Associations of fat mass and fat-free mass accretion in infancy with body composition and cardiometabolic risk markers at 5 years: The Ethiopian iABC birth cohort study

PLoS Medicine, Volume 16, No. 8, Year 2019

Interpretação

Background: Accelerated growth in early childhood is an established risk factor for later obesity and cardiometabolic disease, but the relative importance of fat mass (FM) and fat-free mass (FFM) accretion is not well understood. We aimed to study how FM and FFM at birth and their accretion during infancy were associated with body composition and cardiometabolic risk markers at 5 years. Methods and findings: Healthy children born at term were enrolled in the Infant Anthropometry and Body Composition (iABC) birth cohort between December 2008 and October 2012 at Jimma University Specialized Hospital in the city of Jimma, Ethiopia. FM and FFM were assessed using air displacement plethysmography a median of 6 times between birth and 6 months of age. In 507 children, we estimated individual FM and FFM at birth and their accretion over 0-3 and 3-6 months of age using linear-spline mixed-effects modelling. We analysed associations of FM and FFM at birth and their accretion in infancy with height, waist circumference, FM, FFM, and cardiometabolic risk markers at 5 years using multiple linear regression analysis. A total of 340 children were studied at the 5-year follow-up (mean age: 60.0 months; girls: 50.3%; mean wealth index: 45.5 out of 100; breastfeeding status at 4.5 to 6 months postpartum: 12.5% exclusive, 21.4% almost exclusive, 60.6% predominant, 5.5% partial/none). Higher FM accretion in infancy was associated with higher FM and waist circumference at 5 years. For instance, 100-g/month higher FM accretion in the periods 0-3 and 3-6 months was associated with 339 g (95% CI: 243-435 g, p < 0.001) and 367 g (95% CI: 250-484 g, p < 0.001) greater FM at 5 years, respectively. Higher FM at birth and FM accretion from 0 to 3 months were associated with higher FFM and cholesterol concentrations at 5 years. Associations for cholesterol were strongest for low-density lipoprotein (LDL)-cholesterol, and remained significant after adjusting for current FM. A 100-g higher FM at birth and 100-g/ month higher FM accretion from 0 to 3 months were associated with 0.16 mmol/l (95% CI: 0.05-0.26 mmol/l, p = 0.005) and 0.06 mmol/l (95% CI: 0.01-0.12 mmol/l, p = 0.016) higher LDL-cholesterol at 5 years, respectively. Higher FFM at birth and FFM accretion in infancy were associated with higher FM, FFM, waist circumference, and height at 5 years. For instance, 100-g/month higher FFM accretion in the periods 0-3 and 3-6 months was associated with 1,002 g (95% CI: 815-1,189 g, p < 0.001) and 624 g (95% CI: 419-829 g, p < 0.001) greater FFM at 5 years, respectively. We found no associations of FM and FFM growth with any of the other studied cardiometabolic markers including glucose, HbA1c, insulin, C-peptide, HOMA-IR, triglycerides, and blood pressure. Non-attendance at the 5- year follow-up visit was the main limitation of this study, which may have introduced selection bias and limited the power of the regression analyses. Conclusions: FM accretion in early life was positively associated with markers of adiposity and lipid metabolism, but not with blood pressure and cardiometabolic markers related to glucose homeostasis. FFM accretion was primarily related to linear growth and FFM at 5 years. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Text). Data for the present study were collected following a prospectively written study protocol (S2 Text). The iABC study is a prospective birth cohort study of the determinants and consequences of growth variability in early childhood [13,15]. The study was carried out at Jimma University Specialized Hospital in Jimma, Ethiopia (with a population size of 157,432 [16], and situated 350 km southwest of the capital Addis Ababa). Mother–child pairs meeting our eligibility criteria (residing in Jimma, gestational age at birth ≥ 37 completed weeks of pregnancy, birth weight ≥ 1,500 g, no congenital malformations) were enrolled between 17 December 2008 and 24 October 2012. Eligible mother–child pairs were examined within 48 hours of birth and were invited for a total of 12 scheduled visits between birth and 5 years of age. To estimate FM and FFM accretion in infancy, we used data on FM and FFM at birth and at 1.5, 2.5, 3.5, 4.5, and 6 months of age. To capture the dynamics of FM and FFM accretion in early infancy, we required a minimum of 3 assessments of FM and FFM between 0 and 6 months, including an