Armed conflict, insecurity, and attitudes toward women's and girls' reproductive autonomy in Nigeria.

BACKGROUND: Armed conflict and insecurity have been linked to deteriorations in reproductive health and rights globally. In Nigeria, armed violence has taken a significant toll on women's and girls' health and safety. However, knowledge is limited about how conflict shapes attitudes surrounding their ability to make autonomous decisions on relationships and childbearing. Drawing on a socioecological framework and terror management theory, we aimed to investigate the association between conflict, insecurity, and attitudes toward women's and girls' reproductive autonomy in Nigeria. METHODS: We conducted a cross-sectional study using data from two sources: the World Values Survey (WVS) and the Uppsala Conflict Data Program-Georeferenced Event Dataset (UCDP-GED). Nationally representative data on attitudes of 559 men and 534 women was collected by WVS in 2017-2018. Linear probability models estimated the association between attitudes toward five dimensions of women and girl's reproductive autonomy (contraception, safe abortion, marital decision-making, delayed childbearing, early marriage), respondents' perceptions of neighborhood insecurity using WVS data, and geospatial measures of conflict exposure drawn from UCDP-GED. RESULTS: Exposure to armed conflict and perceived neighborhood insecurity were associated with more supportive attitudes toward access to safe abortion among both men and women. Among women, conflict exposure was associated with higher support for contraception and the perception that early marriage can provide girls with security. Conflict-affected men were more likely to support a delay in girls' childbearing. CONCLUSION: Our findings suggest that conflict and insecurity pose a threat to, but also facilitate opportunities for, women's and girls' reproductive autonomy. Contraception, abortion, early marriage, and postponement or childbearing may be perceived as risk-aversion strategies in response to mortality threats, livelihood losses, and conflict-driven sexual violence. Our findings foreshadow changes in fertility and relationship patterns in conflict-affected Nigeria and highlight the need for health programming to ensure access to contraception and safe abortion services.

Modeling the shared risks of malaria and anemia in Rwanda.

In sub-Saharan Africa, malaria and anemia contribute substantially to the high burden of morbidity and mortality among under-five children. In Rwanda, both diseases have remained public health challenge over the years in spite of the numerous intervention programs and policies put in place. This study aimed at understanding the geographical variations between the joint and specific risks of both diseases in the country while quantifying the effects of some socio-demographic and climatic factors. Using data extracted from Rwanda Demographic and Health Survey, a shared component model was conceived and inference was based on integrated nested Laplace approximation. The study findings revealed similar spatial patterns for the risk of malaria and the shared risks of both diseases, thus confirming the strong link between malaria and anaemia. The spatial patterns revealed that the risks for contracting both diseases are higher among children living in the districts of Rutsiro, Nyabihu, Rusizi, Ruhango, and Gisagara. The risks for both diseases are significantly associated with type of place of residence, sex of household head, ownership of bed net, wealth index and mother's educational attainment. Temperature and precipitation also have substantial association with both diseases. When developing malaria intervention programs and policies, it is important to take into account climatic and environmental variability in Rwanda. Also, potential intervention initiatives focusing on the lowest wealth index, children of uneducated mothers, and high risky regions need to be reinforced.

Modeling the spatial patterns of antenatal care utilization in Nigeria with inference based on Pólya-Gamma mixtures.

Despite the vast advantages of making antenatal care visits, the service utilization among pregnant women in Nigeria is suboptimal. A five-year monitoring estimate indicated that about 24% of the women who had live births made no visit. The non-utilization induced excessive zeroes in the outcome of interest. Thus, this study adopted a zero-inflated negative binomial model within a Bayesian framework to identify the spatial pattern and the key factors hindering antenatal care utilization in Nigeria. We overcome the intractability associated with posterior inference by adopting a Pólya-Gamma data-augmentation technique to facilitate inference. The Gibbs sampling algorithm was used to draw samples from the joint posterior distribution. Results revealed that type of place of residence, maternal level of education, access to mass media, household work index, and woman's working status have significant effects on the use of antenatal care services. Findings identified substantial state-level spatial disparity in antenatal care utilization across the country. Cost-effective techniques to achieve an acceptable frequency of utilization include the creation of a community-specific awareness to emphasize the importance and benefits of the appropriate utilization. Special consideration should be given to older pregnant women, women in poor antenatal utilization states, and women residing in poor road network regions.

