Modelling and forecasting spatio-temporal variation in the risk of chronic malnutrition among under-five children in Ghana

Aheto, Justice Moses K.; Taylor, Benjamin M.; Keegan, Thomas; Diggle, Peter J.

doi:10.1016/j.sste.2017.02.003

Cited by 34 publications

(52 citation statements)

References 26 publications

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“…Our study supports the findings of others that reported geographical differences in health outcomes such as prostate and lung cancers, malaria, malnutrition, mortality among others [5,7,[23][24][25]30].…”

Section: Discussionsupporting

confidence: 92%

“…This is critical because residential location of people could act as a marker for the socioeconomic, personal, and climatic/environmental factors that influence access to healthcare services and the general health of the people. Thus, spatial modelling and mapping provides the required tools to obtain an improved understanding of health outcomes of people by place for targeted public health interventions[7,[23][24][25][26][27]. The predicted…”

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confidence: 99%

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Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012-2016 SEER data

Aheto

Utuama

Dagne

2020

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Background: Prostate cancer (CaP) cases are high in the United States. According to the American Cancer Society, there are an estimated number of 174,650 CaP new cases in 2019. The estimated number of deaths from CaP in 2019 is 31,620, making CaP the second leading cause of cancer deaths among American men with lung cancer been the first. Our goal is to estimate and map prostate cancer relative risk, with the ultimate goal of identifying counties at higher risk where interventions and further research can be targeted.Method: The 2012-2016 Surveillance, Epidemiology, and End Results (SEER) Program data was used in this study. Analyses were conducted on 159 Georgia counties. The outcome variable is incident prostate cancer. We employed a Bayesian geospatial model to investigate both measured and unmeasured spatial risk factors for prostate cancer. We visualised the risk of prostate cancer by mapping the predicted relative risk and exceedance probabilities. We finally developed interactive web-based maps to guide optimal policy formulation and intervention strategies.Results: Number of persons above age 65 years and below poverty, higher median family income, number of foreign born and unemployed were risk factors independently associated with prostate cancer risk in the non-spatial model. Except number of foreign born, all these risk factors were also significant in the spatial model with the same direction of effects. Substantial geographical variations in prostate cancer incidence were found in the study. The predicted mean relative risk was 1.20 with a range of 0.53 to 2.92. Individuals residing in Towns, Clay, Union, Putnam, Quitman, and Greene counties were at increased risk of prostate cancer incidence while those residing in Chattahoochee were at the lowest risk of prostate cancer incidence.Conclusion: Our results can be used as an effective tool in the identification of counties that require targeted interventions and further research by program managers and policy makers as part of an overall strategy in reducing the prostate cancer burden in Georgia State and the United States as a whole.

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Section: Discussionsupporting

confidence: 92%

mentioning

confidence: 99%

Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012-2016 SEER data

Aheto

Utuama

Dagne

2020

Preprint

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“…Increase in the likelihood of severe stunting observed among older ages of children in this study supports previous studies [1,4,28,40,42,59]. A plausible explanation could be a deficit in timely and adequate complementary feeding and presence of progressive childhood diseases [1,4,28,59]. The finding that the children who were born small at birth had higher likelihood of severe stunting is consistent with previous studies.…”

Section: Quantile Plots Of Covariate Effects For the Ranges Of 010 Tsupporting

confidence: 92%

“…Majority of the studies [1,4,[16][17][18][19] conducted to examine risk factors of under-five malnutrition in Ghana used statistical models which might not be optimal to inform targeted nutrition policies and interventions because they could not give much information about the underlying associations, not robust to statistical outliers and lacks flexibility in analysing the determinants of nutritional status. For example, modelling the mean as in the ordinary linear regression models could miss critical aspects of the relationship that may exist between the nutritional status and its determinants, especially in the presence of skewed data as is usually the case with anthropometric data [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous quantile regression and determinants of under-five severe chronic malnutrition in Ghana.

