RESEARCH
Mid-upper arm circumference at age of routine infant vaccination to identify infants at elevated risk of death: a retrospective cohort study in the Gambia
El perímetro braquial a la edad de la vacunación infantil rutinaria para identificar a los lactantes con un riesgo de muerte elevado: un estudio de cohorte retrospectivo en Gambia
Circonférence du bras à mi-hauteur à l'âge de la vaccination systématique des nourrissons pour identifier les enfants dont le risque de décès est élevé: une étude de cohorte rétrospective en Gambie
Martha K MwangomeI,*; Greg FeganI; Tony FulfordII; Andrew M PrenticeII; James A BerkleyI
IKenya Medical Research Institute/Wellcome Trust Research Programme, PO Box 230, Kilifi, 80108, Kenya
IILondon School of Hygiene and Tropical Medicine, University of London, London, England
ABSTRACT
OBJECTIVE: To determine the predictive value for death before 12 months of age of mid-upper arm circumference (MUAC) and weight-for-length Z score (WFLz).
METHODS: A retrospective cohort analysis of infants living in Keneba, in rural Gambia, was conducted. Anthropometric measures were obtained from demographic surveillance system records for infants registered between February 1974 and July 2008 who had had MUAC and WFLz recorded at 6-14 weeks of age and vital status recorded at least once more. Hazard ratios (HRs), population attributable fractions and areas under receiver operating characteristic (ROC) curves were estimated to assess the predictive value for death in infancy of MUAC and WFLz.
FINDINGS: Of 2876 infants included in the analysis, 40 died before the age of 12 months. The HR for death in this group versus in well-nourished infants was 5.8 (95% confidence interval, CI: 1.6-21) for a WFLz < -3. HRs for MUACs below the thresholds of 115 mm, 110 mm and 105 mm were 4.5 (95% CI: 1.4-15), 9.5 (95% CI: 2.6-35) and 23 (95% CI: 4.2-122), respectively. The attributable fractions for a MUAC < 130 mm and a WFLz < 0 were 51% and 13%, respectively. The areas under the ROC curve for death in infancy were 0.55 (95% CI: 0.46 to 0.64) for WFLz and 0.64 (95% CI: 0.55 to 0.73) for MUAC.
CONCLUSION: Among infants aged 6 to 14 weeks, unadjusted MUAC showed good performance in identifying infants at increased risk of death.
RESUMEN
OBJETIVO: Determinar el valor diagnóstico de la muerte antes de los 12 meses de edad del perímetro braquial y de la puntuación Z de peso para la talla.
MÉTODOS; Se llevó a cabo un análisis de cohorte retrospectivo de los lactantes residentes en Keneba, una zona rural de Gambia. Se obtuvieron las medidas antropométricas de los archivos del sistema de vigilancia demográfica para lactantes registrados entre febrero de 1974 y julio del 2008 a los que se había medido el perímetro braquial y la puntuación Z de peso para la talla cuando tenían entre 6 y 14 semanas de edad y cuyo estado vital se registró al menos una vez más. Se calcularon los índices de peligrosidad, las fracciones atribuibles a la población y las áreas con curvas de la característica operativa del receptor (ROC) para evaluar el valor diagnóstico de la muerte en lactantes del perímetro braquial y la puntuación Z de peso para la talla.
RESULTADOS: De los 2876 lactantes incluidos en el análisis, 40 fallecieron antes de alcanzar los 12 meses de edad. El índice de peligrosidad de este grupo comparado con el de lactantes bien alimentados fue del 5,8 (intervalo de confianza del 95%, IC: 1,6-21) para una puntuación Z de peso para la talla < -3. El índice de peligrosidad para el perímetro braquial por debajo de los umbrales de 115 mm, 110 mm y 105 mm fue del 4,5% (95% IC: 1,4-15), 9,5 (95% IC: 2,6-35) y 23 (95% IC: 4,2-122). Las fracciones atribuibles para un perímetro braquial inferior a 130 mm y una puntuación Z de peso para la talla inferior a 0 fueron del 51% y del 13%, respectivamente. Los resultados en las áreas bajo influencia de la curva ROC para la muerte infantil fueron 0,55 (95% IC: 0,46 a 0,64) para la puntuación Z de peso para la talla y 0,64 (95% IC: 0,55 a 0,73) para el perímetro braquial.
CONCLUSIÓN: El perímetro braquial no ajustado mostró resultados positivos para la identificación de lactantes de entre 6 y 14 semanas de edad con un riesgo de muerte superior.