Intimate partner violence and malnutrition among women of reproductive age in Western Africa: A geostatistical analysis.

Intimate partner violence (IPV) is a public health issue, and the experience varies among population sub-groups in Africa. In the West African sub-region, IPV perpetrated against women remains high and is exacerbated by the pertaining cultural milieu. It affects women's health, wellbeing, and nutritional status. We examined the association between women's lifetime experiences of physical, sexual, and emotional IPV and undernutrition by quantifying the association at smaller geographical settings in West African countries. We used a bivariate probit geostatistical technique to explore the association between IPV and undernutrition, combining data from the latest Demographic and Health Survey conducted in ten Western African countries. Bayesian inference relies on Markov chain Monte Carlo simulation. The findings demonstrate spatial clustering in the likelihood of experiencing IPV and being underweight in the regions of Mali, Sierra Leone, Liberia and neighboring Cote d'Ivoire, Ghana, Togo, Benin, Cameroon, and Nigeria. The pattern of clustering was somewhat similar when physical violence was combined with underweight and emotional violence combined with underweight. The findings also indicate protective effects of education, wealth status, employment status, urban residence, and exposure to mass media. Further, the likelihood of experiencing IPV and the likelihood of being underweight or thin declined with age and age-gap between the woman and her partner. The findings provide insight into the location-specific variations that can aid targeted interventions, and underscore the importance of empowering women holistically, in the domains of education, socio-economic and socio-cultural empowerment, in addressing women's vulnerability to IPV and malnutrition (underweight and thinness). Furthermore, IPV prevention programmes will need to address gender inequality and cultural factors such as male dominance that may heighten women's risk of experiencing IPV.

Copula based trivariate spatial modeling of childhood illnesses in Western African countries.

Acute respiratory infections (ARI), diarrhea, and fever are three common childhood illnesses, especially in sub-Saharan Africa. This study investigates the marginal and pairwise correlated effects of these diseases across Western African countries in a single analytical framework. Using data from nationally representative cross-sectional Demographic and Health Surveys, the study analyzed specific and correlated effects of each pair of childhood morbidity from ARI, diarrhea, and fever using copula regression models in fourteen contiguous Western African countries. Data concerning childhood demographic and socio-economic conditions were used as covariates. In this cross-sectional analysis of 152,125 children aged 0-59 months, the prevalence of ARI was 6.9%, diarrhea, 13.8%, and fever 19.6%. The results showed a positive correlation and geographical variation in the prevalence of the three illnesses across the study region. The estimated correlation and 95% confidence interval between diarrhea and fever is 0.431(0.300,0.539); diarrhea and ARI is 0.270(0.096,0.422); and fever and ARI is 0.502(0.350,0.614). The marginal and correlated spatial random effects reveal within-country spatial dependence. Source of water and access to electricity was significantly associated with any of the three illnesses, while television, birth order, and gender were associated with diarrhea or fever. The place of residence and access to newspapers were associated with fever or ARI. There was an increased likelihood of childhood ARI, diarrhea, and fever, which peaked at about ten months but decreased substantially thereafter. Mother's age was associated with a reduced likelihood of the three illnesses. The maps generated could be resourceful for area-specific policy-making to speed up mitigation processes.

Analysis of the space-time trends in open defecation in Nigeria.

The practice of open defecation has persistently remained high in Nigeria despite the grave danger it poses to public and environmental health, and the several intervention programs put in place over the years to curtail the ugly practice. This study quantifies the space and time trends in open defecation practice in Nigeria with the aim of highlighting the changes that have taken place at various locations in Nigeria over a 15-year period. A Bayesian spatio-temporal model was applied to cross-section data obtained from the Nigeria Demographic and Health Survey conducted in 2003, 2008, 2013, and 2018, and inference was based on integrated nested Laplace approximation technique. The findings indicate a north-south spatio-temporal patterns that are similar among the rural and urban dwellers. States such as Kwara, Kogi, Oyo, Ondo, Osun, Ekiti, Enugu, and Ebonyi all of which are neighbors to each other are among those with persistent high prevalence of open defecation in the country. Given the diversity of the Nigerian population groups within the states, a more understanding of the socio-cultural standard of the different communities would be required to implement policies that recognize opportunities to explore, while being culturally responsive to community needs in ending open defecation in Nigeria.

