Community Resilience to Acute Malnutrition: Learning to date from Concern’s programme in Chad January 2016 The purpose of this brief is to share learning from the CRAM programme to date. It draws largely from the results of the CRAM baseline and midline surveys carried out in late 2012 and late 2014, respectively, and an internal review of the programme conducted in October 2015. It highlights some emerging areas of interest as we await the final results of the endline survey and additional qualitative research conducted in November 2015. A full impact evaluation report and additional publications sharing learning from the research will become available in early 2016.
1. Introduction: development of the CRAM programme Concern initiated the Community Resilience to Acute Malnutrition (CRAM) programme in the Sila Region of Eastern Chad in late 2012 as a three-year pilot funded primarily by Irish Aid. The programme was designed to take a more holistic approach to addressing poverty and vulnerability in Goz Beida sub-prefecture, where Concern had been working since 2007. The pilot also presented an opportunity test and generate learning around Concern’s emerging approach to building community resilience, which Concern defines as ‘the ability of all vulnerable households or individuals that make up a community, to anticipate, respond to, cope with, and recover from the effects of shocks, and to adapt to stresses in a timely and effective manner without compromising their long-term prospects of moving out of poverty.’ As the name suggests, the programme specifically aimed to reduce the high and persistent levels of acute malnutrition observed among children in the area, as a proxy indicator of improved resilience. After five years of supporting a series of short term emergency interventions in Goz Beida, Concern felt strongly that a more multi-sectoral programme taking a long term view of vulnerability and strategies to mitigate, prepare and respond to recurring shocks was needed to improve the nutrition and wellbeing of Goz Beida’s population. In a context that traditionally attracted only piecemeal emergency funds, Irish Aid’s multiannual funding provided an ideal opportunity to design and implement such a programme. Irish Aid was also keen to invest in a strong evaluation and research component to strengthen the evidence base for resilience programming, and a research partnership with the Feinstein International Center at Tufts University’s School of Nutrition Science and Policy was established early on to support this learning component.
CRAM Learning Brief Based on an analysis of the prevailing risks and vulnerabilities facing Goz Beida communities and suspected causes of malnutrition, the programme was designed to deliver an integrated set of interventions to promote: food security and diversified livelihoods; optimal hygiene, health and nutrition practices; improved access to water, sanitation and health services; greater gender equality; and better emergency preparedness and timely response to localised shocks, if and when they occurred. In partnership with Tufts, a strong impact evaluation and complementary research were incorporated to generate evidence in the rapidly evolving area of resilience programming. Although the CRAM pilot and its randomised control study will come to an end in December 2015, the CRAM approach is being expanded in Chad and into three states in the Republic of Sudan under the DFID-funded Building Resilience and Adaptation to Climate Extremes and Disasters (BRACED) programme. The BRACED consortium in Chad and the Republic of Sudan is led by Concern working in partnership with the Feinstein International Center at Tufts; the World Agroforestry Centre; and Al Mazar (a Sudanese NGO working largely with pastoralist communities). The Consortium the approach will also be adapted and piloted in pastoralist communities and research will continue to identify effective strategies for building resilience, particularly in the face of climate change.
