SummaryWe performed a prospective matched cohort study to investigate the effects of long-term (>1 month) heparin therapy on lumbar spine bone density. Twenty-five women who received heparin during pregnancy, and 25 matched controls underwent dual photon absorptiometry of the lumbar spine in the post-partum period. Zero of 25 heparin-treated patients developed fractures. Heparin-treated patients had a 0.082 g/cm2 lower bone density compared to untreated controls, which is clinically and statistically significant (p = 0.0077). There were 6 matched pairs in which only the heparin-treated patient had a bone density below 1.0 g/ cm2, compared to only one pair in which only the control patient had a bone density below this level (p = 0.089). The correlation coefficients of the difference .in bone density in each matched pair, and the duration of heparin therapy, the mean daily dose, and the total dose of heparin were 0.042, -0.015, and 0.021, respectively; none of these values is statistically significant. We conclude: 1) long-term heparin therapy was associated with a significant reduction in bone density, although fractures are uncommon, 2) there was no significant correlation between lumbar bone density and the dose or duration of heparin.

Despite concerns about the coexistence of overnutrition, undernutrition and micronutrient deficiencies, which is compositely referred to as the triple burden of malnutrition (TBM), little is known about the phenomenon in sub-Saharan Africa (SSA). We, therefore, aimed to examine the prevalence and investigate the factors associated with TBM in SSA. This study uses cross-sectional survey data collected through the Demographic and Health Surveys (DHS) Program from 2010 to 2019. Data from 32 countries in SSA were used for the analysis. The prevalence of TBM were presented in tables and maps using percentages. The predictors of TBM were examined by fitting a negative log-log regression to the data. The results were then presented using adjusted odds ratios (aORs) at 95% Confidence Intervals (CIs). Out of the 169,394 children, 734 (1%) suffered from TBM. The highest proportion of children with TBM in the four geographic regions in SSA was found in western Africa (0.75%) and the lowest in central Africa (0.21%). Children aged 1 [aOR = 1.283; 95% CI = 1.215–1.355] and those aged 2 [aOR = 1.133; 95% CI = 1.067–1.204] were more likely to experience TBM compared to those aged 0. TBM was less likely to occur among female children compared to males [aOR = 0.859; 95% CI = 0.824–0.896]. Children whose perceived size at birth was average [aOR = 1.133; 95% CI = 1.076–1.193] and smaller than average [aOR = 1.278; 95% CI = 1.204–1.356] were more likely to suffer from TBM compared to those who were larger than average at birth. Children born to mothers with primary [aOR = 0.922; 95% CI = 0.865–0.984] and secondary [aOR = 0.829; 95% CI = 0.777–0.885] education were less likely to suffer from TBM compared to those born to mothers with no formal education. Children born to mothers who attended antenatal care (ANC) had lower odds of experiencing TBM compared to those born to mothers who did not attend ANC [aOR = 0.969; 95% CI = 0.887–0.998]. Children born to mothers who use clean household cooking fuel were less likely to experience TBM compared to children born to mothers who use unclean household cooking fuel [aOR = 0.724; 95% CI = 0.612–0.857]. Essentially, higher maternal education, ANC attendance and use of clean cooking fuel were protective factors against TBM, whereas higher child age, low size at birth and being a male child increased the risk of TBM. Given the regional variations in the prevalence and risk of TBM, region-specific interventions must be initiated to ensure the likelihood of those interventions being successful at reducing the risk of TBM. Countries in Western Africa in particular would have to strengthen their current policies and programmes on malnutrition to enhance their attainment of the SDGs.