Spatial Co-Morbidity of Childhood Acute Respiratory Infection, Diarrhoea and Stunting in Nigeria.

In low- and middle-income countries, children aged below 5 years frequently suffer from disease co-occurrence. This study assessed whether the co-occurrence of acute respiratory infection (ARI), diarrhoea and stunting observed at the child level could also be reflected ecologically. We considered disease data on 69,579 children (0-59 months) from the 2008, 2013, and 2018 Nigeria Demographic and Health Surveys using a hierarchical Bayesian spatial shared component model to separate the state-specific risk of each disease into an underlying disease-overall spatial pattern, common to the three diseases and a disease-specific spatial pattern. We found that ARI and stunting were more concentrated in the north-eastern and southern parts of the country, while diarrhoea was much higher in the northern parts. The disease-general spatial component was greater in the north-eastern and southern parts of the country. Identifying and reducing common risk factors to the three conditions could result in improved child health, particularly in the northeast and south of Nigeria.

A spatio-temporal analysis of cause-specific mortality in São Paulo State, Brazil.

Using five cause-specific mortality data sourced by the Brazilian Ministry of Health, and over 17 years period, we applied Bayesian spatio-temporal models on 644 municipalities of the state of São Paulo, using logistic model to the binary outcome that specifies whether or not the death was from a specific cause. We modeled the temporal mortality effects using B-splines, while the spatial components were considered through Gaussian and Markov random field, and inference was based on Markov chain Monte Carlo simulation. The results demonstrate consistent downward trend in mortality from infectious and parasitic diseases and external causes, while those from neoplasms and respiratory are rising. Cardiovascular is the only cause-specific death that is kept constant in time. All the causes of death considered show heterogeneous spatial and temporal variations among the municipalities, which sometimes change considerably within successive years. Mortality from infectious diseases clustered around the Northwestern municipalities in 2000, but changes to the Southeastern part in 2016, a similar development as external death causes. The study identifies areas with increased and decreased odds mortality and could be useful in disease monitoring, especially if we consider small spatial units.

A spatio-temporal analysis of cause-specific mortality in São Paulo State, Brazil / Uma análise espaço-temporal da mortalidade por causas específicas no estado de São Paulo, Brasil

Abstract Using five cause-specific mortality data sourced by the Brazilian Ministry of Health, and over 17 years period, we applied Bayesian spatio-temporal models on 644 municipalities of the state of São Paulo, using logistic model to the binary outcome that specifies whether or not the death was from a specific cause. We modeled the temporal mortality effects using B-splines, while the spatial components were considered through Gaussian and Markov random field, and inference was based on Markov chain Monte Carlo simulation. The results demonstrate consistent downward trend in mortality from infectious and parasitic diseases and external causes, while those from neoplasms and respiratory are rising. Cardiovascular is the only cause-specific death that is kept constant in time. All the causes of death considered show heterogeneous spatial and temporal variations among the municipalities, which sometimes change considerably within successive years. Mortality from infectious diseases clustered around the Northwestern municipalities in 2000, but changes to the Southeastern part in 2016, a similar development as external death causes. The study identifies areas with increased and decreased odds mortality and could be useful in disease monitoring, especially if we consider small spatial units.

Resumo Usando dados do Ministério da Saúde do Brasil para cinco causa de mortes, e num período de 17 anos, aplicamos modelos espaço-temporais Bayesianos em 644 municípios do estado de São Paulo, utilizando um modelo logístico binário que especifica se o óbito foi (ou não) de uma determinada causa. Modelamos os efeitos temporais da mortalidade com B-splines, e os componentes espaciais foram estimados através de campos aleatórios de Gaussiano e Markov. Simulamos a inferência estatística com Monte Carlo via cadeias de Markov. Os resultados demonstraram tendência consistente de queda nas mortes por doenças infecciosas e causas externas, enquanto mortes por neoplasias e doenças respiratórias aumentaram no tempo. Cardiovascular foi a única causa de morte constante no tempo. As causas de morte apresentaram variações espaciais e temporais entre os municípios, com consideráveis mudanças em anos sucessivos. A mortalidade por doenças infecciosas se concentrou nos municípios do noroeste do estado em 2000, mas mudou para a parte sudeste em 2016, um desenvolvimento semelhante as causas externas de morte. Este estudo identificou áreas com maior e menor chances de morte entre diferentes causas, e pode ser útil no monitoramento de doenças, especialmente se considerarmos pequenas unidades espaciais.