2. Context: risk and vulnerability in Sila Region1 The Sila Region of Eastern Chad lies on the border with Sudan and on the southern edge of the Sahara. The settled communities of Sila rely primarily on subsistence agriculture for their livelihood, while nomadic pastoralists move through the area seasonally. Though sparsely populated, these two groups often compete for access to water and arable land. Since the signing of the peace agreement with Sudan in 2010, Chad has remained relatively stable, but the security situation in the border areas remains volatile. The effects of the large-scale displacement between 2005 and 2010 as a result of the protracted conflict with Sudan can still be felt. Acute and chronic malnutrition are widespread in Sila, as they are for much of Chad. Regular surveys show that more than a third of all children under five in Sila suffer from chronic malnutrition (they are stunted). Furthermore, even during post-harvest periods, roughly 15% suffer from acute malnutrition (they are wasted and/or have nutritional oedema), with more than 3% suffering from severe acute malnutrition.2 3 There appear to be many, interrelated factors contributing to these high levels of malnutrition, which fall generally across the domains of food insecurity; poor maternal and child caring practices; and poor access to water, sanitation and health services. However, the context is dynamic, and the specific causes of undernutrition, therefore, are also complex and changing. 1
Unless otherwise noted all figures in Section 2 were taken from the CRAM baseline or midline surveys. Both reports are available from Concern upon request 2 CRAM baseline Nov 2012: prevalence of stunting 31.5% as measured by height-for-age; 3 CRAM baseline Nov 2012: 15.5% GAM and 3.2% SAM; CRAM midline Nov 2014: 14.4% GAM and 3.6% SAM; Concern/ UNICEF SMART Survey July 2012 results for Sila: 16.3% GAM, 2.5% SAM and 36.4% stunted; DHS 2004; 13.5% GAM, 3.1% SAM and 40% stunted;
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CRAM Learning Brief The semi-arid region remains highly vulnerable to food insecurity. This is due largely to the challenging agro-ecological context, which has been made worse by recent conflict and displacement. Variable rainfall, which regularly results in droughts and to a lesser extent floods; a largely undiversified agricultural system; declining soil fertility and erosion; animal and crop pests; a limited asset base; and poor access to agricultural inputs all contribute to low crop yield year on year. Exceptionally poor harvests were experienced in 2011, and climate change is and will continue to increase variability in agricultural conditions. 4 5 Settled households grow mostly millet, sorghum, groundnuts and sesame on small plots of less than two hectares. The majority of households also rear small livestock as well as donkeys and horses for transport. Cattle and camels are present but less common. Although highly variable, most of the rainfall occurs from June to September. Most crops are planted between April and June; weeding is done between June and August; and harvesting in October/ November. Markets are relatively undeveloped, with sorghum and livestock being the main commodities traded, including across the border with Sudan. The ‘hunger gap’ generally falls during the rainy season from June to September as households await the annual harvest. In a ‘normal’ year, households have reported having insufficient food for five to six months. Feeding and caring practices for infants and young children are poor, particularly exclusive breastfeeding to six months, which at 3.4% nationally is one of the lowest in the world.6 The diet diversity of children is modest with children 6-59 months reportedly eating from an average of 3.4 food categories (of 7 total standard categories) in the control group at midline.7 Only half of all households were accessing their drinking water from an improved source (i.e. a borehole) at baseline, and half reported practicing open defecation. Access to quality health services remains extremely poor. There are four functioning health centres in the programme area (Goz Beida sub-prefecture) and a further ten functioning health centres in the wider Kimiti Department. The average distance to a health centre is 11.7 kilometres, and most (69%) of households must walk over an hour to reach their nearest facility. Vaccination rates are very low, with measles vaccination coverage consistently at just under 50% of children 9-59 months.8 Roughly a third to half of all children is sick at any one time with half of those reportedly brought to a health facility for treatment. Pregnant women generally have very low access to iron-folic acid tablets, despite anaemia being widespread and a critical risk factor for maternal death during the perinatal period.9 Finally, gender equality is poor and women’s workload very high, with close to a quarter of households being female headed. The dependency ratio is 173, meaning there are 4
Executive Brief Post-Harvest National Food Security Assessment of Rural Households in Chad UNDP Climate Change Country Profile: Chad 6 UNICEF estimate for period 2008-2012. http://www.unicef.org/infobycountry/chad_statistics.html . 7 No other reliable measure of child diet diversity is available before the midline due to a data collection error at baseline 8 Concern SMART survey June 2012 (48%) and June 2013 (48%) 9 20% of pregnant women reported taking iron or iron-folate tablets at baseline; An estimated 19% of girls 516 years of age were anaemic in 2000 according to the WHO anaemia database: http://www.who.int/vmnis/anaemia/data/database/countries/tcd_ida.pdf. 5
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CRAM Learning Brief roughly 1.7 non-productive individuals (children, older people and the disabled) for every productive individual in the community. Labour migration is common, although the flow of remittances appears low (only 11% of households reported they had received remittances). Men were reported to be the primary decision-maker across key domains related to child health and nutrition, including a woman’s own health care (47% of maleheaded households), children’s health care (42%), livestock (51%) and the number of children to have (41%). While the findings outlined below reflect the specific context of the Sila Region, the livelihood and socio-economic profile is similar to many communities across the Sahel, meaning learning from the CRAM programme may be relevant to other contexts beyond Goz Beida and even Chad.