assessment at birth, to be included in the BC growth modelling. The outcome data on BC and cardiometabolic markers were collected at the 5-year visit. Standing height at 5 years was measured in duplicate to the nearest 0.1 cm (model 213 stadiometer, Seca, Hamburg, Germany). Weight, FM, and FFM from birth to 6 months were assessed with a PEA POD—an infant air displacement plethysmograph (ADP) designed to measure infants between birth and 6 months of age (COSMED, Rome, Italy). At 5 years, weight, FM, and FFM were assessed with a BOD POD—a child/adult ADP with a paediatric chair insert allowing accurate assessment in children above the age of 2 years (COSMED). These ADP instruments provide accurate, precise, feasible, and safe assessment of FM and FFM in infants and children [17–19]. The PEA POD has previously been validated in the iABC cohort against a 3-compartment model of BC incorporating measurement of total body water by stable isotopes [15]. A comprehensive overview of the theory and methods behind the PEA POD and BOD POD techniques is found elsewhere [20,21]. In short, an ADP relies on densitometry to distinguish the 2 body components FM and FFM. First, by measuring total body weight and volume, the total body density is derived. Subsequently, since the density of FM and FFM differs, the relationship between the total body density and the assumed densities of FM and FFM is used to attribute the body weight to either FM or FFM, using a 2-component model of BC and Archimedes’ principle [22]. The density of FM was assumed to be constant at 0.9007 g/cm3, while age- and sex-specific densities of FFM were used [23]. The calculations were performed by the inbuilt computers of the PEA POD and the BOD POD, software versions 3.3.0 and 5.2.0, respectively. A complete BC assessment lasted 5–10 minutes, and the 2-minute volume measurement occurred in an enclosed transparent test chamber (PEA POD and BOD POD). In the PEA POD the nude infant was placed in supine position on a tray wearing a swim cap, and in the BOD POD the child sat on a paediatric chair insert wearing a swim cap and tight fitted underpants. After relaxing for a minimum of 5 minutes, systolic and diastolic blood pressure were measured in a sitting position using a blood pressure monitor with age-appropriate cuffs (Pressostabil model, Welch Allyn, Skaneateles Falls, NY, US). Measurements were done in duplicate, and the values averaged. After a minimum of 3 hours of fasting, 2 ml of venous blood was drawn from the antecubital fossa. We determined glucose concentrations from whole blood using the HemoCue Glucose 201 RT System (HemoCue, Ängelholm, Sweden). Glycosylated haemoglobin (HbA1c, mmol/mol) was determined from whole blood using a DCCT aligned Quo-Test A1c Analyzer (EKF Diagnostics, Cardiff, Wales). After clotting, the whole blood was centrifuged to isolate serum, divided into three 0.4-ml aliquots and frozen at −80 °C until analysed at the Ethiopian Public Health Institute, Addis Ababa, Ethiopia. Serum concentrations of total cholesterol, low-density lipoprotein (LDL)–cholesterol, high-density lipoprotein (HDL)–cholesterol, and triglycerides (all in mmol/l) were determined using the COBAS 6000, module c501, and insulin (μU/ml) and C-peptide (ng/ml) concentrations were determined using the COBAS 6000, module e601 (Roche Diagnostics International, Rotkreuz, Switzerland). We calculated the homeostasis model assessment of insulin resistance index (HOMA-IR) as insulin × glucose/22.5 [24]. Maternal postpartum height was measured in duplicate to the nearest 0.1 cm using a Seca 214 stadiometer (Seca, Hamburg, Germany). We used an average of the available measurements from birth to the 6-month visit. Data on birth order of the current child (parity), child’s sex, gestational age at birth, maternal age, maternal educational level, and family socioeconomic status were collected through questionnaires at the birth visit. Gestational age at birth of the current child was assessed using the New Ballard Score test instrument [25]. Socioeconomic status of the family was estimated using the International Wealth Index (IWI). The IWI estimates the wealth status of families in low- and middle-income countries using 12 material well-being items, including 7 items on household assets, 2 items on access to public services, and 3 items on characteristics of the house [26]. The IWI has a range of 0 to 100 (highest wealth). Data on breastfeeding status were collected at the follow-up visits at 4.5 and 6 months after birth and divided into 4 categories: exclusive (no other foods given), almost exclusive (no other