Investigating the spatial variation and risk factors of childhood anaemia in four sub-Saharan African countries

Anaemia and associated factors among children aged 6-59 months during the post-ebola period in Sierra Leone: a national cross-sectional survey- 2019

BACKGROUND

Anaemia is a global public health problem associated with early childhood adverse effects on mental, physical, and social development. Sierra Leone had made progress in reducing the prevalence of anaemia pre-Ebola period however this was affected by the Ebola epidemic which further strained an already struggling health system. Therefore, this study aimed to assess the prevalence and factors associated with anaemia during post-Ebola period among children aged 6-59 months in Sierra Leone.

METHODS

We analyzed data from the 2019 Sierra Leone demographic and health survey (SLDHS), a nationally representative cross-sectional study. We used data collected using a stratified two-stage cluster sampling design that resulted in the random selection of a representative sample of 13,872 households. A total sample of 3,459 children aged 6-59 months were included in the study. Multivariable logistic regression was used to calculate the adjusted odds ratios and corresponding 95% confidence intervals.

RESULTS

The prevalence of anaemia was 68.9%, that of mild anaemia was 35.8%, moderate anaemia was 30.3% and for severe anaemia was 2.8%. Children aged 6-36 months were 1.83 times more likely to have anaemia compared to those above 36 months, while boys 1.33 times more likely to be anaemic compared to girls. Children born in poor households, to mothers who had anaemia and had a history of fever had 65%, 85% and 38% increase in likelihood of childhood anaemia respectively. In addition, children living in rural areas and stunted were 1.55 and 1.38 times more likely to be anaemic respectively compared to those living in urban areas and not stunted. Children born to younger mothers (15-24 years) were 1.45 times more likely to be anaemic compared to older mother (35-49 years.

CONCLUSION

The current study demonstrated the predominant existence of anaemia among children aged 6-59 months in Sierra Leone. Owing to the adverse effects of anaemia on the development of children in the future, there is an urgent need for effective and efficient remedial public health interventions to prevent further complications.

Application of machine learning methods for predicting childhood anaemia: Analysis of Ethiopian Demographic Health Survey of 2016

Childhood anaemia is a public health problem in Ethiopia. Machine learning (ML) is a growing in medicine field to predict diseases. Diagnosis of childhood anaemia is resource intensive. The aim of this study is to apply machine learning (ML) algorithm to predict childhood anaemia using socio-demographic, economic, and maternal and child related variables. The study used data from 2016 Ethiopian demographic health survey (EDHS). We used Python software version 3.11 to apply and test ML algorithms through logistic regression, Random Forest (RF), Decision Tree, and K-Nearest Neighbours (KNN). We evaluated the performance of each of the ML algorithms using discrimination and calibration parameters. The predictive performance of the algorithms was between 60% and 66%. The logistic regression model was the best predictive model of ML with accuracy (66%), sensitivity (82%), specificity (42%), and AUC (69%), followed by RF with accuracy (64%), sensitivity (79%), specificity (42%), and AUC (63%). The logistic regression and the RF models of ML showed poorest family, child age category between 6 and 23 months, uneducated mother, unemployed mother, and stunting as high importance predictors of childhood anaemia. Applying logistic regression and RF models of ML can detect combinations of predictors of childhood anaemia that can be used in primary health care professionals.

A district-level geospatial analysis of anaemia prevalence among rural men in India, 2019-21

BACKGROUND

Despite its considerable impact on health and productivity, anemia among men has received limited attention. In a country as diverse as India, characterized by extensive geographic variations, there is a pressing need to investigate the nuanced spatial patterns of anemia prevalence among men. The identification of specific hotspots holds critical implications for policymaking, especially in rural areas, where a substantial portion of India's population resides.

METHODS

The study conducted an analysis on a sample of 61,481 rural men from 707 districts of India, utilizing data from the National Family Health Survey-5 (2019-21). Various analytical techniques, including Moran's I, univariate LISA (Local Indicators of Spatial Association), bivariate LISA, and spatial regression models such as SLM (Spatial Lag Model), and SEM (Spatial Error Model) were employed to examine the geographic patterns and spatial correlates of anaemia prevalence in the study population.

