“I don’t know anything about their Culture”: The Disconnect between Allopathic and Traditional Maternity Care Providers in Rural Northern Ghana

The provision of maternal and neonatal health care in rural northern Ghana is pluralistic, consisting of traditional and allopathic providers. Although  women often use these providers interchangeably, important differences exist. This study explored the differences in approaches to maternal and neonatal care provision by these two different types of providers. This  research was part of the Stillbirth and Neonatal Death Study (SANDS),  conducted in northern Ghana in 2010. Trained field staff of the Navrongo Health Research Centre conducted in-depth interviews with 13 allopathic and 8 traditional providers. Interviews were audio-recorded, transcribed, and analyzed using in vivo coding and discussion amongst the research team. Three overarching themes resulted: 1) many allopathic providers were isolated from the culture of the communities in which they practiced, while traditional providers were much more aware of the local cultural  beliefs and practices. 2) Allopathic and traditional healthcare providers have different frameworks for understanding health and disease, with  allopathic providers relying heavily on their biomedical knowledge, and traditional providers drawing on their knowledge of natural remedies. 3) All providers agreed that education directed at pregnant women, providers (both allopathic and traditional), and the community at large is needed to improve maternal and neonatal outcomes. Our findings suggest that, among other things, programmatic efforts need to be placed on the cultural education of allopathic providers. (Afr J Reprod Health 2014; 18[2]: 36-45).Keywords: Allopathic medicine, traditional medicine, maternal health, delivery care, cultur

Cystic fibrosis in premature infants

There are few reports of cystic fibrosis (CF) diagnosed in premature infants. We describe the clinical course of three patients, from our neonatal intensive care units, who were diagnosed with CF, and discuss the existing literature and treatment considerations

Classifying perinatal mortality using verbal autopsy: is there a role for nonphysicians?

<p>Abstract</p> <p>Background</p> <p>Because of a physician shortage in many low-income countries, the use of nonphysicians to classify perinatal mortality (stillbirth and early neonatal death) using verbal autopsy could be useful.</p> <p>Objective</p> <p>To determine the extent to which underlying perinatal causes of deaths assigned by nonphysicians in Guatemala, Pakistan, Zambia, and the Democratic Republic of the Congo using a verbal autopsy method are concordant with underlying perinatal cause of death assigned by physician panels.</p> <p>Methods</p> <p>Using a train-the-trainer model, 13 physicians and 40 nonphysicians were trained to determine cause of death using a standardized verbal autopsy training program. Subsequently, panels of two physicians and individual nonphysicians from this trained cohort independently reviewed verbal autopsy data from a sample of 118 early neonatal deaths and 134 stillbirths. With the cause of death assigned by the physician panel as the reference standard, sensitivity, specificity, positive and negative predictive values, and cause-specific mortality fractions were calculated to assess nonphysicians' coding responses. Robustness criteria to assess how well nonphysicians performed were used.</p> <p>Results</p> <p>Causes of early neonatal death and stillbirth assigned by nonphysicians were concordant with physician-assigned causes 47% and 57% of the time, respectively. Tetanus filled robustness criteria for early neonatal death, and cord prolapse filled robustness criteria for stillbirth.</p> <p>Conclusions</p> <p>There are significant differences in underlying cause of death as determined by physicians and nonphysicians even when they receive similar training in cause of death determination. Currently, it does not appear that nonphysicians can be used reliably to assign underlying cause of perinatal death using verbal autopsy.</p

Global health partnerships: building multi-national collaborations to achieve lasting improvements in maternal and neonatal health

Abstract Background In response to health care challenges worldwide, extensive funding has been channeled to the world’s most vulnerable health systems. Funding alone is not sufficient to address the complex issues and challenges plaguing these health systems. To see lasting improvement in maternal and infant health outcomes in the developing world, a global commitment to the sharing of knowledge and resources through international partnerships is critical. But partnerships that merely introduce western medical techniques and protocols to low resource settings, without heeding the local contexts, are misguided and unsustainable. Forming partnerships with mutual respect, shared vision, and collaborative effort is needed to ensure that all parties, irrespective of whether they belong to resource rich or resource poor settings, learn from each other so that meaningful and sustained system strengthening can take place. Methods In this paper, we describe the partnership building model of an international NGO, Kybele, which is committed to achieving childbirth safety through sustained partnerships in low resource settings. The Kybele model adapts generic stages of successful partnerships documented in the literature to four principles relevant to Kybele’s work. A multiple-case study approach is used to demonstrate how the model is applied in different country settings. Results The four principle of Kybele’s partnership model are robust drivers of successful partnerships in diverse country settings. Conclusions Much has been written about the need for multi-country partnerships to achieve sustainable outcomes in global health, but few papers in the literature describe how this has been achieved in practice. A strong champion, support and engagement of stakeholders, co-creation of solutions with partners, and involvement of partners in the delivery of solutions are all requirements for successful and sustained partnerships

Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics

Drug target Mendelian randomization (MR) studies use DNA sequence variants in or near a gene encoding a drug target, that alter the target's expression or function, as a tool to anticipate the effect of drug action on the same target. Here we apply MR to prioritize drug targets for their causal relevance for coronary heart disease (CHD). The targets are further prioritized using independent replication, co-localization, protein expression profiles and data from the British National Formulary and clinicaltrials.gov. Out of the 341 drug targets identified through their association with blood lipids (HDL-C, LDL-C and triglycerides), we robustly prioritize 30 targets that might elicit beneficial effects in the prevention or treatment of CHD, including NPC1L1 and PCSK9, the targets of drugs used in CHD prevention. We discuss how this approach can be generalized to other targets, disease biomarkers and endpoints to help prioritize and validate targets during the drug development process

Replication and Characterization of Association between ABO SNPs and Red Blood Cell Traits by Meta-Analysis in Europeans.

