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Integrating anaemia analysis in SMART surveys in Bolivia

By Susana Moreno, Brigitt Olagivel and Elisa Dominguez

Susana Moreno Romero is the Nutrition Programme Manager for Acción contra el Hambre (ACH) (Action Against Hunger - Spain). She has previously worked in Sierra Leone, Niger and Argentina. She has a PhD in Nutritional Anthropology and is a member of Universidad Complutense de Madrid Researching Group EPINUT.

Brigitt Olagivel is a Bolivian nutritionist. She has been working with ACF in El Chaco region since 2009.

Elisa Dominguez is a medical doctor and the Manager of the Health & Nutrition Department at ACF-Spain HQ and nutrition advisor for ACF projects in Latin America. She has previously worked in the field (Guinea, Thailand, Liberia, Mali, Angola, Niger) for more than 10 years.

This study was co-funded by the Swedish International Development Cooperation Agency (Sida) and European Commission (through ACH), WFP, COOPI (Cooperazione Internazionale) and UNICEF. The authors would like to acknowledge the political and health authorities from regional to community level. Thanks are also extended to the teams responsible for the survey implementation and analysis, from surveyors to logistics and administration. A special thanks goes to the families and children who participated in the survey and to all those who helped as guides or in providing essential information, without whose collaboration this survey would not have been possible.

The ‘Chaco’, which is a term derived from the Quechua ‘Chaku’ and means ‘hunting land’, is a geographic area that takes in parts of Bolivia, Paraguay and Argentina. The Bolivian Chaco has a surface area of approximately 128,000 km2. It has very irregular weather patterns affecting temperatures, rains and winds and is characterised by intense rainy summers and dry winters. The region is especially subject to regular floods and droughts related to El Niño and La Niña oscillations. The vulnerability of the Chaco rural population, who subsist on a farming and livestock based economy and have poverty levels higher than those in many other areas of the country (INE 20011), increases the potential impact of disasters.

Between February 2009 and November 2010, the Chaco region, especially the Bolivian Chaco, suffered an exceptional rain deficit according to the Meteorological Hazards and Seasonal Forecasting Group of Benfield UCL Hazard Research Centre. During the austral (southern hemisphere) summer 2009-2010, rains were 60% less than expected.

Because of this protracted drought, the Bolivian government declared a National Emergency Situation in Chaco Region in June 2010 and a Plan of Emergency Assistance and Farming Recuperation was established. Water consumption, farming and livestock of more than 7,600 households were severely affected according to a UNETE evaluation report (2010)3.

In October 2010, global acute malnutrition (GAM) prevalence (based on weight for height z score (WHZ)) in rural children under five years of age was above 10% in two of the three Chaco departments (Cordillera and Chuquisaca). High prevalence rates of acute malnutrition in seven out of 16 Chaco districts were confirmed by CT-CONAN (Comité Técnico – Consejo Nacional de Alimentación y Nutrición4).

Bolivian Chaco Region

To explore the nutritional and food security situation further, Acción contra el Hambre (ACH), in collaboration with WFP, UNICEF and COOPI, implemented ESAE15 and SMART nutritional surveys including anaemia prevalence, in the Bolivian Chaco Region, between March- April 2011. Urban populations were excluded as ACH was only targeting rural areas. Also, it was considered that a bias could be introduced if urban areas were included, given the large social and economic differences between rural and urban populations in Latin America.

Box 1 overviews the SMART survey approach. The inclusion of haemoglobin (Hb) analysis to determine anaemia status in SMART surveys gives a more complete nutritional status assessment, particularly with regard to potential constraints for adequate child growth and development due to iron deficiency. Such analysis is especially important in countries like Bolivia where iron deficiency anaemia is a major nutritional problem, affecting 61% of Bolivian children between 6-59 months old (DHS 2008)6.

This article describes the survey undertaken with a particular focus on the anaemia assessment component.

Box 1: What is a SMART survey?

The Standardised Monitoring and Assessment of Relief and Transition (SMART) programme is an interagency initiative to improve monitoring and evaluation of humanitarian assistance interventions.

The SMART Methodology provides a basic, integrated method for assessing nutritional status and mortality rate in emergency situations. It provides the basis for understanding the magnitude and severity of a humanitarian crisis. The optional food security component provides the context for nutrition and mortality data analysis.

SMART surveys measure acute malnutrition of the whole population via estimates of:

  • Prevalence of Global Acute Malnutrition (GAM) in children aged 6-59 months.
  • Crude mortality rate (CMR) in a given population over a specific period of time.
  • Food security assessments, which are used to understand and interpret nutritional and mortality survey data.