RESULTS

In rural India, three out of every ten men were found to be anemic. The univariate Moran's I value for anaemia was 0.66, indicating a substantial degree of spatial autocorrelation in anaemia prevalence across the districts in India. Cluster and outlier analysis identified five prominent 'hotspots' of anaemia prevalence across 97 districts, primarily concentrated in the eastern region (encompassing West Bengal, Jharkhand, and Odisha), the Dandakaranya region, the Madhya Pradesh-Maharashtra border, lower Assam, and select districts in Jammu and Kashmir. The results of SLM revealed significant positive association between anaemia prevalence at the district-level and several key factors including a higher proportion of Scheduled Tribes, men in the 49-54 years age group, men with limited or no formal education, individuals of the Muslim faith, economically disadvantaged men, and those who reported alcohol consumption.

CONCLUSIONS

Substantial spatial heterogeneity in anaemia prevalence among men in rural India suggests the need for region-specific targeted interventions to reduce the burden of anaemia among men in rural India and enhance the overall health of this population.

The application of machine learning approaches to determine the predictors of anemia among under five children in Ethiopia

Health professionals need a strong prediction system to reach appropriate disease diagnosis, particularly for under-five child with health problems like anemia. Diagnosis and treatment delay can potentially lead to devastating disease complications resulting in childhood mortality. However, the application of machine learning techniques using a large data set provides scientifically sounded information to solve such palpable critical health and health-related problems. Therefore, this study aimed to determine the predictors of anemia among under-5 year's age children in Ethiopia using a machine learning approach. A cross-sectional study design was done using the Ethiopian Demographic and Health Survey 2016 data set. A two-stage stratified cluster sampling technique was employed to select the samples. The data analysis was conducted using Statistical Package for Social Sciences/SPSS version 25 and R-software. Data were derived from Ethiopian Demographic and Health Survey. Boruta algorism was applied to select the features and determine the predictors of anemia among under-5 years-old children in Ethiopia. The machine learning algorism showed that number of children, distance to health facilities, health insurance coverage, youngest child's stool disposal, residence, mothers' wealth index, type of cooking fuel, number of family members, mothers' educational status and receiving rotavirus vaccine were the top ten important predictors for anemia among under-five children. Machine-learning algorithm was applied to determine the predictors of anemia among under- 5 year's age children in Ethiopia. We have identified the determinant factors by conducting a feature importance analysis with the Boruta algorithm. The most significant predictors were number of children, distance to health facility, health insurance coverage, youngest child's stool disposal, residence, mothers' wealth index, and type of cooking fuel. Machine learning model plays a paramount role for policy and intervention strategies related to anemia prevention and control among under-five children.

Spatial variation and clustering of anaemia prevalence in school-aged children in Western Kenya

Anaemia surveillance has overlooked school-aged children (SAC), hence information on this age group is scarce. This study examined the spatial variation of anaemia prevalence among SAC (5-14 years) in western Kenya, a region associated with high malaria infection rates. A total of 8051 SAC were examined from 82 schools across eight counties in Western Kenya in February 2022. Haemoglobin (Hb) concentrations were assessed at the school and village level and anaemia defined as Hb<11.5g/dl for age 5-11yrs and Hb <12.0g/dl for 12-14yrs after adjusting for altitude. Moran's I analysis was used to measure spatial autocorrelation, and local clusters of anaemia were mapped using spatial scan statistics and local indices of spatial association (LISA). The prevalence of anaemia among SAC was 27.8%. The spatial variation of anaemia was non-random, with Global Moran's I 0.2 (p-value < 0.002). Two significant anaemia cluster windows were identified: Cluster 1 (LLR = 38.9, RR = 1.4, prevalence = 32.0%) and cluster 2 (LLR = 23.6, RR = 1.6, prevalence = 45.5%) at schools and cluster 1 (LLR = 41.3, RR = 1.4, prevalence = 33.3%) and cluster 2 (LLR = 24.5, RR = 1.6, prevalence = 36.8%) at villages. Additionally, LISA analysis identified ten school catchments as anaemia hotspots corresponding geographically to SatScan clusters. Anaemia in the SAC is a public health problem in the Western region of Kenya with some localised areas presenting greater risk relative to others. Increasing coverage of interventions, geographically targeting the prevention of anaemia in the SAC, including malaria, is required to alleviate the burden among children attending school in Western Kenya.

