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.

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.

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.

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

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.

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.

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.

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.

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.

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.

Analysis of COVID-19 Cases' Spatial Dependence in US Counties Reveals Health Inequalities

On March 13, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) caused by the novel coronavirus SARS-CoV2 a pandemic. Since then the virus has infected over 9.1 million individuals and resulted in over 470,000 deaths worldwide (as of June 24, 2020). Here, we discuss the spatial correlation between county population health rankings and the incidence of COVID-19 cases and COVID-19 related deaths in the United States. We analyzed the spread of the disease based on multiple variables at the county level, using publicly available data on the numbers of confirmed cases and deaths, intensive care unit beds and socio-demographic, and healthcare resources in the U.S. Our results indicate substantial geographical variations in the distribution of COVID-19 cases and deaths across the US counties. There was significant positive global spatial correlation between the percentage of Black Americans and cases of COVID-19 (Moran I = 0.174 and 0.264, p < 0.0001). A similar result was found for the global spatial correlation between the percentage of Black American and deaths due to COVID-19 at the county level in the U.S. (Moran I = 0.264, p < 0.0001). There was no significant spatial correlation between the Hispanic population and COVID-19 cases and deaths; however, a higher percentage of non-Hispanic white was significantly negatively spatially correlated with cases (Moran I = -0.203, p < 0.0001) and deaths (Moran I = -0.137, p < 0.0001) from the disease. This study showed significant but weak spatial autocorrelation between the number of intensive care unit beds and COVID-19 cases (Moran I = 0.08, p < 0.0001) and deaths (Moran I = 0.15, p < 0.0001), respectively. These findings provide more detail into the interplay between the infectious disease and healthcare-related characteristics of the population. Only by understanding these relationships will it be possible to mitigate the rate of spread and severity of the disease.

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.

Is Nigeria really on top of COVID-19? Message from effective reproduction number

Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population.In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) - this also enables us to estimate the initial daily transmission rate (β0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020.In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37-2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%.Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

Estimating lifetime malnourished period and its statistics based on the concept of Markov chain with reward

Malnutrition among women, accessed through body mass index, has great consequences for achieving key national targets. This study introduces the concept of lifetime malnourished period (LMP): the number of years a woman would remain malnourished, either as underweight or overweight given that she is currently malnourished, and its measures of variation. Markov chain with rewards was used to compute the moments of LMP based on age-specific mortality rates and proportion of women of reproductive age that were either underweight or overweight using data from the 2013 Nigeria Demographic and Health Survey. Each of the two malnutrition status was treated as a Bernoulli-distributed reward with probability taken as the proportion of overweight or underweight women at specific age. Findings indicate that the average LMP for an underweight woman in Nigeria at age 15 years is 2.3 years but 5.8 for overweight. The remaining LMP for underweight is lower among women who attain higher level of education than for those with no or primary level of education with standard deviation reducing with age. Further, we found overweight women from the richest households and those from urban areas to have longer years of remaining in that state of health than their other counterparts, and that longevity contributes more to the variance in LMP for overweight than for underweight women.

Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria

On 31 December 2019, the World Health Organization (WHO) was notified of a novel coronavirus disease in China that was later named COVID-19. On 11 March 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on 27 February 2020. This study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria. We estimated the early transmissibility via time-varying reproduction number based on the Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected individual and the infector), and adjusted for disease importation. By 11 April 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% confidence interval (CI): 0.05-0.10) with a doubling time of 9.84 days (95% CI: 7.28-15.18). Separately for imported cases (travel-related) and local cases, the doubling time was 12.88 days and 2.86 days, respectively. Furthermore, we estimated the reproduction number for each day of the outbreak using a three-weekly window while adjusting for imported cases. The estimated reproduction number was 4.98 (95% CrI: 2.65-8.41) at day 22 (19 March 2020), peaking at 5.61 (95% credible interval (CrI): 3.83-7.88) at day 25 (22 March 2020). The median reproduction number over the study period was 2.71 and the latest value on 11 April 2020, was 1.42 (95% CrI: 1.26-1.58). These 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.