The SMART manual deals specifically with nutrition and mortality surveys, including sampling, nutritional measurements, and mortality rates. It describes general survey procedures and provides information on how to collect data necessary for planning direct interventions in emergency settings or for surveillance. It also provides step-by-step instructions for analysing survey data using Emergency Nutrition Assessment (ENA) software and procedures for food security assessments.

For more information, including access to the SMART Manual, additional resources, ENA software and a SMART forum for practitioner questions, visit: http://www.smartmethodology.org

Methodology

Population

The Bolivian Chaco is located in the southeast of the country and extends over five provinces belonging to the departments of Cordillera, Tarija and Chuquisaca (see map). According to the last national census (2001), the 16 Chaco districts have a population of nearly 300,000 inhabitants, over half (56%) of whom live in rural areas. Districts have a skewed urban population distribution, where three out of the nine urban areas in Chaco Region host more than 84% of total urban population of this area (Yacuiba (51%) and Villamontes (12%) in Gran Chaco Province, Tarija, and Camiri (21%) in Cordillera province, Santa Cruz).

The population of Chaco is ethnically composed of mainly Guaraníes and Mestizos (rather than Quechuas) and other aborigine groups like Aymara, Carai or Weenhayek. However, the ethnic composition varies between the Chaco provinces. More than 80% of households surveyed practice agriculture, and maize is the main subsistence crop. More than 95% of households have livestock – in order of priority, poultry, pigs and cattle.

Due to a number of political and administrative factors and in order to allow comparison, a total of three SMART surveys, one per department, were implemented.

Sampling

Using cluster sampling and the latest published GAM prevalence rates (UNETE 20107), a representative sample for each department was calculated using ENA software for SMART. Values are shown in Table 1.

Table 1: Sample estimations for SMART nutritional survey by Chaco department
Sampling Cordillera Chuquisaca Tarija
Total population* 57,321 53,641 66,667
GAM prevalence (WFP 20108) 13.5% 19.9% 6%
Estimated precision 3 5 2.5
Design effect 1.5 1.5 1.5
Estimated sample size 814 +5%=855 400 +10%=440 348 +10%=383
Final sample size 884 477 420
Average household size (No. of people per household) 5.23 5 5
Children under 5 years (%)** 13% 15% 15%
Households that declined to participate (%) 3% 3% 3%
Estimated no of clusters 48 32 32
Final no. of clusters 48(49)** 32 33
Total no. of communities 58 52 53
No. of children under 5 years in each cluster 18 14 12
No. of children with Hb sample 6-59 months (6-24 months) 859 (302) 469 (161) 420 (154)

* Total population for each department (excluding populations over 2000 inhabitants as considered urban and not included in the survey) **An additional cluster was included to enable the expected number of children to be reached.

 

All rural localities listed in the last census 2001, except Mennonite communities9, were considered for random selection using ENA (Emergency Nutrition Assessment) software. The current population for each community was calculated applying the 2010 district population provision of INE (National Statistics Institute) based on the 2001 national census. Localities with more than 2,000 inhabitants (considered as urban), were not included in the survey. Between 52 and 58 localities were sampled in each department. The final samples were 884 children between 6 and 59 months old in Cordillera (Santa Cruz), 477 in Chuquisaca and 420 in Tarija. Some parents did not authorise Hb analysis, so final sample sizes for collection of anaemia data were slightly lower. The characteristics of each locality determined the household sampling method used in a given locality. The approach employed the sampling decision tree set out as part of the SMART methodology.

Figure 1a: Material for Hb analysis

Equipment

Iron deficiency anaemia was detected through use of a Hb analyser HemoCue Hb 201+ (Figures 1a and 1b). This portable analyser allows field measurement of the Hb concentration in peripheral blood through photometric detection. Material for Hb analysis includes microcuvettes, lancets, gloves, alcohol, toilet paper, cotton, batteries and a waste bottle. The anaemia analysis cost was about 2 dollar/child (excluding the cost of the HemoCue which was approximately 1000 dollars/unit).

Figure 1b: Blood drop for Hb analysis

Microcuvettes do not need refrigeration and as the Hb analysis is implemented right after the blood sample is taken, there is no special requirement for transport or storage. The main field constraint to take into account is the climatic environment. In order for the chemical reaction to work properly in the HemoCue Hb 201+, temperatures lower than 15ºC or higher than 30ºC or humidity more than 90% without condensation must be avoided.

Training

Inclusion of anaemia analysis in the SMART survey did not necessitate increasing the number or qualifications of staff. However, in some countries, lab officers or nurses may be required by the Ministry of Health.

