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Malnutrition

among

children

under

the

age

of

five

in

the

Democratic

Republic

of

Congo

DRC

does

geographic

location

matter

NgiangaBakwin

Kandala

Tumwaka

P

Madungu

Jacques

BO

Emina

Kikhela

PD

Nzita

and

Francesco

P

Cappuccio

Author

information

Article

notes

Copyright

and

License

information

This

article

has

been

cited

by

other

articles

in

PMC

Go

to

Abstract

Background

Although

there

are

inequalities

in

child

health

and

survival

in

the

Democratic

Republic

of

Congo

DRC

the

influence

of

distal

determinants

such

as

geographic

location

on

childrens

nutritional

status

is

still

unclear

We

investigate

the

impact

of

geographic

location

on

child

nutritional

status

by

mapping

the

residual

net

effect

of

malnutrition

while

accounting

for

important

risk

factors

Methods

We

examine

spatial

variation

in

underfive

malnutrition

with

flexible

geoadditive

semiparametric

mixed

model

while

simultaneously

controlling

for

spatial

dependence

and

possibly

nonlinear

effects

of

covariates

within

a

simultaneous

coherent

regression

framework

based

on

Markov

Chain

Monte

Carlo

techniques

Individual

data

records

were

constructed

for

children

Each

record

represents

a

child

and

consists

of

nutritional

status

information

and

a

list

of

covariates

For

the

children

born

within

the

last

five

years

before

the

survey

children

have

information

on

anthropometric

measures

Our

novel

empirical

approach

is

able

to

flexibly

determine

to

what

extent

the

substantial

spatial

pattern

of

malnutrition

is

driven

by

detectable

factors

such

as

socioeconomic

factors

and

can

be

attributable

to

unmeasured

factors

such

as

conflicts

political

environmental

and

cultural

factors

Results

Although

childhood

malnutrition

was

more

pronounced

in

all

provinces

of

the

DRC

after

accounting

for

the

locations

effects

geographic

differences

were

significant

malnutrition

was

significantly

higher

in

rural

areas

compared

to

urban

centres

and

this

difference

persisted

after

multiple

adjustments

The

findings

suggest

that

models

of

nutritional

intervention

must

be

carefully

specified

with

regard

to

residential

location

Conclusion

Childhood

malnutrition

is

spatially

structured

and

rates

remain

very

high

in

the

provinces

that

rely

on

the

mining

industry

and

comparable

to

the

level

seen

in

Eastern

provinces

under

conflicts

Even

in

provinces

such

as

BasCongo

that

produce

foods

childhood

malnutrition

is

higher

probably

because

of

the

economic

decision

to

sell

more

than

the

population

consumes

Improving

maternal

and

child

nutritional

status

is

a

prerequisite

for

achieving

MDG

to

reduce

child

mortality

rate

in

the

DRC

Go

to

Background

Malnutrition

prevents

children

from

reaching

their

full

physical

and

mental

potential

Health

and

physical

consequences

of

prolonged

states

of

malnourishment

among

children

are

delay

in

their

physical

growth

and

motor

development

lower

intellectual

quotient

IQ

greater

behavioural

problems

and

deficient

social

skills

susceptibility

to

contracting

diseases

Furthermore

child

malnutrition

is

associated

with

approximately

percent

of

underfive

mortality

in

SubSaharan

Africa

SSA

countries

The

majority

of

studies

on

child

nutritional

status

have

described

prevalence

of

malnutrition

among

underfive

children

and

analyzed

socioeconomic

demographic

and

cultural

factors

associated

with

child

malnutrition

in

SSA

However

little

is

known

about

the

links

between

childs

nutritional

status

and

distal

determinants

including

geographic

location

and

the

environment

due

to

restricted

methodologies

Our

study

aims

to

investigate

the

impact

of

geographic

location

as

a

proxy

for

distal

factors

and

their

influences

on

nutritional

status

of

children

The

province

of

residence

is

taken

as

a

modifiable

variable

which

can

help

explain

the

variation

of

malnutrition

among

children

between

different

provinces

Four

reasons

justify

the

interest

of

this

study

first

geographic

location

is

an

important

modifier

of

known

predictors

of

malnutrition

and

is

associated

with

food

security

and

accessibility

especially

in

the

context

of

conflict

affected

country

such

as

the

DRC

Second

through

the

use

of

our

empirical

methods

we

can

investigate

inequalities

in

childhood

malnutrition

by

mapping

the

residual

net

effect

of

spatial

pattern

of

malnutrition

more

flexibly

than

most

previous

work

Third

the

methods

also

allow

us

to

investigate

nonlinear

effects

of

some

risk

factors

prior

to

and

after

controlling

for

the

socioeconomic

determinants

This

enables

us

to

determine

to

what

extent

the

substantial

spatial

pattern

of

malnutrition

is

driven

by

socioeconomic

factors

or

point

to

the

influence

of

omitted

variables

with

strong

spatial

structure

or

possibly

conflicts

political

or

environmental

and

cultural

factors

or

even

epidemiological

processes

that

account

for

this

spatial

structure

Fourth

the

worsening

socioeconomic

cultural

and

political

context

of

the

DRC

needs

to

be

investigated

The

DRC

is

one

of

the

SSA

countries

characterised

by

extreme

poverty

high

incidence

of

childhood

diseases

high

mortality

and

poor

infrastructure

percent

of

people

are

malnourished

hundreds

of

thousands

of

children

have

died

due

to

malnutrition

over

the

past

years

Furthermore

the

country

continues

to

experience

armed

conflicts

and

political

instability

since

However

regardless

of

the

worsening

socioeconomic

political

and

health

situations

little

is

known

about

inequalities

in

childhood

malnutrition

across

socioeconomic

strata

or

provinces

although

preliminary

reports

from

the

existing

national

surveys

highlight

the

problem

of

malnutrition

among

children

Go

to

Background

on

study

area

The

DRC

is

the

third

largest

country

by

area

km

in

Africa

and

with

immense

natural

resources

distributed

across

its

provinces

It

is

with

the

population

of

more

than

million

the

eighteenth

most

populous

nation

in

the

world

and

the

fourth

most

populous

nation

in

Africa

percent

of

which

are

under

the

age

of

fifteen

Poverty

and

vulnerability

are

the

main

characteristics

of

