7 Baka Darwin Initiative project: Baka health

1. Enabling Baka attain food security, improved health and sustain biodiversity Lecture 7: Baka health

2. Based on two papers

3. Introduction

4. Baka health in comparison to indigenous health worldwide • Most Indigenous groups are marginalized and excluded from political and economic power, suffer from systematic discrimination, and are often dispossessed of their ancestral lands and deprived from their way of life (UN Department of Economic and Social Affairs 2009). • Consequence, many Indigenous Peoples are • disproportionately poor • suffering from major health issues • suffering from higher rates of disease and premature death than the sympatric majority populations • In a systematic review of indigenous health worldwide, the Baka in Cameroon stand out • exhibiting the highest reported disparity in premature death (Anderson et al. 2016) • Life expectancy at birth for Baka Pygmies is on average 35 years of age, compared to the sympatric Bantu population who live about 22 years longer (The Lancet 2016).

5. Effects of displacement and sedentarisation • Pygmy populations have been • relocated as a result of displacements by other ethnic groups (Dounias and Froment 2006) • have been forced to relocate and sedentarise (Knight 2006) • Have voluntarily accepted sedentarisations, e.g. the Baka in our study area • These changes have had consequences on their lifestyles and nutrition (Reyes-García et al. 2019) • Example: • The declaration of protected areas in 1991 left Batwa Pygmies as squatters on the lands of neighbouring farmer communities (Jackson 2005), resulting in severe poverty. When Batwa families were given land, under-five mortality rates dropped from 59% to 18%, demonstrating the crucial importance of land for survival

6. Malnutrition • Sub-Saharan Africa has one of the highest levels of child malnutrition globally (Akombi et al. 2017) • Malnutrition refers to deficiencies, excesses, or imbalances in a person’s intake of energy and/or nutrients. • ‘undernutrition’ including • stunting (low height for age) • wasting (low weight for height) • underweight (low weight for age) • micronutrient deficiencies or insufficiencies (a lack of important vitamins and minerals) • ‘overnutrition,’ including • overweight • obesity • Gravest single threat to global public health • accounting for about 45% of deaths in children <5 years of age (WHO 2018)

7. Body mass index BMI • measured as weight (kg) divided by the square of height (m2) • only weakly correlated with genetic ancestry, indicating a greater contribution of non-genetic factors to body weight than to height (Pemberton et al. 2018) • useful standard • to assess nutritional status of people • commonly used in epidemiological studies on weight and risk of death (Flegal et al. 2014) • widely used in determining public health policies (Nuttall 2015).

8. Four BMI standard categories • underweight (< 18.5) • Causes • impact of some diseases • malnutrition • Effects • heightened mortality • high disease burden • a conservative estimate is that at least 32%of the global burden would be eliminated by eradicating malnutrition • normal weight (18.5–24.9), • overweight (25.0–29.9) • obesity (≥30)

9. Documented BMI data in Pygmy People are inconsistent • Pygmy populations compared to their sympatric non-Pygmy neighbours • pygmy men and women had consistently lower mean BMI in eight and nine studies, respectively (Froment 2014). • no statistical difference in mean BMI was found by Pemberton et al. 2018 • Possible reasons: • sampling bias • socioeconomic and nutritional status of Pygmies may have improved

10. Body height and stunting • Growth failure leading to stunting is the most prevalent type of undernutrition worldwide (UNICEF et al. 2020). • stunting • increases morbidity and mortality, risk of infection • reduces and delays physical and cognitive development • resulting in low adult incomes and intergenerational transmission of poverty • Short stature is not usually by itself a problem • but because stunting is measured against international standards, mainly derived from non-indigenous people, it is not known how much stunting occurs in Pygmy people as their growth curves • are not part of the international standard • poorly documented

11. Body height • Pygmy people have a characteristically low stature • average adult height of 155 cm • from 142 cm amongst the Western Congo Basin Efe to 161 cm in the Eastern Twa (Froment 2014). • Pygmy growth patterns are unique, stemming from specific genetic foundations that determine the African Pygmy phenotype (Ramírez Rozzi et al. 2015; Pemberton et al. 2018). • The mechanisms linking genetics and nutritional status in Pygmies, such as fat deposition, remain unknown.