RÉSUMÉ
OBJECTIF: Déterminer la valeur prédictive de décès avant l'âge de 12 mois sur la base de la circonférence du bras à mi-hauteur (ou périmètre brachial, soit PB) et du score centré réduit poids-pour-taille (WFLz).
MÉTHODES: Une analyse de cohorte rétrospective a été effectuée sur des nourrissons vivant à Keneba, dans la Gambie rurale. Les mesures anthropométriques ont été extraites des dossiers du système de surveillance démographique pour les nourrissons inscrits entre février 1974 et juillet 2008, dont le PB et le WFLz avaient été enregistrés entre 6 et 14 semaines, et dont le statut vital avait été enregistré au moins une fois par la suite. Les ratios de risque (RR), les fractions étiologiques du risque et les zones sous les courbes de caractéristique de fonctionnement du récepteur (ROC) ont été estimés pour évaluer la valeur du périmètre brachial et du WFLz afin de prédire le décès des nourrissons.
RÉSULTATS: Sur 2 876 nourrissons inclus dans l'analyse, 40 sont morts avant l'âge de 12 mois. Le RR de décès dans ce groupe, par rapport aux nourrissons bien nourris était de 5,8 (intervalle de confiance de 95%, IC: 1,6 à 21) pour un WFLz < -3. Les RR des PB en dessous des seuils de 115 mm, 110 mm et 105 mm étaient de 4,5 (IC de 95%: 1,4 à 15), 9,5 (IC de 95%: 2,6 à 35) et 23 (IC de 95%: 4,2 à 122), respectivement. Les fractions étiologiques du risque pour un PB < 130 mm et un WFLz < 0 étaient de 51% et 13%, respectivement. Les zones sous la courbe ROC pour le décès en bas âge étaient respectivement de 0,55 (IC de 95%: 0,46 à 0,64) et de 0,64 pour le WFLz (IC de 95%: 0,55 à 0,73) et pour le PB.
CONCLUSION: Parmi les nourrissons âgés de 6 à 14 semaines, un PB non ajusté s'est révélé performant afin d'identifier les nourrissons dont le risque de décès est accru.
Introduction
Recent estimates indicate that 8.5 million infants less than 6 months of age throughout the world are wasted by World Health Organization (WHO) growth standards, which define wasting as a weight-for-length Z score (WFLz) of < -2. 1,2 The risk of undernutrition in infancy is increased in preterm and low-birth-weight infants and in infants born to young, rural, poorly nourished mothers of lower socioeconomic or educational status. 3-5 Additionally, in poor regions, low rates of exclusive breastfeeding and mixed feeding as early as 2 months of age expose infants to contamination and to foods with low nutrient density. 6
Anthropometric measures and suitable thresholds for intervention are normally assigned on the basis of their predictive value with respect to death, ideally calculated using data from untreated populations. However, a lack of data for infants aged under 6 months, 7 among whom mortality is higher than in any other paediatric age group, makes it difficult to interpret anthropometric measures to guide interventions in this age group.
Among children aged 6 to 60 months, simple anthropometric indices are strongly associated with the risk of death. 8-14 For children aged 0 to 60 months, WHO recommends using WFLz to define wasting, 15 since WFLz is a measure of undernutrition adjusted for height and therefore independent of stunting in its description of wasting. For any given anthropometric measure, a Z score indicates how many standard deviations below or above a reference median an individual value is found. According to WHO growth standards, WFLz below the cut-off value of -3 standard deviations (SDs) from the median defines severe wasting (also called severe acute malnutrition); WFLz below the cut-off value of -2 but no lower than -3 defines moderate wasting (also called moderate acute malnutrition). 16 In children aged 6 to 60 months, the mid-upper arm circumference (MUAC), with simple cut-offs, is at least as predictive of death as WFLz. 17-20 Within this age group, adjusting MUAC by calculating the Z score or adjusting for height does not improve MUAC's predictive value. 21 MUAC can be measured easily, quickly and affordably. Values below the cut-offs of 125 mm and 115 mm are used to define moderate and severe acute malnutrition, respectively. MUAC is currently not recommended for use among infants aged below 6 months because of a lack of data on its reliability, measurement in practice and predictive value for death. However, we recently reported that in rural Kenya the inter-observer reliability of MUAC among infants aged 0 to 6 months was greater than that of WFLz. 22
In the last twodecades, infant vaccination coverage in rural Africa has greatly improved. 23 Attendance at well baby clinics provides an opportunity for vaccination, nutrition and health screening and intervention. Our primary aim was to use data from a long-standing demographic surveillance system (DSS) in the Gambia to determine whether MUAC, measured at the age when infants attend clinics for routine vaccination (i.e. between 6 to 14 weeks), can predict all-cause infant death. Additionally, we aimed to compare the association between MUAC and infant death with that between WFLz and infant death, as well as to discuss potential MUAC cut-off values for use in infants 6 to 14 weeks of age.
