Routine Paediatric Sickle Cell Disease (SCD) Outpatient Care in a Rural Kenyan Hospital: Utilization and Costs

Background

More than 70% of children with sickle cell disease (SCD) are born in sub-Saharan Africa where the prevalence at birth of this disease reaches 2% or higher in some selected areas. There is a dearth of knowledge on comprehensive care received by children with SCD in sub-Saharan Africa and its associated cost. Such knowledge is important for setting prevention and treatment priorities at national and international levels. This study focuses on routine care for children with SCD in an outpatient clinic of the Kilifi District Hospital, located in a rural area on the coast of Kenya.

Objective

To estimate the per-patient costs for routine SCD outpatient care at a rural Kenyan hospital.

Methods

We collected routine administrative and primary cost data from the SCD outpatient clinic and supporting departments at Kilifi District Hospital, Kenya. Costs were estimated by evaluating inputs - equipment, medication, supplies, building use, utility, and personnel - to reflect the cost of offering this service within an existing healthcare facility. Annual economic costs were similarly calculated based on input costs, prorated lifetime of equipment and appropriate discount rate. Sensitivity analyses evaluated these costs under different pay scales and different discount rate.

Results

We estimated that the annual economic cost per patient attending the SCD clinic was USD 138 in 2010 with a range of USD 94 to USD 229.

Conclusion

This study supplies the first published estimate of the cost of routine outpatient care for children born with SCD in sub-Saharan Africa. Our study provides policy makers with an indication of the potential future costs of maintaining specialist outpatient clinics for children living with SCD in similar contexts.     

Aberrant DNA Methylation: Implications in Racial Health Disparity

BackgroundIncidence and mortality rates of colorectal carcinoma (CRC) are higher in African Americans (AAs) than in Caucasian Americans (CAs). Deficient micronutrient intake due to dietary restrictions in racial/ethnic populations can alter genetic and molecular profiles leading to dysregulated methylation patterns and the inheritance of somatic to germline mutations.ResultsDNA from the tumor of AA CRC patients, compared to adjacent normal tissues, contained 1,588 hypermethylated and 100 hypomethylated differentially methylated regions (DMRs). Whereas, 109 hypermethylated and 4 hypomethylated DMRs were observed in DNA from the tumor of CA CRC patients; representing a 14.6-fold and 25-fold change, respectively. Specifically; CHL1, 4 anti-inflammatory genes (i.e., NELL1, GDF1, ARHGEF4, and ITGA4), and 7 miRNAs (of which miR-9-3p and miR-124-3p have been implicated in CRC) were hypermethylated in DNA samples from AA patients with CRC. From the same sample set, RNAseq analysis revealed 108 downregulated genes (including 14 ribosomal proteins) and 34 upregulated genes (including POLR2B and CYP1B1 [targets of miR-124-3p]) in AA patients with CRC versus CA patients.ConclusionDNA methylation profile and/or products of its downstream targets could serve as biomarker(s) addressing racial health disparity.     

A Minimal Intervention to Promote Smoke-Free Homes among 2-1-1 Callers: North Carolina Randomized Effectiveness Trial

This study examined the extent to which delivery of the minimal Smoke-Free Homes intervention by trained 2-1-1 information and referral specialists had an effect on the adoption of home smoking bans in low-income households. A randomized controlled trial was conducted among 2-1-1 callers (n = 500) assigned to control or intervention conditions. 2-1-1 information and referral specialists collected baseline data and delivered the intervention consisting of 3 mailings and 1 coaching call; university-based data collectors conducted follow-up interviews at 3 and 6 months post-baseline. Data were collected from June 2013 through July 2014. Participants were mostly female (87.2%), African American (61.4%), and smokers (76.6%). Participants assigned to the intervention condition were more likely than controls to report a full ban on smoking in the home at both 3- (38.1% vs 19.3%, p = < .001) and 6-month follow-up (43.2% vs 33.2%, p = .02). The longitudinal intent-to-treat analysis showed a significant intervention effect over time (OR = 1.31, p = .001), i.e. OR = 1.72 at 6 months. This study replicates prior findings showing the effectiveness of the minimal intervention to promote smoke-free homes in low-income households, and extends those findings by demonstrating they can be achieved when 2-1-1 information and referral specialists deliver the intervention. Findings offer support for this intervention as a generalizable and scalable model for reducing secondhand smoke exposure in homes.     

