The geo-spatial distribution of childhood diarrheal disease in West Africa, 2008-2013: A covariate-adjusted cluster analysis

High frequency of antimicrobial resistance in Salmonella and Escherichia coli causing diarrheal diseases at the Yirimadio community health facility, Mali

BACKGROUND

Diarrhoea is a public health problem, especially in developing countries where it is the second leading cause of child mortality. In Low Income Countries like in Mali, self-medication and inappropriate use of antibiotics due to the scarcity of complementary diagnostic systems can lead to the development of multidrug-resistant bacteria causing diarrhoea. The objective of this work was to determine the microorganisms responsible for diarrhoea in children under 15 years of age and to characterize their sensitivity to a panel of antibiotics used in a peri-urban community in Mali. The study involved outpatient children visiting the Yirimadio Community Health Centre and diagnosed with diarrhoea. Stool samples from those patients were collected and analysed by conventional stools culture and the susceptibility to antibiotics of detected bacteria was determined by the disc diffusion method in an agar medium.

RESULT

Overall, 554 patients were included. Children under the age of 3 years accounted for 88.8% (492 of 554) of our study population. Two bacterial species were isolated in this study, Escherichia coli 31.8% (176 of 554) and Salmonella 2.9% (16 of 554). In the 176, E. coli strains resistance to amoxicillin and to cotrimoxazole was seen in 93.8% (165 of 176) and 92.6% ( 163 of 176), respectively. The ESBL resistance phenotype accounted for 39,8% (70 of 176) of E. coli. Sixteen (16) strains of Salmonella were found, of which one strain (6.3%) was resistant to amoxicillin and to amoxicillin + clavulanic acid. Another one was resistant to chloramphenicol (6.3%). Two strains of Salmonella were resistant to cotrimoxazole (12.5%) and two others were resistant to cefoxitin (12.5%).

CONCLUSIONS

The data suggest that E. coli is frequently involved in diarrhoea in children under 3 years of age in this peri-urban setting of Bamako, Mali, with a high rate of resistance to amoxicillin and cotrimoxazole, the most widely used antibiotics in the management of diarrhoea in this setting.

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.

Associations between long-term drought and diarrhea among children under five in low- and middle-income countries

Climate change is projected to intensify drought conditions, which may increase the risk of diarrheal diseases in children. We constructed log-binomial generalized linear mixed models to examine the association between diarrhea risk, ascertained from global-scale nationally representative Demographic and Health Surveys, and drought, represented by the standardized precipitation evapotranspiration index, among children under five in 51 low- and middle-income countries (LMICs). Exposure to 6-month mild or severe drought was associated with an increased diarrhea risk of 5% (95% confidence interval 3-7%) or 8% (5-11%), respectively. The association was stronger among children living in a household that needed longer time to collect water or had no access to water or soap/detergent for handwashing. The association for 24-month drought was strong in dry zones but weak or null in tropical or temperate zones, whereas that for 6-month drought was only observed in tropical or temperate zones. In this work we quantify the associations between exposure to long-term drought and elevated diarrhea risk among children under five in LMICs and suggest that the risk could be reduced through improved water, sanitation, and hygiene practices, made more urgent by the likely increase in drought due to climate change.

Associations Between Eight Earth Observation-Derived Climate Variables and Enteropathogen Infection: An Independent Participant Data Meta-Analysis of Surveillance Studies With Broad Spectrum Nucleic Acid Diagnostics

Diarrheal disease, still a major cause of childhood illness, is caused by numerous, diverse infectious microorganisms, which are differentially sensitive to environmental conditions. Enteropathogen-specific impacts of climate remain underexplored. Results from 15 studies that diagnosed enteropathogens in 64,788 stool samples from 20,760 children in 19 countries were combined. Infection status for 10 common enteropathogens-adenovirus, astrovirus, norovirus, rotavirus, sapovirus, Campylobacter, ETEC, Shigella, Cryptosporidium and Giardia-was matched by date with hydrometeorological variables from a global Earth observation dataset-precipitation and runoff volume, humidity, soil moisture, solar radiation, air pressure, temperature, and wind speed. Models were fitted for each pathogen, accounting for lags, nonlinearity, confounders, and threshold effects. Different variables showed complex, non-linear associations with infection risk varying in magnitude and direction depending on pathogen species. Rotavirus infection decreased markedly following increasing 7-day average temperatures-a relative risk of 0.76 (95% confidence interval: 0.69-0.85) above 28°C-while ETEC risk increased by almost half, 1.43 (1.36-1.50), in the 20-35°C range. Risk for all pathogens was highest following soil moistures in the upper range. Humidity was associated with increases in bacterial infections and decreases in most viral infections. Several virus species' risk increased following lower-than-average rainfall, while rotavirus and ETEC increased with heavier runoff. Temperature, soil moisture, and humidity are particularly influential parameters across all enteropathogens, likely impacting pathogen survival outside the host. Precipitation and runoff have divergent associations with different enteric viruses. These effects may engender shifts in the relative burden of diarrhea-causing agents as the global climate changes.