Methods
Study site
Since 1974, the British Medical Research Council has maintained a field station in Keneba, the Gambia. The site comprises the villages of Keneba, Manduar and Katong Kunda and is collectively known as "Keneba". Keneba has been under a longitudinal Demographic and Health Survey system since 1949. 24,25 The population is predominantly composed of Muslim subsistence farmers with similar socioeconomic status, cultural beliefs and practices. 25 Due to their proximity to each other, the three villages experience a similar climate, with a short rainy season between June and September (wet hungry season) and a longer, dry "harvest" season between October and May. 26-31
Age, anthropometric measurements and death
Before 1975, village births and deaths were reported weekly to the DSS team in Keneba through Arabic-literate village informants, included in the DSS team to ensure the accuracy of the information obtained from the allocated households. However, since 1975 birth dates have been obtained from postnatal care clinic and hospital records, in addition to village informants. 32-34
The anthropometric data described in this study were collected by trained health workers (nurses and midwives) during monthly well baby clinics (established in Keneba in 1975), during postnatal care clinic visits (at 6 weeks and 3 months of age), or through the periodic DSS survey. Hospital deaths were ascertained using hospital records and home deaths using village informants. All data on deaths in hospital and at home were compared with DSS data for verification. Refresher training sessions on anthropometry were organized annually by the DSS team at the Keneba field station. No specific interventions were in place to treat malnutrition in infants aged below 6 months.
Study participants
We included data from infants aged 6 to14 weeks who were registered in the Keneba DSS between February 1974 and July 2008 if their MUAC and WFLz had been recorded at 6 to14 weeks of age and their vital status had been recorded at least once more ( Fig. 1 ). Infants who were not normally living in Keneba were excluded.
Study design
We conducted a retrospective cohort analysis of longitudinal data from infants 6 to14 weeks old who were followed until 12 months of age. The primary outcome was death within 12 months of the date of birth.
Statistical analysis
Data were analysed using STATA 12 (StataCorp. LP, College Station, United States of America). Absolute measures of MUAC and length were excluded from the analysis if they were not biologically plausible for infants aged 6 to 14 weeks (i.e. MUAC < 70 mm; length < 400 mm or > 750 mm). WFLz categories were then defined according to the WHO growth standards. 35 MUAC Z-scores could not be calculated for infants aged less than 3 months because the WHO standards apply to infants aged 3 months or older. We therefore explored the data to determine an appropriate equivalent reference cut-off for the MUAC analysis. We used generalized binomial linear regression models to predict the risk of death in infancy, which was plotted for different MUAC and WFLz values. The number of deaths "plateaus" at a MUAC of approximately 130 mm and a WFLz of 0 ( Fig. 2 ). We therefore chose as the reference category a MUAC greater than or equal to the cut-off value of 130 mm (which also approximates the median MUAC for three-month-old infants in the WHO growth reference population), and we investigated MUAC thresholds of 115 mm, 110 mm and 105 mm. For WFLz, we chose WFLz > 0 as the reference category and investigated integer thresholds of -1, -2 and -3.
We used the Kruskal-Wallis test to detect median differences in MUAC and WFLz and the χ2 test for trend to detect associations between the proportion of infants who died and different anthropometric categories. We used Cox proportional hazards regression to compare hazards of mortality between anthropometric categories. Hazard ratios (HRs) for dying by 12 months of age were estimated for MUAC and WFLz in two ways. First, HRs were estimated by comparing individual MUAC and WFLz measures with the reference categories (i.e. > 0 for WFLz and > 130 mm for MUAC). Then, to examine the performance of potential cut-off values, we estimated HRs by defining exposure as MUAC or WFLz below each of the thresholds that we investigated. The HRs were adjusted for exact age in days at visit, sex, decade and season of birth and are presented with their 95% confidence intervals (CIs). According to one study, children born during June to October in the Gambia are at elevated risk of death, 36 although more recent evidence suggests that this effect is waning. 25 We also controlled for decade of birth because, since the data span four decades, we wanted to account for changes in the health system or other temporal effects. We estimated the sensitivities and specificities of MUAC and WFLz and used receiver operating characteristic (ROC) curves to assess their discriminatory ability to predict death by 12 months of age. To assess the contribution of MUAC and WFLz to infant mortality, we calculated the sample attributable risk following the method of Garenne et al., 37 which considers nutritional status and relative hazards as continuous variables.