Spatial location, temperature and rainfall diversity affect the double burden of malnutrition among women in Kenya.

Studies have looked into how environmental and climate covariates affect under-and over-nutrition, but little is known about the spatial distribution of different forms of malnutrition in Kenya and whether there are locations that suffer from double-burden of malnutrition. This research quantifies spatial variations and estimates how climatic and environmental factors affect under-and over-nutrition among women in Kenya. This enables us to determine if the patterns in which these factors affect the malnutrition indicators are similar and whether there are overlaps in the spatial distributions. The study used data from the Demographic and Health Survey, which included cross-sectional data on malnutrition indicators as well as some climate and environmental variables. A multicategorical response variable that classified the women into one of four nutritional classes was generated from the body mass index (BMI) of the women, and a Bayesian geoadditive regression model with an estimate based on the Markov chain Monte Carlo simulation technique was adopted. Findings show that women in Turkana, Samburu, Isiolo, Baringo, Garissa, and West Pokot counties are more likely to be underweight than women in other counties while being overweight is prevalent in Kirinyag'a and Kitui counties. Obesity is prevalent in Kirinyag'a, Lamu, Kiambu, Murang'a, and Taita Taveta counties. The study further shows that as mean temperature and precipitation increase, the likelihood of being underweight reduces. The chances of being underweight are lower among literate women [OR: 0.614; 95% CrI: 0.513,0.739], married women [OR: 0.702; 95% CrI: 0.608,0.819] and those from rich households [OR: 0.617; 95% CrI: 0.489,0.772], which is not the case for overweight and obesity. The generated spatial maps identify hot spots of the double burden of malnutrition that can assist the government and donor agencies in channeling resources efficiently.

Spatial pattern and determinants of sufficient knowledge of mother to child transmission of HIV and its prevention among Nigerian women.

The lack of sufficient knowledge of mother to child transmission (MTCT) of human immunodeficiency virus (HIV) among pregnant women is considered a major contributor to new pediatric HIV infections globally, and increasing HIV related infant mortality especially in developing countries. Nigeria has the highest number of new HIV infections among children in the world. This study was designed to examine the spatial pattern and determinants of acquisition of sufficient knowledge of MTCT and prevention of mother to child transmission (PMTCT) in Nigeria. The data used in the study were extracted from the 2018 Nigeria Democratic Health Survey. The spatial modeling was through a Bayesian approach with appropriate prior distributions assigned to the different parameters of the model and inference was through the integrated nested Laplace approximation technique (INLA). Results show considerable spatial variability in the acquisition of sufficient knowledge of MTCT and its prevention with women in the southwestern and southeastern part of the country having higher likelihood. The nonlinear effects findings show that acquisition of sufficient knowledge of MTCT and PMTCT increased with age of women and peaked at around age 35yearswhere it thereafter dropped drastically among the older women. Furthermore, sufficient knowledge of MTCT and PMTCT was found to be driven by ethnicity, respondents' education and wealth status.

Double burden of underweight and overweight among Indian adults: spatial patterns and social determinants.

OBJECTIVE: The current study explores the spatial patterns of underweight and overweight among adult men and women in districts of India and identifies the micro-geographical locations where the risks of underweight and overweight are simultaneously prevalent, after accounting for demographic and socio-economic factors. DESIGN: We relied on BMI (weight (kg)/height squared (m2)), a measure of nutritional status among adult individuals, from the 2015-2016 National Family and Health Survey. Underweight was defined as <18·5 kg/m2 and overweight as ≥25·0 kg/m2. SETTING: We adopted Bayesian structured additive quantile regression to model the underlying spatial structure in underweight and overweight burden. PARTICIPANTS: Men aged 15-54 years (sample size: 108 092) and women aged 15-49 years (sample size: 642 002). RESULTS: About 19·7 % of men and 22·9 % of women were underweight, and 19·6 % of men and 20·6 % of women were overweight. Results indicate that malnutrition burden in adults exhibits geographical divides across the country. Districts located in the central, western and eastern regions show higher risks of underweight. There is evidence of substantial spatial clustering of districts with higher risk of overweight in southern and northern India. While finding a little evidence on double burden of malnutrition among population groups, we identified a total of sixty-six double burden districts. CONCLUSIONS: The current study demonstrates that the geographical burden of overweight in Indian adults is yet to surpass that of underweight, but the coexistence of double burden of underweight and overweight in selected regions presents a new challenge for improving nutritional status and necessitates specialised policy initiatives.

