Introduction may only be recognized as a cause

Introduction

Infection and nutrition are intimately
related from the coincidental shared pathways of poverty to effects on
metabolism and immunity1. Distinguishing the contributions of
infectious diseases and nutrition as causes of death is complex. In most
reporting systems and global disease burden estimations, infectious diseases
represent an immediate, direct cause of death, while mortality attributed to
malnutrition may only be recognized as a cause of death when it is severe
enough to cause clinical manifestations2. However, Pelletier et al. suggested
that malnutrition, by virtue of its synergistic relationship with infectious
disease, caused 56% of child mortality, a much larger fraction than classification
of “nutritional deficiencies”2. Similarly, community-based studies
of malaria reveal that this infection contributes to under-five mortality more
than would be attributed to malaria-specific deaths alone2. Importantly, both malaria and
undernutrition are highly prevalent in sub-Saharan Africa and often share
common spatial distributions3–5.  

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

 

The precise clinical relationships
between undernutrition and malaria have been the subject of competing
hypotheses. Nutritional interventions appeared to exacerbate the clinical
outcomes of malaria infection in Nigeria6–9 and Senegal7, leading some to suggest that
nutrient deficiency, notably iron10, may protect against malaria. While
other studies found no significant association11,12 and more recent cross-sectional
studies offer no support to the hypothesis that under-nutrition protection
against malaria infection and disease progression13,14. In fact, increased risks of poor
outcomes of malaria are described in several studies13 indicate that malnutrition and
malaria form a vicious circle that has a large impact on morbidity and
mortality among the most vulnerable in the population, likely operating through
broad effects on the functional capacity of the immune system4,15. In Somalia, there are high levels of
acute malnutrition in the South Central zone, estimated to be at least 35%;
followed by Puntland zone and lowest levels are in Somaliland zone16. A similar pattern is observed in the
distribution of malaria in Somalia17. Plasmodium falciparum parasite rate
(PfPR) is estimated to range from 0–9% in the north of Somalia and from 0–52%
in south of Somalia, with high PfPR locations occurring in the densely
populated regions between the Juba and Shabelle rivers. Majority of the area in
the northern part of Somalia have been reported to have a PfPR of 5–9%. In the south, PfPR is lower
along the two rivers, compared to the area in between18,19.

 

There
are several pathways that may explain the comorbidity. On one hand, children in
developing countries are at a higher risk of both malnutrition and infections
due to environmental conditions, and thus more prone to concurrent conditions
occurring by chance20.  Both are subject to seasonal variation driven
by weather and food supply. On the other hand, malnutrition compromises the
immunity, leaving the child susceptible to infection21;
and malaria may result in anorexia, weight-loss or, when pregnant women are
infected, low birth weight.

 

The overlapping epidemiology may be
explored by joint mapping of the two health conditions to quantify the
correlation structures between their relative risks by modelling common and
disease-specific observed effects and spatial patterns simultaneously22. In this study, we aimed to undertake
the first nationwide investigation of ecological co-morbidity of wasting and
low mid upper arm circumference (MUAC) with falciparum
malaria in Somalia to determine the spatial patterns of these health conditions.
A shared component model was used to fit common and indicator-specific
unobserved and unmeasured spatial risks 23,24.

 

Country context

Somalia is located in the horn of
Africa and is predominately semi-arid and transected by two major rivers,
Shebelle and Juba.  The vast majority of
Somalis depend on two main livelihood systems: pastoralist and agro-pastoralist
as their main source of sustenance. 
Pastoralists are in the rural arid areas predominantly in the northern
and central Somalia, as well as along Ethiopia and Kenyan borders.
Agro-pastoralists are in inter-riverine regions of Bay, Bakool, western Hiran
and eastern Gedo in southern Somalia, and also in small areas of the northern
regions25. A small proportion of the riverine
population along Juba and Shabelle rivers depends on settled agriculture. Fishing
only represents a small livelihood activity, despite Somalia having one of the
longest coastlines in Africa25. Somalia has experienced chronic
harsh climatic conditions with four main seasons around which pastoral and
agricultural activities depend. Two wet seasons: April to June (the main rainy
season) and October to December (the short rainy season); and two dry seasons: December
to March (the main dry season) and July to September (the second dry season).