foods given except water), predominant (breast milk as primary food), and partial/none (breast milk not the primary food/not breastfeeding) [27]. We used the breastfeeding status at the 6-month visit, but if a child did not attend the 6-month visit we used the breastfeeding status from the visit at 4.5 months of age. The study was approved by the Ethical Review Committee of Jimma University (Reference RPGC/279/2013). Written, visual, and oral information about the study was presented in local language prior to obtaining written consent from a parent or caregiver. No risks were associated with the examinations, and a topical anaesthetic (EMLA cream) was used prior to collecting the 2 ml of venous blood sample. Medical conditions noticed by the research nurses were addressed according to local clinical guidelines. Descriptive data are presented as mean (standard deviation [SD]) or median (interquartile range) for continuous variables and count (percentage) for categorical variables. Differences between groups were tested by 1-way ANOVA F-test for continuous variables and Pearson’s chi-squared test of independence or Fisher’s exact test of independence for categorical variables. Continuous variables with a right-skewed distribution were log-transformed (natural logarithm) prior to regression analyses. Estimates from these models were back-transformed and presented as percentwise change. A significance level of 5% was used. All analyses were carried out in R version 3.4.1 (R Foundation for Statistical Computing). Linear-spline mixed-effects (LSME) modelling was used to approximate the non-linear relationship of age with FM and FFM by deriving a number of child-specific and average summary measures of growth over discrete time intervals from 0 to 6 months of age [28,29]. LSME modelling differs from conventional mixed-effects modelling by combining 2 or more linear mixed-effects modelling functions at pre-specified ages (knot points). Thus, the estimated FM and FFM growth velocities are constant within a given time interval but allowed to differ between successive time intervals. Separate LSME models, specified with a knot point at 3 months of age, were fitted for FM and FFM. The LSME models for FM and FFM return 3 average and 3 child-specific growth parameters: estimated FM/FFM at birth and estimated FM/FFM growth velocity in the periods 0–3 and 3–6 months. The child-specific growth parameters are used as continuous exposure variables in the subsequent regression analyses of the BC and cardiometabolic outcomes. A detailed description of the modelling of FM and FFM growth velocity is provided in S3 Text. Associations of estimated FM and FFM at birth and their growth velocity over the periods 0–3 months and 3–6 months with cardiometabolic markers and BC at 5 years were analysed in separate multiple regression models (e.g., FM at 5 years regressed on estimated weight gain velocity from 0 to 3 months and adjusted for relevant covariates in separate models). Model 1 was adjusted for child’s sex, birth order, gestational age at birth, child’s exact age at the 5-year visit, maternal age at delivery, maternal postpartum height, maternal educational status, and IWI. Model 2 was additionally adjusted for FM at the 5-year visit. In the regression analyses of FM and waist circumference at 5 years as outcome, model 2 was adjusted for FFM at the 5-year visit instead of FM. We used a complete case approach, limiting the analyses to children with complete data on the included covariates. Covariates included in model 1 and 2 were identified a priori based on adjustment practices in similar studies and reported associations of the covariates with growth in early life and the outcomes studied [30–38]. To obtain comparable estimates across the different growth periods, exposure variables were standardised prior to the regression analyses. Thus, the estimates indicate the change in outcome per study population SD increase of the exposure variable (e.g., FM accretion from 0 to 3 months). We also present estimates for the change in outcome per 100-g higher birth FM and FFM and 100-g/month higher FM and FFM growth velocity over the periods 0–3 and 3–6 months of age. In additional analyses, we accounted for multiple testing using the Benjamini-Hochberg approach [39], with the number of tests set to 90 (15 outcomes and 3 age periods for the 2 exposures FM and FFM). Finally, as data on breastfeeding were only available on a smaller sub-sample, we ran sensitivity analyses on this sample where we adjusted model 1 and 2 for breastfeeding status at 4.5 to 6 months postpartum.