12. Growth standards • To improve maternal, infant and young child nutrition appropriate diagnostic tools, such as reference growth curves, are therefore essential to guide local, national, and international health policies • Standards • WHO Child Growth Standards from birth to 5 years of age (WHO Multicentre Growth Reference Study Group 2006) • growth curves for the 5 to 19 years age group developed by Bloem 2007 and de Onis et al. 2007

13. Use of growth standards • Malnutrition is defined relative to the WHO child growth standard • stunting = small size for age (z-score of HA ≤ −2 SD) • wasting = low weight per height (z-score of WH ≤ −2 SD) • underweight = low weight per age (z-score of WA ≤ −2 SD) • obesity = high weight per age (z-score of WA > +2 SD) • low BMI = low BMI per age (z-score of BA ≤ −2 SD) • Cut-off criteria for severe cases of these types of malnutrition are three standard deviations (±3 SD) • P.S. z-score (also called a standard score) • gives an idea of how far from the mean a data point is • a measure of how many standard deviations below or above the population mean an observed value is

14. Growth standards for Pygmy People • Child growth charts are considered good models for how children should grow in all countries and for all ethnicities (WHO Multicentre Growth Reference Study Group 2006; Bloem 2007; de Onis et al. 2007; deOnis 2008) • broad consensus for the absence of major genetic differences in the average growth potential among human populations • Environmental and socioeconomic factors affect growth as demonstrated by the widespread upturns in adult height in affluent countries and more recently in developing countries, the so called secular trends, as living conditions have improved over the last century • since 2011, except for a few countries, the WHO growth standards have been adopted worldwide • to guide health policies • to advise mothers regarding nutrition of their children including medication for children

15. Growth standards for Pygmy People • Based on data gathered from healthy breastfed infants and young children from widely diverse ethnic backgrounds and cultural settings (Brazil, Ghana, India, Norway, Oman, and USA). • but excluding rainforest hunter-gatherers in South America and Africa including Pygmies • But what about Pygmy people? • because they are small, children are categorised as stunted • or are they stunted because of nutritional insufficiency and infectious disease that are generally suggested as being the main causes of small stature?

16. Aims Analyse BMI of Pygmy populations and their non-Pygmy neighbours • Baka versus Bantu in Southern Cameroon • Across their distribution range in Central Africa Estimate stunting, wasting, and obesity frequencies for a large sample of Baka children in Southern Cameroon • compare with published Amazonian hunter-gatherers, the indigenous Tsiname of Bolivia, data • to do so, we develop a reference growth curve for the studied Baka population

17. Materials and Methods

18. Cameroonian study villages • Collection of morphometric data of Baka children and adults • 42 villages southeastern Cameroon • we visited 34 of these in four campaigns in 2010, 2017, 2018 and 2019 • 18 villages were visited once, 7 twice, 5 three times, and 4 four times

19. Sampling with informed consent • A day before the clinic, we held meetings with the village chiefs from whom we obtained permission to carry out our work • We verbally informed attendees of the purpose of the study and procedures used. • Participation in data recording was voluntarily and anonymously • Names were recorded for clinical purposes • Prior to data analysis, the names of participants were dissociated from all datasheets to maintain confidentiality • Those who agreed to participate and to provide their data gave written informed consent. • Permit Arrete No. 00034 of the Minstere de L’AdministrationTerritoriales et de la Decentralisation

20. Sampling representativeness • The 2018 and 2019 clinics focused on the same 10 villages • In 2018, we mapped all houses in these 10 villages and identified the number of families and persons per family per village • Sampling representativeness in 2018 • 75% of adults • 49% of 2-12 year old children

21. Anthropometric measurements: mobile clinics • During mobile clinics • run by a team of qualified medics • under the auspices of Zerca y Lejos (ZyL), a Spanish NGO, providing providing health and development support since 2002 • Clinics were open to all villagers • Villagers were encouraged to attend even if they were not unwell