Global warming may significantly increase childhood anemia burden in sub-Saharan Africa

Childhood anemia constitutes a global public health problem, especially in low- and middle-income countries (LMICs). However, it remains unknown whether global warming has an impact on childhood anemia. Here, we examined the association between annual temperatures and childhood anemia prevalence in sub-Saharan Africa and then projected childhood anemia burden attributable to climate change. Each 1°C increment in annual temperature was associated with increased odds of childhood anemia (odd ratio = 1.138, 95% confidence interval: 1.134-1.142). Compared with the baseline period (1985-2014), the attributable childhood anemia cases would increase by 7,597 per 100,000 person-years under a high-emission scenario in the 2090s, which would be almost 2-fold and over 3-fold more than those projected in moderate- and low-emission scenarios. Our results reveal the vulnerabilities and inequalities of children for the excess burden of anemia due to climate warming and highlight the importance of climate mitigation and adaptation strategies in LMICs.

Prevalence and Associated Risk Factors of Anemia Among Hospitalized Children in a Tertiary Level Hospital in Botswana

Anemia is a global health concern and has been associated with long term cognitive and behavioral adverse effects. A cross sectional study was conducted to determine the prevalence of and risk factors for anemia in infants and children between 6 months to 5 years of age admitted to a tertiary hospital in Botswana. Baseline full blood count of every patient admitted during the study period was assessed to determine if anemia was present. Data were collected from patient's medical inpatient chart, electronic medical record (Integrated Patient Management System (IPMS)), and through interviewing parents and caregivers. Multivariate logistic regression model was used to identify risk factors of anemia. A total of 250 patients were included in the study. Prevalence of anemia in this cohort was 42.8%. There were 145 (58%) males. Of the patients with anemia, 56.1%, 39.2%, and 4.7% had mild, moderate, and severe anemia, respectively. Microcytic anemia consistent with iron deficiency was identified in 61 (57%) patients. Age was the only independent predictor of anemia. Children aged 24 months and more had a 50% lower risk of having anemia than their younger counterparts (odds ratio (OR) 0.52; 95% Confidence Interval (95% CI) 0.30 to 0.89). The findings of this study demonstrate anemia as a serious health concern in the pediatric population in Botswana.

Shared component modelling of early childhood anaemia and malaria in Kenya, Malawi, Tanzania and Uganda

Background

Malaria and anaemia contribute substantially to child morbidity and mortality. In this study, we sought to jointly model the residual spatial variation in the likelihood of these two correlated diseases, while controlling for individual-level, household-level and environmental characteristics.

Methods

A child-level shared component model was utilised to partition shared and disease-specific district-level spatial effects.

Results

The results indicated that the spatial variation in the likelihood of malaria was more prominent compared to that of anaemia, for both the shared and specific spatial components. In addition, approximately 30% of the districts were associated with an increased likelihood of anaemia but a decreased likelihood of malaria. This suggests that there are other drivers of anaemia in children in these districts, which warrants further investigation.

Conclusions

The maps of the shared and disease-specific spatial patterns provide a tool to allow for more targeted action in malaria and anaemia control and prevention, as well as for the targeted allocation of limited district health system resources.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03694-4.

Modeling spatial pattern of anemia and malnutrition co-occurrence among under-five children in Ethiopia: A Bayesian geostatistical approach

Anemia and malnutrition among under-five children are some of the challenges to public health in Ethiopia. This study aims to determine the socio-economic, demographic, and geographical risk factors that increase the prevalence of the co-occurrence of anemia and malnutrition among under-five children in Ethiopia. The Ethiopia Demographic and Health Survey data for the survey years 2011 and 2016 were used. A Bayesian hierarchical mixed model with a stochastic partial differential equation was adopted to understand the spatial patterns of co-occurrence of these ailments in Ethiopia. The significant risk factors are gender, maternal education, birth order, preceding births, contraceptive use, vaccination, marital status, distance to a health facility, and birth weight. Findings revealed more vulnerability among children less than twenty months and existing geographical disparity with a higher burden of the prevalence of the co-occurrences of anemia and malnutrition in the North-East regions. For cost-effective intervention, policies and programs that improve individual-level risk factors of parents are a more promising approach to tackle these ailments in high-prevalent regions than the ones on the children and should be of utmost priority in the North-East region of the country.