the

Congolese

population

First

the

World

Bank

estimated

that

the

DRCs

per

capita

gross

domestic

product

GDP

in

was

US

Dollar

The

GDP

has

since

declined

External

debt

at

the

end

of

was

billion

US

which

according

to

the

Word

Bank

equals

roughly

percent

of

the

GDP

and

to

percent

of

the

exports

The

accumulated

debt

and

severe

economic

decline

are

due

to

both

recent

war

and

decades

of

corruption

and

economic

mismanagement

Further

since

the

DRC

has

been

hit

by

conflict

which

has

devastated

and

destabilized

the

country

and

claimed

the

lives

of

an

estimated

six

million

civilians

People

continue

to

live

in

crisis

conditions

in

many

parts

of

the

country

The

eastern

provinces

Orientale

Katanga

North

and

South

Kivu

and

more

recently

the

province

of

Equateur

are

afflicted

by

violence

The

ongoing

Congolese

crisis

has

claimed

more

lives

than

any

conflict

since

World

War

II

and

it

continues

to

be

of

concern

to

the

international

community

Despite

many

political

agreements

signed

since

the

start

of

the

conflict

there

is

little

expectation

and

prospect

for

peace

as

lives

of

vulnerable

groups

such

as

women

and

children

continue

to

be

shattered

as

conflict

reemerged

in

the

eastern

part

of

the

country

and

a

new

front

of

violence

opened

in

the

province

of

Equateur

These

conflicts

have

continued

to

hinder

the

DRCs

ability

to

drive

development

efforts

forward

so

the

population

continues

to

suffer

the

consequences

Compounding

this

situation

is

the

lack

of

leadership

mismanagement

corruption

rapid

deterioration

Thus

humanitarian

needs

in

the

country

remain

colossal

According

to

the

Central

Emergency

Respond

Fund

report

in

conflict

has

generated

up

to

million

internally

displaced

persons

IDPs

in

only

three

provinces

corroding

the

coping

mechanisms

of

millions

of

people

With

the

continuation

of

conflict

and

the

actions

of

abusive

armed

groups

have

increased

food

prices

matched

with

the

limited

ability

of

productive

areas

to

feed

population

centres

due

to

logistic

constraints

have

generated

malnutrition

rates

of

up

to

percent

in

certain

health

zones

Consequently

chronic

malnutrition

is

a

serious

problem

affecting

some

percent

of

children

in

the

DRC

Preliminary

reports

from

three

nutritional

national

surveys

the

and

Multiple

Indicator

Cluster

Surveys

and

MICS

and

the

Demographic

and

Health

Survey

DRCDHS

show

that

nutritional

situation

in

the

DRC

remains

critical

Specifically

nutritional

status

of

children

under

the

age

of

indicated

deterioration

in

terms

of

acute

malnutrition

stunting

wasting

and

underweight

Stunting

rate

was

respectively

percent

in

percent

in

and

percent

in

The

nutritional

status

of

mothers

is

also

critical

about

percent

of

them

were

suffering

from

low

Body

Mass

Index

BMI

in

The

ever

worsening

political

climate

in

Eastern

provinces

resulted

in

war

since

has

created

an

unprecedented

hardship

on

the

population

especially

on

children

as

they

are

more

prone

than

adults

to

suffer

from

nutritional

deficiencies

because

of

their

physiologically

less

stable

situation

Very

high

malnutrition

rates

have

been

recorded

in

the

war

provinces

because

of

insecurity

But

even

in

peace

areas

untouched

by

the

present

conflict

nearly

half

of

the

children

are

malnourished

Malnutrition

remains

one

of

the

main

factors

associated

with

the

high

childhood

morbidity

and

mortality

National

estimates

of

malnutrition

may

conceal

important

intraprovincial

differences

due

to

diverse

cultural

norms

that

might

affect

nutritional

practices

and

the

impact

of

the

ongoing

conflict

on

food

security

It

is

therefore

important

to

examine

patterns

of

malnutrition

at

a

more

disaggregated

province

level

We

recognise

that

a

province

in

a

country

such

as

the

DRC

is

a

large

unit

of

observation

but

the

provinces

estimates

are

more

informative

compared

to

the

use

of

national

estimates

of

malnutrition

There

is

no

specific

empirical

study

undertaken

to

investigate

determinants

of

malnutrition

among

children

in

the

DRC

We

therefore

investigate

the

impact

of

geographic

location

on

childhood

malnutrition

while

taking

into

account

the

effect

of

the

important

risk

factors

of

malnutrition

present

in

the

DHS

database

that

might

confound

or

mediate

the

inequalities

of

the

spatial

patterns

observed

at

the

provincelevel

in

order

to

gain

a

good

understanding

of

the

extent

of

malnutrition

in

a

postconflict

country

The

results

will

enrich

the

current

literature

with

recent

data

on

malnutrition

making

it

more

understandable

and

helping

to

establish

more

effective

intervention

Go

to

Geographic

Location

in

the

DRC

DHS

By

applying

the

spatial

analysis

to

the

disaggregated

provincelevel

we

are

able

to

establish

whether

the

spatial

effects

cross

the

boundaries

between

the

provinces

or

are

distinct

which

would

also

give

us

a

sense

on

the

relative

importance

of

policies

versus

geographic

factors

in

causing

malnutrition

While

the

eastern

provinces

used

to

be

the

major

food

producers

of

the

country

repeated

looting

of

crops

by

armed

groups

and

general

insecurity

over

many

years

has

undermined

production

In

other

parts

of

the

country

with

better

security

conditions

crumbled

infrastructure

has

significantly

decreased

the

countrys

food

production

capacity

Households

and

major

food

importers

maintain

food

reserves

at

a

bare

minimum

because

of

the

volatile

political

and

economic

environment

as

well

as

the

frequent

threats

of

looting

High

prices

have

also

hit

the

DRC

hard

Food

prices

have

increased

by

percent

in

June

compared

to

figures

from

May

This

is

probably

due

to

the

lack

of

national

policy

for

food

production

and

the

reliance

of

the

DRC

on

food

aid

the

DRC

relies

on

aid

The

financial

and

economic

crisis

has

also

affected

mining

activities

Acute

malnutrition

is

at

dangerously

high

levels

in

some

parts

of

the

DRC

Acute

malnutrition

is

above

the

emergency

threshold

in

the

Kasaï

provinces

centre

Even

the

worst

affected

parts

of

North

Kivu

do

not

have

such

high

rates

perhaps

due

to