22. Sampling effort • Weighed and measured • 1917 persons of different ages and ethnicities (551 in 2010, 515 in 2017, 553 in 2018 and 298 in 2019). • Ethnicities: • 1651 Baka • 260 Bantu • 3 from other ethnicities • 3 undeclared • We examined a total of 191 persons in more than one campaign • Anthropometric data analysed • 1092 2-to-12 year old Baka children for the growth curves • 117 of these were seen in two different years, but only the first year was analyzed to avoid statistical biases • 402 adults (331 Baka and 71 Bantu) aged between 16 and 70 years for BMI analysis • 44 persons were measured more than once, but included in the analysis • 28 pregnant women were excluded

23. Anthropometric measurements: data • Residence • Ethnicity (self-declared; Baka and Bantu) • Name, sex, and age (in months or years) • Weight (kg) • Infant and child weights to the nearest 0.1 kg (participants dressed in light clothing) • Up to 25 kg: suspended dial balance (Kerbl®) • Others: digital scale (Accuweight®) • Height (cm) • Infants: mobile Seca® 210 (graduation 5 mm) pediatric measuring mat • Others: barefoot to the nearest 0.1 cm using a portable Seca® 213 stadiometer • Clinical data • hemoglobin levels, Hg, using a portable system (HemoCue ® Hb 201+) • absence/presence of edema

24. Children growth standard: analysis • Using the a Generalized Additive Models for Location Shape and Scale (GAMLSS) using the R-package ‘gamlss’ • the R-package replicated the WHO modelling procedure as close as possible • albeit modelling ages of 0 to 29 in one approach rather than WHO’s modelling of the two age groups 0–5 years and 5–19 years old separately • Analysis of z-scores

25. Baka-specific problems: no exact age data for children and adults • The traditional growth curve (construction and interpretation) requires exact age data • But these are not available for Baka because • Baka have a different concept of time • official documentation of identity and age was generally not available, • Mothers were asked for the age of their children in years • We also crosschecked age estimations by dental eruption, but this method only allows categorization in years • The assessment of the effects of the uncertainty in age classification by Monte-Carlo simulations of the growth curve

26. BMI of adults: analysis • statistical analysis was performed using Wald test statistics of Generalized Estimating Equations (GEE) • a general method for analysing data containing longitudinally collected, possibly autocorrelated observations, as in our case (repeated visist of the clinic in different years) • Non-parametric Mann-Whitney-Wilcoxon Test was used to test whether age and height distributions in Baka and Bantu are identical. • Linear regression was used with age, sex, and ethnicity as independent variables

27. Height and weight data for Pygmy and sympatric populations from Sub-Saharan Africa • Published data from the Demographic and Health Surveys (DHS) from • Cameroon 2004 • Republic of Congo 2005 and 2011 • Democratic Republic of Congo (DRC) 2013 • Gabon 2012. • No such surveys were available for the other countries where Pygmies live or ethnicity was not recorded • DHS • standardised and representative sampling • objective information on anthropometric measures • thus allowing comparisons between countries and over time • Additional DHS data • wealth index and anaemia as estimators of individual living standards and nutrition status • Only data from adult females were available.

28. DHS data: analysis • Filtering: • extreme values for height (< 100 cm or > 200 cm) and weight (< 25 kg or > 200 kg) were excluded to account for data inconsistencies • Statistical analysis was performed using ANOVA models • adequate as DHS data set did not include longitudinal observations • Sign test together with a two-sided binomial test • Non-parametric Mann-Whitney-Wilcoxon Test was used to test whether age and height distributions in Baka and Bantu are identical. • Linear regression was used with age, sex, and ethnicity as independent variables

29. Results

30. Baka child growth curves • Growth curves were constructed from 1092 2-to-12 year old Baka children • 95 Baka babies < 2 years old and 156 Baka between 12 and 29 years to avoid edge effects for estimating the 2-to-12 year old growth curves • Impact of age estimation • Observed and simulated characteristics of the growth curves (z-scores) were on average very similar but variances were large • individual level: dramatic effects on the reliability of estimated individual z-scores • population level: distributions of z-scores were robust

