Child Malnutrition and Climate Change in Sub-Saharan Africa:

An analysis of recent trends in Kenya

Kathryn GraceFrank Davenport

Chris Funk

University of California Santa BarbaraDepartment of Geography

Motivation and Research Question

• Recent climate analyses by the Famine Early Warning System Network (FEWsNET) suggest that Eastern Africa is rapidly getting warmer and dryer

• In Kenya, the evidence suggests shorter growing season rains and diminishing crop lands

• Kenya has a persistently high rate of child stunting (about ~30%)-a common indicator of malnutrition and food insecurity

Question Are warming and drying trends in Kenya linked

to higher rates of child malnutrition?

Linking malnutrition with climate

– Exposure: water reduction(increasing x means decreasingfood)– Linkage: change over timeexpected in certain areas– Vulnerability: dependent on “livelihood”(how reliant are people on water for food)

Definition of Key Terms

• Child Stunting (HAZ)-Height for Age Z-Score (HAZ). An indicator of chronic malnutrition. Measured in the number of standard deviations (Z-score) the child’s height-for-age (HAZ) ratio is away from a reference mean (what they should have).

• Vulnerability: Function of both a system’s exposure and sensitivity to stress and its capacity to absorb or cope as measured by potential for loss (in this case).

• Livelihood Zones: Doing your thing to make ends meet.

Livelihoods Framework

– How do people in different places live? (Scoones1989)

– Why do people do what they do? How do the produce food and money?

– The Pentagon – 5 capitals• Social capital• Natural capital• Human capital• Financial capital• Physical capital

Image

Livelihoods Framework

• Can we incorporate the livelihoods framework into applied research?

• How do we apply this theory to quantitative analysis?

• What about scale?• Data?• Interpretation?

Presenter

Presentation Notes

Map looks wonky – can you adjust the sizing so it isn’t stretched Talk about basic stats on regional malnutrition here since we have both regions and livelihoods so we can start to give people an idea of what to expect based on livelihood zone.

Linking household food security and climate

AccessAvailability

Malnutrition(child stunting)

Food production Cost, Transport

Climate(precipitation and

temperature)

Utilization

Knowledge, Quality

Physical Determinants

Data: DHS and Climate• DHS: 2008 Demographic and Health Survey

– 2268 Children Aged 12-59 months– Controls include physical, maternal, household, and regional

characteristics – Lat/Long coordinates for Community/Sampling Clusters

• Climate (Temperature and Precipitation)– Interpolated from a combination of 70 rainfall stations, 17

temperature stations, and remotely sensed data onto 1 degree (~10 km) grids

– Covers the Years 1969-2009, masked for dominant growing season (MMAJ)

– Matched to Children using DHS Sampling Cluster Coordinates

Matching Children to Climate Data

• One cluster (sampling unit) will contain several children/households

• Grid cell containing the cluster and cells touching that cell are averaged

• Averaging occurs over the child’s age +12 months

Analysis

• Standard linear regression with a set of controls, climate variables, and climate/livelihood interactions

• Standard Errors calculated using Cluster Robust Variance Estimator (CRVE) to correct for within group (g) correlation and heteroskedasticity

Where’s the spatial correlation?

• Other strategies for incorporating similarities in observations?

• Think about scale• Think about the research question• Think about the data collection• Think about the context• Think about policy implications

Presenter

Presentation Notes

Results 2When we construct one model for each of the climate variables, models 2 and 3, the negative relationship between HAZ and temperature and the positive relationship between rainfall and HAZ are clear. This suggests that as Kenya experiences warming and drying, we can anticipate greater incidence of child stunting

Presenter

Presentation Notes

When temperature and rainfall statistically interact with the livelihood zones the significance of these variables on HAZ is evident and we can begin to sort out the differing impacts of these climate variables on the populations within each type of livelihood zone. Within mixed-farming, agro-pastoral and the high intensity cropping zones increasing rainfall is associated with higher HAZ scores. For the high intensity cropping zones increasing temperature is positively associated with HAZ scores. The only livelihood zone where we see a negative relationship between temperature and precipitation and HAZ is the reference category, the coastal/river zone, where livelihoods are primarily dependent on fishing. It is likely that in this low-land, wet area of the country climate variables may impact health in ways that we have not accounted for in this analysis and that supersede those related to agricultural production (McMichael et al., 2006).

Discussion

• Impact of climate variables on malnutrition will vary based on livelihood

• Negative Impacts could be mitigated with investment in agricultural technology and transport infrastructure

• Aggregated (National) impacts may mask more severe impacts in geographic regions that are especially exposed

Refinements

• Compare results to different radii of spatial aggregation

• Refine standard errors

• Compare predicted climate change- use simulations to accommodate for errors in coefficient estimates and climate predictions

Questions?

Kathryn Grace grace@geog.ucsb.eduFrank Davenport davenpor@geog.ucsb.eduChris Funk chris@geog.ucsb.edu

Presenter

Presentation Notes

Do we know all the city names since they are unclear in the map? Are they relevant, really and we should cite/source this map since it isn’t in our document