scholarly journals Geographical distribution of fertility rates in 70 low-income, lower-middle-income, and upper-middle-income countries, 2010–16: a subnational analysis of cross-sectional surveys

2021 ◽  
Vol 9 (6) ◽  
pp. e802-e812
Author(s):  
Carla Pezzulo ◽  
Kristine Nilsen ◽  
Alessandra Carioli ◽  
Natalia Tejedor-Garavito ◽  
Sophie E Hanspal ◽  
...  
Keyword(s):  
Geographical Distribution ◽  
Low Income ◽  
Fertility Rates ◽  
Cross Sectional ◽  
Middle Income ◽  
Middle Income Countries ◽  
Subnational Analysis

scholarly journals Efficacy of deep learning methods for predicting under-five mortality in 34 low-income and middle-income countries

BMJ Open ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. e034524
Author(s):  
Adeyinka Emmanuel Adegbosin ◽  
Bela Stantic ◽  
Jing Sun
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Low Income ◽  
Maternal Education ◽  
Household Wealth ◽  
Secondary Outcome ◽  
Cross Sectional ◽  
Middle Income ◽  
Under Five ◽  
Middle Income Countries

ObjectivesTo explore the efficacy of machine learning (ML) techniques in predicting under-five mortality (U5M) in low-income and middle-income countries (LMICs) and to identify significant predictors of U5M.DesignThis is a cross-sectional, proof-of-concept study.Settings and participantsWe analysed data from the Demographic and Health Survey. The data were drawn from 34 LMICs, comprising a total of n=1 520 018 children drawn from 956 995 unique households.Primary and secondary outcome measuresThe primary outcome measure was U5M; secondary outcome was comparing the efficacy of deep learning algorithms: deep neural network (DNN); convolution neural network (CNN); hybrid CNN-DNN with logistic regression (LR) for the prediction of child’s survival.ResultsWe found that duration of breast feeding, number of antenatal visits, household wealth index, postnatal care and the level of maternal education are some of the most important predictors of U5M. We found that deep learning techniques are superior to LR for the classification of child survival: LR sensitivity=0.47, specificity=0.53; DNN sensitivity=0.69, specificity=0.83; CNN sensitivity=0.68, specificity=0.83; CNN-DNN sensitivity=0.71, specificity=0.83.ConclusionOur findings provide an understanding of determinants of U5M in LMICs. It also demonstrates that deep learning models are more efficacious than traditional analytical approach.

scholarly journals Reporting of inequalities in blindness in low income and middle income countries: a review of cross sectional surveys

2017 ◽  
Vol 46 (1) ◽  
pp. 99-100 ◽  
Author(s):  
Jacqueline Ramke ◽  
Anna Palagyi ◽  
Jennifer Petkovic ◽  
Clare E Gilbert
Keyword(s):  
Low Income ◽  
Cross Sectional ◽  
Middle Income ◽  
Middle Income Countries

scholarly journals State of deworming coverage and equity in low-income and middle-income countries using household health surveys: a spatiotemporal cross-sectional study

2019 ◽  
Vol 7 (11) ◽  
pp. e1511-e1520 ◽  
Author(s):  
Nathan C Lo ◽  
Sam Heft-Neal ◽  
Jean T Coulibaly ◽  
Leslie Leonard ◽  
Eran Bendavid ◽  
...  
Keyword(s):  
Low Income ◽  
Health Surveys ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Middle Income ◽  
Middle Income Countries

scholarly journals Clubfoot treatment in 2015: a global perspective

2018 ◽  
Vol 3 (4) ◽  
pp. e000852 ◽  
Author(s):  
Rosalind M Owen ◽  
Beth Capper ◽  
Christopher Lavy
Keyword(s):  
Low Income ◽  
Income Group ◽  
Cross Sectional Study ◽  
Global Perspective ◽  
Access To Treatment ◽  
Cross Sectional ◽  
Permanent Disability ◽  
Middle Income ◽  
Middle Income Countries ◽  
Treatment Services

IntroductionClubfoot affects around 174 000 children born annually, with approximately 90% of these in low-income and middle-income countries (LMIC). Untreated clubfoot causes life-long impairment, affecting individuals’ ability to walk and participate in society. The minimally invasive Ponseti treatment is highly effective and has grown in acceptance globally. The objective of this cross-sectional study is to quantify the numbers of countries providing services for clubfoot and children accessing these.MethodIn 2015–2016, expected cases of clubfoot were calculated for all countries, using an incidence rate of 1.24/1000 births. Informants were sought from all LMIC, and participants completed a standardised survey about services for clubfoot in their countries in 2015. Data collected were analysed using simple numerical analysis, country coverage levels, trends over time and by income group. Qualitative data were analysed thematically.ResultsResponses were received from 55 countries, in which 79% of all expected cases of clubfoot were born. More than 24 000 children with clubfoot were enrolled for Ponseti treatment in 2015. Coverage was less than 25% in the majority of countries. There were higher levels of response and coverage within the lowest income country group. 31 countries reported a national programme for clubfoot, with the majority provided through public–private partnerships.ConclusionThis is the first study to describe global provision of, and access to, treatment services for children with clubfoot. The numbers of children accessing Ponseti treatment for clubfoot in LMIC has risen steadily since 2005. However, coverage remains low, and we estimate that less than 15% of children born with clubfoot in LMIC start treatment. More action to promote the rollout of national clubfoot programmes, build capacity for treatment and enable access and adherence to treatment in order to radically increase coverage and effectiveness is essential and urgent in order to prevent permanent disability caused by clubfoot.