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.

Investigating the Spatial Patterns of Common Childhood Morbidity in Six Neighboring West African Countries

Children in developing countries have continued to suffer morbidity and mortality arising from a few illnesses. This study was designed to examine the within and between spatial variations in childhood morbidity from cough, fever, and diarrhea among six West African countries in a manner that transcends geographical boundaries. Data from six countries including their geographical boundaries were obtained from Demographic and Health Surveys. The spatial modelling was through Bayesian models and appropriate prior distributions were assigned to the different parameters of the model. Parameter estimation was through integrated nested Laplace approximation. Results show similar significant spatial distributions for the three illnesses, and they demonstrate that children in Benin Republic and Mali are less likely to suffer from these illnesses, whereas higher likelihood were obtained in the case of Cote d'Ivoire, Burkina Faso, Togo, and some parts of Ghana. The nonlinear effects of child's age show that the risks of contracting the illnesses peak among children aged 10-14 months while, as the mothers advance in age, their children have reduced risks. Breastfeeding and a woman's working status and education are among the significant factors that either aggravate or prevent these illnesses in the West African countries. The results pinpointed regions of the West African countries with high and low risks of the illnesses, and this would enhance intervention strategies of policy makers and international donors in the subregion.

Structured additive distributional hurdle Poisson modelling of individual fertility levels in Nigeria

Fertility is one of the dynamic components of population and has been modelled through children ever born per woman, which is a count variable that can be characterized with excessive zeros origination from women without any births. In order to examine the spatial variation across states of Nigeria, we proposed the use of hurdle models that classifies the data into a truncated count and point mass of zeros. We adopt distributional regression model that allows all parameters of the hurdle model to be linked to covariates of different types so as to allow for accessing the spatial variations and nonlinear forms of metrical covariates on the level of fertility and in the likelihood of having no child. Data was sourced from the 2013 Nigeria Demographic and Health Survey. Findings reveal the existence of north-south divide in the average level of fertility and in the likelihood of a woman not giving birth to any child. Women with higher level of education and those from richer or richest households have higher likelihood of having no child, but this is not the case for women with primary or secondary education, users of traditional or modern contraceptive, ever-married women and those working. There is therefore the need to strengthen family planning policies so that investment in contraceptive would yield the expected results in Nigeria.

Regional variations in the use of postnatal care in Nigeria: a spatial analysis

Maternal health outcomes vary considerably in Nigeria, with maternal mortality ratio ranging from 165 per 100,000 live births in the South-west to 1549 per 100,000 live births in the North-east. One important maternal health indicator is an adequate use of postnatal care (PNC); however, the evidence is sparse on its spatial distribution across regions in Nigeria. This paper thus examined the spatial distribution of uptake of postnatal care in Nigeria using data from the 2013 Nigeria Demographic and Health Survey, with a sample of 12,127 women aged 15-49 years. The Bayesian-structured additive regression of the logit model was used to examine the spatial relationships. The results revealed a north-south divide in the use of postnatal care, with higher PNC uptake established in the latter. Interestingly, results showed significant intra-region residual spatial variations with higher PNC use in Yobe and Bauchi in North-east Nigeria compared to other states within the region. The findings indicate the need for policymakers to develop state- and region-specific health policy and intervention programs to address the inequity in postnatal care coverage and usage across regions in Nigeria.