 

The collapse of the national
government resulted to emergence of a North West zone, “Republic of Somaliland”
in 1991; of a North East zone, “Puntland State of Somalia” in 1998 and of a
military administration in the South Central region in 1999. These self-formed
autonomous states are not internationally recognized26. Consequently, this led to
destruction of social and economic infrastructure, massive internal and
external migration, degradation of the environment, which profoundly altered
human development27. Due to inadequate governance
structures in parts of Somalia, nutrition response programming is mainly
undertaken by organizations in the nutrition cluster formed in 2006. So far, efforts
are primarily focused on responding to alarming rates of acute malnutrition
throughout the country. Surveillance of food security and nutrition status, and
early warning reports are key activities aimed at appropriate and timely
responses to changing needs in the country. Several feeding programmes for the
management of severe acute malnutrition have been implemented across Somalia by
UN agencies, international and local NGOs. However coverage and quality of
interventions is limited due to overall weaknesses of the public health system27.

 

Methods

 

Survey Data

The data used for this study were
obtained from food security and nutrition analysis unit (FSNAU). Food Security and Nutrition Unit
(FSNAU) is a unit in World Food Programme of United Nations (UNFAO), which was
set up in 1994 to provide evidence-based analysis of Somali food, nutrition and
livelihood security to enable both short-term emergency responses and long-
term strategic planning. Therefore, in partnership with UNICEF, FSNAU has been
conducting bi-annual seasonal nutrition assessment surveys since 2001. Our
study focuses on survey data ranging from 2007 – 2010Figure 1. Within this
period, FSNAUconducted cross-sectional nutrition assessment surveys twice a
year where information on falciparum
malaria parasitaemia was included upon request of United Nations Children’s
Emergency Fund (UNICEF).26,28. In each survey, a stratified multi-stage
cluster sampling design was adopted where the sampling frame of a selected
district was based on the four livelihood definitions (pastoral, agro-pastoral,
riverine and fishing) within which 30 communities and 30 households within each
village were selected using systematic  random sampling method and the urban population were clearly
defined and considered separately. Vulnerable groups that could not be
classified in any of the livelihood such as IDPs were surveyed separately. Respective
samples sizes (number of households and number of children) were calculated
using the Epiinfo/Ena 2008 software after considering the population size,
estimated prevalence and desired precision. A list of all villages and
population within each of the assessed livelihoods served as a sampling frame
and was used to construct cumulative population for the assessment area. Villages
were then randomly selected from these villages with the chance of any village
being selected being proportional to the size of its population. This is called
sampling with “probability proportional to population size (PPS)”. Selection
of households within the village was done using systematic random sampling,
preferably from a list of eligible names or a map of households. Where these
were not available, the number of households in the village was estimated from
the population figures (the total population divided by the mean household
size). Starting from a random household, every nth household was selected and
all eligible children (aged 6-59) in that household measured (Figure SI 1).
Retrospective mortality data was collected from all the households in each village
from each livelihood, including even those that did not have children aged 6-59
months. At the individual child level, age, gender, weight, height, mid-upper
arm circumference (MUAC), vitamin A supplementation in the last six months,
diarrhoea, acute respiratory infections (ARI) and febrile illness in the two
weeks before the survey, and Polio and Measles vaccination history were
collected. At the household level, information recorded included the household
size and age structure, gender of the household head, and access to different
types of foods in the last 24 hours. Data on falciparum malaria infection in children aged 5-59 months were
collected in sub-sets of villages at the request of UNICEF29–31. The data used in this study were
therefore a subset of the whole survey dataset with information on both the
childhood malnutrition and malaria.

 

We considered two outcome measurements
to describe the anthropometric indicators of malnutrition, low
weight-for-height (wasting) and low-MUAC, which detect different sets of children
as malnourished. Wasting is traditionally the main indicator in community
surveys. Although MUAC is a better predictor of mortality32, few studies have examined
associations between MUAC and specific pathogens.

 

A child was defined as wasted when
s/he was below -2 Z scores for weight-for-height, according to World Health
Organization (WHO) 2006 standards33. A child with MUAC below 125mm was
classified as having low-MUAC. Malaria parasitaemia was determined using
Paracheck Pf™ (Orchid Biomedical Systems, Goa, India) in a subset of the sample
in every FSNAU surveys during this period33. A child was regarded as malaria infected
when s/he had a positive Paracheck Pf™ test result, regardless of any clinical
symptoms.

 

A detailed search were undertaken to
establish a set of spatial coordinates for each village in Somalia using the village
names in the data. The location of village was verified by using Google Earth
(Google, Seattle, USA) and other online databases to visually inspect whether
the coordinates matched evidence of human settlement. Those settlements for
which no reliable source of the coordinates was obtained were excluded from the
analysis.