31. Height, weight and BMI of Baka children

32. Z-scores for using Baka Tziname and WHO growth standards • Scores • HA, top left: height per age • WA, top right: weight per age • WH, bottom left: weight per height • BA, bottom right: BMI per age • Distributions of z-scores for Baka children with z-scores derived from WHO, Tsiname and Baka growth standards • Very similar for weight per height (WH) and BMI per age (BA) • large shifts to negative values for height per age (HA) and weight per age (WA)

33. Malnutrition calculated from Tziname- and WHO-derived z-scores • Prevalence of malnutrition, defined relative to theWHO child growth standard (based on WHO-derived z-scores) • stunting at 57.8% • wasting at 25.5% • underweight, obesity and low BMI at 5% • All values were < 11% when estimated against the Tsiname reference population

34. Comparing WHO-derived z-scores ≤ − 2 SD between Baka and Tsimane children • For the age classes 2 to 5 years and 5 to 10 years • Percentages are consistently larger for Baka than for Tsimane for all parameters and age classes

35. BMI data for Baka and Bantu adults in the field study • The three-year age difference between Baka and Bantu was not significant (Mann-Whitney U test, n = 402,W= 10,216, p = 0.07) • but reflected the lower life expectancy of Baka recorded in the literature (The Lancet 2016). • Baka were on average 13.2 cm smaller than Bantu (Mann-Whitney U test, n = 402, W= 3685, p < 0.0001).

36. BMI of Baka men and women in relation to age • Baka (right) exhibited a strong decline in BMI with increasing age. GEE analysis confirmed • significant age-specificity (Wald statistics: 19.3, p < 0.0001) • no gender-specificity (Wald statistics: 0.7, p = 0.4) • Bantu (not shown) had a BMI distribution independent of age (p = 0.21) but with a strong sex bias (p = 0.0009)

37. BMI of Baka and Bantu in relation to age with age and sex • Mean ± SD BMI was with 22.8 ± 3.58 on average 2.45points higher for Bantu than Baka • Pooled data showed significant • age(Wald statistics: 11.4, p = 0.007) • sex (Wald statistics: 5.6, p =0.018) • ethnicity (Wald statistics: 35.5, p < 0.0001)

38. DHS surveys: BMI over age 1 • No or negative growth of BMI of Pygmies with increasing age (A). With only one exception, the non-Pygmy Bakukuya people in Congo 2005, the slope of the growth for Pygmy populations was less than all non-Pygmy African ethnicities in • Cameroon in 2004 (n = 47 non-Pygmy ethnicities), • Congo 2005 and 2011 (n = 90 and n = 10, respectively), • DRC in 2013 (n = 8), • Gabon in 2012 (n = 7)

39. DHS surveys: BMI over age 2 • Comparison of BMI of Pygmy individuals and individuals of the non-Pygmy ethnicity with the largest sample size from five DHS surveys across the Pygmy distribution where ethnicity was recorded • Pygmies: negative slopes • Non-pygmies: positive slopes

40. DHS surveys: other parameters • Pygmy people were also theleast wealthy populations in all surveys (B) • When includingthe wealth index in the ANOVA, wealth was alwayssignificant • Mean BMI showed the same pattern • however, the mean for Cameroon 2004 wasnot amongst the smallest of the surveyed non-Pygmy populations • consistent with the fact that only the Cameroon 2014 survey did not include Baka aged over 35 years • thus excluding smaller BMI values for older peoplethat can be expected due to the significantly negative slope of BMI with increasing age (previous slide) • Anemia (D) inconclusive

41. Discussion

42. Methodological limitation: age estimation • Reliable age estimations are important for clinicians for assessing child growth and for research in evolutionary anthropology • however, not available for Baka • simulations • for individuals, the ranges of possible differences between observed and simulated z-scores was very high, indicating that the estimators for malnutrition can be completely wrong in some cases • the current Baka growth curve is of limited use for the clinician to assess individuals • thus, longitudinal studies for the construction of reliable reference growth curves required • for populations: results were robust • ranges for the percentages of stunted and wasted children was only between 2% and 3% and less than 1% for BMI