Theoretical and practical training for the anaemia test took around four hours. With good team organisation, the average time to do the Hb analysis in the field is about five minutes per child, although this does depend on the child’s amenability and team experience. The chemical reaction in the HemoCue Hb 201+ usually takes one minute.

Figure 2: Results card for the families, one per child

The survey was implemented in March and April of 2011 coinciding with the last two months of the rainy season and the first ‘choclo’ (green corn) harvest. One to two weeks before the survey, local and selected community authorities as well as health posts/centres were informed and authorisation for the survey was requested. Some ethnic groups, or populations from specific geographic zones, were averse to blood tests so this prior sensitisation and mobilisation stage is especially important for anaemia studies.

Figure 3: Stamps representing anaemia status

Family authorisation was also required. For this it is important to distinguish between permission for anthropometrical measurements and bilateral oedema which is non-invasive, to permissions for blood tests which are invasive.

The fact that results are obtained immediately allows the family to have the Hb status of their children confirmed instantaneously. This is highly appreciated. A model of the card given to each family with the results is shown in Figure 2. Different stamps were designed and used to represent a child with anaemia, without anaemia and borderline and made it easier for caregivers to understand (Figure 3). Although only 1 to 3 blood drops are needed for the reaction analysis, one common belief and reason for resistance to the test was the suspicion that blood may be sold. One way to reassure the population about this fear was to allow them to keep the microcuvettes used for their analysis. However, we do not recommend standardising this practice due to waste management difficulties.

Figure 4: Puncture zone on middle hand finger

The peripheral blood sample was taken from the left hand middle finger (Figure 4) and when this extraction was not possible (under 1% of cases), the sample was taken from the heel (Figure 5). The specific methodology used to take the blood sample and conduct the Hb analysis can be found in INS (2005)10.

Height, weight, mid-upper arm circumference (MUAC) measurements and bilateral oedema check-up, as well as the blood test, were done for each selected child following standardised protocols.

Figure 5: Puncture zone on heel

Analysis

Each child’s information was entered in the ENA software in order to check and correct possible mistakes. The same software was used to analyse indicators based on weight and height measurements and presence of oedema, and to calculate prevalence of acute and chronic malnutrition. Anaemia and MUAC data were analysed with Excel 2007 and SPSS 17.0.

MUAC cut-off points were established at 115 mm for severe acute malnutrition and 125 mm for moderate acute malnutrition.

Cut off points for anaemia and the severity scale varies according to authors, age, pregnancy, countries and geographic altitude (INS 2005). In this case, and largely to allow comparisons, anaemia classification was based on the same cutoff points used for children between 6-59 months old in the previous Demographic and Health National Survey (2008) in Bolivia. These are shown in Table 2.

Table 2: Cut-off points for anaemia classification
Age (months) Category upper limit
Mild g/dl Moderate g/dl Severe g/dl
0-3 8.9 6.9 4.9
3-6 10.4 8.4 5.4
7-23 10.9 8.9 5.9
24+ 11.4 9.4 6.4

Source: ENDSA, 2008. Encuesta Nacional de Demografía y Salud. INE.

Results and discussion

According to these first SMART surveys in Bolivia, GAM prevalence in Chaco rural population under five years is less than 1.5%. There is less than 6% underweight and approximately 20% stunting. Malnutrition prevalence in each department is specified in Table 3. The highest values were found in Cordillera province (Santa Cruz). The child nutritional situation after the 2010 drought seems to have returned to normal values, when compared with ENDSA 2008 and PMA 200611 survey findings. Anthropometric results in conjunction with the ESAE analysis showed that causes of malnutrition in Bolivian Chaco are generally structural and only change when occasional dramatic events like severe droughts or floods occur, as occurred in the 2009-2010 drought.

Table 3: Underweight, acute and chronic malnutrition prevalence and confidence intervals per department of Bolivian Chaco Region
  Cordillera Chuquisaca Tarija
n=884 n=477 n=420
% (n) (95% C.I.) % (n) (95% C.I.) % (n) (95% C.I.)
Acute malnutrition

1.4% (12)

(0.8 – 2.4

0.8% (4)

(0.3 – 2.3)

0.7% (3)

(0.2 – 2.3)

Underweight

5.9% (52)

(4.2 – 8.1)

2.5% (12)

(1.2 – 5.4)

3.6% (15)

(2.1 – 6.1)

Chronic malnutrition

22.3% (197)

(18.9-26.0)

18.8% (90)

(15.0-23.4)

16.9% (71)

(12.5-22.5)

 

Based on MUAC, a total of nine cases of acute malnutrition were detected in all Chaco Region. Out of these nine cases, only one was classified as acutely malnourished using weight for height zscore.