 

Environmental data

A set of four plausible environmental
covariates, together with wasting, low-MUAC and malaria in children were included
in the analysis18,34. These were rainfall, enhanced
vegetation index (EVI), mean temperature, and urbanization. Rainfall and mean
temperature were derived from the monthly average grid surfaces obtained from
WorldClim database35. The EVI values were derived from the
MODerate-resolution Imaging Spectroradiometer (MODIS) sensor imagery for period
2007-2010 while the urbanization information was obtained from Global Rural
Urban Mapping Project (GRUMP)36,37. All the environmental covariates
were extracted from 1 x 1 km spatial resolution grids. Rainfall, temperature
and EVI were summarized to compute seasonal averages corresponding to the time
of survey.

 

Statistical methods

The
overall aim of this study was to model the ecological comorbidity of wasting
and low-MUAC with malaria parasitaemia among children aged 6-59 month in
Somalia from 2007 – 2010. To achieve this, we implemented the Bayesian
geostatistical shared component model through stochastic partial differential
equation (SPDE) approach in integrated nested Laplace approximations (INLA)
using R-INLA library in R project version 3.2.3. 22–24,38,39. Therefore, we modelled two
underlying spatial risks common to: (1) wasting and malaria and (2) low-MUAC and
malaria at child level. The relative risk of each condition depends on a latent
spatial component shared by each pair and a condition-specific component after
controlling for environmental covariates40,41. The household survey and
environmental predictors of malnutrition and malaria were controlled at
individual, household and village level.

 

Finally, to determine if the risks
were correlated, we performed a significant test by looking at the 2.5% and
97.5% quantiles of each element of the random effect using the quintile
correction (QC) method as implemented by Bolin and Lindgren 201242. 
Further, the empirical correlation between the conditions were explored
using correlation plots. Detailed methods on covariate selection,
geostatisitcal shared component modeling, validation procedures are described
elsewhere31.

 

Results

A summary of individual level data is
shown in Table 1. A total of 49,227 children aged 5-59 months, with a mean age
of 32 months (51% male) were examined from 888 villages. Of which 8,542 (17%), 5276 (11%) and 6,840 (14%) were wasted, had low-MUAC and had
malaria parasitaemia respectively (Figure 1 and Figure SI 2). By livelihood,
28%, 29% and 20% of children were from areas of agro-pastoral, pastoral and
riverine livelihoods respectively while 17% lived in internally displaced
people (IDP) camps and 6% lived in urban areas. Fifty seven percent had
received Vitamin A supplementation in the two weeks before the survey and 51%
and 82% reported to have received measles and polio vaccination respectively.
Fever in the last two weeks was reported for 21% of children, while 26% and 17%
reported symptoms consistent with acute respiratory infection (ARI) and
diarrhoea respectively. The mean household size was 6 with a median of 2 in
children 5-59 months. Eighty one percent of the household had male head. Approximately,
97%, 87% and 79% of children were reported to have consumed sources of carbohydrate,
protein or fats in the last 24 hours before the survey respectively.

 

The empirical correlation estimates in
Figure 2 show that the correlation was highest between malaria infection and
low-MUAC at 0.23, and relatively lower between wasting and malaria 0.16. As a
first step, the associations of child-level, household and environmental
covariates with wasting and low-MUAC were examined in a univariate and multiple
variable binomial regression analysis. The effects of the covariates can be
found in Table SI 1 in the supplementary information file. The shared spatial
residual effects were significant with (odds ratio (OR) =1.06, 95% credible
interval (CrI): 1.04 – 1.09) and (OR=2.74, 95% CrI: 2.38 – 3.14) for wasting
and malaria and low-MUAC and malaria respectively. The common spatial effects
from the geostatistical shared latent component analysis are shown in Figure 3.
The component displayed a strong spatial gradient in the South-North direction
in all the shared components examined in this study. The relative risk between
wasting and malaria ranged from 0.11 – 3.55 and relative risk between low-MUAC
and malaria was 0.19 – 5.40.  In South
Central region, the hotspots were consistently found in Bakool, Bay and
Shabelle Dhexe regions for both the components while in the North the hotspot
were found to be high in Nugaal and Awdal for the component between low-MUAC
and malaria.