humanitarian

interventions

Malaria

malnutrition

acute

respiratory

infections

tuberculosis

and

diarrhea

are

the

main

causes

of

child

mortality

according

to

the

As

conflict

continues

to

prevail

in

Province

Orientale

South

North

Kivu

and

Equateur

children

are

subject

to

starvation

and

there

is

an

increase

in

child

mortality

and

morbidity

An

almost

total

lack

of

basic

health

and

social

infrastructure

has

had

a

negative

impact

on

child

health

Go

to

Methods

Go

to

Nutritional

status

According

to

the

World

Health

Organization

WHO

malnutrition

has

three

commonly

used

comprehensive

types

named

stunting

wasting

and

underweight

measures

by

height

for

age

weight

for

height

and

weight

for

age

indexes

respectively

Stunting

or

growth

retardation

or

chronic

proteinenergy

malnutrition

PEM

is

deficiency

for

calories

and

protein

available

to

the

body

tissues

and

it

is

inadequate

intake

of

food

over

a

long

period

of

time

or

persistent

and

recurrent

illhealth

This

heightforage

index

stunting

is

less

sensitive

to

temporary

food

shortages

and

thus

seems

to

be

considered

as

the

most

reliable

indicator

Because

studies

have

shown

that

wasting

is

volatile

over

seasons

and

periods

of

sickness

and

underweight

shows

seasonal

weight

recovery

and

being

overweight

for

some

children

can

also

affect

weightforage

index

Wasting

or

acute

proteinenergy

malnutrition

captures

the

failure

to

receive

adequate

nutrition

during

the

period

immediately

before

the

survey

resulting

from

recent

episodes

of

illness

and

diarrhoea

in

particular

or

from

acute

food

shortage

Underweight

status

is

a

composite

of

the

two

preceding

ones

and

can

be

due

to

either

chronic

acute

malnutrition

or

PEM

In

the

three

surveys

nutritional

status

was

assessed

according

to

weightforage

weightforheight

and

heightforage

using

the

US

National

Center

for

Health

StatisticsWHO

international

reference

tables

and

charts

Wasting

stunting

and

underweight

were

defined

as

weightforheight

heightforage

and

weightfor

age

of

or

more

below

the

corresponding

median

of

the

reference

population

respectively

while

severe

wasting

severe

stunting

was

defined

as

or

more

below

the

same

median

respectively

We

focused

on

stunted

children

SD

of

heightforage

below

the

median

value

as

our

covariates

were

better

able

to

explain

chronic

than

acute

malnutrition

We

used

the

ZScore

in

a

standardized

form

as

a

continuous

variable

to

maximize

the

amount

of

information

available

in

the

data

set

It

is

worth

mentioning

that

because

of

the

drawback

of

the

international

reference

population

in

correctly

capturing

nutritional

status

of

children

around

the

world

recently

a

new

reference

standard

has

been

generated

from

which

Zscores

can

be

calculated

For

the

purpose

of

this

paper

the

use

of

the

new

reference

standard

would

not

change

the

qualitative

results

A

detailed

discussion

on

the

new

reference

standard

can

be

found

in

Figure

shows

a

histogram

and

kernel

density

estimates

of

the

distribution

of

the

Zscores

together

with

a

normal

density

with

mean

and

variance

estimated

from

the

sample

This

gave

us

clear

evidence

that

a

Gaussian

regression

model

is

a

reasonable

choice

for

our

inference

for

the

dependent

variable

stunting

Figure

Histogram

kernel

density

of

stunting

left

and

mean

standardized

Zscore

for

stunting

by

childs

age

right

Go

to

Correlates

of

Malnutrition

Child

nutritional

status

is

actually

caused

by

multiple

factors

including

but

not

exclusively

those

with

illness

disease

and

biological

causes

A

fuller

understanding

of

illness

and

disease

must

include

considerations

of

cultural

psychological

social

and

political

factors

present

in

the

physical

environment

where

the

child

lives

This

premise

has

been

expanded

in

many

different

areas

such

as

medical

child

psychology

and

sociology

and

now

forms

a

fundamental

part

of

a

great

deal

of

social

science

research

and

practice

Mosley

and

Chen

in

their

study

of

the

causes

of

death

in

children

in

developing

and

low

income

countries

placed

risk

factors

within

an

analytical

framework

or

including

the

interactions

among

socioeconomic

cultural

environmental

and

biomedical

factors

The

framework

focuses

on

the

factors

or

determinants

according

to

how

direct

the

impact

of

the

determinant

was

on

the

risk

of

death

ie

the

proximity

of

the

risk

posed

to

the

children

The

Proximate

factors

include

biological

agents

of

disease

eg

microbes

and

vectors

and

other

elements

which

directly

influence

childs

exposure

to

the

agents

of

disease

and

ill

health

Distal

factors

include

features

of

the

wider

sociocultural

environmental

and

political

context

affecting

both

the

child

hisher

care

givers

eg

public

health

policies

and

safety

as

well

as

cultural

norms

environmental

degradation

which

dictate

how

a

family

may

respond

to

an

illness

These

associations

illustrate

the

vulnerability

of

children

in

any

population

who

live

in

the

environment

where

many

of

these

determinants

become

unavailable

or

unstable

Since

we

are

interested

in

multiple

causes

of

malnutrition

when

modelling

the

determinants

of

malnutrition

we

can

distinguish

between

immediate

intermediate

and

underlying

determinants

While

malnutrition

is

always

immediately

related

to

either

insufficient

nutrient

intake

or

the

inability

of

the

body

to

absorb

nutrients

primarily

due

to

illness

these

are

themselves

caused

by

food

security

care

practises

and

the

health

environment

at

the

household

level

which

themselves

are

influenced

by

the

socioeconomic

and

demographic

situation

of

households

communities

and

public

health

policies

Factors

such

as

food

security

care

practises

and

health

environment

are

a

matter

of

public

health

policies

We

refer

to

them

as

distal

determinants

of

malnutrition

In

order

to

capture

this

complex

chain

of

causation

various

approaches

have

been

taken

each

focusing

on

a

particular

level

of

causality

Studies

have

estimated

structural

equations

that

address

the

interactions

Caputo

et

al

have

used

graphical

chain

models

to

assess

the

causal

pathways