Bayesian spatial modelling of Ebola outbreaks in Democratic Republic of Congo through the INLA-SPDE approach.

Ebola virus (EBV) disease is a globally acknowledged public health emergency, endemic in the west and equatorial Africa. To understand the epidemiology especially the dynamic pattern of EBV disease, we analyse the EBV case notification data for confirmed cases and reported deaths of the ongoing outbreak in the Democratic Republic of Congo (DRC) between 2018 and 2019, and examined the impact of reported violence on the spread of the virus. Using fully Bayesian geo-statistical analysis through stochastic partial differential equations (SPDE) allows us to quantify the spatial patterns at every point of the spatial domain. Parameter estimation was based on the integrated nested Laplace approximation (INLA). Our findings revealed a positive association between violent events in the affected areas and the reported EBV cases (posterior mean = 0.024, 95% CI: 0.005, 0.045) and deaths (posterior mean = 0.022, 95% CI: 0.005, 0.041). Translating to an increase of 2.4% and 2.2% in the relative risks of EBV cases and deaths associated with a unit increase in violent events (one additional Ebola case is associated with an average of 45 violent events). We also observed clusters of EBV cases and deaths spread to neighbouring locations in similar manners. Findings from the study are therefore useful for hot spot identification, location-specific disease surveillance and intervention.

Geostatistical modeling of malaria prevalence among under-five children in Rwanda.

BACKGROUND: Malaria has continued to be a life-threatening disease among under-five children in sub-Saharan Africa. Recent data indicate rising cases in Rwanda after some years of decline. We aimed at estimating the spatial variations in malaria prevalence at a continuous spatial scale and to quantify locations where the prevalence exceeds the thresholds of 5% and 10% across the country. We also consider the effects of some socioeconomic and climate variables. METHODS: Using data from the 2014-2015 Rwanda Demographic and Health Survey, a geostatistical modeling technique based on stochastic partial differential equation approach was used to analyze the geospatial prevalence of malaria among under-five children in Rwanda. Bayesian inference was based on integrated nested Laplace approximation. RESULTS: The results demonstrate the uneven spatial variation of malaria prevalence with some districts including Kayonza and Kirehe from Eastern province; Huye and Nyanza from Southern province; and Nyamasheke and Rusizi from Western province having higher chances of recording prevalence exceeding 5%. Malaria prevalence was found to increase with rising temperature but decreases with increasing volume for rainfall. The findings also revealed a significant association between malaria and demographic factors including place of residence, mother's educational level, and child's age and sex. CONCLUSIONS: Potential intervention programs that focus on individuals living in rural areas, lowest wealth quintile, and the locations with high risks should be reinforced. Variations in climatic factors particularly temperature and rainfall should be taken into account when formulating malaria intervention programs in Rwanda.

Change in outbreak epicentre and its impact on the importation risks of COVID-19 progression: A modelling study.

BACKGROUND: The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first detected in the city of Wuhan, China has now spread to every inhabitable continent, but now the attention has shifted from China to other epicentres. This study explored early assessment of the influence of spatial proximities and travel patterns from Italy on the further spread of SARS-CoV-2 worldwide. METHODS: Using data on the number of confirmed cases of COVID-19 and air travel data between countries, we applied a stochastic meta-population model to estimate the global spread of COVID-19. Pearson's correlation, semi-variogram, and Moran's Index were used to examine the association and spatial autocorrelation between the number of COVID-19 cases and travel influx (and arrival time) from the source country. RESULTS: We found significant negative association between disease arrival time and number of cases imported from Italy (r = -0.43, p = 0.004) and significant positive association between the number of COVID-19 cases and daily travel influx from Italy (r = 0.39, p = 0.011). Using bivariate Moran's Index analysis, we found evidence of spatial interaction between COVID-19 cases and travel influx (Moran's I = 0.340). Asia-Pacific region is at higher/extreme risk of disease importation from the Chinese epicentre, whereas the rest of Europe, South-America and Africa are more at risk from the Italian epicentre. CONCLUSION: We showed that as the epicentre changes, the dynamics of SARS-CoV-2 spread change to reflect spatial proximities.