Modeling the spatial variability in the spread and correlation of childhood malnutrition in Nigeria

The average nutritional status of children in Nigeria is, just as in most developing countries, still in an alarmingly bad condition. Prior studies have shown that this status relies on a series of different influences and can be measured by three anthropometric variables for stunting, wasting, and underweight. Different regression modeling techniques have been adopted over the years to explain the determinants and spatial clustering. Those indicators, however, show patterns that are not necessarily full filling requirements for ordinary regression models for the mean and are correlated among each other, a fact that has until now been ignored by most studies. Methods to model outcomes in the light of both, the whole distribution of and the correlation between two or more outcomes based on a set of covariates, have lately been developed. The aim of this paper is to make use of those methods to explain the underlying spatial structure in malnutrition in Nigeria. The study brings to limelight the pattern of spread as well as the interwoven relationships among childhood malnutrition indicators that would have otherwise remained unknown in Nigeria.

Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula

BACKGROUND

Middle East respiratory syndrome coronavirus is a contagious respiratory pathogen that is contracted via close contact with an infected subject. Transmission of the pathogen has occurred through animal-to-human contact at first followed by human-to-human contact within families and health care facilities.

DATA AND METHODS

This study is based on a retrospective analysis of the Middle East respiratory syndrome coronavirus outbreak in the Kingdom of Saudi Arabia between June 2012 and July 2015. A Geoadditive variable model for binary outcomes was applied to account for both individual level risk factors as well spatial variation via a fully Bayesian approach.

RESULTS

Out of 959 confirmed cases, 642 (67%) were males and 317 (33%) had died. Three hundred and sixty four (38%) cases occurred in Ar Riyad province, while 325 (34%) cases occurred in Makkah. Individuals with some comorbidity had a significantly higher likelihood of dying from MERS-CoV compared with those who did not suffer comorbidity [Odds ratio (OR) = 2.071; 95% confidence interval (CI): 1.307, 3.263]. Health-care workers were significantly less likely to die from the disease compared with non-health workers [OR = 0.372, 95% CI: 0.151, 0.827]. Patients who had fatal clinical experience and those with clinical and subclinical experiences were equally less likely to die from the disease compared with patients who did not have fatal clinical experience and those without clinical and subclinical experiences respectively. The odds of dying from the disease was found to increase as age increased beyond 25 years and was much higher for individuals with any underlying comorbidities.

CONCLUSION

Interventions to minimize mortality from the Middle East respiratory syndrome coronavirus should particularly focus individuals with comorbidity, non-health-care workers, patients with no clinical fatal experience, and patients without any clinical and subclinical experiences.

Joint modeling of Anaemia and Malaria in children under five in Nigeria

Malaria and anaemia which jointly account for high proportion of morbidity and mortality among young children in developing countries have been individually studied using binary regression model. We adopt geoadditive latent variable model for binary/ordinal indicators to analyze the influence of variables of different types on the morbidity among young children in Nigeria. Latent variable models allow for the analysis of multidimensional response variables that reveal the indicator's underlying relationship that are caused by the latent variables. We extend the structural model to a semi-parametric geoadditive model in order to quantify the joint spatial structure of morbidity from malaria and anaemia. Findings revealed substantial geographical variations and the generated maps can guide policy makers and donors on how to prudently utilize the scarce resources for designing more cost-effective interventions.

Exclusive breastfeeding practice in Nigeria: a bayesian stepwise regression analysis

Despite the importance of breast milk, the prevalence of exclusive breastfeeding (EBF) in Nigeria is far lower than what has been recommended for developing countries. Worse still, the practise has been on downward trend in the country recently. This study was aimed at investigating the determinants and geographical variations of EBF in Nigeria. Any intervention programme would require a good knowledge of factors that enhance the practise. A pooled data set from Nigeria Demographic and Health Survey conducted in 1999, 2003, and 2008 were analyzed using a Bayesian stepwise approach that involves simultaneous selection of variables and smoothing parameters. Further, the approach allows for geographical variations at a highly disaggregated level of states to be investigated. Within a Bayesian context, appropriate priors are assigned on all the parameters and functions. Findings reveal that education of women and their partners, place of delivery, mother's age at birth, and current age of child are associated with increasing prevalence of EBF. However, visits for antenatal care during pregnancy are not associated with EBF in Nigeria. Further, results reveal considerable geographical variations in the practise of EBF. The likelihood of exclusively breastfeeding children are significantly higher in Kwara, Kogi, Osun, and Oyo states but lower in Jigawa, Katsina, and Yobe. Intensive interventions that can lead to improved practise are required in all states in Nigeria. The importance of breastfeeding needs to be emphasized to women during antenatal visits as this can encourage and enhance the practise after delivery.