and

other

studies

have

used

multilevel

modelling

techniques

However

with

the

available

data

it

is

not

always

clear

how

to

separate

intermediate

from

underlying

determinants

For

example

mothers

education

might

be

influencing

care

practises

an

intermediate

determinant

and

the

resources

available

to

the

household

an

underlying

determinant

On

the

other

hand

child

province

of

residence

a

distal

determinant

might

influence

food

prices

and

security

intermediate

determinants

and

food

availability

an

underlying

determinant

Given

these

difficulties

our

approach

is

to

estimate

models

that

mainly

focus

on

factors

that

are

mostly

underlying

determinants

of

malnutrition

although

some

might

also

be

considered

intermediate

determinants

and

distal

determinants

The

most

important

covariate

included

in

this

analysis

is

the

geographic

location

where

the

child

lives

that

includes

features

of

the

wider

sociocultural

and

political

context

affecting

both

the

child

and

hisher

care

givers

Other

selected

sociodemographics

variables

available

in

the

data

are

grouped

as

individual

childs

characteristics

mothers

characteristics

household

economic

level

and

communitys

characteristics

Regarding

the

covariates

we

were

guided

by

the

previous

literature

on

the

Unfortunately

the

surveys

do

not

generate

an

income

variable

and

we

therefore

rely

on

a

household

asset

index

as

a

proxy

for

the

socioeconomic

status

of

the

households

which

has

been

found

to

be

quite

reliable

Ownership

of

consumer

items

such

as

a

radio

or

car

as

well

as

characteristics

of

the

dwelling

such

as

floor

or

roof

type

toilet

facilities

and

water

source

are

items

that

measure

poverty

in

these

setting

and

the

World

Bank

and

others

have

used

these

items

to

generate

an

asset

index

using

Principal

Components

Analysis

PCA

We

use

the

first

principal

component

derived

from

Table

Distribution

of

stunted

children

by

selected

variables

Among

the

underlying

determinants

of

chronic

malnutrition

we

considered

as

a

proxy

measure

of

current

or

recent

socioeconomic

status

SES

the

asset

index

household

size

the

nutritional

status

of

the

mother

measured

by

her

BMI

health

knowledge

and

care

practices

measured

by

mothers

education

mothers

marital

status

birth

interval

and

place

of

delivery

of

children

We

also

control

for

the

sex

of

the

child

urban

rural

location

and

the

age

of

child

Based

on

prior

own

work

as

well

as

other

literature

we

investigated

a

potentially

nonlinear

pattern

of

effects

of

the

mothers

BMI

as

well

as

the

age

pattern

on

malnutrition

For

illustration

the

empirical

distribution

of

the

stunting

Zscore

by

childs

age

is

shown

in

Figures

right

It

is

obvious

that

the

effect

of

childs

age

on

the

mean

Zscore

of

stunting

is

nonlinear

It

will

be

difficult

to

model

the

possibly

nonlinear

effect

of

such

covariates

through

a

parametric

functional

form

which

well

justifies

our

use

of

a

flexible

semiparametric

model

Empirical

distributions

of

all

factors

used

in

the

analysis

are

given

in

Table

Statistical

analysis

Historically

variations

in

malnutrition

prevalence

has

been

related

to

household

socioeconomic

factors

because

it

determines

the

amount

of

resources

such

as

food

good

sanitation

and

health

care

that

are

available

to

infants

and

neglected

temporal

and

geographic

gradients

and

other

variations

in

risk

in

order

to

generate

hypotheses

towards

the

cause

of

malnutrition

We

examine

spatial

variation

in

underfive

malnutrition

with

flexible

geoadditive

semiparametric

mixed

model

while

simultaneously

controlling

for

spatial

dependence

and

possibly

nonlinear

effects

of

covariates

within

a

simultaneous

coherent

regression

framework

Individual

data

records

were

constructed

for

children

Each

record

represents

a

child

and

consists

of

nutritional

status

and

a

list

of

covariates

For

the

children

born

within

the

last

five

years

before

the

survey

children

have

information

on

anthropometric

measures

Because

the

predictor

contains

usual

linear

terms

nonlinear

effects

of

metrical

covariates

and

geographic

effects

in

additive

form

such

models

are

also

called

geoadditive

models

Kammann

proposed

this

type

of

models

within

an

empirical

Bayesian

approach

Here

we

apply

a

fully

Bayesian

approach

as

suggested

in

which

is

based

on

Markov

priors

and

uses

Markov

Chain

Monte

Carlo

MCMC

techniques

for

inference

and

model

checking

For

model

choice

we

routinely

used

the

Deviance

Information

Criterion

DIC

developed

in

Spiegelhalter

et

al

as

a

measure

of

fit

and

model

complexity

Geoadditive

and

georeferenced

disaggregated

province

level

or

sitespecific

analysis

is

a

means

of

managing

spatial

and

temporal

variability

of

determinant

of

different

types

distal

proximate

and

intermediate

factors

which

are

deemed

to

affect

child

nutritional

status

The

aim

of

sitespecific

province

analysis

is

to

accelerate

policy

interventions

optimise

inputs

unobserved

factors

such

as

distal

ones

food

security

and

prices

policies

environmental

etc

improve

child

nutrition

by

taking

into

account

the

environmental

impact

and

reduce

the

timescale

to

attain

the

Millennium

Development

Goals

MDGs

It

is

an

approach

that

deals

with

multiple

groups

of

factors

input

to

improve

child

nutritional

status

in

order

to

satisfy

the

actual

needs

of

parts

of

the

provinces

rather

than

average

needs

of

the

whole

country

The

analysis

was

carried

out

using

version

of

the

BayesX

software

package

which

permits

Bayesian

inference

based

on

MCMC

simulation

techniques

The

statistical

significance

of

apparent

associations

between

potential

risk

factors

and

stunting

was

explored

in

chisquare

and

MannWhitney

U

tests

as

appropriate

Multivariate

analysis

was

used

to

evaluate

the

significance

of

the

posterior

mean

determined

for

the

fixed

nonlinear

effects

and

spatial

effects

A

P

value

of

was

considered

indicative

of

a

statistically

significant

difference

We

also

run

a

sensitivity

analysis

for

the

choice

of

priors

Standard

choices

for

the

hyperparameters

are

a

and

b

or

a

b

Jereys

Noninformative