3. Concern’s CRAM programme Programme components The CRAM programme was planned as a three-year pilot implemented in 35 villages in the sub-prefecture of Goz Beida. The programme goal and objectives are outlined in Box 1, below. The programme aimed to achieve these by delivering an integrated set of multi-sectoral interventions, establishing an early warning system and providing timely emergency response, such as the provision of cash, seeds or food, if and when a shock overwhelmed local coping capacity. The CRAM conceptual model and main impact indicators are outlined in Annex 1. Box 1: CRAM programme goal and objectives The overall goal of the CRAM programme is to improve health, nutrition and livelihood security as well as resilience to shocks for the rural population of Sila Region (Eastern Chad). The programme’s interventions were designed to achieve the following objectives: 1) Improve food security and diversify livelihoods 2) Improve health and nutrition outcomes; 3) Improve access to safe and sustainable water and sanitation services 4) Improve health, hygiene and feeding practices for mothers and young children; 5) Increase participation of women in community and household decision making 6) Support social and behaviour change to promote all of the above 7) Establish an Early Warning System 8) Improve emergency preparedness and effective emergency response 9) Undertake quality research, monitoring and advocacy Most of the integrated project activities have been implemented as planned, though some have been on a smaller scale than first envisioned. The Early Warning System, for example, is not yet a complete ‘system’, but a computer-based model using satellite rainfall data has been developed with some promising results (see below). The promotion of small business and other off-farm income generating activities to diversify livelihoods was focused largely on supporting home gardens and animal health workers who charge a fee for their services. However, a value chain analysis was conducted for 4
CRAM Learning Brief several local crops, and more intensive market-based activities are planned under BRACED. Finally, some of the gender equality activities, including Community Conversations to facilitate open dialogue on gender dynamics, were not advanced, but a clear gender strategy has been developed for implementation under BRACED.
Impact evaluation The quantitative component of the impact evaluation follows a cluster randomised control design, whereby half the working area (35 programme villages) were randomly assigned to receive the integrated set of CRAM interventions and the other half (35 control villages) were not.10 This randomisation allows outcomes between the two groups to be compared and changes directly attributable to the programme to be distinguished from those due to broader contextual factors. A quantitative baseline survey was conducted in November/ December 2012 for which female respondents from 1420 households were interviewed and their Mid-Upper Arm Circumference (MUAC) and the weight, height and MUAC of their children were measured. A midline survey was conducted at the same time of year in 2014 with the same households.11 Additional qualitative research was also undertaken at baseline and at endline to provide context for the quantitative findings and help explain why any positive changes may or may not have occurred. The themes explored in the qualitative research include current livelihoods strategies and how they have changed over time; the specific shocks and stresses experienced by each livelihood group, particularly in relation to climate variability and conflict, and how the capacity to cope has changed over time; the causes of malnutrition; and the gender roles and responsibilities in relation to each. Longitudinal data has also been collected monthly from a subsample of 60 households in the programme and control villages, documenting actions the households had taken to cope with food shortages over the course of 2014. The questions were an abbreviated list of those used in the Coping Strategy Index (CSI) (see findings below).12
A rainfall-based model for predicting poor harvests As a first step to establishing a robust early warning system (EWS), the programme has developed a computer-based model to predict the quality of the annual harvest several months before harvesting begins. The model is currently based on satellite imagery showing total rainfall and its spatial distribution over critical months of crop growth (May to September).13 Tufts developed and refined the rainfall-based model by comparing the ability of different iterations to retrospectively predict harvest yields (millet) for the past ten years as compared to the actual historical harvest data from the Chadian Ministry of Agriculture for the same period. The rainfall-based model was then used to forecast crop productivity in Goz Beida for each year of the CRAM programme, and the predictions were validated using a number of qualitative and quantitative programme data. The 10
Note, some interventions, such as support to the four government health centres benefited project and control villages, but the rest of the package was delivered only to project villages 11 Roughly 12% of the original respondents from the baseline were not available at midline due to mortality, migration and relocation 12 13
Maxwell D and Caldwell C (2008) The Coping Strategy Index Field Methods Manual. CARE, Tango, et al.
Remote sensing rainfall data is taken from from two satellites, jointly managed by NASA and the Japanese Space Agency JAXA: the Tropical Rainfall Measuring Mission (TRMM) launched in 1997, and its sibling Global Precipitation Monitor Mission (GCP) launched in 2014
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CRAM Learning Brief rainfall model has demonstrated strong potential to predict crop harvests and the programme is now working to determine how this predictive model can be operationalised and integrated into wider early warning systems at local, regional and national level.