Hb analysis showed that iron deficiency anaemia in rural children between 6 and 59 months old in Bolivian Chaco is above 40%. Based on the FAO (2006) scale12, this signifies a severe iron deficiency problem in this region. Age, sex and district differences were found: anaemia was higher in children under 24 months of age, in boys and in Cordillera province (Santa Cruz Chaco) (see Table 4). Anaemia prevalence in 6-23 months aged boys from Cordillera was 75.3%, compared to 67.9% in girls. Overall prevalence in 6-59 months (both sexes) was highest in Cordillera (57%) and lowest in Tarija (48.8%). Approximately 70-80% anaemia cases were mild and less than 1.5% were classified as severe (Figure 6).

Table 4: Anaemia prevalence in boys and girls aged 6-23 months and 6-59 months per Bolivian Chaco Departments
Age (months) Sex % anaemia prevalence
Cordillera Chuquisaca Tarija
6 – 23 Girls 67.9 55.5 55.1
  Boys 75.3 57.7 67.1
  Total 71.9 56.5 61.1
6 – 59 Girls 55.5 49.1 47.0
  Boys 58.6 50.3 50.5
  Total 57.0 49.7 48.8

 

 

These results confirm anaemia as one of the major nutritional problems of rural children in Bolivian Chaco. Iron deficiency anaemia prevalence found in ENDSA 200813 was also above 40% showing that iron deficiency anaemia is an endemic problem with structural causes amongst the Bolivian Chaco population. Iron deficiency anaemia in children has adverse effects on physical and psychomotor development, the immune system and physical performance14. Its reduction through program- mes and policies which impact the structural causes must therefore be a key government goal.

Conclusions

Anaemia analysis is easily integrated into SMART surveys without an excessive increase of budget or resources required. It provides a more profound understanding of the nutritional problems affecting a population than anthropometric surveys alone and hence the policies, strategies and programmes that must be pursued to eradicate malnutrition. Its application is especially important in areas or groups with high risk of “hidden hunger” due to micronutrient deficiencies, i.e. in regions like Latin America or urban settlements in developing countries. ACF is currently evaluating whether the urban population of the Bolivian Chaco needs to be included in future anaemia surveys.

If you have any comments regarding this article, please contact: hom-pe@acf-e.org

Show footnotes

1INE 2001. Censo Nacional de Población y Vivienda. Instituto Nacional de Estadística. http://www.ine.gob.bo/anda/ddibrowser/?id=31

2The full assessment report is available in Spanish at: http://www.reliefweb.int/rw/rwb.nsf/db900sid/VDUX-89GRUC?OpenDocument&query=bolivia

3UNETE. RED HUMANITARIA 2010. Bolivia: Sequía en el Chaco – Reporte de misión de valoración y validación de información. Report Government of Bolivia, UN Country Team in Bolivia. http://reliefweb.int/node/368278

4Technical committee of the National Council on Food and Nutrition

5Evaluacion de seguridad alimentaria de emergencia (food security assessment in emergencies). More information at: http://documents.wfp.org/stellent/groups/public/documents/manual_guide_proced/wfp203212.pdf

6Available at: http://www.measuredhs.com

7See footnote 3

8WFP 2010. Rapid assessment of nutrition and food security in Chaco municipalities affected by droughts.

9Mennonite communities were not included as they are not targeted by ACF programmes. Also, Mennonite communities have significantly different socio-economic and cultural backgrounds to the wider rural population in the Chaco, hence were not included even if they were set in rural areas.

10INS. Instituto Nacional de Salud. 2005. Manual del Encuestador. Monitoreo Nacional de Indicadores Nutricionales. Perú. Capitulo II: Manual de diagnóstico de anemia por hemoglobinómetro y su utilización. Perú. http://www.ins.gob.pe/portal/jerarquia/5/311/monitoreonacional-de-indicadores-nutricionales-monin/jer.311

11PMA. 2006. Diagnóstico de la Seguridad Alimentaria y Nutricional en el Chaco Boliviano. Programa Mundial de Alimentos de las Naciones Unidas.

12FAO. 2006. Indicadores de Nutrición para el Desarrollo. Organización de las Naciones Unidas para la Agricultura y la Alimentación. Roma, 2006

13ENDSA. 2008. Encuesta Nacional de Demografía y Salud. INE.

14World Health Organization 2001. Iron Deficiency Anaemia. Assessment, Prevention and Control. A Guide for Programme Managers. WHO/NHD/01.3

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Susana Moreno, Brigitt Olagivel, Elisa Dominguez (2012). Integrating anaemia analysis in SMART surveys in Bolivia. Field Exchange 44, December 2012. p35. www.ennonline.net/fex/44/integrating

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