Red blood cell (RBC) traits are routinely measured in clinical practice as important markers of health. Deviations from the physiological ranges are usually a sign of disease, although variation between healthy individuals also occurs, at least partly due to genetic factors. Recent large scale genetic studies identified loci associated with one or more of these traits; further characterization of known loci and identification of new loci is necessary to better understand their role in health and disease and to identify potential molecular mechanisms. We performed meta-analysis of Metabochip association results for six RBC traits-hemoglobin concentration (Hb), hematocrit (Hct), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV) and red blood cell count (RCC)-in 11 093 Europeans from seven studies of the UCL-LSHTM-Edinburgh-Bristol (UCLEB) Consortium. We identified 394 non-overlapping SNPs in five loci at genome-wide significance: 6p22.1-6p21.33 (with HFE among others), 6q23.2 (with HBS1L among others), 6q23.3 (contains no genes), 9q34.3 (only ABO gene) and 22q13.1 (with TMPRSS6 among others), replicating previous findings of association with RBC traits at these loci and extending them by imputation to 1000 Genomes. We further characterized associations between ABO SNPs and three traits: hemoglobin, hematocrit and red blood cell count, replicating them in an independent cohort. Conditional analyses indicated the independent association of each of these traits with ABO SNPs and a role for blood group O in mediating the association. The 15 most significant RBC-associated ABO SNPs were also associated with five cardiometabolic traits, with discordance in the direction of effect between groups of traits, suggesting that ABO may act through more than one mechanism to influence cardiometabolic risk.British Heart Foundation (Grant ID: RG/10/12/28456, RG/08/013/25942, RG/13/16/30528, RG/98002, RG/07/008/23674); Medical Research Council (Grant ID: G0000934, G0500877, MC_UU_12019/1, K013351); Wellcome Trust (Grant ID: 068545/Z/02, 097451/Z/11/Z); European Commission Framework Programme 6 (Grant ID: 018996); French Ministry of Research; Department of Health Policy Research Programme (England); Chief Scientist Office of Scotland (Grant ID: CZB/4/672, CZQ/1/38); National Institute on Ageing (NIA) (Grant ID: AG1764406S1, 5RO1AG13196); Pfizer plc (Unrestricted Investigator Led Grant); Diabetes UK (Clinical Research Fellowship 10/0003985); Stroke Association; National Heart Lung and Blood Institute (5RO1HL036310); Agency for Health Care Policy Research (HS06516); John D. and Catherine T. MacArthur Foundation Research Networks on Successful Midlife Development and Socio-economic Status and Health; Swiss National Science Foundation (33CSCO-122661); GlaxoSmithKline. Faculty of Biology and Medicine of Lausanne,Switzerland.This is the final version of the article. It first appeared from Public Library of Science (PLOS) via http://dx.doi.org/10.1371/journal.pone.015691

The druggable genome and support for target identification and validation in drug development

Target identification (determining the correct drug targets for a disease) and target validation (demonstrating an effect of target perturbation on disease biomarkers and disease end points) are important steps in drug development. Clinically relevant associations of variants in genes encoding drug targets model the effect of modifying the same targets pharmacologically. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome-wide association studies to an updated set of genes encoding druggable human proteins, to agents with bioactivity against these targets, and, where there were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, which will enable association studies of druggable genes for drug target selection and validation in human disease

NMR metabolomic modeling of age and lifespan: A multicohort analysis.

Metabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. Ninety-eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24-86 years). We used nonlinear and penalized regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all-cause mortality. Seven-fold cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability

A comparison of physicians and medical assistants in interpreting verbal autopsy interviews for allocating cause of neonatal death in Matlab, Bangladesh: can medical assistants be considered an alternative to physicians?

Objective. This study assessed the agreement between medical physicians in their interpretation of verbal autopsy (VA) interview data for identifying causes of neonatal deaths in rural Bangladesh. Methods. The study was carried out in Matlab, a rural sub-district in eastern Bangladesh. Trained persons conducted the VA interview with the mother or another family member at the home of the deceased. Three physicians and a medical assistant independently reviewed the VA interviews to assign causes of death using the International Classification of Diseases - Tenth Revision (ICD-10) codes. A physician assigned cause was decided when at least two physicians agreed on a cause of death. Cause-specific mortality fraction (CSMF), kappa (k) statistic, sensitivity, specificity, and positive predictive values were applied to compare agreement between the reviewers.Results. Of the 365 neonatal deaths reviewed, agreement on a direct cause of death was reached by at least two physicians in 339 (93%) of cases. Physician and medical assistant reviews of causes of death demonstrated the following levels of diagnostic agreement for the main causes of deaths: for birth asphyxia the sensitivity was 84%, specificity 93%, and kappa 0.77. For prematurity/low birth weight, the sensitivity, specificity, and kappa statistics were, respectively, 53%, 96%, and 0.55, for sepsis/meningitis they were 48%, 98%, and 0.53, and for pneumonia they were 75%, 94%, and 0.51. Conclusion. This study revealed a moderate to strong agreement between physician- assigned and medical assistant- assigned major causes of neonatal death. A well-trained medical assistant could be considered an alternative for assigning major causes of neonatal deaths in rural Bangladesh and in similar settings where physicians are scarce and their time costs more. A validation study with medically confirmed diagnosis will improve the performance of VA for assigning cause of neonatal death