4. Learning to date Overall, the programme appears to be having a positive impact on acute malnutrition and a clear and significant effect on several factors that Concern believes are central to community resilience and ultimately to reducing acute malnutrition. Due to the randomised control design, the impact outlined below can be directly attributed to the programme. The main findings to date are as follows:
Programme impact on acute malnutrition The CRAM programme is reducing levels of acute malnutrition in children in smaller programme villages. CRAM programme villages with fewer than 150 households have seen a significant reduction in the prevalence of acute malnutrition since the baseline (from 17.5% to 11.7%) as compared to control villages of the same size. However, the same effect is not yet seen in larger villages (150-500 households). The prevalence of global acute malnutrition (GAM) among under-fives in control versus programme (aka ‘treatment’) villages according to village size is shown in Figure 1 – the arrow indicates the statistically significant reduction from baseline in the smaller programme villages. The reasons for the greater impact in smaller villages are not yet clear, but one possible explanation is that the per capita coverage of interventions is higher in smaller villages, for example the per capita coverage of a borehole, community health agents, or hygiene campaigns may be higher in villages with fewer people. Social dynamics may also be a factor, as there may be a lower ‘threshold’ in smaller villages to achieve widespread social and behaviour change than in larger villages with more diffuse social structures. We are hopeful more clues will emerge from the endline regarding how and why village size is affecting nutritional and hygiene outcomes. For now, the findings suggest that it is important to ensure the optimal ratio of inputs per person across all villages, regardless of size.
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Figure 1: Prevalence of global acute malnutrition in control villages versus programme (aka ‘treatment’) villages according to village size
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Programme impact on food security The programme has had a significant impact on the ability of households to cope with the hunger gap. This finding comes exclusively from the longitudinal data, which assessed an abbreviated form of the Coping Strategies Index (CSI) among a subsample of programme and control villages each month. The CSI provides a standardised method to assess the degree to which households are changing their food consumption practices to cope with food shortages. It is therefore widely used as a proxy measure for food insecurity. While programme and control villages were relatively similar in their reported coping behaviours post-harvest, a clear pattern emerged during the months of the hunger gap (July to September): households from programme villages were not employing coping strategies (e.g. eating less preferred foods, borrowing food on credit, reducing meals or portions size) to the same degree as households in control villages. The difference in the modified CSI seen between the two groups during these critical months was statistically significant. The programme’s impact on the ability of households to cope with the hunger gap was only detectable (by a statistically significant margin) via the seasonal longitudinal data collected – not by the post-harvest surveys. This is likely because the baseline and endline surveys were conducted soon after the harvest when almost all households tend to enjoy at least a short period of improved food security. As such, any difference in coping strategies would be temporarily ‘washed out’. This points to the intensely seasonal nature of food insecurity in such contexts and the importance of developing and employing more seasonal, month-to-month data collection methods to assess factors affecting resilience. Relying solely on cross-sectional surveys risks missing or seriously underestimating programme impact or important causal relationships.
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Figure 2: Monthly trends in food insecurity in programme (‘intervention’) versus control (non-intervention) villages January to December 2014
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Programme impact on health seeking behaviours The programme is leading to more sick children being brought for treatment at a health centre, hospital or mobile clinic. This does not appear to be due to physical proximity to one of the four health centres supported by Concern, as programme and control villages were, on average, the same distance from these health centres (both according to GPS mapping and self-reported travel time). While the difference may be partially explained by variations in the quality of the services provided at the nearest health centre (i.e. having had a good experience of the service, they returned the next time a child was sick), Concern was supporting all four health facilities fairly equally. The greater degree of care-seeking seen in the programme villages (by a statistically significant margin) is therefore more likely due to the community-level health promotion activities carried out by community health volunteers in the programme villages, as these were not implemented in control villages. These included active screening for children with acute malnutrition or illness and behaviour change communication focused on identifying signs of illness and seeking prompt treatment, which are delivered via mother to mother support groups and through mobilisation of other community members, including traditional leaders and men.
Programme impact on access to clean water and hygiene The programme has resulted in households reporting greater use of improved sources for drinking water, namely boreholes. This is not entirely surprising as one of the main interventions of the programme was to dig a borehole in each programme village where there wasn’t one already. It is interesting to note, however, that this increase was, again, significantly greater in smaller villages, where a higher percent of
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CRAM Learning Brief households are likely to be benefiting from a single borehole than in a larger village, for the reasons outlined above. The programme has had a significant impact on the knowledge of mothers regarding the two most critical times to wash hands, and (in smaller programme villages only) the programme has also resulted in actually improving hand washing practice. Handwashing practice was measured by survey interviewers observing the presence of a handwashing station with soap and the mother displaying the correct hand washing procedure. Figure 3 outlines the prevalence of handwashing knowledge and practice in control versus programme villages according to village size.