Bayesian spatio-temporal modelling and mapping of malaria and anaemia among children between 0 and 59 months in Nigeria

BACKGROUND/M&M

A vital aspect of disease management and policy making lies in the understanding of the universal distribution of diseases. Nevertheless, due to differences all-over host groups and space-time outbreak activities, data are subject to intricacies. Herein, Bayesian spatio-temporal models were proposed to model and map malaria and anaemia risk ratio in space and time as well as to ascertain risk factors related to these diseases and the most endemic states in Nigeria. Parameter estimation was performed by employing the R-integrated nested Laplace approximation (INLA) package and Deviance Information Criteria were applied to select the best model.

RESULTS

In malaria, model 7 which basically suggests that previous trend of an event cannot account for future trend i.e., Interaction with one random time effect (random walk) has the least deviance. On the other hand, model 6 assumes that previous event can be used to predict future event i.e., (Interaction with one random time effect (ar1)) gave the least deviance in anaemia.

DISCUSSION

For malaria and anaemia, models 7 and 6 were selected to model and map these diseases in Nigeria, because these models have the capacity to receive strength from adjacent states, in a manner that neighbouring states have the same risk. Changes in risk and clustering with a high record of these diseases among states in Nigeria was observed. However, despite these changes, the total risk of malaria and anaemia for 2010 and 2015 was unaffected.

CONCLUSION

Notwithstanding the methods applied, this study will be valuable to the advancement of a spatio-temporal approach for analyzing malaria and anaemia risk in Nigeria.

Impact of a 5-Year Mass Drug Administration Programme for Soil-Transmitted Helminthiases on the Spatial Distribution of Childhood Anaemia in Burundi from 2007 to 2011

Background: Childhood anaemia affects 1.8 billion people globally. Little is known about the long-term impact of mass drug administration (MDA) for the control of soil-transmitted helminthiases (STH) on the spatiotemporal variation of anaemia prevalence and severity. We describe the long-term spatiotemporal impact of a 5-year STH MDA programme (2007−2011) on the prevalence of anaemia and anaemia severity in school-aged children (SAC) in Burundi. Methodology/Principal Findings: We used annual haemoglobin concentration and STH data collected during 2007−2011 in 31 schools in Burundi. Spatial dependence in prevalence and severity of anaemia was assessed using semivariograms. Bayesian geostatistical models were developed to (a) quantify the role of STH (adjusted for other anaemia determinants) in the spatiotemporal distribution of anaemia prevalence/severity, and (b) predict the geographical variation of both outcomes across Burundi. Adjusted population data were used to estimate the geographical distribution of the number of SAC at risk of anaemia and with low and moderate/severe anaemia. Infections with Ascaris lumbricoides and Trichuris trichiura were positively and significantly associated with childhood anaemia; hookworm infections were not. A significant decrease in anaemia prevalence, from 40−50% (2008) to 10−20% (2011) was predicted in western areas. The predicted prevalence of low-severity anaemia decreased from 40−50% (2008) to <20% (2011) in southern and eastern areas. Moderate/high-severity anaemia was concentrated in western regions of Burundi, with pockets of moderate/high-severity anaemia in central and northern regions in 2008. The overall number of predicted anaemic children decreased from 443,657 (2008) to 232,304 (2011), with a resurgence after MDA disruption in 2010 (to 480,605). Prevalence of low- and moderate-severity anaemia was higher in boys than in girls. Conclusions/Significance: Despite ongoing MDA, the prevalence of anaemia in SAC remained high and increased in certain parts of the country. It is recommended that MDA programmes targeting STH are complemented with specific anaemia interventions.

Spatiotemporal Variation and Predictors of Unsuppressed Viral Load among HIV-Positive Men and Women in Rural and Peri-Urban KwaZulu-Natal, South Africa