Ethical considerations
Ethical approval was granted by the Gambian government/Medical Research Council Laboratories Joint Ethics Committee (L2008.82vs01, 11 November 2008) and the London School of Hygiene and Tropical Medicine Ethics committee (21 July 2009).
Results
Baseline characteristics
A total of 3541 infants aged between 0 to 12 months were registered in the Keneba DSS between February 1974 and July 2008. Of these infants, 56 died before they reached 12 months of age. MUAC measurements taken at the age of interest (6-14 weeks) were missing for 556 (16%) infants, 125 of whom had their initial records generated before the age of 6 weeks and 431 after the age of 14 weeks. Ninety six (3%) infants were non-residents of Keneba; 13 (0.4%) infants could not be traced after recruitment ( Fig. 1 ). A total of 2876 infants were included in the analysis (equivalent to 839 747 child-days of observation); their median age at enrolment was 61 days (interquartile range, IQR: 53-76 days). Of these infants, 2033 (71%) were recruited during the long dry season.
At recruitment, the median WFLz was 0.15 (IQR: -0.65 to 0.93) and the median MUAC was 121 mm (IQR: 114-128). Wasting (WFLz < -2) was present in 144 (5%) infants ( Table 1 ). MUAC and WFLz both showed distributions that varied significantly by season of birth (P < 0.01) but not by decade of birth.
Forty infants died and 147 infants were censored between recruitment and 12 months of age ( Fig. 1 ). The median values for age, MUAC, weight and length did not differ significantly between the censored and uncensored infants (P > 0.05). Of the 125 infants whose first record was generated before 6 weeks of age, 5 died before reaching the age of 12 months ( Fig. 1 ). The proportion of infants who died increased as MUAC and WFLz decreased (χ2 test for trend P < 0.001) ( Table 1 ).
Predictive value
WFLz identified very few of the 40 infants who subsequently died. Only 3 of these 40 infants had a baseline WFLz < -2. WFLz was only significantly associated with death at values < -3, whereas MUAC categories of < 115 mm, < 110 mm and < 105 mm were associated with HR estimates of 4.5 (95% CI: 1.4-15), 9.5 (95% CI: 2.6-35) and 23 (95% CI: 4.2-122), respectively ( Table 1 ).
Severe wasting (WFLz < -3) predicted death before 12 months of age with a sensitivity of 7.5% (95% CI: 2.5-20) and a specificity of 98% (95% CI: 97-99), whereas MUAC < 105 mm predicted death with a sensitivity of 20% (95% CI: 11-35) and specificity of 93% (95% CI: 92-94) ( Fig. 3 ). The area under the ROC curve for predicting death was 0.55 (95% CI: 0.46-0.64) for WFLz and 0.64 (95% CI: 0.55-0.73) for MUAC ( Fig. 3 ). Although the point estimate for MUAC was higher, this difference was not statistically significant at the conventional level (P = 0.07).
The cumulative attributable risk associated with values of WFLz < 0 within the study population was 13.0%, while that associated with a MUAC < 130 mm was 51.7% ( Fig. 4 ).
Discussion
We have shown that a single MUAC measurement in infants around the age of vaccination (6-14 weeks) has predictive value with respect to infant death. Contrarily, WFLz had poor predictive value with respect to infant death: the CIs of the area under the ROC curve included 0.5, which suggests that the WFLz values observed were not significantly different from those that would be obtained by randomly allocating children to different risk of death categories. The observed HR for MUAC < 110 mm was broadly comparable to reported pooled odds ratios for all-cause deaths associated with severe wasting among children aged less than 5 years. 38 WFLz identified very few of the infants who died and thus had low sensitivity for predicting infant death.