Environmental determinants of E. coli, link with the diarrheal diseases, and indication of vulnerability criteria in tropical West Africa (Kapore, Burkina Faso)

In 2017, diarrheal diseases were responsible for 606 024 deaths in Sub-Saharan Africa. This situation is due to domestic and recreational use of polluted surface waters, deficits in hygiene, access to healthcare and drinking water, and to weak environmental and health monitoring infrastructures. Escherichia coli (E. coli) is an indicator for the enteric pathogens that cause many diarrheal diseases. The links between E. coli, diarrheal diseases and environmental parameters have not received much attention in West Africa, and few studies have assessed health risks by taking into account hazards and socio-health vulnerabilities. This case study, carried out in Burkina Faso (Bagre Reservoir), aims at filling this knowledge gap by analyzing the environmental variables that play a role in the dynamics of E. coli, cases of diarrhea, and by identifying initial vulnerability criteria. A particular focus is given to satellite-derived parameters to assess whether remote sensing can provide a useful tool to assess the health hazard. Samples of surface water were routinely collected to measure E. coli, enterococci and suspended particulate matter (SPM) at a monitoring point (Kapore) during one year. In addition, satellite data were used to estimate precipitation, water level, Normalized Difference Vegetation Index (NDVI) and SPM. Monthly epidemiological data for cases of diarrhea from three health centers were also collected and compared with microbiological and environmental data. Finally, semi-structured interviews were carried out to document the use of water resources, contact with elements of the hydrographic network, health behavior and condition, and water and health policy and prevention, in order to identify the initial vulnerability criteria. A positive correlation between E. coli and enterococci in surface waters was found indicating that E. coli is an acceptable indicator of fecal contamination in this region. E. coli and diarrheal diseases were strongly correlated with monsoonal precipitation, in situ SPM, and Near Infra-Red (NIR) band between March and November. Partial least squares regression showed that E. coli concentration was strongly associated with precipitation, Sentinel-2 reflectance in the NIR and SPM, and that the cases of diarrhea were strongly associated with precipitation, NIR, E. coli, SPM, and to a lesser extent with NDVI. Moreover, E. coli dynamics were reproduced using satellite data alone, particularly from February to mid-December (R2 = 0.60) as were cases of diarrhea throughout the year (R2 = 0.76). This implies that satellite data could provide an important contribution to water quality monitoring. Finally, the vulnerability of the population was found to increase during the rainy season due to reduced accessibility to healthcare and drinking water sources and increased use of water of poor quality. During this period, surface water is used because it is close to habitations, easy to use and free from monetary or political constraints. This vulnerability is aggravated by marginality and particularly affects the Fulani, whose concessions are often close to surface water (river, lake) and far from health centers.

Understanding the Impact of Rainfall on Diarrhea: Testing the Concentration-Dilution Hypothesis Using a Systematic Review and Meta-Analysis

BACKGROUND

Projected increases in extreme weather may change relationships between rain-related climate exposures and diarrheal disease. Whether rainfall increases or decreases diarrhea rates is unclear based on prior literature. The concentration-dilution hypothesis suggests that these conflicting results are explained by the background level of rain: Rainfall following dry periods can flush pathogens into surface water, increasing diarrhea incidence, whereas rainfall following wet periods can dilute pathogen concentrations in surface water, thereby decreasing diarrhea incidence.

OBJECTIVES

In this analysis, we explored the extent to which the concentration-dilution hypothesis is supported by published literature.

METHODS

To this end, we conducted a systematic search for articles assessing the relationship between rain, extreme rain, flood, drought, and season (rainy vs. dry) and diarrheal illness.

