District-­Level  Variability  in  Nutrition  Outcomes  and  Drivers  of  Stunting  at  the  District  Level  

Phuong  Hong  Nguyenwith  Derek  Headey,  Rasmi  Avula,  Sneha  Mani,  Lan  Mai  Tran,  Purnima  Menon

Poverty,  Health  and  Nutrition  Division,  International  Food  Policy  Research  Institute

May  3,  2017

BackgroundØ India’s  progress  on  nutrition  has  

accelerated  over  the  last  decade

Ø Despite  these  improvements,  levels  of  stunting  and  most  other  nutrition  indicators  remain  unacceptably  high.  

Ø The  positive  trends  mask  wide  variation  among  the  Indian  states  and  districts.    

Ø Three  previous  NFHS  surveys  were  only  representative  at  the  state  level

Ø NFHS-­4,  with  district-­level  data,  offers  tremendous  learning  opportunity

0

10

20

30

40

50

60

70

Stunting Wasting Anemia  among  women

Exclusive  breastfeeding

Low  birthweight

%

2006 2016

Objectives

Ø Describe  the  inter-­district  variabilities  in  nutrition  outcomes

Ø Analyze  drivers  of  stunting  at  the  district  level  

Ø How  do  stunting  rates  and  absolute  numbers  of  stunted  children  vary  across  India?

Ø Which  underlying  determinants  have  associations  with  stunting  rates?

Ø Which  underlying  determinants  account  for  the  variation  in  stunting  rates  observed  across  high  and  low  burden  districts.  

Methods

Data  sources

Ø District  level  data  set  from  NHFS-­4  Fact  Sheets  

Ø 2011  Census  of  India  data

Analysis

Ø Mapping  nutrition  outcomes  per  district,  using  WHO  cut-­off  values  for  public  health  significance.  

Ø Bivariate  analyses  and  multivariate  regression  analyses  of  association  between  various  determinants  and  stunting.  

Ø Regression-­decomposition  to  assess  the  contributions  of  various  underlying  determinants  to  predict  spatial  patterns  in  stunting,  and  differences  between  high  burden  and  low  burden  districts

Top 5  districtsErnakulam (KL) 12.4%Pathanamthitta (KL) 13.3%Kollam (KL) 14.4%Alappuzha  (KL) 14.5%Idukki  (KL) 15.1%

Bottom  5  districtsSiddharthnagar (UP) 57.9%Pashchimi Singhbhum (JH) 59.4%Balrampur (UP) 62.8%Shrawasti (UP) 63.5%Bahraich (UP) 65.1%

Stunting-­ high  and  very  high  burden  (>30%)  in  441  districts

Districts with no

data

Districts with

10⎼20%

Districts with

20⎼30%

Districts with

30⎼40%

Districts with

>40%

1 29 170 202 239

48.0  %  -­‐-­‐-­‐à 38.4  %

Wasting  and  severe  wasting,  by  district,  2016

Districts with no

data

Districts with

0⎼5%

Districts with

5⎼10%

Districts with

10⎼15%

Districts with

>15%

1 175 307 121 37

Districts with no

data

Districts with 0⎼5%

Districts with

5⎼10%

Districts with

10⎼15%

Districts with

>15%

1 6 42 105 487

19.8  %  -­‐-­‐-­‐à 21  % 6.4  %  -­‐-­‐-­‐à 7.5  %

Top 5  districtsPhek  (NL) 9.0%Champhai (MZ) 12.8%Ukhrul (MN) 16.0%Peren  (NL) 17.5%Aizawl  (MZ) 18.5%

Bottom  5  districtsSaraikela  Kharsawan  (JH) 78.8%Dadra  and  Nagar  Haveli  (DN) 79.5%Puruliya  (WB) 80.0%Kinnaur  (HP) 80.8%Lahul  and  Spiti  (HP) 83.2%

Anemia  among  women  of  reproductive  age,  by  district,  2016

Districts with no

data

Districts with

10⎼20%

Districts with

20⎼30%

Districts with

30⎼40%

Districts with

>40%

1 7 122 360 151

55.3  %  -­‐-­‐-­‐à 53.0  %

Top 5  districtsSurguja  (CH) 84.3%Narsimhapur (MP) 84.3%Rajnandgaon (CH) 84.6%Kabirdham  (CH) 84.9%Tinsukia  (AS) 86.2%

Bottom  5  districtsSouth  Garo  Hills  (ML) 10.4%Muzaffarnagar (UP) 13.3%Rampur  (UP) 15.0%Meerut  (UP) 15.2%Ambedkar  Nagar    (UP) 17.4%

Exclusive  breastfeeding,  by  district,  2016

Districts with no

data

Districts with

10⎼40%

Districts with

40⎼50%

Districts with

50⎼60%

Districts with

>60%

216 72 70 89 194

46.4  %  -­‐-­‐-­‐à 54.9  %

Overweight  and  obesity,  by  district,  2016Men Women

Districts with no

data

Districts with

0⎼10%

Districts with

10⎼20%

Districts with

20⎼30%

Districts with

>30%

1 288 170 149 62

Districts with no

data

Districts with

0⎼10%

Districts with

10⎼20%

Districts with

20⎼30%

Districts with

>30%

1 98 289 160 93

18.6 20.7

Summary  of  insights  from  mapping  of  district  variability

§ The  distribution  of  multiple  forms  of  malnutrition  varies  tremendously  across  the  country

§ The  most  widespread  problems  of  malnutrition  are  stunting,  anemia  and  wasting,  the  burden  of  which  is  medium  or  high  from  a  public  health  significance  perspective  in  a  majority  of  districts

§ Non-­communicable  diseases  are  an  emerging  problem

§ Further  analyses  needed  to  help  to  understand  and  unpack  drivers  of  levels  and  changes  at  district-­level

Drivers  of  stunting  at  the  district  level  – variable  selection

Stunting  rates  and  absolute  numbers  of  stunted  children  among  different  district  categorizations

29

170202

239

0

50

100

150

200

250

300

Low  burden  (<20%)

Medium  burden  (20-­‐30%)

High  burden  (30-­‐40%)

Very  high  burden  (≥  40%)

Num

ber  o

f  districts

723,651

8,872,991

16,363,83037,179,537

Low  burden  (<20%) Medium  burden  (20-­‐30%)

High  burden  (30-­‐40%) Very  high  burden  (≥  40%)

Summary  of  findings  on  drivers  of  stunting

Ø Stunting  in  India  remains  high  and  variable  among  districts

Ø Inter-­district  differences  in  stunting  are  not  explained  by  any  single  factor,  but  

rather  by  a  multitude  of  economic,  health,  hygiene  and  demographic  factors.  

Ø Multi-­sectoral  strategies  with  enhanced  targeting  towards  higher  burden  

districts  are  needed  to  reduce  inequalities  in  childhood  stunting  in  India

Ø Next  steps:    to  analyze  drivers  of  change  in  stunting  and  other  forms  of  

malnutrition  at  the  district