Little has been published on the use and interpretation of anthropometric measurements in infants aged less than 6 months. A recent study using data from Ghana, India and Peru reported that moderate wasting (WFLz < -2 but > -3) observed during the first immunization visit (i.e. between weeks 6 and 10 after birth) poorly predicted death before 6 months of age. 39 Our findings are concordant with those of this study, which also show that, in the age group of infants aged 6 to 14 weeks, a WFLz < -3 identified a very small proportion of the infants at risk of dying. Similar findings have been reported among infants aged 0 to 12 months in the Congo. 40 Although the age range in this study was broader than ours, the finding suggests that the WFLz cut-offs in current use may be of little value in discriminating younger infants at higher risk of death. Several factors may explain the poor discriminatory and predictive value of WFLz. First, it is possible that WFLz is inaccurately or unreliably measured in infants aged less than 6 months. 22,41 Second, WFLz is a very indirect measure of muscle and fat mass, unlike MUAC. These body compartments, and in particular muscle mass, seem important for health and survival. 42,43
Currently, MUAC is not being measured in infants aged less than 6 months because of lack of evidence to direct its interpretation. In children aged 6 to 60 months, MUAC shows a known bias towards identifying younger and smaller infants as malnourished 44 and the rapid growth experienced by younger infants may make it difficult to establish an appropriate single MUAC cut-off value. In this study we minimized the effects of this age-selection bias by adjusting the hazard model for the infant's exact age in days and by limiting the analysis to an age band of 6 to 14 weeks, which coincides with the age range for routine infant vaccination. The idea was to use a selection criterion that would enhance the operability of our findings within the existing health system. In the case of infants who do not access routine vaccination services, MUAC could also be measured routinely as part of active community-based screening. However, because of improved vaccination coverage throughout Africa, the proportion of infants likely to be missed using this criterion is expected to be small. 23 Further studies exploring the use of MUAC to assess the nutritional status of infants aged less than 6 months are recommended to gather evidence from which to determine the optimum age bands for its use.
A potential MUAC threshold
In our analysis, we focused on investigating the sensitivity, specificity and positive predictive value of MUAC and had intended to use the findings as criteria for selecting an appropriate threshold for this measure, as suggested by Myatt et al. 17 We found that most deaths in the study population were associated with MUAC below a threshold of 130 mm. Specifically, 36% of the deaths in infancy would hypothetically be prevented if MUAC among infants aged 6 to 14 weeks were sustained above a cut-off of 110 mm, whereas practically no deaths would be prevented if WFLz were sustained above -3.
It makes sense to use a MUAC value that identifies infants at high risk of death but likely to benefit from intervention. Thus, the best MUAC cut-off depends on the potential effectiveness and cost-effectiveness of any intervention that might be applied. 17 In the absence of these data, information on predictive and discriminatory value are used instead to define cut-offs. Although in our study infants aged 6 to 14 weeks with MUAC < 115 mm had a fourfold greater risk of dying than those with MUAC > 130 mm, this 115 mm cut-off identified about one fourth (27%) of the infants in the sample and hence lacks specificity (Table 1 ). This finding does highlight, however, the need to improve nutrition in the general population.
From our data, a MUAC cut-off of less than 105 mm would be highly specific in that it would select 7% of the total infant sample, specifically the fraction with a dramatically elevated risk of death (HR = 23). These infants are probably too sick to survive even when treated or require a highly intensive and invasive therapeutic intervention to be rehabilitated. On the other hand, a MUAC cut-off of 110 mm is less specific and would select 14% of the target population, i.e. the fraction having a risk of death nearly 10 times higher than well-nourished infants (HR = 9.5). This 110 mm cut-off may identify a group of infants who, if not acutely ill, could probably benefit from home-based preventive, low-intensity interventions focused on breastfeeding, micronutrient supplementation, good hygiene and prevention of infections. Thus, the choice of a MUAC cut-off value depends on the type of interventions available. The selected cut-off should be tested in practice and verified across various settings.
Study strengths and limitations
To examine the relationship between MUAC in early infancy and death in the first year of life we relied on data from a well-maintained and well-resourced surveillance system with good anthropometric data covering four decades. This data source lends strength to our findings. However, one important limitation of our study is that only 40 deaths were observed among the 2876 infants who were followed up to 12 months of age. This very low death rate, which has been previously noted in Keneba, is believed to result from direct and indirect exposure to improved health interventions in this DSS setting, where research has been conducted for many years. 25 Thus, the presence of the DSS might have undermined our ability to detect the association between anthropometric measures and survival. It may be impossible to find another prospective cohort for validating MUAC cut-offs, but historical data from older cohorts is feasible and can also serve the purpose.
