WHO lunchtime seminar Mapping child growth failure in Africa between 2000 and Professor Simon I. Hay March 12, 2018

Transcription

1 WHO lunchtime seminar Mapping child growth failure in Africa between 2000 and 2015 Professor Simon I. Hay March 12, 2018

2 Outline Local Burden of Disease (LBD) at IHME Child growth failure From global to local o Data o Model o Results Implications and impact Limitations Future directions

3 Outline Local Burden of Disease (LBD) at IHME Child growth failure From global to local o Data o Model o Results Implications and impact Limitations Future directions

4 Prevalence of stunting (%) Precision public health: a new approach National (or even subnational) averages can hide important local health variation The use of data to guide interventions that benefit populations more efficiently and increase equity in outcomes National Admin 1 Admin 2 5x5 km

5 Prevalence of stunting (%) Local Burden of Disease project goals Assemble the world s largest geopositioned dataset on key diseases and risk factors Create high-resolution (5x5 km) maps of prevalence, incidence, or mortality Create compelling and useful interactive data visualization tools to illuminate levels, trends, and disparities over time Disseminate results and encourage uptake by donors, policymakers, and researchers to inform evidence-based decision-making

6 Location Burden of Disease team 50+ team members o Data Analysts o Data Extraction Analysts o Data Mapping Specialist o Data Services Specialist o o o o o Director Engagement Officer Faculty Fellows PhD Student o o o o Policy Translation Specialist Project Officers Research Coordinator Research Managers o o o Researchers Senior Research Manager Software Engineers

7 What we are mapping Malaria (P.f. and P.v.) Diarrhea Lower respiratory infections (LRI) Tuberculosis HIV/AIDS Under 5 mortality Educational attainment Child growth and nutrition o o o o Stunting, wasting, underweight Low birth weight Child overweight Exclusive breastfeeding NTD: lymphatic filariasis, onchocerciasis, and schistosomiasis Water and sanitation Vaccine coverage (DTP3, measles, etc.). Ebola and other hemorrhagic fevers Pandemic potential of five emerging zoonotic infectious diseases AMR: 17 bacteria-antibacterial drug combinations

8 Outline Local Burden of Disease (LBD) at IHME Child growth failure From global to local o Data o Model o Results Implications and impact Limitations Future directions

9 Child growth failure Stunting Height-for-age z-score <-2 SD Wasting Weight-for-height z-score <-2 SD Underweight Weight-for-age z-score <-2 SD Specific subset of child undernutrition, excluding micronutrient deficiencies Relationship between insufficient height and weight at a given age Described in terms of univariate growth standards by WHO, where agespecific height and weight are compared to healthy reference populations

10 Future directions Policy relevant analysis WHO Global Targets 2025 to improve young child nutrition Sustainable Development Goal 2.2 to end all forms of malnutrition by 2030, including achievement of the Global Targets 2025

11 Outline Local Burden of Disease (LBD) at IHME Child growth and nutrition From global to local o Data o Model o Results Implications and impact Limitations Future directions

12 Geospatial data Point o GPS coordinates (latitude/longitude) o Infinitesimal representation Polygon o Aerial representation (mean over a region) o Typically data matched to shape files Raster o o Data discretized over continuous space, represented by pixel values in a bitmap Covariates and outputs

13 Data coverage

14 Sparse data Some areas have robust data coverage, and we can make confident predictions Others have more sparse coverage, so our predictions are less certain

15 How do we generate predictions in areas with sparse data? 256 geo-located datasets Household surveys A suite of geospatial covariates Satellite imagery and modeled surfaces of relevant environmental and human activity

16 DATA COVARIATES ENSEMBLE OF MACHINE LEARNING MODELS Maximize the predictive power of the covariates

17 MODEL-BASED GEOSTATISTICS Borrow strength from observations nearby in space and time, accounting for leftover variation

18 CALIBRATION TO GBD Leverage validated Global Burden of Disease (GBD) estimates which utilize additional data sources RESULTS Pixel-level estimates with uncertainty intervals, extremely flexible with many use cases

