This data story considers different factors from multiple sources that have a high correlation with Under 5 Mortality Rate (U5MR) across taluks, and builds predictive models of expected impact of changing each factor for each district and taluk. Based on the correlations, the recommended budget allocation for different schemes/factors is also given. At the bottom, you can also see an interactive dashboard where you can perform a particular intervention and see whether it is stable or not.
Factor-wise correlations
Factors with significant correlation
- Number of Asha Working Per 1000 Population (-ve correlation)
- Per Capita Income (at constant prices) (+ve correlation)
- Literacy (-ve correlation)
- Deprivation % – Households that have no electricity (+ve correlation)
- Deprivation % – Households which do not own more than one asset (radio, TV, telephone, computer, animal cart, bicycle, motorbike (+ve correlation)
- Gap in Number of Primary Health Care (+ve correlation)
- children under 5 who are stunted or underweight or severely wasted (+ve correlation)
- Children age 12-23 months who should fully vaccinated (+ve correlation)
- Children who are not receiving postnatal care from a doctor/nurse/Lady Health Visitor/Auxiliary Nurse and Midwife/other health personnel within 2 days of delivery (+ve correlation)
- Severe Acute Malnutrition (+ve correlation)
- Children age 6-23 who should receive adequate diet (+ve correlation)
- Children age 6-59 months who are anaemic (<11.0 g/dl) (+ve correlation)
- Moderate Acute Malnutrition (+ve correlation)
- Mothers who consumed iron folic acid for 180 days or more when they were pregnant (%) (+ve correlation)
- Women (15-49 yrs) whose Body Mass Index (BMI) is below normal (BMI <18.5 kg/m2)(+ve correlation)
- Women aged 20-24 who were married under age 18 yrs (+ve correlation)
Factors without significant correlation
- Gap in Number of Community Health Centers (+ve correlation)
- Number of Chief Health Officers Working Per 1000 Population
- Gap in Primary Health Care Doctor
- Gap in Community Health Centers Doctor
- Deprivation % – Households that cook using solid fuel
- Deprivation % – Households with no safe drinking water or 30 min away
- Deprivation % – Households with with any woman has not received at least 4 antenatal care visits for the most recent birth
- Deprivation % – Households with no sanitation facility
- Received Health Insurance (%)
- Deprivation % – Households that have inadequate housing material in either floor, roofor wall
- No of Pregnant women age 15-49 years who are anaemic (%)
- Children age 9-35 months who received a vitamin A dose (%)
- children low birth weight (%)
- Prevalence of diarrhoea under 5 years of age (%)
- Multidimensional Poverty Index (%)
- Children under age 3 years breastfed within one hour of birth (%)
Predictive Impact Analysis
o see the impact of each of these factors, click on the tabs given above. Every dashboard has a slider using which, you can change the factor of interest. Initially, the slider is at 0%. If you want to decrease the factor by 10%, you can move the slider to the left. Similarly, if you want to increase the factor by 10%, you can move the slider to the right.
For example, say you want to decrease the percentage of children who are underweight by 20% of the current value, you can do it by moving the slider two times to the left (which corresponds to -20%)
The dashboards has district level map as well as taluk level map. The colours on these maps represent the change in under 5 mortality after making the desired change in the factor of interest. If you click on a particular district in the district level map, you get a magnified image of those taluks belonging to the selected district.
Below the maps, you can see the score of each taluk sorted in descending order. The scoring is based on how good each taluk has performed in changing yield after changing the factor.
At the very bottom, you can see the dataset that is being used for making this interactive visualization
Recommended Budget Allocation
The budget allocation is calculated based on the correlation of the various significant factors with U5MR. If a factor has high correlation with U5MR, then that factor has to be allocated more budget than others. The method also takes into account the population of each district/taluk and distributes the budget in such a way that if a particular district/taluk has more people who are affected by a factor x, then we allocate more amount to that district/taluk in order to control that factor.