Spatial pattern of COVID-19 deaths and infections in small areas of Brazil.

As of mid-August 2020, Brazil was the country with the second-highest number of cases and deaths by the COVID-19 pandemic, but with large regional and social differences. In this study, using data from the Brazilian Ministry of Health, we analyze the spatial patterns of infection and mortality from Covid-19 across small areas of Brazil. We apply spatial autoregressive Bayesian models and estimate the risks of infection and mortality, taking into account age, sex composition of the population and other variables that describe the health situation of the spatial units. We also perform a decomposition analysis to study how age composition impacts the differences in mortality and infection rates across regions. Our results indicate that death and infections are spatially distributed, forming clusters and hotspots, especially in the Northern Amazon, Northeast coast and Southeast of the country. The high mortality risk in the Southeast part of the country, where the major cities are located, can be explained by the high proportion of the elderly in the population. In the less developed areas of the North and Northeast, there are high rates of infection among young adults, people of lower socioeconomic status, and people without access to health care, resulting in more deaths.

Structured additive distributional zero augmented beta regression modeling of mortality in Nigeria.

Child mortality has remained persistently high in most sub-Saharan African countries. Majority of the effort in analyzing the determinants, or covariables did not consider the duration of exposure to mortality risks. In addition, covariates are usually linked to the mean of the response variable, thereby neglecting the possible association with other higher moments. In this paper, we account for the duration of exposure via the child mortality index, defined as the ratio of observed to expected child death, for all women captured in the 2013 Nigeria Demographic and Health Survey. Based on this index, a structured additive distributional beta regression model was adopted to examine covariate effects on the probability of a woman experiencing no child mortality, the conditional expectation of mortality, and the mortality spread, controlling for latent spatial associations. Our inferential framework is Bayesian inference, powered by generic MCMC tools based on iterative weighted least squares. Results confirm the existence of significant variation in the likelihood of a woman experiencing no child mortality, and in the spread of mortality, across Nigerian states. Findings also show that although mortality is fairly spread among women aged ≥30 years, it is concentrated among the younger women.

The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa.

Corona virus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first detected in the city of Wuhan, China in December 2019. Although, the disease appeared in Africa later than other regions, it has now spread to virtually all countries on the continent. We provide early spatio-temporal dynamics of COVID-19 within the first 62 days of the disease's appearance on the African continent. We used a two-parameter hurdle Poisson model to simultaneously analyse the zero counts and the frequency of occurrence. We investigate the effects of important healthcare capacities including hospital beds and number of medical doctors in different countries. The results show that cases of the pandemic vary geographically across Africa with notably high incidence in neighbouring countries particularly in West and North Africa. The burden of the disease (per 100 000) mostly impacted Djibouti, Tunisia, Morocco and Algeria. Temporally, during the first 4 weeks, the burden was highest in Senegal, Egypt and Mauritania, but by mid-April it shifted to Somalia, Chad, Guinea, Tanzania, Gabon, Sudan and Zimbabwe. Currently, Namibia, Angola, South Sudan, Burundi and Uganda have the least burden. These findings could be useful in guiding epidemiological interventions and the allocation of scarce resources based on heterogeneity of the disease patterns.

Spatial prediction of childhood malnutrition across space in Nigeria based on point-referenced data: an SPDE approach.

Malnutrition remains a leading cause of child mortality in Nigeria. The spatial analysis based on areal level approaches could, in reality, conceal variations at smaller units. Using point-referenced data from Nigeria Demographic and Health Survey, we quantify the prevalence of malnutrition among under-five children in Nigeria at 1.63 by 1.63 km spatial resolution, and compute the exceedance probability maps for stunting, wasting and underweight at 20% threshold level using the stochastic partial differential equation approach with Bayesian inference based on integrated nested Laplace approximation. Results show divergence prevalence of the malnutrition indicators among children living in neighbouring locations and that the prevalence of stunting and underweight increase with age. The prevalence of stunting was uneven among children living in Kebbi, Zamfara, Sokoto, Kaduna, Kano, Katsina, Bauchi, Gombe and Taraba states with more concentrations in the northern fringes of some of the states. Except for few locations in about three states, the probability is more than 90% that the prevalence of stunting in all parts of the country exceeds 20% but this was not the case for wasting. The findings can assist in location-specific policy formulation and implementations.