Unsuppressed HIV viral load is an important marker of sustained HIV transmission. We investigated the prevalence, predictors, and high-risk areas of unsuppressed HIV viral load among HIV-positive men and women. Unsuppressed HIV viral load was defined as viral load of ≥400 copies/mL. Data from the HIV Incidence District Surveillance System (HIPSS), a longitudinal study undertaken between June 2014 to June 2016 among men and women aged 15−49 years in rural and peri-urban KwaZulu-Natal, South Africa, were analysed. A Bayesian geoadditive regression model which includes a spatial effect for a small enumeration area was applied using an integrated nested Laplace approximation (INLA) function while accounting for unobserved factors, non-linear effects of selected continuous variables, and spatial autocorrelation. The prevalence of unsuppressed HIV viral load was 46.1% [95% CI: 44.3−47.8]. Predictors of unsuppressed HIV viral load were incomplete high school education, being away from home for more than a month, alcohol consumption, no prior knowledge of HIV status, not ever tested for HIV, not on antiretroviral therapy (ART), on tuberculosis (TB) medication, having two or more sexual partners in the last 12 months, and having a CD4 cell count of <350 cells/μL. A positive non-linear effect of age, household size, and the number of lifetime HIV tests was identified. The higher-risk pattern of unsuppressed HIV viral load occurred in the northwest and northeast of the study area. Identifying predictors of unsuppressed viral load in a localized geographic area and information from spatial risk maps are important for targeted prevention and treatment programs to reduce the transmission of HIV.

Prevalence of, and factors associated with anaemia in children aged 1-3 years in Aceh, Indonesia: A cross-sectional study

Background: Child anaemia continues to represent a major public health challenge in lower-and-middle income countries. It has serious long-term consequences for child growth and development. In Indonesia, there was a 10% increase in the national prevalence of child anaemia between 2013 and 2018. Aim: This study aims to assess the prevalence of, and factors associated with anaemia among children aged one to three years in eight districts in Aceh Province, Indonesia. Methods: A cross-sectional study was conducted on a sample of 1148 mother-child dyads aged one to three years between November and December 2018. The sampling process involved a three-stage cluster sampling design using the probability proportionate to size methodology. Anaemia status was determined using haemoglobin level (Hb < 11.0 g/dL). Data were analysed using multivariable logistic regression to estimate adjusted odds ratios (aOR) and their 95% confidence intervals (95% CI) for associated factors. Results: The overall prevalence of anaemia was 76.1% (869/1142). 44.7% (510/1142) and 28.6% (327/1142) had moderate and mild anaemia, respectively. Child aged 12-24 months (aOR: 2.00, 95% CI: 1.26-3.17), not receiving routine immunisation (aOR: 2.62, 95% CI: 1.34-5.10), and maternal anaemia (aOR: 2.15, 95% CI: 1.59-2.90) were significantly associated with anaemia. Conclusion: The prevalence of anaemia among the children in this study was high, and was associated with child age, immunisation status, and maternal anaemia. These findings provide further insight into anaemia as a public health issue at a sub-national level in Indonesia and for development of targeted programmes to address associated risk factors of child anaemia.

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

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.

Kharsany A, Zewotir T, North D. Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women in an HIV Hyperendemic Area of KwaZulu-Natal, South Africa

New HIV infections among young women remains exceptionally high and to prevent onward transmission, UNAIDS set ambitious treatment targets. This study aimed to determine the prevalence, spatial variation and factors associated with unsuppressed HIV viral load at ≥400 copies per mL. This study analysed data from women aged 15–49 years from the HIV Incidence Provincial Surveillance System (HIPSS) enrolled in two sequential cross-sectional studies undertaken in 2014 and 2015 in rural and peri-urban KwaZulu-Natal, South Africa. Bayesian geoadditive model with spatial effect for a small enumeration area was adopted using Integrated Nested Laplace Approximation (INLA) function to analyze the findings. The overall prevalence of unsuppressed HIV viral load was 45.2% in 2014 and 38.1% in 2015. Factors associated with unsuppressed viral load were no prior knowledge of HIV status, had a moderate-to-low perception of acquiring HIV, not on antiretroviral therapy (ART), and having a low CD4 cell count. In 2014, women who ever consumed alcohol and in 2015, ever ran out of money, had two or more lifetime sexual partners, ever tested for tuberculosis, and ever diagnosed with sexually transmitted infection were at higher risk of being virally unsuppressed. The nonlinear effect showed that women aged 15 to 29 years, from smaller households and had fewer number of lifetime HIV tests, were more likely to be virally unsuppressed. High viral load risk areas were the north-east and south-west in 2014, with north and west in 2015. The findings provide guidance on identifying key populations and areas for targeted interventions.