ObjectiveThis study investigated the risk factors associated with the coexistence of stunting, underweight, and wasting among children under age 5 in sub-Saharan Africa (SSA).DesignData of 127, 487 under-5 children from 31 countries in SSA were pooled from the Demographic and Health Surveys collected between 2010 and 2019. We examined the risk of coexistence of stunting, underweight, and wasting using multinomial logistic regression models. The results were presented using relative risk ratios (RRR) with corresponding confidence intervals (CIs).SettingThirty-one sub-Saharan African countries.ParticipantsChildren under age 5.Outcome measuresThe outcome variables were three child anthropometrics: stunting (height-for-age z-scores); underweight (weight-for-age z-scores) and wasting (weight-for-height z-scores).ResultsThe prevalence of coexistence of stunting, underweight, and wasting varied across countries, with the highest (12.14%) and lowest (0.58%) prevalences of coexistence of stunting, underweight and wasting in Benin and Gambia respectively. The risk of coexistence of the three indicators of undernutrition was higher among children aged 1 year (RRR=3.714; 95% CI 3.319 to 4.156) compared with those aged 0. The risk of coexistence of the three dimensions was lower among female children (RRR=0.468 95% CI 0.420 to 0.51), but higher for those with small size at birth (RRR=3.818; CI 3.383 to 4.308), those whose mothers had no education (RRR=3.291; 95% CI 1.961 to 5.522), not working (RRR=1.195; 95% CI 1.086 to 1.314), had no antenatal visits during pregnancy (RRR=1.364; 95% CI 1.20 to 1.541), children delivered at home (RRR=1.372; CI 1.232 to 1.529), those from poor households (RRR=1.408; 95% CI 1.235 to 1.605), those whose mothers had no access to media (RRR=1.255; 95% CI 1.144 to 1.377) and living in households with an unimproved toilet facility (RRR=1.158; 95% CI 1.032 to 1.300).ConclusionsFindings suggest the urgent need for consideration of the coexistence of stunting, wasting and underweight among under-5 children in policy design and programming of interventions to eradicate child malnutrition in SSA. In the short-term, national-level policies and interventions need to be well tailored considering the compositional characteristics.

Introduction The coronavirus 2019 (COVID-19) has overwhelmed the health systems of several countries, particularly those within the African region. Notwithstanding, the relationship between health systems and the magnitude of COVID-19 in African countries have not received research attention. In this study, we investigated the relationship between the pervasiveness of the pandemic across African countries and their Global Health Security Index (GHSI) scores. Materials and methods The study included 54 countries in five regions viz Western (16); Eastern (18); Middle (8); Northern (7); and Southern (5) Africa. The outcome variables in this study were the total confirmed COVID-19 cases (per million); total recoveries (per million); and the total deaths (per million). The data were subjected to Spearman’s rank-order (Spearman’s rho) correlation to determine the monotonic relationship between each of the predictor variables and the outcome variables. The predictor variables that showed a monotonic relationship with the outcome were used to predict respective outcome variables using multiple regressions. The statistical analysis was conducted at a significance level of 0.05. Results Our results indicate that total number of COVID-19 cases (per million) has strong correlations (rs >0.5) with the median age; aged 65 older; aged 70 older; GDP per capita; number of hospital beds per thousand; Human Development Index (HDI); recoveries (per million); and the overall risk environment of a country. All these factors including the country’s commitments to improving national capacity were related to the total number of deaths (per million). Also, strong correlations existed between the total recoveries (per million) and the total number of positive cases; total deaths (per million); median age; aged 70 older; GDP per capita; the number of hospital beds (per thousand); and HDI. The fitted regression models showed strong predictive powers (R-squared>99%) of the variances in the total number of COVID-19 cases (per million); total number of deaths (per million); and the total recoveries (per million). Conclusions The findings from this study suggest that patient-level characteristics such as ageing population (i.e., 65+), poverty, underlying co-morbidities–cardiovascular disease (e.g., hypertension), and diabetes through unhealthy behaviours like smoking as well as hospital care (i.e., beds per thousand) can help explain COVID-19 confirmed cases and mortality rates in Africa. Aside from these, other determinants (e.g., population density, the ability of detection, prevention and control) also affect COVID-19 prevalence, deaths and recoveries within African countries and sub-regions.