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Figure 3: The prevalence of hand washing knowledge and practice among control versus programme (aka ‘treatment’) households by village size
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treatment know the two main times for handwashing have a wash station with soap and water correctly practice handwashing
Strong causal link between hygiene practices and acute malnutrition Evidence is emerging of a very strong link between household hygiene practices and acute malnutrition in children. Specifically, the frequency with which a household washes its water transport containers appears to be closely associated with child wasting. The proximity of livestock to human drinking water sources also appears to be linked to child wasting. Both these hygiene factors showed a statistically significant association with child wasting, while no such link was seen between wasting and the three measures of food security (although this may be due to the the baseline and midline surveys being undertaken post-harvest, see above).14 Further details of these causal links will be explored once the endline data is available and will be shared in a forthcoming journal article.
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The three measures of food insecurity assessed in the baseline and midline surveys were total months of (self-reported) food insecurity during the previous year; the Coping Strategy Index; and household diet diversity
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Access to land influences food security Access to land is a strong and robust predictor of food security in the Goz Beida context. Specifically, households that reported ‘owning’ any land experienced food insecurity for two months less over the course of the previous year than households that had no access to land at all. Furthermore, those who reported having access to land for sharecropping were more food secure (by one month out of twelve) than those who had no access to land at all. This is perhaps not surprising in a context where subsistence farming is the predominant livelihood. However, it highlights the importance of better understanding the somewhat complex arrangements surrounding land tenure in Goz Beida – particularly what it means to ‘own’ land. The programme’s initial qualitative research suggests that the system for allocating land is currently quite subjective and favours more ‘productive’ community members, leading to vulnerable community members such as those from female-headed households consistently being allocated fewer productive fields. A comprehensive land tenure study is therefore planned for 2016 under the BRACED programme.
No improvements seen (yet) in women’s decision making Unfortunately, the data shows no impact on the levels of female-decisionmaking at the household level. This is likely due to several reasons. First, gender attitudes and practice are notoriously difficult to change in a short period. Second, while Concern has initiated some gender-focused activities, these have, to date, focused on ensuring representation on community-level committees and sensitising our own staff. Third, it is likely that the measurement of joint decision making at the household level requires a more nuanced spectrum as households are likely to move more gradually along the continuum.
A rainfall-based model shows promise for use in early warning system The rainfall-based predictive model appears able to accurately forecast millet production up to two months before the final harvest is normally completed. As explained above, Tufts assessed several iterations of a computerbased predictive model using satellite imagery showing total rainfall and its spatial distribution (with ten-kilometre precision) across the programme area. They then compared the retrospective predictions of different iterations of the model against historical crop production data (for millet) provided by the Ministry of Agriculture for the period 2000 to 2010. The current model proved highly predictive of the annual millet harvest, which is central to the food security of Goz Beida’s non-pastoralist communities.15 When the model was used to predict the annual harvest for 2012 and
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The rainfall data explained 90% of the variability in crop production. Furthermore, the retrospective annual crop production data itself was compared to results from a retrospective household survey where respondents were asked to rate each of the past ten years on a scale from very bad to very good (without specific reference to crop production)
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CRAM Learning Brief 2013, it correlated well with the actual crop estimates for millet. The model may offer a useful basis for more localised predictions of poor harvest in contexts such as Goz Beida. Meanwhile, Concern and Tufts continue to work on the wider early warning system to identify with communities and other stakeholders a) how the model could be used and/or improved to support better disaster risk management b) establish appropriate thresholds and identify the type of early response required and how it should be triggered c) how to link this emerging Goz Beida systems with early warning systems being developed at the national level and d) explore how the model could be adapted to better predict shocks and stresses affecting pastoralist livelihoods.
5. Recommendations and emerging hypotheses WASH may be at least as important as food security in improving nutrition in contexts such as Goz Beida. It appears that integrated, multi- sectoral programmes – particularly inclusion of WASH interventions - will almost definitely be required to improve acute malnutrition and resilience in contexts such as Goz Beida. The CRAM findings to date show that hygiene practices – specifically, washing water containers and lower concentrations of animals at human water points - were closely linked to acute malnutrition at household level, and more links are likely to emerge. While we do not yet (and may never) understand all the causal links, it is clear that coordinated interventions from multiple sectors will be essential to achieve and sustain improvements in outcomes as dynamic as child nutrition and community resilience.