prior

is

closer

to

the

later

choice

and

since

practical

experience

shows

that

regression

parameters

depend

on

the

choice

of

hyperparameters

we

have

investigated

in

our

application

the

sensitivity

to

this

choice

It

would

be

beyond

the

scope

of

this

paper

to

go

into

the

details

of

estimation

procedures

Please

refer

to

Appendix

for

a

detailed

explanation

of

the

statistical

methods

The

method

has

also

been

discussed

in

more

detail

in

Go

to

Results

Table

shows

individual

characteristics

of

the

sample

population

prior

to

multiple

adjustments

of

all

factors

that

might

confound

or

mediate

the

observed

spatial

variation

within

provinces

on

stunting

Of

the

overall

sample

of

children

percent

of

the

sample

children

had

measurement

on

their

height

and

weight

to

ascertain

their

nutritional

status

Of

those

percent

was

female

and

the

overall

prevalence

of

malnutrition

stunting

was

percent

The

prevalence

of

stunting

was

higher

among

boys

compared

to

girls

versus

percent

has

an

inverse

linear

association

with

the

age

of

the

child

higher

in

the

age

groups

ranging

from

years

followed

by

years

years

years

but

lower

in

the

younger

age

year

versus

percent

higher

in

rural

areas

compared

with

urban

areas

versus

percent

higher

among

children

born

outside

the

hospital

compared

with

their

counterpart

born

in

hospitals

versus

percent

linearly

associated

with

maternal

education

higher

among

children

from

non

educated

mother

On

the

other

hand

there

were

no

statistically

significant

association

observed

between

the

prevalence

of

stunting

and

gender

of

the

households

head

mothers

marital

status

preceding

birth

interval

of

the

child

and

households

size

The

geographical

distribution

of

the

crude

prevalence

of

the

standardized

Zscores

for

the

response

variable

stunting

by

province

display

in

Table

shows

distinct

spatial

patterns

While

in

Kinshasa

Orientale

and

Equateur

provinces

it

appears

that

stunting

was

lower

there

seem

to

be

more

areas

of

high

stunting

in

NorthEastern

of

the

DRC

that

is

affected

by

conflict

and

the

three

provinces

that

relied

heavily

on

local

mineral

mining

Katanga

and

the

two

Kasai

In

addition

to

local

smallarea

variability

there

might

also

be

an

underlying

smooth

spatial

component

which

crosses

provincial

borders

due

to

displacement

of

population

during

the

conflicts

something

we

investigated

below

The

provincial

prevalence

shown

in

Table

also

suggested

that

we

should

examine

the

spatial

pattern

of

stunting

at

a

more

disaggregated

province

level

as

the

national

prevalence

of

percent

glossed

over

important

intraprovince

differentials

In

the

multivariate

analysis

the

results

for

the

fixed

effects

in

Table

suggest

that

female

children

are

slightly

less

stunted

as

found

in

other

studies

In

fact

the

corresponding

posterior

mean

for

male

is

negative

and

the

and

quintiles

are

both

negative

indicating

that

the

effect

is

statistically

significant

Children

living

in

rural

areas

are

more

stunted

than

their

counterpart

in

urban

areas

Maternal

education

rather

than

paternal

education

has

a

positive

impact

on

childrens

nutritional

status

as

well

as

households

socioeconomic

status

Children

from

low

socioeconomic

households

were

as

expected

more

stunted

than

children

from

high

income

backgrounds

Table

Provincial

posterior

mean

for

fixed

effect

parameters

of

malnourished

children

in

DRC

We

also

estimated

the

posterior

mean

of

stunting

and

plotted

it

against

childs

age

and

mothers

BMI

As

hypothesised

Figure

shows

that

there

is

a

bell

shaped

nonlinear

relationship

between

the

effects

of

childs

age

left

mothers

BMI

right

and

stunting

Shown

are

the

posterior

means

together

with

the

and

pointwise

credible

intervals

As

found

in

other

countries

of

SSA

these

data

show

that

the

effect

of

mothers

BMI

on

childs

nutritional

status

to

be

in

the

form

of

an

inverse

U

shape

While

the

inverse

U

looks

nearly

symmetric

the

descending

portion

exhibits

a

much

larger

range

in

the

credible

region

This

appears

quite

reasonable

as

obesity

of

the

mother

possibly

due

to

a

poor

quality

diet

is

likely

to

pose

less

of

a

risk

for

the

nutritional

status

of

the

child

as

very

low

BMIs

which

suggest

acute

undernutrition

of

the

mother

The

Zscore

is

highest

and

thus

stunting

lowest

at

a

BMI

of

around

The

figure

also

shows

that

there

are

few

women

with

high

BMI

or

higher

in

the

survey

but

this

is

likely

to

represent

an

artefact

of

the

small

numbers

sampled

at

this

BMI

range

Figure

Nonlinear

effects

of

and

childs

age

left

and

mothers

body

mass

index

right

on

stunting

Shown

are

posterior

mean

of

stunting

within

the

and

credible

interval

Figure

left

shows

the

effect

of

the

childs

age

on

its

nutritional

status

As

hypothesised

and

commonly

suggested

by

the

nutritional

literature

we

are

able

to

discern

the

continuous

worsening

of

the

nutritional

status

up

until

about

months

of

age

This

deterioration

sets

in

right

after

birth

and

continues

more

or

less

linearly

until

months

Such

an

immediate

deterioration

in

nutritional

status

is

not

as

expected

as

the

literature

typically

suggests

that

the

worsening

is

associated

with

weaning

at

around

months

One

reason

for

this

finding

could

be

that

according

to

the

surveys

most

parents

give

their

children

liquids

other

than

breast

milk

shortly

after

birth

which

might

contribute

to

infections

at

these

early

ages

After

months

stunting

stabilizes

at

a

low

level

Through

reduced

growth

and

the

waning

impact

of

infections

children

are

apparently

able

to

reach

a

lowlevel

equilibrium

that

allows

their

nutritional

status

to

stabilize

We

also

see

a

sudden

improvement

of

the

Zscore

around

months

of

age

This

is

picking

up

the

effect

of

a

change

in

the

data

set

that

makes

up

the

reference

standard

Until

months

the

currently

used

international

reference

standard

is

based

on

white

children

in

the

US

of

high

socioeconomic

status

while

after

months

it

is

based

on

a

representative

sample

of

all

US

children

Since

the

latter

sample