RESULTS

A total of 111 articles met our inclusion criteria. Overall, the literature largely supports the concentration-dilution hypothesis. In particular, extreme rain was associated with increased diarrhea when it followed a dry period [incidence rate ratio ( IRR ) = 1.26 ; 95% confidence interval (CI): 1.05, 1.51], with a tendency toward an inverse association for extreme rain following wet periods, albeit nonsignificant, with one of four relevant studies showing a significant inverse association (IRR = 0.911 ; 95% CI: 0.771, 1.08). Incidences of bacterial and parasitic diarrhea were more common during rainy seasons, providing pathogen-specific support for a concentration mechanism, but rotavirus diarrhea showed the opposite association. Information on timing of cases within the rainy season (e.g., early vs. late) was lacking, limiting further analysis. We did not find a linear association between nonextreme rain exposures and diarrheal disease, but several studies found a nonlinear association with low and high rain both being associated with diarrhea.

DISCUSSION

Our meta-analysis suggests that the effect of rainfall depends on the antecedent conditions. Future studies should use standard, clearly defined exposure variables to strengthen understanding of the relationship between rainfall and diarrheal illness. https://doi.org/10.1289/EHP6181.

Correlates of childhood morbidity in Nigeria: Evidence from ordinal analysis of cross-sectional data

BACKGROUND

Child mortality records show that 1 in every 13 children dies before age five in sub-Saharan Africa with diseases such as pneumonia, diarrhoea and malaria considered to be the leading causes of such deaths. In Nigeria where 50% of all under-five deaths are attributed to morbidity, much attention has been directed to single health conditions. This study aims at examining the factors that are associated with single health conditions and comorbidity among children in Nigeria.

MATERIALS AND METHODS

This study was based on data from 2013 Nigeria Demographic and Health Survey (DHS) which involved 27,571 under-five children who suffered from acute respiratory infection, diarrhoea or fever within two weeks of data collection exercise. Descriptive statistics and generalized ordinal logistic regression model were used for the analysis.

RESULTS

About 14% of children suffered from a single health condition and 9% suffered from comorbidity. The likelihood of suffering from a single health condition and comorbidity is higher for children who are of third order birth or more (OR = 1.24, 95% CI = 1.11-1.39 & OR = 1.31, 95% CI = 1.12-1.55) compared to those who are of first order birth. The likelihood also increased for children whose mothers live in Northeast (OR = 3.19, 95% CI = 2.86-3.55 & OR = 3.88, 95% CI = 3.30-4.57) compared to children whose mothers live in North Central. The odds of suffering from a single health condition and comorbidity reduced for children who are from richest households, aged 3 years and above and were of average size at birth. Children of women who obtained water from improved source are less likely to experience any morbidity (OR = 0.93, 95% CI = 0.87-0.99) compared to children whose mothers obtained water from non-improved source.

CONCLUSIONS

The study has demonstrated that children in Nigeria are not only exposed to the risk of single health conditions but they are also exposed to the risk of comorbidity. Efforts should be made to design appropriate health care models that would facilitate a considerable reduction in childhood morbidity in the country.

A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa

Spatial analysis has become an increasingly used analytic approach to describe and analyze spatial characteristics of disease burden, but the depth and coverage of its usage for health surveys data in Sub-Saharan Africa are not well known. The objective of this scoping review was to conduct an evaluation of studies using spatial statistics approaches for national health survey data in the SSA region. An organized literature search for studies related to spatial statistics and national health surveys was conducted through PMC, PubMed/Medline, Scopus, NLM Catalog, and Science Direct electronic databases. Of the 4,193 unique articles identified, 153 were included in the final review. Spatial smoothing and prediction methods were predominant (n = 108), followed by spatial description aggregation (n = 25), and spatial autocorrelation and clustering (n = 19). Bayesian statistics methods and lattice data modelling were predominant (n = 108). Most studies focused on malaria and fever (n = 47) followed by health services coverage (n = 38). Only fifteen studies employed nonstandard spatial analyses (e.g., spatial model assessment, joint spatial modelling, accounting for survey design). We recommend that for future spatial analysis using health survey data in the SSA region, there must be an improve recognition and awareness of the potential dangers of a naïve application of spatial statistical methods. We also recommend a wide range of applications using big health data and the future of data science for health systems to monitor and evaluate impacts that are not well understood at local levels.