scholarly journals Maternal exposure to intimate partner violence and breastfeeding practices in 51 low-income and middle-income countries: A population-based cross-sectional study

PLoS Medicine ◽  
2019 ◽  
Vol 16 (10) ◽  
pp. e1002921 ◽  
Author(s):  
Rishi Caleyachetty ◽  
Olalekan A. Uthman ◽  
Hana Nekatebeb Bekele ◽  
Rocio Martín-Cañavate ◽  
Debbie Marais ◽  
...  
Keyword(s):  
Low Income ◽  
Partner Violence ◽  
Population Based ◽  
Cross Sectional Study ◽  
Intimate Partner ◽  
Sectional Study ◽  
Cross Sectional ◽  
Middle Income ◽  
Middle Income Countries ◽  
Breastfeeding Practices

Influenza vaccination policies for health workers in low-income and middle-income countries: A cross-sectional survey, January-March 2020

Vaccine ◽  
2020 ◽  
Vol 38 (47) ◽  
pp. 7433-7439
Author(s):  
Helena C. Maltezou ◽  
Kalliopi Theodoridou ◽  
Maria Tseroni ◽  
Vasilios Raftopoulos ◽  
Amanda Bolster ◽  
...  
Keyword(s):  
Influenza Vaccination ◽  
Low Income ◽  
Health Workers ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Middle Income ◽  
Middle Income Countries

scholarly journals Association of Undernutrition and Neonatal, Infant and Under-5 Mortality: Evidence from 62 Low-Income and Middle-Income Countries

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 911-911
Author(s):  
Paddy Ssentongo ◽  
Joseph Lewcun ◽  
Anna Ssentongo ◽  
Djibril Ba ◽  
Claudio Fronterre ◽  
...  
Keyword(s):  
Infant Mortality ◽  
Neonatal Mortality ◽  
Child Mortality ◽  
Low Income ◽  
Household Survey ◽  
Cross Sectional ◽  
Middle Income ◽  
Middle Income Countries ◽  
Country Level ◽  
Nationally Representative

Abstract Objectives During the Millennium Development Goals (MDG) era, many low- and middle-income countries (LMICs) failed to achieve the MDG 4 of reducing neonatal, infant, and under-5 mortality. In this study, we aimed to assess whether reductions in early childhood undernutrition is associated with a reduction in neonatal, infant and under-5 mortality rate in LMICs. Methods We analyzed demographic and health household survey data from 62 LMICs collected between 2006 and 2018. The sample consisted of nationally representative cross-sectional surveys of children aged 0–59 months (n = 600,390). We examined country-level prevalence of stunting, wasting and underweight (based on z scores < −2 per the WHO Growth Standard) each as predictors of neonatal, infant and under-5 mortality incidence using multivariate Poisson regression models adjusted for country-level mean duration of breastfeeding and gross domestic product per capita. We also examined the association between breastfeeding and mortality. Results Overall, 28.4% (95% CI: 26.3%, 30.7%) of young children were stunted, 5.4% (95% CI: 4.5%, 6.6%) were wasted, 12.3% (95% CI: 10.4%, 14.6%) were underweight. Per 1000 live births, neonatal mortality was 23.6 (95% CI: 19.3–27.1), infant mortality was 43.4 (95% CI: 30.2–50.1) and under-5 mortality was 61.6 (95% CI: 55.3- 68.3). At the country level, a 10-fold decrease in stunting was associated with a relative risk (RR) of 0.81 (95% CI 0.66–0.98; P < 0.001) for neonatal mortality, 0.66 (95% CI 0.55–0.80; P < 0.001) for infant mortality, and 0.63 (95% CI 0.52–0.76; P < 0.001) for under-5 mortality. No association was seen between wasting or underweight and child mortality. Breastfeeding was associated with lower rates of child mortality. A one standard deviation (16 months) increase in breastfeeding was associated with a RR of 0.86 (95% CI 0.76–0.97; P = 0.015) for neonatal mortality, 0.79 (95% CI 0.70–0.89; P < 0.001) for infant mortality, and 0.75 (95% CI 0.67–0.85; P < 0.001) for under-5 mortality. Conclusions In a very large, multi-country sample of nationally-representative surveys in LMICs, stunting was strongly associated with child mortality from birth to 5 years. Stunting should be a focus in the effort to achieve the Sustainable Development Goal 3.2 target to reduce neonatal and under-5 mortality in all countries by 2030. Funding Sources National Institute of Health.

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