PMID- 35798952
OWN - NLM
STAT- MEDLINE
DCOM- 20220711
LR  - 20230410
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 12
IP  - 1
DP  - 2022 Jul 7
TI  - Spatial variation and risk factors of malaria and anaemia among children aged 0 
      to 59 months: a cross-sectional study of 2010 and 2015 datasets.
PG  - 11498
LID - 10.1038/s41598-022-15561-4 [doi]
LID - 11498
AB  - Malaria and anaemia are common diseases that affect children, particularly in 
      Africa. Studies on the risk associated with these diseases and their synergy are 
      scanty. This work aims to study the spatial pattern of malaria and anaemia in 
      Nigeria and adjust for their risk factors using separate models for malaria and 
      anaemia. This study used Bayesian spatial models within the Integrated Nested 
      Laplace Approach (INLA) to establish the relationship between malaria and 
      anaemia. We also adjust for risk factors of malaria and anaemia and map the 
      estimated relative risks of these diseases to identify regions with a 
      relatively high risk of the diseases under consideration. We used data obtained 
      from the Nigeria malaria indicator survey (NMIS) of 2010 and 2015. The spatial 
      variability distribution of both diseases was investigated using the convolution 
      model, Conditional Auto-Regressive (CAR) model, generalized linear mixed model 
      (GLMM) and generalized linear model (GLM) for each year. The convolution and 
      generalized linear mixed models (GLMM) showed the least Deviance Information 
      Criteria (DIC) in 2010 for malaria and anaemia, respectively. The Conditional 
      Auto-Regressive (CAR) and convolution models had the least DIC in 2015 for 
      malaria and anaemia, respectively. This study revealed that children in rural 
      areas had strong and significant odds of malaria and anaemia infection [2010; 
      malaria: AOR = 1.348, 95% CI = (1.117, 1.627), anaemia: AOR = 1.455, 95% 
      CI = (1.201, 1.7623). 2015; malaria: AOR = 1.889, 95% CI = (1.568, 2.277), 
      anaemia: AOR = 1.440, 95% CI = (1.205, 1.719)]. Controlling the prevalence of 
      malaria and anaemia in Nigeria requires the identification of a child's location 
      and proper confrontation of some socio-economic factors which may lead to the 
      reduction of childhood malaria and anaemia infection.
CI  - (c) 2022. The Author(s).
FAU - Ibeji, Jecinta U
AU  - Ibeji JU
AD  - School of Mathematics, Statistics and Computer Science, University of KwaZulu 
      Natal, Durban, South Africa. jecintaanyaogu@gmail.com.
FAU - Mwambi, Henry
AU  - Mwambi H
AD  - School of Mathematics, Statistics and Computer Science, University of KwaZulu 
      Natal, Durban, South Africa. mwambih@ukzn.ac.za.
FAU - Iddrisu, Abdul-Karim
AU  - Iddrisu AK
AD  - School of Science, Mathematics and Statistics, University of Energy and Natural 
      Resources, Sunyani, Ghana.
LA  - eng
PT  - Journal Article
DEP - 20220707
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - *Anemia/etiology
MH  - Bayes Theorem
MH  - Child
MH  - Cross-Sectional Studies
MH  - Humans
MH  - *Malaria/complications/epidemiology
MH  - Nigeria/epidemiology
MH  - Prevalence
MH  - Risk Factors
PMC - PMC9262914
COIS- The authors declare no competing interests.
EDAT- 2022/07/08 06:00
MHDA- 2022/07/12 06:00
PMCR- 2022/07/07
CRDT- 2022/07/07 23:33
PHST- 2022/01/07 00:00 [received]
PHST- 2022/06/27 00:00 [accepted]
PHST- 2022/07/07 23:33 [entrez]
PHST- 2022/07/08 06:00 [pubmed]
PHST- 2022/07/12 06:00 [medline]
PHST- 2022/07/07 00:00 [pmc-release]
AID - 10.1038/s41598-022-15561-4 [pii]
AID - 15561 [pii]
AID - 10.1038/s41598-022-15561-4 [doi]
PST - epublish
SO  - Sci Rep. 2022 Jul 7;12(1):11498. doi: 10.1038/s41598-022-15561-4.