AI Digest

Study Justification:

This study aimed to investigate the associations between fat mass (FM) and fat-free mass (FFM) accretion in infancy with body composition and cardiometabolic risk markers at 5 years. The study aimed to fill the gap in understanding the relative importance of FM and FFM accretion in early childhood growth and their impact on later obesity and cardiometabolic disease.

Study Highlights:

– The study enrolled healthy children born at term in Ethiopia.
– FM and FFM were assessed using air displacement plethysmography.
– Associations of FM and FFM at birth and their accretion in infancy with body composition and cardiometabolic risk markers at 5 years were analyzed.
– Higher FM accretion in infancy was associated with higher FM and waist circumference at 5 years.
– Higher FM at birth and FM accretion from 0 to 3 months were associated with higher FFM and cholesterol concentrations at 5 years.
– Higher FFM at birth and FFM accretion in infancy were associated with higher FM, FFM, waist circumference, and height at 5 years.
– No associations were found between FM and FFM growth with other studied cardiometabolic markers including glucose, HbA1c, insulin, C-peptide, HOMA-IR, triglycerides, and blood pressure.

Recommendations:

Based on the findings of the study, the following recommendations can be made:

1. Early interventions should focus on reducing excessive FM accretion in infancy to prevent the development of obesity and related health risks.
2. Strategies to promote healthy FFM accretion in infancy should be implemented to support linear growth and overall body composition.
3. Further research is needed to explore the long-term effects of FM and FFM accretion on cardiometabolic health beyond 5 years of age.

Key Role Players:

To address the recommendations, the following key role players are needed:

1. Healthcare professionals: Pediatricians, nutritionists, and public health experts can provide guidance and support in implementing interventions and strategies to promote healthy growth in infancy.
2. Policy makers: Government officials and policymakers can create and implement policies that support healthy growth and development in early childhood.
3. Community leaders and organizations: Local community leaders and organizations can play a role in raising awareness and promoting healthy behaviors related to infant growth and development.

Cost Items for Planning Recommendations:

While the actual cost of implementing the recommendations may vary, the following cost items should be considered in the planning process:

1. Healthcare services: Costs associated with healthcare services, including consultations, screenings, and interventions, should be budgeted.
2. Education and training: Costs for educating and training healthcare professionals, policy makers, and community leaders on infant growth and development should be included.
3. Public awareness campaigns: Budget should be allocated for public awareness campaigns to promote healthy behaviors and raise awareness about the importance of early childhood growth.
4. Research and monitoring: Funding for further research and monitoring of the long-term effects of FM and FFM accretion on cardiometabolic health should be considered.
Please note that the provided cost items are general suggestions and may vary depending on the specific context and resources available.

Força da prova

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on a prospective birth cohort study with a large sample size. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline and collected data using validated methods. The associations between fat mass (FM) and fat-free mass (FFM) accretion in infancy and body composition and cardiometabolic risk markers at 5 years were analyzed using multiple linear regression analysis. The study found significant associations between FM and FFM accretion in infancy with markers of adiposity and lipid metabolism at 5 years. However, the study had limitations, such as non-attendance at the 5-year follow-up visit, which may have introduced selection bias and limited the power of the regression analyses. To improve the evidence, future studies could focus on addressing the limitations by ensuring high follow-up rates and considering additional confounding factors.