Another important limitation is that the direct causes of death of the infants could not be ascertained because they had not been systematically registered in the DSS. While accurate cause-of-death data would be of considerable interest, collecting it is not easy. Verbal autopsy methods have very poor sensitivity for the most common causes of death in the age group we studied, including pneumonia and gastroenteritis. 45,46 Importantly, the studies that have sought to establish anthropometric criteria for malnutrition in children aged 6 to 60 months have relied on estimates of all-cause mortality rather than cause-specific mortality. 13,14
Conclusion
In infants aged 6 to 14 weeks, which is the age of routine vaccination, MUAC below 115 mm identifies infants more likely to die before the age of one year than well-nourished infants. MUAC can be accurately, affordably and reliably measured with ease, and we recommend measuring it during routine infant vaccination. In the absence of data on the effectiveness of interventions for the management of malnourished infants in a given context, we suggest using a MUAC cut-off of 110 mm to identify infants with a markedly increased risk of death. Research on appropriate clinical guidelines for the treatment of severe acute malnutrition in infants aged less than 6 months is needed to support effective interventions. 
Acknowledgements
We acknowledge the Medical Research Council (MC-A760-5QX00) for its support of the Keneba field station, as well as Keneba staff, study participants and the rest of the Keneba community for their continued cooperation.
Funding: This work was supported by the Kenya Medical Research Institute (KEMRI) through a strategic award (084538) and personal fellowship (083576) from the Wellcome Trust and is published with the permission of the Director of KEMRI.
Competing interests: None declared.
References
1. Kerac M, Blencowe H, Grijalva-Eternod C, McGrath M, Shoham J, Cole TJ et al. Prevalence of wasting among under 6-month-old infants in developing countries and implications of new case definitions using WHO growth standards: a secondary data analysis. Arch Dis Child 2011;96:1008-13. doi: 10.1136/adc.2010.191882 PMID: 21288999
2. Emergency Nutrition Network [Internet]. Management of acute malnutrition in infants less than 6 months (MAMI). Oxford: ENN; 2009. Available from: http://www.ennonline.net/research/mami [accessed 9 October 2012] .
3. Saleemi MA, Ashraf RN, Mellander L, Zaman S. Determinants of stunting at 6, 12, 24 and 60 months and postnatal linear growth in Pakistani children. Acta Paediatr 2001;90:1304-8. doi: 10.1111/j.1651-2227.2001.tb01580.x PMID: 11808904
4. Medhin G, Hanlon C, Dewey M, Alem A, Tesfaye F, Worku B et al. Prevalence and predictors of undernutrition among infants aged six and twelve months in Butajira, Ethiopia: the P-MaMiE Birth Cohort. BMC Public Health 2010;10:27. doi: 10.1186/1471-2458-10-27 PMID: 20089144
5. Santos IS, Matijasevich A, Domingues MR, Barros AJD, Victora CG, Barros FC. Late preterm birth is a risk factor for growth faltering in early childhood: a cohort study. BMC Pediatr 2009;9:71. doi: 10.1186/1471-2431-9-71 PMID: 19917121
6. Florescu L, Bălănică G, Vremeră T, Matei M. [Cross-sectional study to evaluate risk factors in infant malnutrition]. Rev Med Chir Soc Med Nat Iasi 2011;115:699-704. PMID: 22046774
7. Lopriore C, Dop M-C, Solal-Céligny A, Lagnado G. Excluding infants under 6 months of age from surveys: impact on prevalence of pre-school undernutrition. Public Health Nutr 2007;10:79-87. doi: 10.1017/S1368980007219676 PMID: 17212846
8. Pelletier DL. The relationship between child anthropometry and mortality in developing countries: implications for policy, programs and future research. J Nutr 1994;124:2047S-81S. PMID: 7931716
9. Pelletier DL, Frongillo EA. Changes in child survival are strongly associated with changes in malnutrition in developing countries. J Nutr 2003;133:107-19. PMID: 12514277
10. Pelletier DL, Frongillo EA Jr, Habicht JP. Epidemiologic evidence for a potentiating effect of malnutrition on child mortality. Am J Public Health 1993;83:1130-3. doi: 10.2105/AJPH.83.8.1130 PMID: 8342721
11. Pelletier DL, Frongillo EA Jr, Schroeder DG, Habicht JP. The effects of malnutrition on child mortality in developing countries. Bull World Health Organ 1995;73:443-8. PMID: 7554015
12. Briend A, Dykewicz C, Graven K, Mazumder RN, Wojtyniak B, Bennish M. Usefulness of nutritional indices and classifications in predicting death of malnourished children. Br Med J (Clin Res Ed) 1986;293:373-5. doi: 10.1136/bmj.293.6543.373 PMID: 3089529
13. Vella V, Tomkins A, Borghesi A, Migliori GB, Ndiku J, Adriko BC. Anthropometry and childhood mortality in northwest and southwest Uganda. Am J Public Health 1993;83:1616-8. doi: 10.2105/AJPH.83.11.1616 PMID: 8238688
14. Chen LC, Chowdhury A, Huffman SL. Anthropometric assessment of energy-protein malnutrition and subsequent risk of mortality among preschool aged children. Am J Clin Nutr 1980;33:1836-45. PMID: 6773410
15. Management of severe malnutrition: a manual for physicians and other senior health workers. Geneva: World Health Organization; 1999. Available from: http://www.who.int/nutrition/publications/en/manage_severe_malnutrition_eng.pdf [accessed 9 October 2012] .