Inequalities in the prevalence of stunting, anemia and exclusive breastfeeding among African children

Background

Childhood stunting and anemia are on the increase in many resource-constrained settings, without a counter increase in proper feeding practices such as exclusive breastfeeding. The objective of this study was to explore the prevalence of stunting, anemia and exclusive breastfeeding across African countries.

Methods

Demographic and Health Survey (DHS) data from 39 African countries was analyzed. Data from under 5 children were analyzed. Forest plot was used to determine inequalities in the prevalence of the outcome variables.

Results

The prevalence of stunting was highest in Burundi (56%), Madagascar (50%) and Niger (44%). In addition, Burkina Faso (88%), Mali (82%), Cote d’Ivoire and Guinea (75% each) and Niger (73%) had the highest prevalence of anemia. Furthermore, Burundi (83%), Rwanda (81%) and Zambia (70%) had the highest exclusive breastfeeding. We found statistical significant difference in the prevalence of stunting, anemia and exclusive breastfeeding (p < 0.001). Higher prevalence of stunting and anemia were estimated among the male, rural residents, those having mothers with low education and from poor household wealth.

Conclusion

Concerted efforts are required to improve childhood health, survival and proper feeding practice. Reduced stunting and anemia could be achieved through sustained socioeconomic improvement that is shared in equity and equality among the population. Interventions aimed at increasing food availability can also aid in the reduction of hunger, particularly in impoverished communities.

Effect of individual, household and regional socioeconomic factors and PM2.5 on anaemia: A cross-sectional study of sub-Saharan African countries

There is limited knowledge on the effect of contextual and environmental factors on the risk of anaemia, as well as the spatial distribution of anaemia in the Sub-Saharan Africa region. In this study, we used multi-country data from the Demographic & Health survey (DHS) with 270,011 observations and PM_2.5 data from NASA, applied to the spatial risk pattern of anaemia in the SSA region. The prevalence of anaemia amongst women (41%) was almost twice that of men (22%). A Bayesian hierarchical model showed that individual household, neighbourhood and regional socioeconomic factors were significantly associated with the likelihood of being anaemic. 1 μg/m³ increase in cumulative lifetime PM_2.5 exposure accounted for 1% (β = 0.011, CI = 0.008 - 0.015) increase in the likelihood of being anaemic. The results suggest the need for a multidimensional approach to tackle anaemia in the Sub-Saharan African region and identify high-risk areas for target intervention policies or programs.

Prevalence of anemia and its associated factors among under-five age children in Shanan gibe hospital, Southwest Ethiopia

BACKGROUND

Anemia is a major health problem in the worldwide. Because of health and socioeconomic problems, the prevalence of anemia is higher in developing countries. However, there was a limited finding in our study area. Therefore, the aim of this study was to determine the prevalence of anemia and its associated factors among under-five age children in Shanan Gibe Hospital (SGH), Southwest Ethiopia.

METHODS

Institution based cross sectional study was conducted at SGH, Ethiopia using consecutive convenient sampling technique during 1 January to 30 April, 2021. Data was collected by interviewing and capillary blood was taken from the fingertip for hemoglobin determination by using HaemoCue digital photometer. Additionally, stool sample was processed using wet mount and formal-ether concentration technique. Then after, the data were entered to Epidata version 3.1 and analysed with Statistical Package for the Social Sciences (SPSS) version 20. Factors associated with anaemia were assessed by bivariable and multivariable logistic regression model by considering P < 0.05 as statistical significance.

RESULTS

A total of 368 under five children were recruited to the study and the current prevalence of anemia was 48.9%. Of this anemia, 25.0% mild, 15.8% moderate and 8.2% were severely anemic. More ever, being rural resident (AOR = 6.11; 95% CI = 1.49-8.99, P = 0.002), family low income (AOR = 6.27, 95% CI = 1.35-11.43, P = 0.004), family size greater than five (AOR = 3.12; 95% CI =1.47-7.11, P = 0.002) and intestinal parasite infections such as Enteameoba histolytica (AOR =3.37; 95%CI = 2.16-11.31, P = 0.005), Hookworm (AOR = 6.09; 95%CI = 2.37-11.56, P = 0.001), and Trichuris trichuria (AOR = 2.79; 95%CI = 1.45-9.13, P = 0.002) (P < 0.05) were factors significantly associated with anemia among under five children.

