IJE Advance Access originally published online on March 27, 2006
International Journal of Epidemiology 2006 35(3):648-656; doi:10.1093/ije/dyl016
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Article |
Health impacts of macroeconomic crises and policies: determinants of variation in childhood malnutrition trends in Cameroon
1 Harvard School of Public Health, Boston, MA, USA
2 Population Studies & Training Center, Brown University, Providence, RI, USA
* Corresponding author. 68 Waterman Street, Providence, RI 02912, USA. E-mail: Roland_Pongou{at}brown.edu
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Abstract |
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Background It is generally hypothesized that macro economic shocks worsen child health by lowering household economic status and limiting access to health care, but this proposition seldom has been tested empirically. We examined the effects of economic crises and adjustment programmes during the 1990s in Cameroon on childhood malnutrition in population subgroups and evaluated the household and health system mediators of these effects.
Methods We used pooled cross-sectional data from two Demographic and Health Surveys conducted in 1991 and 1998. In multivariate analysis, we stratified data on child sex and age, maternal education, and place and region of residence. We used a linear regression model to estimate the net effects of changes in average household economic status and maternal health seeking behaviour (MHSB) on changes in the prevalence of malnutrition for each stratum, adjusting for all other variables.
Results The prevalence of malnutrition in children younger than 3 years increased from 16 to 23% (P < 0.001) between 1991 and 1998. The increase in urban areas, from 13 to 15% (P = 0.391), mostly occurred in children of low-educated mothers. The increase in rural areas, from 19 to 25% (P < 0.001), mostly occurred in boys, children older than 6 months of age, those born to low-educated mothers, and those of low economic status. In urban areas, the advantage associated with higher maternal education was robust to all controls, and declines in economic status and MHSB were the mediators of increasing malnutrition. In rural areas, increase in malnutrition was higher in children with lower baseline economic status; decline in MHSB was a significant mediator of worsening nutritional status.
Conclusions The negative nutritional effects during economic crises and adjustment programmes of the 1990s in Cameroon were largest among children of low socioeconomic status. Declines in household economic status and access to health care were the mediators of increasing malnutrition.
Keywords Economic crisis, child health, malnutrition, household economic status, access to health care, developing countries, Cameroon
Accepted 18 January 2006
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Introduction |
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Childhood and maternal undernutrition is the single leading cause of the global burden of disease.1 Malnutrition is likely to worsen during periods of economic crises, a common phenomenon in many developing countries. The nutritional consequences of economic crises and adjustment programmes implemented in many developing countries since the 1970s or 1980s have been the subject of ongoing research. Behrman2 argued that adjustment programmes may have little effect on the health and nutritional status of the poor; other studies have emphasized their negative impact.3–7
Poverty increased after the mid-1980s in Cameroon following a period of economic crisis and subsequent adjustments by the government. From 1985 until 1995, GDP per capita and private consumption per capita fell by 40 and 42%, respectively (Figure 1). After 1988, the government implemented a series of adjustments aimed at achieving macroeconomic stability by reducing public expenditures.8–9 Measures such as reduction of public sector employees and a salary cut of 
60% for public sector workers in 1993, exacerbated by the 50% devaluation of the national currency in late 1993, lowered nominal earnings and purchasing power for a large majority of the population, and resulted in a sharp contraction in household consumption including food and health (Tables S-1 and S-2 in annex). Between 1984 and 1993, a decline in the prices of cash crops (by 38–77%) and a deterioration of the urban–rural terms of trade due to a decline in the prices of food crops resulted in lower income and fuelled poverty in rural areas.10 The fragile return to economic growth beginning in 1995 had no significant economic benefits for households; in 1996–2000, 50.5% of the population lived below the poverty line, compared with 40% in 1984.10–13 In addition to increasing household poverty, public spending on health and agriculture fell by 23% over 1989–92 and by 39% over 1993–94.9
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Based on cross-country data, recent studies have found that lower GDP per worker and income per capita are, respectively, associated with higher frequency of low birth weight14 and underweight in preschool children.15 At the micro-level, a positive relationship between economic status and health status also is well documented.16–17 Studies from developing countries also suggest that declines in household economic status, due for instance to national economic downturns18 or natural catastrophes such as rainfall shocks, drought, or floods, may adversely affect child mortality and nutritional status.19–21 In Cameroon, studies found declining child nutrition in the 1990s,9–10,22–24 mirroring the trends in under-5-mortality rates that rose from 126 to 152 per 1000 between 1991 and 1998.25–26 Some of these studies have related this worsening health situation to the economic crisis, but none of them have addressed in detail the socioeconomic or geographical subgroups that were the most vulnerable. Moreover, there is no study that has identified the routes through which macrolevel economic crises worsen childhood nutrition. Examples of these mediators include lower household income and lower access to, or utilization of, health care. The health effects of these mediators generally have been acknowledged, but a direct association between lowering household economic status and worsening health outcomes has not been demonstrated. Some studies, however, have tested the positive child health effects of added income. Duflo27 found that cash transfer to grandmothers through a social pension programme improved the nutritional status of girls in South Africa, consistent with another study from the same country.28 In contrast, findings from a large experimental rural anti-poverty programme in Mexico (PROGRESA/Oportunidades) showed that reducing monetary poverty through randomized allocations had no impact on child nutrition.29 In the latter study, the transfers were not pure income transfers, but were conditional on certain behaviours such as school and health clinic attendance,30,31 a potential source of selection bias. These apparently conflicting studies motivate further research on the health impact of household economic status changes, especially in developing countries where the economic crises of recent years have generally increased household and community poverty.
We used nationally representative household surveys from 1991 and 1998 to assess changes in levels of malnutrition in children of different socioeconomic groups as defined by place and region of residence, maternal education, maternal health seeking behaviour (MHSB), and household economic status. By considering data from two periods, between which poverty increased at the community and household levels, we also considered how increased malnutrition due to macroeconomic factors may have been affected by changes in household economic status and MHSB. Knowledge of these intermediate factors is needed to target specific interventions that aim to reduce the negative health effects of economic crisis.
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Methods |
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Data sources
We used pooled cross-sectional data from Demographic and Health Surveys (DHS) (http://www.measuredhs.com) conducted in Cameroon in 1991 and 1998, and representative at the country, regional, and urban–rural level. A two-stage probabilistic sampling technique was used to select clusters at the first stage and households at the second. Household response rate was 82.8% in 1991 and 93.7% in 1998. In each household, data were collected on household possessions and on the demographic and socioeconomic characteristics of its members, including maternal education, MHSB, and child sex and age. Additional information included measured data on child anthropometry (weight and height) for children younger than 5 years in 1991 and younger than 3 years in 1998. For comparability between the two surveys, we restricted analysis to children younger than 3 years. We used weight-for-age Z-scores (WAZ), as in many epidemiological studies of undernutrition and child mortality, including in the latest systematic review and meta-analysis.32 The response rate for this anthropometric indicator was 80.7% in 1991 and 85% in 1998, yielding weighted sample sizes of 1587 and 1923.
Variable definitions
The prevalence of malnutrition was defined as the proportion of children with weight-for-age 2 standard deviations (SDs) or more below the median of the NCHS/CDC/WHO international reference population. In descriptive analyses of trends, covariates considered included child sex and age, maternal education and health seeking behaviour, household economic status, and place and region of residence—factors that were associated with nutritional status in Cameroon, with varying effects during the 1990s.17
We measured household economic status with an index variable constructed using the statistical model developed by Ferguson et al.33 The model is designed to measure economic status based on possession of household consumer durables such as electricity, television, bicycle, and car. The basic premise of the model is that wealthier households are more likely to own any given set of assets; and, some assets are likely to be owned at relatively low levels of economic status (e.g. radio or bicycle) while others will be owned only at higher levels (e.g. television or car). The model postulates a continuous (but unobserved) level of economic status predicted by a series of socio-demographic covariates (age and sex of the head of the household, mother's education and occupation, and place of residence), with observed ownership of each asset captured in a set of indicator variables. The inclusion of certain assets presumed to be owned at roughly the same level on an internationally comparable economic status scale allows comparisons across countries or over time. A similar approach was used to estimate levels of MHSB, with predictive covariates including mother's education, occupation and place of residence, and dichotomous indicator variables for prenatal visit, tetanus injection during pregnancy, medical assistance at delivery, knowledge of oral rehydration salt, and possession of a health card for the child. The scales of the resulting estimates of economic status and MHSB are each defined by the identifying assumptions of the model, so we linearly transformed each index into a scale ranging from 0 to 100 to facilitate interpretation. Figure S-1 shows the mapping from the latent scale to the transformed scale for each index and locates the average levels at which people are more likely than not to respond positively to each indicator variable on this scale. Details on the distributions of variables are given in Table 1.
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Statistical analysis
We estimated the effects of cross-year changes in economic status and MHSB on cross-year changes in the prevalence of malnutrition, adjusting for other relevant covariates. Such analysis would ideally use individual cohort data reporting changes in nutritional status, household income, and other factors likely to affect child health during a period of economic crisis. We are not aware of any such longitudinal data in Cameroon. We therefore used repeated cross-sectional DHS data from 1991 and 1998 to build cross-year homogeneous socio-demographic strata defined by child sex and age, maternal education, and place and region of residence. Thus, 208 strata were defined, each with five fixed characteristics matched between the two surveys. 21 strata with observations in 1991 were empty in 1998, 17 strata with observations in 1998 were empty in 1991, and 4 strata were empty in both years (Figure S-2). The remaining 166 strata were used in the analysis. Table S-3 shows the distributions of variables in these strata nationally, and for rural and urban areas. In urban and rural areas, children born to non-educated mothers were present in fewer strata compared with those born to mothers with a primary, secondary, or higher educational level.
In each stratum, we calculated changes in average prevalence of malnutrition, household economic status, and MHSB between 1991 and 1998. The skewness and kurtosis test for normality of the distribution of cross-year changes in the prevalence of malnutrition was statistically significant (P < 0.001). In multivariate analysis, we used ordinary least-squares regression to estimate the effects of changes in economic status and MHSB on changes in the prevalence of malnutrition, adjusting for other cross-year fixed explanatory factors (child sex and age, maternal education, place and region of residence) that defined the strata (Table S-3). Because poor households may be more affected by macroeconomic and structural shocks than affluent households, we also adjusted for the average economic status (ES; average between the 2 years for each stratum) in addition to the change between the 2 years (
ES). This model is described by:
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- PREV: Change in prevalence of malnutrition in each stratum
ES: Change in stratum-average economic status
MHSB: Change in stratum-average MHSB
- ß: Estimated model parameters
- X: Vector of fixed factors (child sex and age, maternal education, place and region of residence)
: Stratum-specific error term, assumed to be normally distributed.
The analysis was conducted nationally, and separately for urban and rural areas.
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Results |
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Changes in prevalence of malnutrition between 1991 and 1998
Weight-for-age malnutrition increased from 16 to 23% (P < 0.001) in children younger than 3 years between 1991 and 1998 (Table 2). The increase in malnutrition was larger in rural areas (from 19 to 25%; P < 0.001) than urban areas (from 13 to 15%; P = 0.39). The proportion of malnourished children increased from 9 to 14% (P = 0.06) in the North-West and South-West provinces, and from 29 to 34% (P = 0.07) in Northern Cameroon. In the West and Littoral provinces, the increase was larger in rural areas where prevalence doubled from 6 to 12% (P = 0.05).
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The rise in malnutrition was greater in boys (from 15 to 25%; P < 0.001) than in girls (from 18 to 21%; P = 0.10). In both sexes, increases were larger in rural areas. Among girls in urban areas, there was a slight decrease, statistically not significant, P = 0.80. Malnutrition increased for all age groups above 6 months, and with an increasing age gradient. In children aged 0–5 months, the prevalence of malnutrition dropped from 3 to 2% (P = 0.23). Children aged 12–23 months had the poorest nutritional status in both years, as well as the highest absolute increase in malnutrition (from 24 to 33%; P < 0.001).
Increase in malnutrition occurred in all maternal educational groups and household economic status quartiles, but low educational groups and low-economic-status households were more affected (Figures 2 and 3). In children born to non-educated mothers, the prevalence of malnutrition increased from 27 to 36% (P = 0.001), with similar changes in urban and rural areas. In children whose mothers attained a primary level, increase was larger in rural areas (from 11 to 20%; P < 0.001). There was no significant increase in malnutrition among children whose mothers had secondary or higher education. In urban areas, children born to educated mothers had higher absolute declines in economic status and MHSB compared with those born to non-educated mothers (Table S-4). Prevalence of malnutrition rose from 23 to 31% (P = 0.01) and from 19 to 26% (P = 0.01) in the lowest two quartiles of economic status. Increase in malnutrition in children of the third or highest quartile was smaller except in rural areas where malnutrition rose from 4 to 10% (P = 0.15) in the highest quartile. Almost all MHSB groups were affected by increasing malnutrition (Figure 4). The increase was not significant in children of the bottom quartile (from 30 to 35%; P = 0.12), was lowest in the second and the top quartile, and was the highest in the third quartile (from 10 to 17%; P < 0.001), with higher concentration in rural areas (from 7 to 19%; P < 0.001).
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Multivariate results
Results for the multivariate regression analysis of the changes in the prevalence of malnutrition nationally and at the urban–rural level are presented in Table 3. Region of residence did not predict the changes in malnutrition and was removed from the analysis; in general, its inclusion reduced the goodness-of-fit of the model in rural areas as the P-value for the F-statistics fell from 0.059 to 0.037 after region was removed (the log-likelihood ratio test was not significant; P = 0.31). Region of residence was a significant predictor of child nutritional status in 1991 and 1998 in Cameroon.17 The present study suggests that differential regional changes in malnutrition during the 1990s were mediated by other socioeconomic factors (e.g. education, economic status, and MHSB).
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Nationally, children born to mothers with a secondary or higher educational level had lower increase in malnutrition than those born to non-educated mothers (P = 0.077). This means that the advantage associated with maternal education increased during the 1990s in Cameroon, and confirms the result found in descriptive analysis. The relative disadvantage of boys over girls during that period was also robust to all controls. The bivariate urban–rural differentials in rise in malnutrition were no longer significant after controlling for other factors. Reductions in economic status (P = 0.058) and MHSB (P = 0.04) were both positively associated with increase in the prevalence of malnutrition. Higher baseline economic status was associated with a smaller increase in malnutrition, but this relationship was not significant.
Separate analyses for urban and rural areas showed that factors associated with increase in malnutrition varied by place of residence. In urban areas, the relationships were similar to those observed at the national level, but the effects of education and changes in economic status and MHSB were larger. Increase in malnutrition was inversely related to maternal education (ß = –0.317 and P = 0.029 for primary level, and ß = –0.388 and P = 0.016 for secondary or higher level). Reductions in economic status (P = 0.073) and MHSB (P = 0.024) were associated with increases in malnutrition. Baseline economic status had no effect in urban areas, meaning that poor and rich children had the same increase in malnutrition after controlling for other factors.
In rural areas, boys had a greater increase in malnutrition than girls (P = 0.053). Children aged 6–11 months or 24–35 months had larger increase than those aged 0–5 months (P = 0.009 and 0.036, respectively). Declines in economic status were associated with worsening nutritional status, but this result was not statistically significant after controlling for other factors. Declines in MHSB were significantly associated with increased risk of malnutrition (P = 0.009). Above and beyond changes in economic status and MHSB, baseline economic status had a significant effect in rural areas; poor children had a greater increase in malnutrition even after controlling for changes in economic status and MHSB (P = 0.095).
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Discussion |
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Weight-for-age malnutrition increased from 16 to 23% in children under 3 years of age in Cameroon between 1991 and 1998, a period of decline in national earnings and consumption. We found that the increase was larger in rural areas, similar to the differential changes in poverty (poverty rose from 43 to 63% and from 14 to 22% between 1984 and 1996 in rural and urban areas, respectively).10,34
We found that children of low economic status and those with lower maternal education were most vulnerable to consequences of the economic crisis. Educated mothers may have greater capacity to substitute with less costly sources of nutrients during periods of recessions, thereby alleviating risks of malnutrition in their children. In contrast, the ability for such substitution can be very limited in mothers with limited education and low economic status as suggested by Weil et al.,3 increasing risks of household food insecurity. In addition, declining income in deprived households may result in the inability to purchase health care, especially in a context of low health supply and declines in health workers' performance as in Cameroon during the 1990s,35,36 thereby increasing risks of untreated infectious diseases (e.g. diarrhoea, malaria, etc.) and malnutrition in poor children.
The advantage associated with education disappeared after adjusting for other socioeconomic factors in rural areas, but it was robust to all controls in urban areas. Urban infrastructure increases the availability of food and health care, and provides potential for improved environmental conditions. More educated mothers, therefore, have higher access to alternative choices. In rural areas, the absence of such community factors exposes children born to educated and low-educated mothers to similar community poverty (e.g. lack of health care facilities or potable water, seasonal shortages of food, unhygienic environment, etc.) This may explain why differential trends in malnutrition among children of different maternal educational groups are entirely explained by household socioeconomic factors in rural areas.
Declines in economic status and MHSB were among the routes through which economic crisis and subsequent adjustment measures worsened malnutrition during the 1990s in Cameroon. Malnutrition in urban children was more affected by changes in economic status than in rural children. In urban areas, people are tied to the monetary economy, and child nutritional status can be very sensitive to any changes in income. Rural households may be less sensitive to changes in income because agricultural subsistence economy partially enables them to meet their nutritional needs. Declines in MHSB were the most important socioeconomic predictor of increasing malnutrition in rural areas. In these areas, declining access to health exacerbated by seasonal food shortages, imbalanced dietary intake, and unclean environment are likely to increase the prevalence of malnutrition. It is also important to note that economic status and MHSB are not really separable, since MHSB may be affected by economic change. Beyond changes in economic status and MHSB in rural areas, increase in malnutrition was higher in children with lower baseline economic status. These children may have experienced a greater burden of untreated infectious diseases during this period due to imbalanced dietary intake, poverty, and difficult access to health care facilities.
A recent study forecast the prevalence of malnutrition to decrease by 15% in Middle Africa between 1990 and 2015.37 Our results indicate that Cameroon is unlikely to benefit from this general trend. Block et al.38 found that the prevalence of anaemia in children under 5 increased during Indonesia's 1997–98 crisis, but the prevalence of malnutrition or average weight-for-age Z-scores were not affected.39–41 This finding could stem from the fact that these indicators are not very sensitive to short-term shocks as in Indonesia, in contrast to the 1990s economic crisis in Cameroon which lasted for years. However, Block et al.38 also suggested the role of mothers in buffering children's caloric intake to explain little childhood nutritional effects of the 1997–98 Indonesian crisis. Our study is consistent with the long-established finding that the adverse nutritional effects of economic crises and resulting adjustment programmes in developing countries are magnified in the poor.4
The quasi simultaneity of the economic crisis and the structural adjustment programmes in response generally makes it difficult to separate the effects of these phenomena. In Cameroon, although structural adjustment began in 1988, only in 1992–94 were there large reductions of the government spending on social sectors including health.36 Therefore, children born in 1995–98 were likely to receive a greater shock compared with children born in 1988–91, even though they all lived through the economic crisis. The significant role of MHSB, after adjustment for baseline economic status and changes in economic status, may also reflect the impacts of structural adjustment on health care delivery. This would be consistent with findings that, in Indonesia, donor assistance towards health care financing mitigated the macroeconomic shock of 1997–9842 and that, in Cuba, social policy responses to the 1990s economic crisis lessened the negative impact on households and children.43
The demonstrated negative impacts of large economic adjustment on child nutrition should motivate systematic reconsideration of the potential health impacts of future macroeconomic policies. Even when public health cannot influence economic policy and avert economic crisis, identifying the socioeconomic groups most affected by macro shocks provides an opportunity for targeted interventions to lessen the negative impacts. Interventions focusing on maternal knowledge of alternative sources of nutrition,41 nutritional supplements, access to health care delivery, household and community hygiene (water, sanitation, and fuel) may help prevent increasing malnutrition during periods of economic crisis. Beyond public health interventions, promotion of maternal education as a long-term measure is important.45,46
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Supplementary material |
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Supplementary tables and figures can be found at IJE Online (http://ije.oxfordjournals.org).
KEY MESSAGES
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Acknowledgments |
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This work was sponsored by the National Institute on Aging (Grant PO1-AG17625). We thank Michael R. Reich, George Zeidenstein, Gervais Beninguisse, Laura Reichenbach, Muna S. Meky, Kevin JA Thomas, Zacharie T Dimbuene and two anonymous referees for comments on previous versions. We thank Ajay Tandon and Emmanuela Gakidou for help with the construction of economic status and MHSB index.
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