 

Table 2 gives the effect of the
covariates controlled in the joint model. High risks of ARI and febrile
illnesses were associated with high risk of wasting, low-MUAC and malaria.
Consumption of food high in carbohydrate and proteins was associated lower risk
of these three health issues. 
Precipitation and EVI were associated with decreased risk of wasting (OR=0.94,
95% CrI: 0.91- 0.97); OR=0.67, 95% CrI: 0.65-0.69 and low-MUAC (OR=0.91, 95%
CrI: 0.87-0.94; OR=0.96, 95% CrI: 0.92-0.99), and increased risk of malaria (OR=1.20,
95% CrI: 1.14-1.25; OR=1.27, 95% CrI: 1.21-1.33) respectively. The ambient
local temperature was associated with higher risk of the wasting (OR=1.15, 95%
CrI: 1.11-1.19); low-MUAC (OR=1.17, 95% CrI: 1.12-1.24) and lower risk of
malaria (OR=0.80, 95% CrI: 0.75-0.86). Urbanization was associated with lower
risk of malaria (OR=0.60, 95% CrI: 0.53-0.68) but was not associated with
wasting and low-MUAC. Children who slept under the net had a lower risk of
malaria (OR=0.83, 95% CrI: 0.79-0.87).

 

Discussion

The main objective of this study was
to investigate the nationwide spatial comorbidity between wasting and low-MUAC
with malaria. To achieve this, we implemented two geostatistical shared
component methods to model the comorbidity between (1) wasting and malaria and
(2) low-MUAC and malaria at child level. The findings showed a strong co-occurrence
of these health conditions. The relative risk was highest between low-MUAC and
malaria and relatively lower between wasting and malaria. The common risks were
greater in the South compared to the North of Somalia.

 

Numerous studies have investigated the
epidemiological relationship between child malnutrition with either malaria
morbidity or intensity of infection13,15,43,44. In contrast, only a few studies have
incorporated the spatial underlying component in the analysis45,46. This is the first study that has modelled
the co-distribution of wasting and low-MUAC with malaria in a geostatistical
framework to produce continuous national maps of common relative risk at high
spatial resolution. The shared component statistical framework has an advantage
in that its latent component have a direct interpretation in terms of the
prevalence of the comorbidity of the health conditions and related risk factors
which are either shared by several or specific to one of the health conditions.

 

In Somalia, the rates of malnutrition have
persistently remained at critical levels throughout the country47.
The rates are seen to be higher in the South compared to North of the country47.
These tenacious nutritional setbacks compromises the immune system and thus
leaving the child susceptible to subsequent infections21.  The risk of malaria is high in the South as
well17,19
and therefore children in these regions are at high risk of presenting with
multiple health conditions. The development of more serious infections
simultaneously increases the risk of mortality in children48.
In addition, in developing countries in settings where resources are scarce,
children are at a higher risk of infections due to poor environmental
conditions and thus prone to concurrent infections occurring by chance and this
could lead to additive or greater risk of multiple infections49.   

 

This study provide important new
information about the subnational priority areas for targeting integrated
interventions for malnutrition and malaria. Our predictive maps of the common
relative risk indicates that integrated control programmes should be
prioritized in the South of Somalia within the high endemicity areas. The
hotspots in this study correlates with areas where malaria risk has been shown
in previous studies18,19. The hotspots present opportunities
for integrating malaria interventions with the nutrition interventions
delivered through health campaigns by World Food Programme (WFP) and UNICEF
which include vitamin A distribution, deworming and nutritional screening during
bi-annual child health days with a full course of antimalarial treatment during
the peak malaria season which coincided with seasons of high malnutrition
levels50. Importantly, seasonal malaria
chemoprevention (SMC) has been shown to be 75% protective against uncomplicated
and severe malaria in children51–54 and may be effective in this setting.

 

The present study has some
limitations. There are important socio-demographic and environmental
confounding factors that were not measured in this study and therefore not
accounted for in the analysis. Information on access to water and sanitation,
which contributes to the prevalence of diarrhoea, was not collected in the Food
Security and Analysis Unit (FSNAU) surveys used herein. In addition,
information on market prices and purchasing power, food distribution, and
household economic status, that might influence household food security were
not available and therefore not included in the analysis.

 

Conclusion

This study has demonstrated that there
is significant spatial correlation between wasting and low-MUAC with falciparum
malaria risk in children aged 6-59 months in Somalia, indicating common
underlying determinant that causes the spatial distribution of these health
conditions be similar. The findings reinforce the need for integrating malaria and
nutrition interventions.

Comments are closed.