable.
It isbetterthan havingpivotviewsforYears.Thisaydata can be modelled/reshaped/edited/
updated/deletedeasily.
A sample ERDis givenbelow:

4. Challenges Faced:
Missing Data: A lot of data points were missing but my idea was to compare for those
countries which have the highest MMR.
Columns Indicator Code and Country code were not giving any useful information in the
table and were also just additional columns which would be problematic while reshaping
data, so I have removed these two columns in Excel itself.
Pivoted year-wise columns from 1990 to current: I used unpivot and found the average values
over the years. I then pivoted the table again to show Indicators as column.
UNPIVOT: The Indicator names were lengthy and with a lot of special characters, this was
updated manually as total no. of distinct indicators were 10. Some manipulation and data
shaping was done in SQL for this. A lot of temporary tables/tables were used to save the
different stages of data manipulation, as these could be used for different sort of
charting/values in Tableau.
Default Data formats: While importing the dataset, the default data types were float,
nvarchar, etc. For calculating Avg., these had to be either inserted into a different table/ re-
imported with correct data type.
Unsaved Work Lost: Learnt a hard lesson when the data and tableau workbook data was lost
as it wasn’t saved due to low battery shutdown.
Incorrect Data: There were values like ‘High Income’ in country column. Since these were
not among the Top countries I was analysing, I did not correct such anomalies. (There were
many, up to a point where replacing the dataset was considered, very close to the deadline.)
Any spelling mistakes were treated directly in tableau.

5. However, to save the data in a cleaner way, normalization would definitely help.
Format: Format of the sheets were in .csv, saved them to .xlsx
Footnotes appended: Raw data was not present in the workbooks, there were footnotes
appended below the actual data rows. Realized this after importing the data. Had to delete,
save and again import the sheet data.
Visualization:
Tableau was used to visualize key findings from the data.
Data Connection: Learnt how to connect more than one data table from SQL in the same
workbook, although not in the same sheet. (I have tried that too through one of the joins, but
later figured joining in SQL is better)
View/Graphs:
Countries with Highest MMR
MMR Worldwide By year
Trends MMR Vs. Other factors
MMR Detailed Box Plot for All Countries
Insights:
1. Most of the Top 20 Countries with highest MMR are Sub-Saharan / African Countries
(Tableau 1st Graph Countries with Highest MMR)
2. A detailed model will be required to find direct correlation between MMR and other
Factors.
(Tableau: Trends for MMR vs. Possible Impact Factors)
3. MMR seems related to Avg. Life Expectancy, Poverty, Female Mortality Ratio, and
Lifetime Risk of Maternal Death
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AdultMortalityFemale

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Chart Title

7. - Most of the Countries are repeated in all three Factors. (Graphs Made in Excel)
4. Only Guinea and Malawi differ somewhat slightly in having higher comparative
mortality rates for female but in continuous decreasing trend for MMR.
5. Factor affecting MMR: Poverty and Malnourishment, Health Conditions, Political
Unrest and Violence, Geography and Climate. To reduce MMR, countries need to
directly work on solving nation-wide issues, few of which are stated above.
6. Decreasing Trend for MMR Each Year in last 15 years world-wide
7. Sierra Leone, having the Highest Avg. MMR has the most significant decrease in
MMR over the years.
(Tableau: MMR-Detailed View)