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Point-of-Care Diagnostics for Improving Maternal Health in South Africa

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      Abstract

      Improving maternal health is a global priority, particularly in high HIV-endemic, resource-limited settings. Failure to use health care facilities due to poor access is one of the main causes of maternal deaths in South Africa. “Point-of-care” (POC) diagnostics are an innovative healthcare approach to improve healthcare access and health outcomes in remote and resource-limited settings. In this review, POC testing is defined as a diagnostic test that is carried out near patients and leads to rapid clinical decisions. We review the current and emerging POC diagnostics for maternal health, with a specific focus on the World Health Organization (WHO) quality-ASSURED (Affordability, Sensitivity, Specificity, User friendly, Rapid and robust, Equipment free and Delivered) criteria for an ideal point-of-care test in resource-limited settings. The performance of POC diagnostics, barriers and challenges related to implementing POC diagnostics for maternal health in rural and resource-limited settings are reviewed. Innovative strategies for overcoming these barriers are recommended to achieve substantial progress on improving maternal health outcomes in these settings.

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      Metabolic syndrome in childhood: association with birth weight, maternal obesity, and gestational diabetes mellitus.

      Childhood obesity has contributed to an increased incidence of type 2 diabetes mellitus and metabolic syndrome (MS) among children. Intrauterine exposure to diabetes and size at birth are risk factors for type 2 diabetes mellitus, but their association with MS in childhood has not been demonstrated. We examined the development of MS among large-for-gestational-age (LGA) and appropriate-for-gestational age (AGA) children. The major components of MS (obesity, hypertension, dyslipidemia, and glucose intolerance) were evaluated in a longitudinal cohort study of children at age 6, 7, 9, and 11 years who were LGA (n = 84) or AGA (n = 95) offspring of mothers with or without gestational diabetes mellitus (GDM). The cohort consisted of 4 groups, ie, LGA offspring of control mothers, LGA offspring of mothers with GDM, AGA offspring of control mothers, and AGA offspring of mothers with GDM. Biometric and anthropometric measurements were obtained at 6, 7, 9, and 11 years. Biochemical testing included measurements of postprandial glucose and insulin levels and high-density lipoprotein (HDL) cholesterol levels at 6 and 7 years and of fasting glucose, insulin, triglyceride, and HDL cholesterol levels at 9 and 11 years. We defined the components of MS as (1) obesity (BMI >85th percentile for age), (2) diastolic or systolic blood pressure >95th percentile for age, (3) postprandial glucose level >140 mg/dL or fasting glucose level >110 mg/dL, (4) triglyceride level >95th percentile for age, and (5) HDL level 85th percentile) at 11 years was present in 25% to 35% of the children, but rates were not different between LGA and AGA offspring. There was a trend toward a higher incidence of insulin resistance, defined as a fasting glucose/insulin ratio of or =2 components of MS was 50% for the LGA/GDM group, which was significantly higher than values for the LGA/control group (29%), AGA/GDM group (21%), and AGA/control group (18%). The prevalence of > or =3 components of MS at age 11 was 15% for the LGA/GDM group, compared with 3.0% to 5.3% for the other groups. Cox regression analysis was performed to determine the independent hazard (risk) of developing MS attributable to birth weight, gender, maternal prepregnancy obesity, and GDM. For Cox analyses, we defined MS as > or =2 of the following 4 components: obesity, hypertension (systolic or diastolic), glucose intolerance, and dyslipidemia (elevated triglyceride levels or low HDL levels). LGA status and maternal obesity increased the risk of MS approximately twofold, with hazard ratios of 2.19 (95% CI: 1.25-3.82) and 1.81 (95% CI: 1.03-3.19), respectively. GDM and gender were not independently significant. To determine the cumulative hazard of developing MS with time, we plotted the risk according to LGA or AGA category for the control and GDM groups from 6 years to 11 years, with Cox regression analyses. The risk of developing MS with time was not significantly different between LGA and AGA offspring in the control group but was significantly different between LGA and AGA offspring in the GDM group, with a 3.6-fold greater risk among LGA children by 11 years. We showed that LGA offspring of diabetic mothers were at significant risk of developing MS in childhood. The prevalence of MS in the other groups was similar to the prevalence (4.8%) among white adolescents in the 1988-1994 National Health and Nutrition Examination Survey. This effect of LGA with maternal GDM on childhood MS was previously demonstrated for Pima Indian children but not the general population. We also found that children exposed to maternal obesity were at increased risk of developing MS, which suggests that obese mothers who do not fulfill the clinical criteria for GDM may still have metabolic factors that affect fetal growth and postnatal outcomes. Children who are LGA at birth and exposed to an intrauterine environment of either diabetes or maternal obesity are at increased risk of developing MS. Given the increased obesity prevalence, these findings have implications for perpetuating the cycle of obesity, insulin resistance, and their consequences in subsequent generations.
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        Policy: Sustainable development goals for people and planet.

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          Point-of-Care Testing for Infectious Diseases: Diversity, Complexity, and Barriers in Low- And Middle-Income Countries

          Madhukar Pai and colleagues discuss a framework for envisioning how point-of-care testing can be applied to infectious diseases in low- and middle-income countries.
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            Author and article information

            Affiliations
            [1 ]Discipline of Public Health, School of Nursing and Public Health, University of KwaZulu-Natal, Durban 4001, South Africa; Sartorius@ 123456ukzn.ac.za
            [2 ]International Clinical Research Center, Department of Global Health, University of Washington, Seattle, WA 98195, USA; pkdrain@ 123456uw.edu
            [3 ]Division of Infectious Diseases, Department of Medicine, University of Washington, Seattle, WA 98195, USA
            [4 ]Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
            [5 ]Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA
            Author notes
            [* ]Correspondence: Mashamba-Thompson@ 123456ukzn.ac.za ; Tel.: +27-031-260-2530
            Contributors
            Role: Academic Editor
            Journal
            Diagnostics (Basel)
            Diagnostics (Basel)
            diagnostics
            Diagnostics
            MDPI
            2075-4418
            31 August 2016
            September 2016
            : 6
            : 3
            27589808 5039565 10.3390/diagnostics6030031 diagnostics-06-00031
            © 2016 by the authors; licensee MDPI, Basel, Switzerland.

            This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license ( http://creativecommons.org/licenses/by/4.0/).

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