Spatial analysis of choice of place of delivery in Nigeria

OBJECTIVE

Access to quality healthcare during childbirth is a crucial factor for taming maternal and child mortality and morbidity. Increasing this access in developing countries depends on understanding the factors influencing maternal healthcare decision at a geographical location. This study analyzes spatial pattern in choice of place of delivery in Nigeria.

METHOD

Data analyzed came from Nigerian Demographic and Health Survey data set. The choice of place delivery was considered a multi-categorical response and a multinomial logistic regression model used to evaluate spatial variations in choosing a particular place to deliver against home delivery.

RESULTS

Results show a north-south divide in choosing health facilities against homes for delivery. The likelihood of institutional delivery was significantly lower for women residing in Bayelsa and the majority of the states in northern Nigeria. As women advance in age, they have more likelihood of having institutional deliveries. Other contributing factors that favor institutional deliveries include use of antenatal care services, urban dwelling, mass media and parity.

CONCLUSION

Usage of mass media to campaign for institutional deliveries particularly in northern Nigeria, among younger women and those of higher parity; encouraging the use of antenatal services and even distribution of health facilities making them easily accessible to rural women are important for enhancing chances of institutional deliveries. Also, state-specific policies in this regard are indispensable.

Possible determinants and spatial patterns of anaemia among young children in Nigeria: a Bayesian semi-parametric modelling

BACKGROUND

Anaemia is a global public health problem affecting both developing and developed countries with major consequences for human health and socioeconomic development. This paper examines the possible relationship between Hb concentration and severity of anaemia with individual and household characteristics of children aged 6-59 months in Nigeria; and explores possible geographical variations of these outcome variables.

METHODS

Data on Hb concentration and severity of anaemia in children aged 6-59 months that participated in the 2010 Nigeria Malaria Indicator Survey were analysed. A semi-parametric model using a hierarchical Bayesian approach was adopted to examine the putative relationship of covariates of different types and possible spatial variation. Gaussian, binary and ordinal outcome variables were considered in modelling.

RESULTS

Spatial analyses reveal a distinct North-South divide in Hb concentration of the children analysed and that states in Northern Nigeria possess a higher risk of anaemia. Other important risk factors include the household wealth index, sex of the child, whether or not the child had fever or malaria in the 2 weeks preceding the survey, and children under 24 months of age.

CONCLUSIONS

There is a need for state level implementation of specific programmes that target vulnerable children as this can help in reversing the existing patterns.

Investigating polycyclic aromatic hydrocarbons profiles in higher plants using statistical models

Thirty-six higher plants sampled from Olomoro, Irri, Uzere, and Oginni exploration sites in the Niger Delta region of Nigeria were subjected to GC/MS analysis to assess the occurrence, distribution and profiles of polycyclic aromatic hydrocarbons (PAHs) contained in them. The sigma28PAHs ranged from 335 to 3094 ng/g. The results of the nonparametric regression models showed that PAHs concentration in a plant cannot be used in isolation to deduce the total PAHs concentration in soils hosting the plant since PAHs concentration in a plant is influenced by the presence (or absence) of other plants in that location. A combination of Factor analysis (FA) and principal component analysis (PCA) were used to recognize PAHs concentration patterns among the plants in the studied locations and individual PAHs compounds. Woody annuals and perennial plants formed similar patterns in Oginni and Irri locations. Three main clusters were formed by all the compounds with naphthalene and 2-methylnaphthalene standing as outliers in all the four locations.