Aheto¹

2020

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Background Under-five malnutrition is a major public health issue contributing to mortality and morbidity, especially in developing countries like Ghana where the rates remain unacceptably high. Identification of critical risk factors of under-five malnutrition using appropriate and advanced statistical methods can help formulate appropriate health programmes and policies aimed at achieving the United Nations SDG Goal 2 target 2. This study attempts to develop a simultaneous quantile regression, an in-depth statistical model to identify critical risk factors of under-five severe chronic malnutrition (severe stunting). Methods Based on the nationally representative data from the 2014 Ghana Demographic and Health Survey, height-for-age z-score (HAZ) was estimated. Multivariable simultaneous quantile regression modelling was employed to identify critical risk factors for severe stunting based on HAZ (a measure of chronic malnutrition in populations). Quantiles of HAZ with focus on severe stunting were modelled and the impact of the risk factors determined. Significant test of the difference between slopes at different selected quantiles of severe stunting and other quantiles were performed. A quantile regression plots of slopes were developed to visually examine the impact of the risk factors across these quantiles. Results Data on a total of 2716 children were analysed out of which 144 (5.3%) were severely stunted. The models identified child level factors such as type of birth, sex, age, place of delivery and size at birth as significant risk factors of under-five severe stunting. Maternal and household level factors identified as significant predictors of under-five severe stunting were maternal age and education, maternal national health insurance status, household wealth status, and number of children under-five in households. Highly significant differences exist in the slopes between 0.1 and 0.9 quantiles. The quantile regression plots for the selected quantiles from 0.1 to 0.9 showed substantial differences in the impact of the covariates across the quantiles of HAZ considered. Conclusion Critical risk factors that can aid formulation of child nutrition and health policies and interventions that will improve child nutritional outcomes and survival were identified. Modelling under-five severe stunting using multivariable simultaneous quantile regression models could be beneficial to addressing the under-five severe stunting.

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“…This is in the expected direction because higher level of maternal education is likely to result in improved health seeking behaviour and utilization of health services for their offspring and themselves, and this is expected to improve the health outcomes of both the children and their mothers. It is also expected to result in optimal childcare and feeding practices with its resultant improved health outcomes for the child [3,[17][18][19]. U5M is significantly higher among children born multiple compared to those born singleton which could be due to competition for nutrients and health complications that usually occur more among children who are products of multiple births [3,17,19,20].…”

Section: Discussionmentioning

confidence: 99%

Geostatistical analysis and mapping: Social and environmental determinants of under-five child mortality, evidence from the 2014 Ghana Demographic and Health Survey.

Aheto

Yankson

Chipeta

2019

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Background Under-five mortality (U5M) rates are among the health indicators of utmost importance globally. It is the goal 3 target 2.1 of the Sustainable Development Goals that is expected to be reduced to at least 25 per 1000 livebirths by 2030. Despite a considerable reduction in U5M was observed globally, several countries especially those in sub-Saharan Africa (SSA) like Ghana are struggling to meet this target. Evidence-based targeting and utilization of the available limited public health resources are critical for effective design of intervention strategies that will enhance under-five child survival. We aimed to estimate and map U5M risk, with the ultimate goal of identifying communities at high risk where interventions can be targeted.Methods The 2014 Ghana Demographic and Health Survey data was used in this study. Geostatistical analyses were conducted on 5,801 children residing in 423 geographical clusters. The outcome variable is child survival status (alive or dead). We employed a geostatistical generalised linear mixed model to investigate both measured and unmeasured child specific and spatial risk factors for child survival. We then visualise child mortality by mapping the predictive probability of survival.Results Of the total sampled under 5 children, 280 (4.83%) experienced the outcome of interest. Children born as multiple births were at increased risk of mortality with AOR (9.28, 95% CI: 6.35 – 13.58) compared to singletons. Maternal education AOR (0.80, 95% CI: 0.68 – 0.93) and number of children under 5 within each household AOR (0.34, 95% CI: 0.28 – 0.41) were shown to have a protective effect. The predicted U5M risk in 2014 was at 8.3%. High altitude areas were highly associated with high U5M.Conclusion The analysis found that multiple births and high elevation are highly associated with U5M in Ghana. The high-resolution maps show areas and communities where interventions for U5M can be prioritised to have health impact.

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Modelling and forecasting spatio-temporal variation in the risk of chronic malnutrition among under-five children in Ghana

Cited by 34 publications

References 26 publications

Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012-2016 SEER data

Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012-2016 SEER data

Simultaneous quantile regression and determinants of under-five severe chronic malnutrition in Ghana.

Geostatistical analysis and mapping: Social and environmental determinants of under-five child mortality, evidence from the 2014 Ghana Demographic and Health Survey.

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