43. Methodological limitations: health status • Sample coverage of our study villages was high both in terms • number of villages sampled (34 out of the 42 in the region) • percentage of the population samples (75% of adults and 49% of 2-12 year old children in 2018) • we focused on general health screening of children and adults and did not concentrate on malnourished or sick individuals • field studies like ours cannot exclude sampling biases • very small observed skews in the Baka growth distributions (see paper) indicate that the sample is not biased towards malnourished individuals

44. Methodological limitations: DHS • DHS data are designed to be representative • Pygmy populations in the DHS surveys have very small sample sizes • approximately 1% to 4% of the largest sample of any non-Pygmy population in any of the five DHS surveys • absolute sample sizes are small, ranging from n = 10 (Cameroon 2004) to n = 90 (Congo 2011) • This reflects the number Pygmy People in the total population but might be sensitive to sampling bias • The joint results of the five DHS surveys together with the field survey are robust as they all show the same trends.

45. Stunting in Baka children • Stunting (z-score of HA ≤ −2 SD) is very high in Baka – higher than the Tsiname and other reported data • In particular, stunting of 64% and 73% for 2-to-4 year oldgirls and boys, respectively, is astonishingly high • compared toanywhere in the world • including sub-Saharan Africa, wherethe rate was highest in Burundi, Eastern Africa with 57.7%(Akombi et al. 2017) • approximately twice as high compared to the rate of 32.5% reported from the most recent DHS survey for mainly non-Pygmy people from Cameroon, and theaverage of 28.8% for Central African countries (Akombi et al. 2017)

46. How much of this stunting is caused by health and the environment? • Reasons why these extremely hight stunting rates do not allow conclusions about Baka children health include: • Stunting rate dramatically different (i.e. lower) when calculated relative to the Tsimane and not the WHO-standard • Wasting (z-score of WH ≤ −2 SD) is similar to other populations • within the same range reported by the DHS data for • Cameroon (5.6%) • Central African countries (6.7%, 95% CI: 4.2–9.2) • the highest level of 18% was reported for Niger (Akombi et al. 2017) • Evidence for genetic basis of growth in Pygmy People • Several lines of evidence • there is also support that these genetic foundations are the result of adaptation to the particular rainforest environment

47. One child growth standard for size fits all? • WHO growth curves are also being vehemently defended as growth standards regardless of ethnicity with the explicit aim to gear health interventions and policies towards achieving these idealized growth standards • Indeed, facilitate inter-population comparisons • BUT, the growth standard height-for-age is clearly not adequate for Baka • this, the most-often applied and normally powerful parameter height-for-age (stunting) will leave the clinician without the background information needed to interpret these data • Our Baka growth standard gives the clinician an indication, but it is still unprecise because it is based on inadequate age data • Requiring a Baka-specific growth curve based on exact age data • Neither our nor the study on the Tsimane demonstrate a deviation of the standards for weight-for-height and BMI-for-age • these should continue to be used

48. BMI trends with age • Pygmy populations: • BMI either declined with age or remained stagnant • All non-Pygmy-populations • BMI either declined with age • This age dependence in western populations has been well documented, leading to the use of age adjusted weight standards for health monitoring and the insurance industry

49. BMI in Baka adults is of high concern • Baka BMI is on average lower that that of their non-Pygmy neighbours • DHS data demonstrate a correlation between BMI and household wealth and correspond with Bakas’ generally low socioeconomic status • Baka represent a general trend that “the poor stay thinner” (Neuman et al. 2011) • Pygmy populations had lower obesity percentages and higher underweight percentage than their neighbours • the declining or stagnant BMI trajectory over age is of particular concern • sets the Pygmy populations apart not only from all other ethnic entities in the region but from the general worldwide trend of increasing body weight and, thus, BMI, over age • the observed negative trend in the field study and in three out of five DHS studies increases health risks associated with underweight and is, consequently, fully aligned with the extraordinary high premature death rate among Baka, who die on average 22 years earlier than their non-Pygmy neigbours (Anderson et al. 2016).

50. Next: Ethnobotanical survey of wild edible plants used by Baka People in southeastern Cameroon