19 Results 2015, under 5 stunting prevalence National Admin 1 Admin 2 5x5 km

20 Results 2000, under 5 stunting prevalence

21 Results 2005, under 5 stunting prevalence

22 Results 2010, under 5 stunting prevalence

23 Results 2015, under 5 stunting prevalence

24 Results , Overlapping populationweighted lowest and highest 10% of pixels and annualised rates of change (AROC) in stunting prevalence

25 Results Annualized decrease in stunting prevalence from relative to rates needed during to meet the WHO GNT Regressing On track Exceeding

26 Results 2025, Predicted stunting prevalence based on annualised decrease achieved between 2000 and 2015

27 Results , Acceleration in the annualized decrease in stunting required to meet the WHO GNT by 2025 Met goal by 2015 On track 2x rate of progress needed 4x rate of progress needed

28 Results 2015, Probability that the Global Nutrition Target for stunting has been achieved at the first administrate subdivision and 5x5 km pixel level

29 Results 2015, under 5 wasting prevalence National Admin 1 Admin 2 5x5 km

30 Results 2000, under 5 wasting prevalence

31 Results 2005, under 5 wasting prevalence

32 Results 2010, under 5 wasting prevalence

33 Results 2015, under 5 wasting prevalence

34 Results 2015, under 5 underweight prevalence National Admin 1 Admin 2 5x5 km

35 Results 2000, under 5 underweight prevalence

36 Results 2005, under 5 underweight prevalence

37 Results 2010, under 5 underweight prevalence

38 Results 2015, under 5 underweight prevalence

39 Outline Local Burden of Disease (LBD) at IHME Child growth and nutrition From global to local o Data o Model o Results Implications and impact Limitations Future directions

40 Implications and impact: publishing

41 Implications and impact: precision public health

42 Implications and impact: in the media

43 Implications and impact: how can decision-makers use the research?

44 Interactive data visualization tool Explore further at

45 Outline Local Burden of Disease (LBD) at IHME Child growth and nutrition From global to local o Data o Model o Results Implications and impact Limitations Future directions

46 Limitations Data coverage and quality o Of 256 data sources, only 127 contain GPS coordinates o Areas of greatest uncertainty correspond to those in need of more/recent information Prediction, not inference o Optimize for prediction, cannot perform correlation inference i.e. relationships between covariates and outcomes Uncertainty propagation o Uncertainty in covariates and population estimates not incorporated

47 Outline Local Burden of Disease (LBD) at IHME Child growth and nutrition From global to local o Data o Model o Results Implications and impact Limitations Future directions

48 Future directions: expanding geographic scope Stage >99% CGF attributable DALYS

49 Future directions: additional Global Targets 2025

50 Future directions Further exploring geographic inequalities Recent or forthcoming publications o Educational attainment (Africa) o Diarrhea (Africa) o Water and sanitation (Africa) o Lower respiratory infections (LRI) (Africa) o Under 5 mortality (Global)

51 Thank you!

52 Additional slides on methods 5

53 Model-based geostatistics Model Assume data arises from underlying random process following a known distribution Bayesian hierarchical model Use a generalized linear model framework, which allows us to incorporate covariates (X i ) in our model logit(p i ) = α + X i β + Z i Z GP 0, C 5

54 Model-based geostatistics Covariates Geospatial team is home to a continually growing spatial covariate repository Both external (e.g. satellite data) and internal (i.e. model outputs) covariates available, in a standardized format 5

55 Value of interest Model-based geostatistics 1-D example X 1 X 2 X 3 1D index of space 5

56 Value of interest Model-based geostatistics 1-D example α + X i β + Z i X 1 X 2 X 3 1D index of space 5

57 Value of interest Model-based geostatistics 1-D example α + X i β + Z i X 1 X 2 X 3 1D index of space 5

58 Residual Model-based geostatistics 1-D example α + X i β + Z i X 1 X 2 X 3 1D index of space 5

59 Residual Model-based geostatistics 1-D example α + X i β + Z i X 1 X 2 X 3 1D index of space 5

60 Value of interest Model-based geostatistics 1-D example α + X i β + Z i X 1 X 2 X 3 1D index of space 6