CONCLUSION

The current prevalence of anemia among under five age children is relatively high. On the other hand, the rural residence, large family size, low family income, infection with Enteameoba histolytica, hookworm and Trichuris trichuria were the identified factors associated with anemia among under five children. Therefore, there should be massive and routine deworming program in addition to imperative targeting anemia prevention, and nutritional supplementation to reduce the burden of anemia.

Relationship between distance of referring facilities and haemoglobin change in children in Malawi

BACKGROUND

Anaemia is a significant cause of mortality in children in sub-Saharan Africa where blood transfusion is often available only at referral hospitals. Understanding the pattern of referrals by health facilities is essential to identify the delays that affect child survival.

AIM

To determine if there was a correlation between change in haemoglobin (Hb) level and distance from referring facilities to Kamuzu Central Hospital (KCH) in Malawi, and whether distance affected mortality rates.

METHODS

This was a retrospective cohort study of 2259 children referred to KCH whose Hb was measured at the referring facility or at KCH. Maps were created using ArcGIS® software. The relationship between distance from KCH and change in Hb was assessed by χ2 analysis and multiple linear regression with SAS© software.

RESULTS

The majority of children were referred by health facilities in the Lilongwe District. When categorised as Hb <4, 4-6 or >6 g/dL, 87.0% of children remained in the same category during transfer. There was no significant relationship between Hb drop and distance from KCH. Distance from KCH was not a significant predictor of Hb level at KCH or Hb change. However, mortality rates were significantly higher in facilities that were 10-50 km from KCH than in those which were <10 km away.

CONCLUSIONS

Using distance as a proxy for time, this suggests that referring facilities are transferring children sufficiently quickly to avert significant reductions in Hb. Despite this, there is a need to identify the factors that influence the decision to transfer anaemic children.

Spatio-temporal distribution and associated factors of anaemia among children aged 6-59 months in Ethiopia: a spatial and multilevel analysis based on the EDHS 2005-2016

OBJECTIVES

Anaemia is a global public health problem with major health and socioeconomic consequences. Though childhood anaemia is a major public health problem in Ethiopia, there is limited evidence on the spatio-temporal variability of childhood anaemia over time in the country. Therefore, this study aimed to assess the spatio-temporal distribution and associated factors of childhood anaemia using the Ethiopian Demographic and Health Survey (EDHS) data from 2005 to 2016.

DESIGN

Survey-based cross-sectional study design was employed for the EDHS.

SETTING

Data were collected in all nine regions and two city administrations of Ethiopia in 2005, 2011 and 2016.

PARTICIPANTS

The source population for this study was all children in Ethiopia aged 6-59 months. A total of 21 302 children aged 6-59 months were included in this study.

OUTCOME MEASURE

The outcome variable was child anaemia status.

RESULTS

The prevalence of anaemia declined from 53.9% in 2005 to 44.6% in 2011, but it showed an increase in 2016 to 57.6%. The spatial analysis revealed that the spatial distribution of anaemia varied across the regions. The spatial scan statistics analysis indicated a total of 22 clusters (relative risk (RR)=1.5, p<0.01) in 2005, 180 clusters (RR=1.4, p<0.01) in 2011 and 219 clusters (RR=1.4, p<0. 0.01) in 2016, significant primary clusters were identified. The child's age, mother's age, maternal anaemia status, wealth index, birth order, fever, stunting, wasting status and region were significant predictors of childhood anaemia.

CONCLUSIONS

In this study, childhood anaemia remains a public health problem. The spatial distribution of childhood anaemia varied significantly across the country. Individual-level and community-level factors were associated with childhood anaemia. Therefore, in regions with a high risk of childhood anaemia, individual-level and community-level factors should be intensified by allocating additional resources and providing appropriate and tailored strategies.

Household cooking fuel type and childhood anaemia in sub-Saharan Africa: analysis of cross-sectional surveys of 123, 186 children from 29 countries

OBJECTIVE

This study sought to investigate the joint effect of household cooking fuel type and urbanicity (rural-urban residency) on anaemia among children under the age of five in sub-Saharan Africa.

DESIGN

We analysed cross-sectional data of 123, 186 children under the age of five from 29 sub-Saharan African countries gathered between 2010 and 2019 by the Demographic and Health Survey programme. Bivariate (χ² test of independence) and multilevel logistic regression were used to examine the effect of urbanicity-household cooking fuel type on childhood anaemia. Results were reported as adjusted odds ratios (aORs) with 95% CIs at p<0.05.