Resumo

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Text). Data for the present study were collected following a prospectively written study protocol (S2 Text). The iABC study is a prospective birth cohort study of the determinants and consequences of growth variability in early childhood [13,15]. The study was carried out at Jimma University Specialized Hospital in Jimma, Ethiopia (with a population size of 157,432 [16], and situated 350 km southwest of the capital Addis Ababa). Mother–child pairs meeting our eligibility criteria (residing in Jimma, gestational age at birth ≥ 37 completed weeks of pregnancy, birth weight ≥ 1,500 g, no congenital malformations) were enrolled between 17 December 2008 and 24 October 2012. Eligible mother–child pairs were examined within 48 hours of birth and were invited for a total of 12 scheduled visits between birth and 5 years of age. To estimate FM and FFM accretion in infancy, we used data on FM and FFM at birth and at 1.5, 2.5, 3.5, 4.5, and 6 months of age. To capture the dynamics of FM and FFM accretion in early infancy, we required a minimum of 3 assessments of FM and FFM between 0 and 6 months, including an assessment at birth, to be included in the BC growth modelling. The outcome data on BC and cardiometabolic markers were collected at the 5-year visit. Standing height at 5 years was measured in duplicate to the nearest 0.1 cm (model 213 stadiometer, Seca, Hamburg, Germany). Weight, FM, and FFM from birth to 6 months were assessed with a PEA POD—an infant air displacement plethysmograph (ADP) designed to measure infants between birth and 6 months of age (COSMED, Rome, Italy). At 5 years, weight, FM, and FFM were assessed with a BOD POD—a child/adult ADP with a paediatric chair insert allowing accurate assessment in children above the age of 2 years (COSMED). These ADP instruments provide accurate, precise, feasible, and safe assessment of FM and FFM in infants and children [17–19]. The PEA POD has previously been validated in the iABC cohort against a 3-compartment model of BC incorporating measurement of total body water by stable isotopes [15]. A comprehensive overview of the theory and methods behind the PEA POD and BOD POD techniques is found elsewhere [20,21]. In short, an ADP relies on densitometry to distinguish the 2 body components FM and FFM. First, by measuring total body weight and volume, the total body density is derived. Subsequently, since the density of FM and FFM differs, the relationship between the total body density and the assumed densities of FM and FFM is used to attribute the body weight to either FM or FFM, using a 2-component model of BC and Archimedes’ principle [22]. The density of FM was assumed to be constant at 0.9007 g/cm3, while age- and sex-specific densities of FFM were used [23]. The calculations were performed by the inbuilt computers of the PEA POD and the BOD POD, software versions 3.3.0 and 5.2.0, respectively. A complete BC assessment lasted 5–10 minutes, and the 2-minute volume measurement occurred in an enclosed transparent test chamber (PEA POD and BOD POD). In the PEA POD the nude infant was placed in supine position on a tray wearing a swim cap, and in the BOD POD the child sat on a paediatric chair insert wearing a swim cap and tight fitted underpants. After relaxing for a minimum of 5 minutes, systolic and diastolic blood pressure were measured in a sitting position using a blood pressure monitor with age-appropriate cuffs (Pressostabil model, Welch Allyn, Skaneateles Falls, NY, US). Measurements were done in duplicate, and the values averaged. After a minimum of 3 hours of fasting, 2 ml of venous blood was drawn from the antecubital fossa. We determined glucose concentrations from whole blood using the HemoCue Glucose 201 RT System (HemoCue, Ängelholm, Sweden). Glycosylated haemoglobin (HbA1c, mmol/mol) was determined from whole blood using a DCCT aligned Quo-Test A1c Analyzer (EKF Diagnostics, Cardiff, Wales). After clotting, the whole blood was centrifuged to isolate serum, divided into three 0.4-ml aliquots and frozen at −80 °C until analysed at the Ethiopian Public Health Institute, Addis Ababa, Ethiopia. Serum concentrations of total cholesterol, low-density lipoprotein (LDL)–cholesterol, high-density lipoprotein (HDL)–cholesterol, and triglycerides (all in mmol/l) were determined using the COBAS 6000, module c501, and insulin (μU/ml) and C-peptide (ng/ml) concentrations were determined using the COBAS 6000, module e601 (Roche Diagnostics International, Rotkreuz, Switzerland). We calculated the homeostasis model assessment of insulin resistance index (HOMA-IR) as insulin × glucose/22.5 [24]. Maternal postpartum height was measured in duplicate to the nearest 0.1 cm using a Seca 214 stadiometer (Seca, Hamburg, Germany). We used an average of the available measurements from birth to the 6-month visit. Data on birth order of the current child (parity), child’s sex, gestational age at birth, maternal age, maternal educational level, and family socioeconomic status were collected through questionnaires at the birth visit. Gestational age at birth of the current child was assessed using the New Ballard Score test instrument [25]. Socioeconomic status of the family was estimated using the International Wealth Index (IWI). The IWI estimates the wealth status of families in low- and middle-income countries using 12 material well-being items, including 7 items on household assets, 2 items on access to public services, and 3 items on characteristics of the house [26]. The IWI has a range of 0 to 100 (highest wealth). Data on breastfeeding status were collected at the follow-up visits at 4.5 and 6 months after birth and divided into 4 categories: exclusive (no other foods given), almost exclusive (no other foods given except water), predominant (breast milk as primary food), and partial/none (breast milk not the primary food/not breastfeeding) [27]. We used the breastfeeding status at the 6-month visit, but if a child did not attend the 6-month visit we used the breastfeeding status from the visit at 4.5 months of age. The study was approved by the Ethical Review Committee of Jimma University (Reference RPGC/279/2013). Written, visual, and oral information about the study was presented in local language prior to obtaining written consent from a parent or caregiver. No risks were associated with the examinations, and a topical anaesthetic (EMLA cream) was used prior to collecting the 2 ml of venous blood sample. Medical conditions noticed by the research nurses were addressed according to local clinical guidelines. Descriptive data are presented as mean (standard deviation [SD]) or median (interquartile range) for continuous variables and count (percentage) for categorical variables. Differences between groups were tested by 1-way ANOVA F-test for continuous variables and Pearson’s chi-squared test of independence or Fisher’s exact test of independence for categorical variables. Continuous variables with a right-skewed distribution were log-transformed (natural logarithm) prior to regression analyses. Estimates from these models were back-transformed and presented as percentwise change. A significance level of 5% was used. All analyses were carried out in R version 3.4.1 (R Foundation for Statistical Computing). Linear-spline mixed-effects (LSME) modelling was used to approximate the non-linear relationship of age with FM and FFM by deriving a number of child-specific and average summary measures of growth over discrete time intervals from 0 to 6 months of age [28,29]. LSME modelling differs from conventional mixed-effects modelling by combining 2 or more linear mixed-effects modelling functions at pre-specified ages (knot points). Thus, the estimated FM and FFM growth velocities are constant within a given time interval but allowed to differ between successive time intervals. Separate LSME models, specified with a knot point at 3 months of age, were fitted for FM and FFM. The LSME models for FM and FFM return 3 average and 3 child-specific growth parameters: estimated FM/FFM at birth and estimated FM/FFM growth velocity in the periods 0–3 and 3–6 months. The child-specific growth parameters are used as continuous exposure variables in the subsequent regression analyses of the BC and cardiometabolic outcomes. A detailed description of the modelling of FM and FFM growth velocity is provided in S3 Text. Associations of estimated FM and FFM at birth and their growth velocity over the periods 0–3 months and 3–6 months with cardiometabolic markers and BC at 5 years were analysed in separate multiple regression models (e.g., FM at 5 years regressed on estimated weight gain velocity from 0 to 3 months and adjusted for relevant covariates in separate models). Model 1 was adjusted for child’s sex, birth order, gestational age at birth, child’s exact age at the 5-year visit, maternal age at delivery, maternal postpartum height, maternal educational status, and IWI. Model 2 was additionally adjusted for FM at the 5-year visit. In the regression analyses of FM and waist circumference at 5 years as outcome, model 2 was adjusted for FFM at the 5-year visit instead of FM. We used a complete case approach, limiting the analyses to children with complete data on the included covariates. Covariates included in model 1 and 2 were identified a priori based on adjustment practices in similar studies and reported associations of the covariates with growth in early life and the outcomes studied [30–38]. To obtain comparable estimates across the different growth periods, exposure variables were standardised prior to the regression analyses. Thus, the estimates indicate the change in outcome per study population SD increase of the exposure variable (e.g., FM accretion from 0 to 3 months). We also present estimates for the change in outcome per 100-g higher birth FM and FFM and 100-g/month higher FM and FFM growth velocity over the periods 0–3 and 3–6 months of age. In additional analyses, we accounted for multiple testing using the Benjamini-Hochberg approach [39], with the number of tests set to 90 (15 outcomes and 3 age periods for the 2 exposures FM and FFM). Finally, as data on breastfeeding were only available on a smaller sub-sample, we ran sensitivity analyses on this sample where we adjusted model 1 and 2 for breastfeeding status at 4.5 to 6 months postpartum.