exhibits

worse

nutritional

status

comparing

the

Congolese

children

to

that

sample

leads

to

a

sudden

improvement

of

their

nutritional

status

at

months

This

anomaly

of

the

reference

standard

is

one

reason

for

the

replacement

of

this

reference

population

by

a

new

reference

standard

from

the

WHO

Figure

explores

province

specific

net

spatial

effects

of

undernutrition

We

report

results

of

the

model

that

includes

the

total

residual

spatial

effects

of

the

province

ie

the

sum

of

both

the

structured

and

unstructured

spatial

effects

The

left

panel

of

Figure

shows

the

total

residual

spatial

effects

of

the

province

and

the

right

panel

of

Figure

Figure

Total

residual

spatial

effect

of

stunting

left

and

posterior

probabilities

right

of

stunting

for

the

full

model

In

the

two

Kasai

one

explanation

for

the

high

rate

of

stunting

may

be

due

to

the

fact

that

the

first

livelihood

activities

are

wage

labour

and

mining

activities

and

few

people

are

involved

in

agriculture

This

explanation

might

also

be

true

for

for

higher

malnutrition

observed

in

Katanga

which

also

relies

on

mining

in

addition

to

the

impact

of

war

To

compare

our

provincespecific

nonlinear

spatial

effects

with

our

simple

fixed

effects

for

provinces

Table

Figure

presents

a

map

that

shows

those

provincial

effects

One

can

only

distinguish

three

main

provinces

effects

Better

nutritional

status

is

found

in

the

Orientale

province

and

Equateur

province

as

well

as

Kinshasa

worse

nutritional

status

in

the

eastern

provinces

under

conflicts

and

non

significant

effect

for

provinces

in

the

south

of

the

DRC

In

contrast

the

crude

provincial

fixed

effects

shown

in

Table

miss

most

of

the

findings

we

discussed

above

In

particular

the

sharp

NorthSouth

gradient

present

in

the

province

analysis

is

clearly

now

visible

as

the

three

eastern

provinces

include

provinces

on

both

sides

of

that

divide

Moreover

the

positive

effect

of

Kinshasa

is

simply

averaged

in

with

the

Bas

Congo

and

Bandundu

provinces

Clearly

a

lot

is

lost

when

relying

on

these

crude

estimates

of

modelling

spatial

effects

Go

to

Discussion

The

DHS

data

provides

a

consistent

large

and

national

database

that

can

be

used

to

analyze

patterns

of

malnutrition

in

the

DRC

This

study

has

shown

the

relationships

between

malnutrition

and

the

geographic

location

as

well

as

a

number

of

other

risk

factors

that

could

explain

the

sitespecific

variation

at

the

province

level

Our

results

show

that

childrens

chronic

malnutrition

is

highly

prevalent

in

the

entire

country

with

rates

largely

above

percent

The

DRC

has

a

deficit

of

food

and

limited

food

productivity

despite

the

countrys

enormous

potential

for

agricultural

production

Only

the

western

part

of

the

country

is

a

net

producer

in

particular

the

province

of

Bas

Congo

Over

the

last

ten

years

there

has

been

a

significant

decline

of

the

production

of

almost

all

agricultural

products

According

to

the

World

Food

Programme

WFP

the

production

of

cassava

has

decreased

by

percent

between

and

the

production

of

plantain

has

decreased

by

percent

between

and

There

has

been

an

increase

of

the

maize

production

by

percent

between

and

however

in

Maniema

and

North

Kivu

the

production

has

decreased

by

in

Katanga

by

percent

The

deterioration

in

food

productivity

is

the

result

of

many

factors

but

can

be

attributed

mainly

to

distal

factors

such

as

lack

of

implementation

of

national

policy

for

food

production

security

and

conflicts

The

agricultural

system

is

mainly

subsistenceoriented

According

to

the

WFP

more

than

percent

of

households

have

access

to

land

however

the

majority

cultivates

less

than

hectare

which

does

not

allow

for

adequate

production

for

sale

or

own

consumption

Cultivation

techniques

are

still

very

traditional

and

households

lack

farming

tools

Few

households

have

a

plough

or

a

tractor

Agricultural

inputs

such

as

fertilizers

are

not

available

Seeds

are

often

of

low

quality

and

productivity

is

low

These

are

clearly

areas

where

if

there

were

a

national

policy

this

could

make

a

difference

for

the

DRC

Also

in

the

Eastern

provinces

people

do

not

cultivate

due

to

the

violence

in

the

provinces

such

as

Katanga

the

two

Kasai

and

Orientale

the

young

generation

has

left

the

agricultural

sector

to

work

in

the

mining

industries

gold

diamond

and

coltan

In

the

eastern

provinces

only

percent

of

households

own

livestock

When

they

do

it

is

usually

in

small

quantity

Goat

is

the

main

livestock

owned

The

results

of

these

rates

are

similar

to

the

one

of

the

other

countries

Likewise

the

risk

of

stunting

is

higher

in

rural

areas

among

children

from

less

educated

mothers

and

living

in

poorer

household

after

controlling

for

other

variables

in

the

model

As

in

most

countries

of

SSA

there

is

substantial

spatial

province

difference

in

child

nutritional

status

in

the

DRC

Kinshasas

population

is

essentially

urban

the

proportion

of

the

most

educated

women

is

higher

compared

to

other

provinces

and

accessibility

to

health

facilities

and

safe

drinking

water

is

better

whereas

rural

children

or

less

educated

mothers

have

difficult

access

to

health

facilities

and

consume

about

half

the

calories

daily

than

their

urban

counterparts

The

major

finding

of

this

study

is

that

malnutrition

rates

remain

very

high

in

the

provinces

that

rely

on

the

mining

industry

two

Kasai

and

Katanga

comparable

to

the

level

seen

in

Eastern

provinces

under

war

One

possible

explanation

may

be

found

in

the

nutritional

behaviour

of

the

population

that

do

not

give

certain

types

of

food

to

children

on

cultural

grounds

even

though

the

food

is

nutritious

and

in

the

reliance

of

the

population

living

in

these

provinces

on

artisanal

mining

industry

and

the

neglect

of

agriculture

A

survey

on

food

security

showed

that

Kasai

occidental

has

the

worst

Another

observation

drawn

from

this

paper

Table

is

the

gap

in

malnutrition

rates

between

the

province

of

Kinshasa

and

all

other

provinces

In

fact

Kinshasas

stunting

prevalence

is

very

low

compared