OUTCOME MEASURES

Anaemia status of children.

RESULTS

More than half (64%) of children had anaemia. The percentage of children who suffered from anaemia was high in those born to mothers in Western Africa (75%) and low among those born in Southern Africa (54%). Children from rural households that depend on unclean cooking fuels (aOR=1.120; 95% CI 1.033 to 1.214) and rural households that depend on clean cooking fuels (aOR=1.256; 95% CI 1.080 to 1.460) were more likely to be anaemic as compared with children from urban households using clean cooking fuel. Child's age, sex of child, birth order, perceived birth size, age of mother, body mass index of mother, education, marital status, employment status, antenatal care, wealth quintile, household size, access to electricity, type of toilet facility, source of drinking water and geographic region had significant associations with childhood anaemia status.

CONCLUSIONS

Our study has established a joint effect of type of household cooking fuel and urbanicity on anaemia among children under the age of five in sub-Saharan Africa. It is therefore critical to promote the usage of clean cooking fuels among households and women in rural areas. These should be done taking into consideration the significant child, maternal, household, and contextual factors identified in this study.

Bayesian Spatial Modeling of Anemia among Children under 5 Years in Guinea

Anemia is a major public health problem in Africa, affecting an increasing number of children under five years. Guinea is one of the most affected countries. In 2018, the prevalence rate in Guinea was 75% for children under five years. This study sought to identify the factors associated with anemia and to map spatial variation of anemia across the eight (8) regions in Guinea for children under five years, which can provide guidance for control programs for the reduction of the disease. Data from the Guinea Multiple Indicator Cluster Survey (MICS5) 2016 was used for this study. A total of 2609 children under five years who had full covariate information were used in the analysis. Spatial binomial logistic regression methodology was undertaken via Bayesian estimation based on Markov chain Monte Carlo (MCMC) using WinBUGS software version 1.4. The findings in this study revealed that 77% of children under five years in Guinea had anemia, and the prevalences in the regions ranged from 70.32% (Conakry) to 83.60% (NZerekore) across the country. After adjusting for non-spatial and spatial random effects in the model, older children (48-59 months) (OR: 0.47, CI [0.29 0.70]) were less likely to be anemic compared to those who are younger (0-11 months). Children whose mothers had completed secondary school or above had a 33% reduced risk of anemia (OR: 0.67, CI [0.49 0.90]), and children from household heads from the Kissi ethnic group are less likely to have anemia than their counterparts whose leaders are from Soussou (OR: 0.48, CI [0.23 0.92]).

Copula geoadditive modelling of anaemia and malaria in young children in Kenya, Malawi, Tanzania and Uganda

BACKGROUND

Anaemia and malaria are the leading causes of sub-Saharan African childhood morbidity and mortality. This study aimed to explore the complex relationship between anaemia and malaria in young children across the districts or counties of four contiguous sub-Saharan African countries, namely Kenya, Malawi, Tanzania and Uganda, while accounting for the effects of socio-economic, demographic and environmental factors. Geospatial maps were constructed to visualise the relationship between the two responses across the districts of the countries.

METHODS

A joint bivariate copula regression model was used, which estimates the correlation between the two responses conditional on the linear, non-linear and spatial effects of the explanatory variables considered. The copula framework allows the dependency structure between the responses to be isolated from their marginal distributions. The association between the two responses was set to vary according to the district of residence across the four countries.

RESULTS

The study revealed a positive association between anaemia and malaria throughout the districts, the strength of which varied across the districts of the four countries. Due to this heterogeneous association between anaemia and malaria, we further considered the joint probability of each combination of outcome of anaemia and malaria to further reveal more about the relationship between the responses. A considerable number of districts had a high joint probability of a child being anaemic but not having malaria. This might suggest the existence of other significant drivers of childhood anaemia in these districts.

CONCLUSIONS

This study presents an alternative technique to joint modelling of anaemia and malaria in young children which assists in understanding more about their relationship compared to techniques of multivariate modelling. The approach used in this study can aid in visualising the relationship through mapping of their correlation and joint probabilities. These maps produced can then help policy makers target the correct set of interventions, or prevent the use of incorrect interventions, particularly for childhood anaemia, the causes of which are multiple and complex.