Materiais

Inovações

Based on the provided description, it seems that the study focuses on understanding the associations between fat mass (FM) and fat-free mass (FFM) accretion in infancy and their impact on body composition and cardiometabolic risk markers at 5 years. The study was conducted in Ethiopia and involved a birth cohort of healthy children.

To improve access to maternal health, it is important to consider innovations that can address the specific challenges faced in this context. While the provided description does not directly mention innovations for improving maternal health access, here are some potential recommendations:

1. Telemedicine and Telehealth: Implementing telemedicine and telehealth solutions can help overcome geographical barriers and improve access to maternal health services. This can involve remote consultations, monitoring, and education for pregnant women and new mothers.

2. Mobile Health (mHealth) Applications: Developing mobile applications that provide maternal health information, reminders for appointments and medication, and access to teleconsultations can enhance accessibility and engagement with healthcare services.

3. Community Health Workers: Training and deploying community health workers who can provide basic maternal health services, education, and support in remote or underserved areas can help bridge the gap in access to care.

4. Maternal Health Vouchers: Introducing voucher programs that provide financial assistance for maternal health services can help reduce financial barriers and increase access to quality care.

5. Transport and Emergency Referral Systems: Establishing efficient transport and emergency referral systems, such as ambulance services or community transportation networks, can ensure timely access to emergency obstetric care for pregnant women in remote areas.

6. Maternal Health Clinics and Centers: Setting up dedicated maternal health clinics or centers in underserved areas can provide comprehensive care, including prenatal care, skilled birth attendance, postnatal care, and family planning services.

7. Health Information Systems: Implementing robust health information systems that capture and analyze maternal health data can help identify gaps in access, monitor progress, and inform evidence-based decision-making for improving maternal health services.

These are just a few potential innovations that can be considered to improve access to maternal health. It is important to tailor these recommendations to the specific context and needs of the population being served.

AI Innovations Description

Based on the provided description, it seems that the study focuses on the associations between fat mass (FM) and fat-free mass (FFM) accretion in infancy and body composition and cardiometabolic risk markers at 5 years. The study was conducted in Ethiopia and involved healthy children born at term.

To develop an innovation to improve access to maternal health based on this study, here is a recommendation:

1. Implement early childhood growth monitoring programs: Based on the findings of the study, accelerated growth in early childhood is associated with later obesity and cardiometabolic disease. By implementing growth monitoring programs, healthcare providers can identify children who are at risk of developing these conditions and provide appropriate interventions and support. This can be done through regular check-ups and measurements of FM and FFM in infancy, similar to the methods used in the study.

2. Provide nutritional counseling and support: Since FM and FFM accretion in infancy were found to be associated with body composition and cardiometabolic risk markers at 5 years, it is important to provide mothers with nutritional counseling and support during pregnancy and early childhood. This can include guidance on healthy eating habits, breastfeeding promotion, and the importance of balanced nutrition for both the mother and child.

