Assessment of county-level poverty alleviation progress by deep learning and satellite observations

2021 ◽  
Author(s):  
Liqiang Zhang ◽  
Yanxiao Jiang ◽  
Yang Li ◽  
Alicia J Zhou ◽  
Jing Cao ◽  
...  
Keyword(s):  
Growth Rate ◽  
Deep Learning ◽  
Low Income ◽  
Poverty Alleviation ◽  
County Level ◽  
Average Growth Rate ◽  
Middle Income ◽  
Average Growth ◽  
Middle Income Countries ◽  
Per Capita

Abstract Poverty alleviation is one of the greatest challenges faced by low-income and middle-income countries. China, which had the largest rural poverty-stricken population, has made tremendous efforts in alleviating poverty especially since the implementation of the targeted poverty alleviation (TPA) policy in 2014. Yet it remains unknown about the successfulness of the policy, because the official statistics are not timely available and in some cases questionable. This study combines deep learning with multiple satellite datasets to estimate county-level economic development from 2008 to 2019 and assess the effect of the TPA policy for 592 national poverty-stricken counties (NPCs) at country, provincial and county levels. Per capita gross domestic product (GDP) is used to measure the affluence level. From 2014 through 2019, the 592 NPCs experience an average growth rate of per capita GDP at 7.6%±0.4%, higher than the average growth rate of 310 adjacent non-NPC counties (7.3%±0.4%) and of the whole country (6.3%). This indicates an overall success of TPA policy so far. We also reveal 42 counties with weak growth recently and that the average affluence level of the NPCs in 2019 is still much lower than the national or provincial averages. The inexpensive, timely and accurate method proposed here can be applied to other low-income and middle-income countries for affluence assessment.

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 Cost of scaling up mental healthcare in low-and middle-income countries

2007 ◽  
Vol 191 (6) ◽  
pp. 528-535 ◽  
Author(s):  
Dan Chisholm ◽  
Crick Lund ◽  
Shekhar Saxena
Keyword(s):  
Low Income ◽  
Scale Up ◽  
Mental Healthcare ◽  
Scaling Up ◽  
Psychosocial Treatment ◽  
Economic Evaluations ◽  
Middle Income ◽  
Middle Income Countries ◽  
Per Capita ◽  
Low And Middle Income

BackgroundNo systematic attempt has been made to calculate the costs of scaling up mental health services in low-and middle-income countries.AimsTo estimate the expenditures needed to scale up the delivery of an essential mental healthcare package over a 10-year period (2006–2015).MethodA core package was defined, comprising pharmacological and/or psychosocial treatment of schizophrenia, bipolar disorder, depression and hazardous alcohol use. Current service levels in 12 selected low-and middle-income countries were established using the WHO–AIMS assessment tool. Target-level resource needs were derived from published need assessments and economic evaluations.ResultsThe cost per capita of providing the core package attarget coverage levels (in US dollars) ranged from $1.85 to $2.60 per year in low-income countries and $3.20 to $6.25 per year in lower-middle-income countries, an additional annual investment of $0.18–0.55 per capita.ConclusionsAlthough significant new resources need to be invested, the absolute amount is not large when considered at the population level and against other health investment strategies.

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