Generic reclassification and species boundaries in the rediscovered freshwater mussel ‘<Emphasis Type="Italic">Quadrula’ mitchelli</Emphasis> (Simpson in Dall, 1896)

The Central Texas endemic freshwater mussel, Quadrula mitchelli (Simpson in Dall, 1896), had been presumed extinct until relict populations were recently rediscovered. To help guide ongoing and future conservation efforts focused on Q. mitchelli we set out to resolve several uncertainties regarding its evolutionary history, specifically its unknown generic position and untested species boundaries. We designed a molecular matrix consisting of two loci (cytochrome c oxidase subunit I and internal transcribed spacer I) and 57 terminal taxa to test the generic position of Q. mitchelli using Bayesian inference and maximum likelihood phylogenetic reconstruction. We also employed two Bayesian species validation methods to test five a priori species models (i.e. hypotheses of species delimitation). Our study is the first to test the generic position of Q. mitchelli and we found robust support for its inclusion in the genus Fusconaia. Accordingly, we introduce the binomial, Fusconaia mitchelli comb. nov., to accurately represent the systematic position of the species. We resolved F. mitchelli individuals in two well supported and divergent clades that were generally distinguished as distinct species using Bayesian species validation methods, although alternative hypotheses of species delineation were also supported. Despite strong evidence of genetic isolation within F. mitchelli, we do not advocate for species-level status of the two clades as they are allopatrically distributed and no morphological, behavioral, or ecological characters are known to distinguish them. These results are discussed in the context of the systematics, distribution, and conservation of F. mitchelli.     

Into the deep: the functionality of mesopelagic excursions by an oceanic apex predator

Comprehension of ecological processes in marine animals requires information regarding dynamic vertical habitat use. While many pelagic predators primarily associate with epipelagic waters, some species routinely dive beyond the deep scattering layer. Actuation for exploiting these aphotic habitats remains largely unknown. Recent telemetry data from oceanic whitetip sharks (Carcharhinus longimanus) in the Atlantic show a strong association with warm waters (>20°C) less than 200 m. Yet, individuals regularly exhibit excursions into the meso‐ and bathypelagic zone. In order to examine deep‐diving behavior in oceanic whitetip sharks, we physically recovered 16 pop‐up satellite archival tags and analyzed the high‐resolution depth and temperature data. Diving behavior was evaluated in the context of plausible functional behavior hypotheses including interactive behaviors, energy conservation, thermoregulation, navigation, and foraging. Mesopelagic excursions (n = 610) occurred throughout the entire migratory circuit in all individuals, with no indication of site specificity. Six depth‐versus‐time descent and ascent profiles were identified. Descent profile shapes showed little association with examined environmental variables. Contrastingly, ascent profile shapes were related to environmental factors and appear to represent unique behavioral responses to abiotic conditions present at the dive apex. However, environmental conditions may not be the sole factors influencing ascents, as ascent mode may be linked to intentional behaviors. While dive functionality remains unconfirmed, our study suggests that mesopelagic excursions relate to active foraging behavior or navigation. Dive timing, prey constituents, and dive shape support foraging as the most viable hypothesis for mesopelagic excursions, indicating that the oceanic whitetip shark may regularly survey extreme environments (deep depths, low temperatures) as a foraging strategy. At the apex of these deep‐water excursions, sharks exhibit a variable behavioral response, perhaps, indicating the presence or absence of prey.     

Fusing Data Mining,Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

Background

Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.

Methods

The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.

Results

After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).

Conclusion

The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.