with

the

national

rate

But

it

is

above

the

emergency

threshold

by

humanitarian

standard

In

spite

of

the

generalized

state

of

poverty

in

the

country

incomes

are

higher

in

Kinshasa

as

a

result

economically

the

population

of

Kinshasa

enjoys

better

access

to

food

products

The

presence

of

more

educated

mothers

and

their

partners

in

Kinshasa

and

the

lowest

rate

of

poorest

people

living

there

may

permit

better

nutritional

practices

The

strong

evidence

of

statistically

significant

difference

of

malnutrition

between

socio

economic

groups

mainly

between

poorest

poorer

middle

and

richer

groups

compared

to

the

richest

group

confirms

the

reality

that

in

the

DRC

affording

food

for

the

majority

of

the

population

is

still

a

challenge

According

to

the

WFP

about

percent

of

households

expenditure

is

spent

on

food

only

percent

in

Bandundu

The

main

source

of

food

is

peoples

own

production

The

second

source

of

food

is

the

market

except

for

the

two

provinces

of

Kivu

where

households

rely

first

on

the

markets

to

access

food

Hence

in

richer

households

often

children

are

well

fed

and

cared

for

and

provided

with

a

safe

and

stimulating

environment

through

which

they

are

more

likely

to

survive

to

have

fewer

diseases

and

illnesses

and

to

fully

develop

thinking

language

emotional

and

social

skills

But

in

poorer

households

most

children

are

affected

by

the

resurgence

of

kwashiorkor

lack

of

proteins

in

the

diet

although

this

remains

controversial

This

is

certainly

due

to

the

increasing

poverty

among

parents

who

can

not

afford

to

buy

proteins

groundnuts

beans

meat

fish

and

milk

for

their

children

Findings

are

largely

consistent

with

findings

of

others

studies

on

malnutrition

by

socio

economic

status

SES

in

SSA

and

highlight

that

poorer

children

have

a

higher

risk

of

becoming

stunted

than

richer

ones

The

gap

observed

on

stunting

prevalence

between

children

from

uneducated

mothers

or

those

whose

mothers

have

a

primary

school

level

of

education

compared

with

those

from

mothers

with

secondary

or

high

level

of

education

remains

high

In

fact

education

could

make

a

difference

by

empowering

mothers

decision

on

type

of

nutrition

andor

use

of

preventive

medicine

Similar

results

have

been

found

in

Cameroon

and

in

most

developing

countries

Education

could

also

help

the

mothers

make

informed

nutritional

decisions

about

cultural

norms

on

certain

types

of

food

for

children

With

reference

to

other

variables

male

children

seem

to

be

more

exposed

to

the

risk

of

malnutrition

than

female

children

There

is

no

obvious

explanation

for

this

gender

difference

but

in

Asia

for

instance

genders

difference

has

been

attributed

to

boys

preference

over

girls

Also

older

children

are

more

prone

to

be

exposed

to

anthropometric

failure

than

their

counterparts

aged

less

than

one

Mainly

older

children

are

mixed

breastfed

even

not

breastfed

at

times

while

younger

children

may

be

protected

by

the

mothers

immune

system

at

birth

The

risk

could

be

also

due

to

lack

of

foods

in

the

households

due

to

poverty

or

the

lack

of

hygiene

by

mothers

when

cooking

children

foods

The

direct

causes

of

malnutrition

are

the

lack

of

access

to

drinking

water

in

the

DRC

it

is

estimated

that

more

than

two

thirds

of

the

population

has

no

access

to

drinking

water

morbidity

malaria

respiratory

infections

and

diarrhoea

and

poor

food

consumption

Also

breast

feeding

practices

are

inadequate

and

according

to

the

WFP

about

percent

of

the

under

children

are

orphans

The

prevalence

changes

significantly

across

the

country

and

it

is

higher

in

the

East

more

than

percent

in

province

Orientale

Go

to

Conclusion

This

study

has

been

able

to

determine

that

in

the

DRC

childhood

malnutrition

is

spatially

structured

and

rates

remain

very

high

in

the

provinces

that

rely

on

the

mining

industry

and

comparable

to

the

level

seen

in

Eastern

provinces

under

war

In

waraffected

provinces

we

are

able

to

determine

that

childhood

malnutrition

is

higher

probably

because

of

the

environmental

impact

caused

by

war

because

these

provinces

are

known

as

traditionally

pastoral

and

agricultural

provinces

Furthermore

the

massive

influx

of

population

especially

from

Rwanda

Uganda

and

Sudan

fleeing

conflicts

has

further

exacerbated

the

food

crisis

Food

aids

has

helped

but

it

In

summary

in

the

DRC

the

improvement

of

the

nutritional

status

of

children

would

help

avert

child

deaths

from

diarrhoea

pneumonia

malaria

HIV

and

measles

Consequently

it

would

reduce

neonatal

mortality

helping

achieve

MDG

which

main

aim

is

to

reduce

poverty

and

hunger

There

is

an

urgent

need

for

national

policies

to

improve

the

security

of

people

and

implement

agricultural

policies

for

autodependent

agriculture

the

DRC

has

the

potential

with

plenty

of

land

for

agriculture

In

other

words

improving

maternal

and

child

nutrition

is

a

prerequisite

for

achieving

MDG

to

reduce

the

child

mortality

rate

Also

nutritional

programmes

Go

to

Competing

interests

The

authors

declare

that

they

have

no

competing

interests

Go

to

Authors

contributions

NBK

Conception

and

design

literature

review

data

analysis

and

interpretation

drafting

the

article

critical

revisions

for

important

intellectual

content

and

approval

of

final

article

for

submission

PTM

Literature

review

interpretation

of

results

drafting

the

article

critical

revisions

for

important

intellectual

content

and

approval

of

final

article

for

submission

JBOE

Interpretation

of

results

and

critical

revisions

for

important

intellectual

content

PDNK

Interpretation

of

results

and

critical

revisions

for

important

intellectual

content

FPC

Interpretation

of

results

and

critical

revisions

for

important

intellectual

content

and

all

authors

read

and

approved

the

final

manuscript

Go

to

Appendix

Statistical

analysis

Classical

linear

regression

models

of

the

form

for

observations

y

i

w

i

i

n

on

a

response

variable

y

and

a

vector

w

of

covariates

assume

that

the

mean

E

y

i

w

i

can

be

modeled

through

a

linear

predictor

w

i

γ

In

our