16. WHO child growth standards and the identification of severe acute malnutrition in infants and children: a joint statement by the World Health Organization and the United Nations Children's Fund. Geneva: World Health Organization & United Nations Children's Fund; 2009. Available from: http://www.who.int/nutrition/publications/en/manage_severe_malnutrition_eng.pdf [accessed 9 October 2012] .
17. Myatt M, Khara T, Collins S. A review of methods to detect cases of severely malnourished children in the community for their admission into community-based therapeutic care programs. Food Nutr Bull 2006;27:S7-23. PMID: 17076211
18. Berkley J, Mwangi I, Griffiths K, Ahmed I, Mithwani S, English M et al. Assessment of severe malnutrition among hospitalized children in rural Kenya: comparison of weight for height and mid upper arm circumference. JAMA 2005;294:591-7. doi: 10.1001/jama.294.5.591 PMID: 16077053
19. Briend A, Wojtyniak B, Rowland MG. Arm circumference and other factors in children at high risk of death in rural Bangladesh. Lancet 1987;2:725-8. doi: 10.1016/S0140-6736(87)91084-1 PMID: 2888951
20. Alam N, Wojtyniak B, Rahaman . mmAnthropometric indicators and risk of death. Am J Clin Nutr 1989;49:884-8. PMID: 2718923
21. Rasmussen J, Andersen A, Fisker AB, Ravn H, Sodemann M, Rodrigues A et al. Mid-upper-arm-circumference and mid-upper-arm circumference z-score: the best predictor of mortality? Eur J Clin Nutr 2012;66:998-1003. doi: 10.1038/ejcn.2012.95 PMID: 22805497
22. Mwangome MK, Fegan G, Mbunya R, Prentice AM, Berkley JA. Reliability and accuracy of anthropometry performed by community health workers among infants under 6 month in rural Kenya. Trop Med Int Health 2012;17:622-9. doi: 10.1111/j.1365-3156.2012.02959.x
23. Arevshatian L, Clements C, Lwanga S, Misore A, Ndumbe P, Seward J et al. An evaluation of infant immunization in Africa: is a transformation in progress? Bull World Health Organ 2007;85:449-57. doi: 10.2471/BLT.06.031526 PMID: 17639242
24. McGregor IA, Smith DA. A health, nutrition and parasitological survey in a rural village (Keneba) in west Kiang, Gambia. Trans R Soc Trop Med Hyg 1952;46:403-27. doi: 10.1016/0035-9203(52)90058-8 PMID: 14958820
25. Rayco-Solon P, Moore SE, Fulford AJ, Prentice AM. Fifty-year mortality trends in three rural African villages. Trop Med Int Health 2004;9:1151-60. doi: 10.1111/j.1365-3156.2004.01325.x PMID: 15548310
26. Lamb WH, Foord FA, Lamb CM, Whitehead RG. Changes in maternal and child mortality rates in three isolated Gambian villages over ten years. Lancet 1984;2:912-4. doi: 10.1016/S0140-6736(84)90664-0 PMID: 6148628
27. McGregor IA, Billewicz WZ, Thomson AM. Growth and mortality in children in an African Village. BMJ 1961;2:1661-6. doi: 10.1136/bmj.2.5268.1661 PMID: 20789304
28. Billewicz WZ, Thomson AM, Thompson B, Illsley R, Rahman AK, McGregor IA. A study of growth and health of young children in tropical Africa. Trans R Soc Trop Med Hyg 1968;62:330-40. doi: 10.1016/0035-9203(68)90083-7 PMID: 5659227
29. Tully M. Nursing with a research unit in Africa. Nurs Times 1978;74:401-5. PMID: 628592
30. Prentice AM. Can maternal dietary supplements help in preventing infant malnutrition? Acta Paediatr Scand Suppl 1991;374:67-77. doi: 10.1111/j.1651-2227.1991.tb12009.x PMID: 1957632
31. Weaver LT, Beckerleg S. Is health a sustainable state? A village study in the Gambia. Lancet 1993;341:1327-30. doi: 10.1016/0140-6736(93)90827-4 PMID: 8098458