3. Strengthen maternal and child healthcare services: To improve access to maternal health, it is crucial to strengthen healthcare services, particularly in areas with limited resources. This can involve training healthcare providers on maternal and child health, improving infrastructure and equipment for accurate measurements, and ensuring the availability of essential medications and supplies.

4. Promote community awareness and education: Increasing community awareness about the importance of maternal health and early childhood development can help improve access to care. This can be done through community-based education programs, workshops, and campaigns that focus on the significance of regular check-ups, proper nutrition, and early intervention.

5. Collaborate with local stakeholders: To ensure the successful implementation of these recommendations, it is essential to collaborate with local stakeholders, including government agencies, non-profit organizations, and community leaders. By working together, resources can be pooled, and efforts can be coordinated to maximize the impact on improving access to maternal health.

It is important to note that these recommendations are based on the information provided in the study description. Further research and evaluation may be needed to assess the feasibility and effectiveness of these recommendations in specific contexts.

AI Innovations Methodology

The study you provided focuses on the associations between fat mass (FM) and fat-free mass (FFM) accretion in infancy and body composition and cardiometabolic risk markers at 5 years. While this study does not directly address access to maternal health, I can provide some general recommendations for innovations that can improve access to maternal health, as well as a methodology to simulate the impact of these recommendations.

Innovations to improve access to maternal health:

1. Telemedicine: Implementing telemedicine programs can provide remote access to healthcare professionals, allowing pregnant women in remote or underserved areas to receive prenatal care and consultations without the need for travel.
2. Mobile health (mHealth) applications: Developing mobile applications that provide information and resources for maternal health, such as tracking pregnancy progress, providing educational materials, and connecting women with healthcare providers.
3. Community health workers: Training and deploying community health workers who can provide basic prenatal care, education, and support to pregnant women in their communities, particularly in areas with limited access to healthcare facilities.
4. Transportation solutions: Addressing transportation barriers by providing affordable and reliable transportation options for pregnant women to reach healthcare facilities for prenatal care and delivery.
5. Maternal health clinics: Establishing dedicated maternal health clinics in underserved areas, staffed with healthcare professionals who specialize in prenatal and postnatal care.

Methodology to simulate the impact of these recommendations:

1. Define the target population: Identify the specific population that would benefit from improved access to maternal health, such as pregnant women in rural areas or low-income communities.
2. Collect baseline data: Gather data on the current state of maternal health in the target population, including indicators such as maternal mortality rates, prenatal care utilization, and birth outcomes.
3. Develop a simulation model: Create a mathematical or computational model that simulates the impact of the recommended innovations on access to maternal health. This model should consider factors such as the number of healthcare providers, geographic distribution, transportation infrastructure, and population characteristics.
4. Input data and parameters: Input the baseline data and parameters into the simulation model, including information on the proposed innovations (e.g., number of telemedicine consultations, availability of community health workers).
5. Run simulations: Run the simulation model to project the potential impact of the innovations on access to maternal health. This can include estimating changes in prenatal care utilization, reduction in maternal mortality rates, and improvements in birth outcomes.
6. Analyze results: Analyze the simulation results to assess the effectiveness of the recommended innovations in improving access to maternal health. Evaluate the potential benefits, challenges, and cost-effectiveness of each innovation.
7. Refine and iterate: Use the simulation results to refine and iterate on the proposed innovations. Adjust parameters, consider alternative scenarios, and identify potential barriers or limitations that need to be addressed.
8. Implement and monitor: Based on the simulation findings, implement the recommended innovations and closely monitor their impact on access to maternal health. Continuously evaluate and adjust the interventions based on real-world data and feedback.

It is important to note that the methodology described above is a general framework and may need to be tailored to the specific context and resources available for the simulation study. Additionally, involving stakeholders, including healthcare providers, policymakers, and community members, in the simulation process can help ensure the relevance and feasibility of the recommendations.

Autores & Coautores

Statistics:

Citations: 12

Authors: 12

Identifiers:

DOI: 10.1371/journal.pmed.1002888

ISSN: 15491277

Research Areas:

Environmental, Food Security, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

Partilhar isto: