Evaluation of Linear Growth at Higher Altitudes

JAMA Pediatrics, Volume 174, No. 10, Year 2020

Interpretation

Importance: Irrespective of their genetic makeup, children living in an ideal home environment that supports healthy growth have similar growth potential. However, whether this potential is true for children residing at higher altitudes remains unknown. Objective: To investigate whether altitude is associated with increased risk of linear growth faltering and evaluate the implications associated with the use of the 2006 World Health Organization growth standards, which have not been validated for populations residing 1500 m above sea level. Design, Settings, and Participants: Analysis of 133 nationally representative demographic and health cross-sectional surveys administered in 59 low- A nd middle-income countries using local polynomial and multivariate regression was conducted. A total of 964299 height records from 96552 clusters at altitudes ranging from-372 to 5951 m above sea level were included. Demographic and Health Surveys were conducted between 1992 and 2018. Exposures: Residence at higher altitudes, above and below 1500 m above sea level, and in ideal home environments (eg, access to safe water, sanitation, and health care). Main Outcomes and Measures: The primary outcome was child linear growth deficits expressed in length-for-age/height-for-age z scores (HAZ). Associations between altitude and height among all children and those residing in ideal home environments were assessed. Child growth trajectories above and below 1500 m above sea level were compared and the altitude-mediated height deficits were estimated using multivariable linear regression. Results: In 2010, a total of 842 million people in the global population (approximately 12%) lived 1500 m above sea level or higher, with 67% in Asia and Africa. Eleven percent of the sample was children who resided 1500 m above sea level or higher. These children were born at shorter length and remained on a lower growth trajectory than children residing in areas less than 1500 m above sea level. The negative association between altitude and HAZ was approximately linear through most part of the altitude distribution, indicating no clear threshold for an abrupt decrease in HAZ. A 1000-m above sea level increase in altitude was associated with a 0.163-unit (95% CI,-0.205 to-0.120 units) decrease in HAZ after adjusting for common risk factors using multivariable linear regressions. The HAZ distribution of children residing in ideal home environments was similar to the 2006 World Health Organization HAZ distribution, but only up to 500 m above sea level. Conclusions and Relevance: The findings of this study suggest that residing at a higher altitude may be associated with child growth slowing even for children living in ideal home environments. Interventions addressing altitude-mediated growth restrictions during pregnancy and early childhood should be identified and implemented.. While researchers have used different definitions for high altitude,16 we used the 1500 m above sea level threshold because sites located above this altitude were not considered eligible in the Multicentre Growth Reference Study. The analysis took place in stages. Surveys were conducted between 1992 and 2018. We first estimated the number of people residing 1500 m above sea level in each country. We then compared child growth trajectories at lower than 1500 m above sea level and 1500 m or more above sea level altitudes using local polynomial regression methods. Previous literature has attributed high-altitude growth deficits to poorer nutrition, health, and socioeconomic conditions at high-altitude localities.13,17 Considering these issues, we accounted for the role of confounding factors in 3 ways. First, we used linear regression methods to quantify the child height deficit associated with altitude after adjusting for confounding factors. Second, we used the same multivariable regression models to assess how altitude is associated with immediate causes of malnutrition: diets and disease.4 Third, we restricted the sample to children who resided in ideal home environments8 and used regression methods to assess the association between altitude and child height within this sample. The protocols and questionnaires of DHS surveys have been reviewed and approved by ICF institutional review board and the institutional review boards of the host countries. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies. The country-level population data disaggregated by altitude were based on data from the Center for International Earth Science Information Network (CIESIN), Columbia University.18 We used nationally representative and publicly available cross-sectional DHS survey data from 59 low- and middle-income countries. We used all available DHS surveys that collected anthropometric measures for children younger than 5 years and restricted the analysis to surveys that recorded the altitude of the survey cluster or the global positioning system coordinates of the cluster that permitted us to obtain the altitude from external sources. A cluster was equivalent to a village in rural areas or a neighborhood in urban areas. Linear growth (faltering) was expressed in HAZ that measures the distance in height to the median child in the 2006 WHO growth standard.7 For convenience, we used the term HAZ to refer to both length for age (<24 months) and height for age (≥24 months). Children with biologically implausible height measures (HAZ <−6 or HAZ >6) were excluded from the analysis. A total of 964 299 height records (51% boys, 49% girls) from 96 552 clusters from 133 surveys administered in 59 countries were used (eTable 1 in the Supplement). The clusters’ altitudes ranged from −372 to 5951 m above sea level. To examine HAZ age trajectories in low- and middle-income countries,19 we used local polynomial regressions that regressed the child’s HAZ on their age in months. We ran separate regressions for children residing in clusters lower than 1500 m above sea level (n = 857 858) and children residing in clusters 1500 m or more above sea level (n = 106 441). We compared the regression lines and their 95% CIs between the 2 groups to assess whether the differences in growth trajectories were statistically different from zero. The weights in these regressions were based on the Epanechnikov kernel-density function, and the bandwidth was selected using the rule-of-thumb method.20 The HAZ of children was then regressed on the altitude of the DHS cluster in which the child resided using a local polynomial regression, where the weights and the bandwidth were selected using the methods described above. Unadjusted and adjusted linear regression models were used to quantify the HAZ deficit per increments of 1000 m above sea level in the altitude of the DHS cluster in which the child resided (eFigure 1 in the Supplement). Adjusted models controlled for differences in biological and underlying causes of linear growth faltering4,19,21: child age (set of binary variables for each month) and sex, maternal age and level of education (years), household wealth (access to electricity and ownership of radio, television, refrigerator, bicycle, motorcycle, car, and improved floor material), and binary variables capturing access to improved water and sanitation (eTable 2 in the Supplement). We also controlled for residence in a rural area and used subnational region (highest administrative unit in each country) fixed effects to control for economic, political, climatic, and other factors shared by the residents in the same administrative area. We had 1348 survey-specific subnational regions in the full sample. Both unadjusted and adjusted regressions were based on the full sample of children aged 0 to 59 months as well as subsamples consisting of different age groups: 0 to 5, 6 to 11, 12 to 23, and 24 to 59 months. In adjusted models, children with missing control variable values were omitted from the sample. We clustered our SEs at the subnational region level.22 We used multivariable regression models to assess how altitude was associated with immediate causes of undernutrition: inadequate dietary intake and disease.4 As measures of inadequate dietary intake, we used prevalence of exclusive breastfeeding (children aged 0-6 months), dietary diversity (age 6-23 months), and minimum acceptable diet (age 6-23 months) as dependent variables. For disease risk, we used incidence of diarrhea, fever, or cough in the 2 weeks preceding the survey (age 0-59 months). More details are provided in eAppendix 1 and eTable 3 in the Supplement. The adjusted logistic regressions were based on the same set of control variables as described above and the SEs were clustered at the subnational region level. The estimated HAZ deficits per 1000-m above sea level increments in altitude are expressed as odds ratios (ORs). In the final part of the analysis, we restricted the sample to children residing in ideal home environments. Using the Karra et al guidelines,8 children were classified as having lived in an ideal home environment based on the following criteria: (1) singleton birth; (2) access to safe water and sanitation; (3) living in a house with finished floors, parents owned a television and a car; (4) born to highly educated mothers (>13 years of schooling); (5) born in a hospital; and (6) received BCG and first diphtheria, pertussis, and tetanus vaccinations (eAppendix 2 in the Supplement). Unadjusted and adjusted linear models were used to regress the HAZ of children living in ideal environments on the altitude of the cluster in which the child resided. Adjusted models controlled for child sex and age. We used heteroskedasticity robust SEs with this ideal home environment sample. We ran multiple robustness checks to assess the sensitivity of our results. We assessed whether the multivariable regression results were robust to replacing the continuous altitude variable with a binary variable capturing 1500-m above sea level clusters. Climatic patterns differ in low- and high-altitude locations and may also directly affect linear growth. While our subnational fixed effects controlled for climatic differences between regions, we augmented the model with data on long-term average rainfall and temperature in the cluster. We explored sensitivity by adding maternal height as a control in the multivariable regression model. We restricted the sample to children whose mother had lived in the same cluster at least since conception. There were 18 countries that had no clusters in areas 1500 m or more above sea level. We assessed sensitivity by omitting these 18 countries from the sample. We explored sensitivity of the regression results by omitting each country from the sample. We assessed the association between altitude and stunting (HAZ <−2) using the same regression methods as described above. Stata, version 16.1 (StataCorp LLC) was used for all analyses.

AI Digest

Study Justification:

This study aimed to investigate the association between altitude and linear growth in children, specifically focusing on whether residing at higher altitudes is associated with increased risk of growth faltering. The study also aimed to evaluate the implications of using the 2006 World Health Organization growth standards for populations residing 1500 meters above sea level. The importance of this study lies in understanding the potential impact of altitude on child growth, regardless of genetic makeup, and identifying interventions to address altitude-mediated growth restrictions.

Study Highlights:

– The study analyzed 133 nationally representative demographic and health cross-sectional surveys conducted in 59 low- and middle-income countries.
– A total of 964,299 height records from 96,552 clusters at altitudes ranging from -372 to 5951 meters above sea level were included.
– Children residing at higher altitudes, above 1500 meters, showed slower growth compared to children residing at lower altitudes.
– The negative association between altitude and height-for-age z scores (HAZ) was approximately linear, indicating no clear threshold for an abrupt decrease in HAZ.
– Children residing in ideal home environments had similar HAZ distribution to the 2006 World Health Organization standards, but only up to 500 meters above sea level.

Study Recommendations:

– Interventions should be identified and implemented to address altitude-mediated growth restrictions during pregnancy and early childhood.
– Further research is needed to understand the specific factors contributing to growth faltering at higher altitudes, such as nutrition, health, and socioeconomic conditions.
– Validation of growth standards for populations residing 1500 meters above sea level is necessary to ensure accurate assessment of child growth.

Key Role Players:

– Researchers and scientists specializing in child growth and development.
– Public health officials and policymakers responsible for implementing interventions and policies related to child health and nutrition.
– Healthcare providers and practitioners involved in maternal and child healthcare.
– Non-governmental organizations (NGOs) working in low- and middle-income countries to improve child health and nutrition.

Cost Items for Planning Recommendations:

– Research funding for further studies on altitude-mediated growth restrictions and interventions.
– Budget allocation for implementing interventions addressing altitude-mediated growth restrictions, such as nutrition programs, healthcare services, and education campaigns.
– Resources for training healthcare providers and practitioners on identifying and addressing growth faltering at higher altitudes.
– Funding for monitoring and evaluation of interventions to assess their effectiveness and make necessary adjustments.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a detailed description of the study design, data sources, and statistical methods used. However, it does not mention the specific results or findings of the study. To improve the evidence, the abstract should include a summary of the main outcomes and measures, as well as the conclusions and implications of the study.

Abstract

While researchers have used different definitions for high altitude,16 we used the 1500 m above sea level threshold because sites located above this altitude were not considered eligible in the Multicentre Growth Reference Study. The analysis took place in stages. Surveys were conducted between 1992 and 2018. We first estimated the number of people residing 1500 m above sea level in each country. We then compared child growth trajectories at lower than 1500 m above sea level and 1500 m or more above sea level altitudes using local polynomial regression methods. Previous literature has attributed high-altitude growth deficits to poorer nutrition, health, and socioeconomic conditions at high-altitude localities.13,17 Considering these issues, we accounted for the role of confounding factors in 3 ways. First, we used linear regression methods to quantify the child height deficit associated with altitude after adjusting for confounding factors. Second, we used the same multivariable regression models to assess how altitude is associated with immediate causes of malnutrition: diets and disease.4 Third, we restricted the sample to children who resided in ideal home environments8 and used regression methods to assess the association between altitude and child height within this sample. The protocols and questionnaires of DHS surveys have been reviewed and approved by ICF institutional review board and the institutional review boards of the host countries. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies. The country-level population data disaggregated by altitude were based on data from the Center for International Earth Science Information Network (CIESIN), Columbia University.18 We used nationally representative and publicly available cross-sectional DHS survey data from 59 low- and middle-income countries. We used all available DHS surveys that collected anthropometric measures for children younger than 5 years and restricted the analysis to surveys that recorded the altitude of the survey cluster or the global positioning system coordinates of the cluster that permitted us to obtain the altitude from external sources. A cluster was equivalent to a village in rural areas or a neighborhood in urban areas. Linear growth (faltering) was expressed in HAZ that measures the distance in height to the median child in the 2006 WHO growth standard.7 For convenience, we used the term HAZ to refer to both length for age (<24 months) and height for age (≥24 months). Children with biologically implausible height measures (HAZ 6) were excluded from the analysis. A total of 964 299 height records (51% boys, 49% girls) from 96 552 clusters from 133 surveys administered in 59 countries were used (eTable 1 in the Supplement). The clusters’ altitudes ranged from −372 to 5951 m above sea level. To examine HAZ age trajectories in low- and middle-income countries,19 we used local polynomial regressions that regressed the child’s HAZ on their age in months. We ran separate regressions for children residing in clusters lower than 1500 m above sea level (n = 857 858) and children residing in clusters 1500 m or more above sea level (n = 106 441). We compared the regression lines and their 95% CIs between the 2 groups to assess whether the differences in growth trajectories were statistically different from zero. The weights in these regressions were based on the Epanechnikov kernel-density function, and the bandwidth was selected using the rule-of-thumb method.20 The HAZ of children was then regressed on the altitude of the DHS cluster in which the child resided using a local polynomial regression, where the weights and the bandwidth were selected using the methods described above. Unadjusted and adjusted linear regression models were used to quantify the HAZ deficit per increments of 1000 m above sea level in the altitude of the DHS cluster in which the child resided (eFigure 1 in the Supplement). Adjusted models controlled for differences in biological and underlying causes of linear growth faltering4,19,21: child age (set of binary variables for each month) and sex, maternal age and level of education (years), household wealth (access to electricity and ownership of radio, television, refrigerator, bicycle, motorcycle, car, and improved floor material), and binary variables capturing access to improved water and sanitation (eTable 2 in the Supplement). We also controlled for residence in a rural area and used subnational region (highest administrative unit in each country) fixed effects to control for economic, political, climatic, and other factors shared by the residents in the same administrative area. We had 1348 survey-specific subnational regions in the full sample. Both unadjusted and adjusted regressions were based on the full sample of children aged 0 to 59 months as well as subsamples consisting of different age groups: 0 to 5, 6 to 11, 12 to 23, and 24 to 59 months. In adjusted models, children with missing control variable values were omitted from the sample. We clustered our SEs at the subnational region level.22 We used multivariable regression models to assess how altitude was associated with immediate causes of undernutrition: inadequate dietary intake and disease.4 As measures of inadequate dietary intake, we used prevalence of exclusive breastfeeding (children aged 0-6 months), dietary diversity (age 6-23 months), and minimum acceptable diet (age 6-23 months) as dependent variables. For disease risk, we used incidence of diarrhea, fever, or cough in the 2 weeks preceding the survey (age 0-59 months). More details are provided in eAppendix 1 and eTable 3 in the Supplement. The adjusted logistic regressions were based on the same set of control variables as described above and the SEs were clustered at the subnational region level. The estimated HAZ deficits per 1000-m above sea level increments in altitude are expressed as odds ratios (ORs). In the final part of the analysis, we restricted the sample to children residing in ideal home environments. Using the Karra et al guidelines,8 children were classified as having lived in an ideal home environment based on the following criteria: (1) singleton birth; (2) access to safe water and sanitation; (3) living in a house with finished floors, parents owned a television and a car; (4) born to highly educated mothers (>13 years of schooling); (5) born in a hospital; and (6) received BCG and first diphtheria, pertussis, and tetanus vaccinations (eAppendix 2 in the Supplement). Unadjusted and adjusted linear models were used to regress the HAZ of children living in ideal environments on the altitude of the cluster in which the child resided. Adjusted models controlled for child sex and age. We used heteroskedasticity robust SEs with this ideal home environment sample. We ran multiple robustness checks to assess the sensitivity of our results. We assessed whether the multivariable regression results were robust to replacing the continuous altitude variable with a binary variable capturing 1500-m above sea level clusters. Climatic patterns differ in low- and high-altitude locations and may also directly affect linear growth. While our subnational fixed effects controlled for climatic differences between regions, we augmented the model with data on long-term average rainfall and temperature in the cluster. We explored sensitivity by adding maternal height as a control in the multivariable regression model. We restricted the sample to children whose mother had lived in the same cluster at least since conception. There were 18 countries that had no clusters in areas 1500 m or more above sea level. We assessed sensitivity by omitting these 18 countries from the sample. We explored sensitivity of the regression results by omitting each country from the sample. We assessed the association between altitude and stunting (HAZ <−2) using the same regression methods as described above. Stata, version 16.1 (StataCorp LLC) was used for all analyses.

Materials

https://efashare.b-cdn.net/materials/a96e73934c713ee5118d7c72971587fa56bb0ebc8851f32fce0a4847b120a4cc/jamapediatr-e202386-s001.pdf

Innovations

Based on the provided information, it appears that the study is focused on investigating the association between altitude and linear growth faltering in children, particularly those residing at higher altitudes. The study aims to evaluate the implications of using the 2006 World Health Organization growth standards for populations residing 1500 meters above sea level. The findings suggest that residing at higher altitudes may be associated with child growth slowing, even for children living in ideal home environments.

In terms of potential innovations to improve access to maternal health, based on the information provided, it is not directly related to maternal health. However, the study highlights the importance of identifying and implementing interventions to address altitude-mediated growth restrictions during pregnancy and early childhood. These interventions could potentially include:

1. Maternal nutrition programs: Implementing targeted nutrition programs for pregnant women residing at higher altitudes to ensure adequate nutrient intake and support optimal fetal growth.

2. Prenatal care services: Strengthening prenatal care services in areas with higher altitudes to monitor maternal health, provide necessary interventions, and address any potential risks associated with altitude.

3. Health education and awareness: Conducting health education campaigns to raise awareness among pregnant women and their families about the potential impact of altitude on maternal and child health, and providing guidance on appropriate measures to mitigate risks.

4. Access to healthcare facilities: Ensuring that healthcare facilities, including skilled birth attendants and emergency obstetric care, are available and accessible to pregnant women residing at higher altitudes.

5. Telemedicine and remote monitoring: Exploring the use of telemedicine and remote monitoring technologies to provide access to specialized maternal health services for women in remote areas at higher altitudes.

It is important to note that these recommendations are based on the general understanding of maternal health and the potential implications of residing at higher altitudes. Further research and consultation with healthcare professionals specializing in maternal health in high-altitude regions would be necessary to develop specific and targeted interventions.

AI Innovations Description

The recommendation to improve access to maternal health based on the provided information is to identify and implement interventions that address altitude-mediated growth restrictions during pregnancy and early childhood. This recommendation is supported by the findings of the study, which suggest that residing at a higher altitude may be associated with child growth slowing, even for children living in ideal home environments.

To develop this recommendation into an innovation, further research and collaboration with healthcare professionals, policymakers, and community members are needed. Here are some steps that can be taken to develop this recommendation into an innovation:

1. Conduct additional research: Further research should be conducted to understand the specific factors contributing to altitude-mediated growth restrictions during pregnancy and early childhood. This research can help identify effective interventions and strategies to address these issues.

2. Collaborate with healthcare professionals: Engage with healthcare professionals, including obstetricians, pediatricians, and community health workers, to gather insights and expertise on maternal health in high-altitude areas. Their input can help inform the development of targeted interventions and strategies.

3. Involve policymakers: Collaborate with policymakers at the local, regional, and national levels to raise awareness about the impact of altitude on maternal health and advocate for the implementation of evidence-based interventions. Policymakers can play a crucial role in allocating resources and implementing policies that support improved access to maternal health services in high-altitude areas.

4. Engage community members: Involve community members, including pregnant women, mothers, and community leaders, in the development and implementation of interventions. Their perspectives and experiences can provide valuable insights and help ensure that interventions are culturally appropriate and meet the specific needs of the community.

5. Develop targeted interventions: Based on the research findings and input from healthcare professionals, policymakers, and community members, develop targeted interventions to address altitude-mediated growth restrictions during pregnancy and early childhood. These interventions may include improving access to prenatal care, nutrition education, and healthcare services in high-altitude areas.

6. Implement and evaluate interventions: Implement the developed interventions in collaboration with healthcare providers and community stakeholders. Monitor and evaluate the impact of these interventions on maternal health outcomes, including child growth and development. This evaluation will help refine and improve the interventions over time.

7. Share best practices and lessons learned: Share the findings, best practices, and lessons learned from the implementation of these interventions with other communities and stakeholders. This knowledge sharing can contribute to the broader understanding of maternal health in high-altitude areas and inform future interventions and policies.

By following these steps, the initial recommendation to address altitude-mediated growth restrictions can be developed into an innovation that improves access to maternal health in high-altitude areas.

AI Innovations Methodology

The methodology used in this study to evaluate the impact of altitude on linear growth and access to maternal health involved several steps:

1. Data Collection: The researchers used nationally representative and publicly available cross-sectional surveys from 59 low- and middle-income countries. These surveys collected anthropometric measures for children under 5 years of age and recorded the altitude of the survey cluster.

2. Altitude Classification: The researchers classified the clusters into two groups based on altitude: those below 1500 m above sea level and those at or above 1500 m above sea level. This classification was chosen because sites above 1500 m were not considered eligible in the Multicentre Growth Reference Study.

3. Growth Trajectory Analysis: Local polynomial regression methods were used to compare the growth trajectories of children in the two altitude groups. Separate regressions were run for children below 1500 m and children at or above 1500 m. The regression lines and their confidence intervals were compared to assess statistically significant differences in growth trajectories.

4. Adjusting for Confounding Factors: The researchers accounted for confounding factors by using linear regression methods. They quantified the height deficit associated with altitude after adjusting for factors such as child age, sex, maternal age and education, household wealth, access to improved water and sanitation, and residence in a rural area. They also controlled for subnational region fixed effects to account for shared factors within the same administrative area.

5. Assessing Immediate Causes of Undernutrition: Multivariable regression models were used to assess how altitude was associated with immediate causes of undernutrition, such as inadequate dietary intake and disease. Prevalence of exclusive breastfeeding, dietary diversity, minimum acceptable diet, and incidence of diarrhea, fever, or cough were used as dependent variables.

6. Analysis of Ideal Home Environments: The researchers further analyzed the association between altitude and child height within the sample of children residing in ideal home environments. Linear regression models were used, adjusting for child sex and age.

7. Sensitivity Analysis: The researchers conducted several robustness checks to assess the sensitivity of their results. These checks included replacing the continuous altitude variable with a binary variable, controlling for climatic differences, adding maternal height as a control, and omitting certain countries from the sample.

8. Statistical Analysis: Stata, version 16.1, was used for all analyses.

By following this methodology, the researchers were able to evaluate the association between altitude and linear growth, as well as identify potential interventions to address altitude-mediated growth restrictions during pregnancy and early childhood.

Author

Dima Health

Statistics:

Citations: 8

Identifiers:

DOI: 10.1001/jamapediatrics.2020.2386

ISSN: 21686203

Research Areas:

Community Interventions, Disparities, Environmental, Food Security, Genetics and Genomics, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Male & Female