32. Rayco-Solon P, Fulford AJ, Prentice AM. Differential effects of seasonality on preterm birth and intrauterine growth restriction in rural Africans. Am J Clin Nutr 2005;81:134-9. PMID: 15640472
33. Poskitt EM, Cole TJ, Whitehead RG. Less diarrhoea but no change in growth: 15 years' data from three Gambian villages. Arch Dis Child 1999;80:115-9, discussion 119-20. doi: 10.1136/adc.80.2.115 PMID: 10325724
34. Moore SE, Cole TJ, Collinson AC, Poskitt EM, McGregor IA, Prentice AM. Prenatal or early postnatal events predict infectious deaths in young adulthood in rural Africa. Int J Epidemiol 1999;28:1088-95. doi: 10.1093/ije/28.6.1088 PMID: 10661652
35. The WHO Child Growth Standards. Geneva; World Health Organization; 2006. Available from: http://www.who.int/childgrowth/standards/en/ [accessed 9 October 2012] .
36. Moore SE, Cole TJ, Poskitt EME, Sonko BJ, Whitehead RG, McGregor IA et al. Season of birth predicts mortality in rural Gambia. Nature 1997;388:434. doi: 10.1038/41245 PMID: 9242401
37. Garenne M, Maire B, Fontaine O, Briend A. Distributions of mortality risk attributable to low nutritional status in Niakhar, Senegal. J Nutr 2006;136:2893-900. PMID: 17056819
38. Black RE, Allen LH, Bhutta ZA, Caulfield LE, de Onis M, Ezzati M et al.; Maternal and Child Undernutrition Study Group. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet 2008;371:243-60. doi: 10.1016/S0140-6736(07)61690-0 PMID: 18207566
39. Vesel L, Bahl R, Martines J, Penny M, Bhandari N, Kirkwood BR. WHO Immunization-linked Vitamin A Supplementation Study Group. Use of new World Health Organization child growth standards to assess how infant malnutrition relates to breastfeeding and mortality. Bull World Health Organ 2010;88:39-48. doi: 10.2471/BLT.08.057901 PMID: 20428352
40. O' Neill SM, Fitzgerald A, Briend A, Van den Broeck J. Child mortality as predicted by nutritional status and recent weight velocity in children under two in rural Africa. J Nutr 2012;142:520-5.
41. Ayele B, Aemere A, Gebre T, Tadesse Z, Stoller NE, See CW et al. Reliability of measurements performed by community-drawn anthropometrists from rural Ethiopia. PLoS ONE 2012;7:e30345. doi: 10.1371/journal.pone.0030345 PMID: 22291939
42. Wolfe RR. The underappreciated role of muscle in health and disease. Am J Clin Nutr 2006;84:475-82. PMID: 16960159
43. Heymsfield SB, McManus C, Stevens V, Smith J. Muscle mass: reliable indicator of protein-energy malnutrition severity and outcome. Am J Clin Nutr 1982;35:1192-9. PMID: 6805298
44. Briend A, Golden MH, Grellety Y, Prudhon C, Hailey P. Use of mid-upper-arm circumference for nutritional screening of refugees. Lancet 1995;345:1119-20. doi: 10.1016/S0140-6736(95)90852-8 PMID: 7715368
45. Snow RW, Armstrong JR, Forster D, Winstanley MT, Marsh VM, Newton CR et al. Childhood deaths in Africa: uses and limitations of verbal autopsies. Lancet 1992;340:351-5. doi: 10.1016/0140-6736(92)91414-4 PMID: 1353814
46. Chandramohan D, Maude GH, Rodrigues LC, Hayes RJ. Verbal autopsies for adult deaths: issues in their development and validation. Int J Epidemiol 1994;23:213-22. doi: 10.1093/ije/23.2.213 PMID: 8082945
Submitted: 16 June 2012
Revised version received: 28 September 2012
Accepted: 1 October 2012
Published online: 16 October 2012
* Correspondence to Martha K Mwangome (e-mail: mmwangome@kemri-wellcome.org).