application

to

childhood

undernutrition

and

in

many

other

regression

situations

we

are

facing

the

following

problems

First

for

the

continuous

covariates

in

the

data

set

the

assumption

of

a

strictly

linear

effect

on

the

response

y

may

not

be

appropriate

In

our

study

such

covariates

are

the

childs

age

age

the

mothers

age

at

birth

mab

and

the

mothers

Body

Mass

Index

BMI

Generally

it

will

be

difficult

to

model

the

possibly

nonlinear

effect

of

such

covariates

through

a

parametric

functional

form

which

has

to

be

linear

in

the

parameters

prior

to

any

data

analysis

Second

in

addition

to

usual

covariates

geographical

smallarea

information

was

given

in

form

of

a

location

variable

s

indicating

the

province

district

or

community

where

individuals

or

units

in

the

sample

size

live

or

come

from

In

our

study

this

geographical

information

is

given

by

the

provinces

of

the

DRC

Attempts

to

include

such

smallarea

information

using

provincespecific

dummyvariables

would

in

our

case

entail

more

than

dummyvariables

and

using

this

approach

we

would

not

assess

spatial

interdependence

The

latter

problem

can

not

also

be

resolved

through

conventional

multilevel

modeling

using

uncorrelated

random

effects

It

is

reasonable

to

assume

that

areas

close

to

each

other

are

more

similar

than

areas

far

apart

so

that

spatially

correlated

random

effects

To

overcome

these

difficulties

we

replace

the

strictly

linear

predictor

through

a

geoadditive

predictor

leading

to

the

geoadditive

regression

model

here

f

f

p

are

nonlinear

smooth

effects

of

the

metrical

covariates

and

f

spat

is

the

effect

of

the

spatial

covariate

s

i

S

labelling

the

provinces

in

the

DRC

Regression

models

with

predictors

as

in

are

sometimes

referred

to

as

geoadditive

models

In

a

further

step

we

may

split

up

the

spatial

effect

f

spat

into

a

spatially

correlated

structured

and

an

uncorrelated

unstructured

effect

f

spat

s

i

f

str

s

i

f

unstr

s

i

The

rationale

is

that

a

spatial

effect

is

usually

a

surrogate

of

many

unobserved

influences

some

of

them

may

obey

a

strong

spatial

structure

and

others

may

be

present

only

locally

The

observation

model

may

be

extended

by

including

interaction

fxw

between

a

continuous

covariate

x

and

a

binary

component

of

w

say

leading

to

so

called

varying

coefficient

models

or

by

adding

a

nonlinear

interaction

f

x

x

of

two

continuous

covariates

In

a

Bayesian

approach

unknown

functions

f

j

and

parameters

γ

as

well

as

the

variance

parameter

σ

are

considered

as

random

variables

and

have

to

be

supplemented

with

appropriate

prior

assumptions

In

the

absence

of

any

prior

knowledge

we

assume

independent

diffuse

priors

γ

j

α

const

j

for

the

parameters

of

fixed

effects

Another

common

choice

is

highly

dispersed

Gaussian

priors

Several

alternatives

are

available

as

smoothness

priors

for

the

unknown

functions

f

j

x

j

see

We

use

Bayesian

Penalized

Splines

It

is

assumed

that

an

unknown

smooth

function

f

j

x

j

can

be

approximated

by

a

polynomial

spline

of

low

degree

The

usual

choices

are

cubic

splines

which

are

twice

continuously

differentiable

piecewise

cubic

polynomials

defined

for

a

grid

of

k

equally

spaced

knot

p

on

the

relevant

interval

a

b

of

the

xaxis

Such

a

spline

can

be

written

in

terms

of

a

linear

combination

Bspline

basis

functions

B

m

x

ie

These

basis

functions

have

finite

support

on

four

neighbouring

intervals

of

the

grid

and

are

zero

elsewhere

A

comparably

small

number

of

knots

usually

between

and

is

chosen

to

ensure

enough

flexibility

in

combination

with

a

roughness

penalty

based

on

second

order

difference

of

adjacent

Bspline

coefficients

to

guarantee

sufficient

smoothness

of

the

fitted

curves

In

our

Bayesian

approach

this

corresponds

to

second

order

random

walks

with

Gaussian

errors

u

m

N

0τ

The

variance

parameter

τ

controls

the

amount

of

smoothness

and

is

also

estimated

from

the

data

More

details

on

Bayesian

PSplines

can

be

found

in

Note

that

random

walks

are

the

special

case

of

BSplines

of

degree

zero

We

now

turn

our

attention

to

the

spatial

effects

f

str

and

f

unstr

For

the

spatially

correlated

effect

f

str

s

s

S

we

choose

Markov

random

field

priors

common

in

spatial

statistics

These

priors

reflect

spatial

neighbourhood

relationships

For

geographical

data

one

usually

assumes

that

two

sites

or

regions

s

and

r

are

neighbours

if

they

share

a

common

boundary

Then

a

spatial

extension

of

random

walk

models

leads

to

the

conditional

spatially

autoregressive

specification

where

N

s

is

the

number

of

adjacent

regions

and

r

s

denotes

that

region

r

is

a

neighbour

of

region

s

Thus

the

conditional

mean

of

f

str

s

is

an

average

of

function

evaluations

f

str

s

of

neighbouring

regions

Again

the

variance

τ

str

controls

the

degree

of

smoothness

For

a

spatially

uncorrelated

unstructured

effect

f

unstr

a

common

assumption

is

that

the

parameters

f

unstr

s

are

iid

Gaussian

Variance

or

smoothness

parameters

τ

j

j

p

str

unstr

are

also

considered

as

unknown

and

estimated

simultaneously

with

corresponding

unknown

functions

f

j

Therefore

hyperpriors

are

assigned

to

them

in

a

second

stage

of

the

hierarchy

by

highly

dispersed

inverse

gamma

distributions

pτ

j

IGa

j

b

j

with

known

hyperparameters

a

j

and

b

j

For

model

choice

we

routinely

used

the

Deviance

Information

Criterion

DIC

developed

in

as

a

measure

of

fit

and

model

complexity

Go

to

Prepublication

history

The

prepublication

history

for

this

paper

can

be

accessed

here

Go

to

Acknowledgements

This

research

was

supported

by

the

British

Council

under

the

DelPHE

Development

Partnership

in

Higher

Education

scheme

The

authors

thank

Macro

international

for

providing

free

the

DHS

datasets

for

the

DR

Congo

Go

to

References

FAO

State

of

Food

Insecurity

in

the

World

Food

Security

Statistics

httpwwwfaoorgesessfaostatfoodsecurityindex_enhtm

Retrieved

on

March

Black

R

Morris

S

Jennifer

B

Where

and

Why

Are

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O B I

biomed central
democratic republic of congo