The intense seasonality of many risks and vulnerabilities in disasterprone contexts should be a key consideration when designing resilience programme, including their monitoring and evaluation systems. Specifically, the collection of more longitudinal data (e.g. monthly assessments of food security indicators) should be a key aspect of monitoring and evaluating resilience programmes to capture these seasonal factors. Relying only on annual cross-sectional surveys, may miss critical changes, programme impact and important opportunities to improve resilience.
Village size affects programme outcomes. Concern and Tufts hope to better understand soon which aspects of village size are affecting programme outcomes. In the meantime, we encourage other researches to consider this factor in any similar studies. Based on our current hypothesis, it appears critical that an optimum ratio of programme inputs per person or target household is achieved, regardless of village size. This means ensuring sufficient equal per capita coverage of material inputs such as boreholes or latrine construction as well as other ‘softer’ resources such as hygiene campaigns and community health promoters. The role of social dynamics in smaller villages versus larger villages and whether e.g. smaller villages have a lower ‘tipping point’ widespread social and behaviour change should be explored further.
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The community outreach component of health and nutrition programmes is essential to increasing health-seeking behaviours. Programmes must balance support to health service delivery (ensuring supply) with mobilisation, screening and support to encourage caretakers to utilise those services (ensuring demand). While it isn’t clear exactly which aspects of the CRAM community health component is making the greatest difference, a community component with all three aspects is likely important.
Land tenure is likely to have a significant impact on food security and, in turn, on community resilience and malnutrition in the Goz Beida context. It is hoped more on this will emerge from the CRAM endline survey and land tenure study to be undertaken in 2016.
CRAM's rainfall-based model for accurately predicting local millet production several months before the harvest has great potential to improve local early warning systems and timely response in Sila and similar contexts. The model, which is based on satellite rainfall imagery, has proven quite effective in predicting the annual millet harvest in Sila, allowing appropriate response to be triggered several months earlier than previously possible. Concern and Tufts will continue to share learning as we work with stakeholders to refine the model and embed it within the wider early warning and response systems emerging in Chad.
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Conclusion
The results of the CRAM programme at the end of year two are encouraging, with signs that the programme approach is, in fact, having a positive effect on child malnutrition and other aspects of resilience. Important links between malnutrition and other causal factors, particularly hygiene, are also emerging, which may have wider implications for resilience and development programming. As the CRAM evaluation was designed to compare only two arms – the full set of interventions versus no intervention – it is not possible to determine the effectiveness of individual programme components relative to others. However, the findings to date underscore the multi-causal nature of child malnutrition, the importance of designing a multi-sectoral programme to address it, and the need for robust, evaluation and research components to capture and better understand the causal pathways and programme impact in the rapidly evolving area of resilience programming. Further learning briefs and other publications will be shared throughout 2016 as results of the full impact evaluation and related research are finalised.
For more information, please contact Kate Golden (
[email protected]) or Anne Radday (
[email protected])
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Annex 1: The CRAM conceptual model
Improved:
• Child nutrition (% global acute malnutrition in under-5s) • Food security (length of the annual hunger gap; coping strategy index) • Resilience to future shocks & stresses
Integrated set of interventions (delivered in all years) •
Promote climate-smart agriculture practices (e.g. no slash & burn; legume rotation) utilizing two lead farmers per village to promote uptake by others
• Promote communal (rain-fed) vegetable gardens among women • Promote improved health & feeding practices via community health agents (1 per 15 HHs), mother-to-mother support groups, hygiene campaigns • Drill boreholes (1 per village), train/ support water management committees; trigger latrine building with community led total sanitation (CLTS) approach and improved latrine construction; • Support delivery of basic maternal and child health and nutrition services via technical training / mentoring to gov’t staff and some material support • Promote animal health via training & equipping community animal health workers who work on a cost-recovery basis • Promote participation of women in community groups and household decision making via equality trainings • Promote local disaster risk management planning via DRM committees 13
Tailored & timely emergency response (e.g. distribution of cash, seeds or food)
Delivered in a bad year when agreed thresholds are passed
Early Warning System (currently based on rainfall patterns)
