Predictive Utility of Composite Child Feeding Indices (CCFIs) for Child Nutritional Status: Comparative Analyses for the Most Suitable Formula for Constructing an Optimum CCFI
Abstract
:1. Introduction
2. Methods
2.1. Study Setting
2.2. Study Design and Population of Interest
2.3. Sample Size and Sampling Procedures
2.4. Data Collection Procedures and Instrument
2.5. Construction of Various Composite Child Feeding Indices (CCFIs)
2.6. Measures of Child Nutritional Status
2.7. Independent Variables
2.8. Measurement of Infant and Young Child Feeding (IYCF) Practices
2.9. Statistical Analyses
2.10. Ethical Clearance and Community Entry Protocols
3. Results
3.1. Distribution of the CFFI Scores
3.2. Comparative Analyses of the Predictive Utility of CCFIs for Child Nutritional Status
3.3. Validity and Reliability Analyses of the Statistically Significant CCFIs
3.4. Sensitivity Analyses and Model Predictive Performance of Statistically Significant CCFIs
4. Discussion
4.1. Validity and Reliability of Significantly Predictive CCFIs
4.2. Strengths and Limitations
5. Conclusions and Recommendations
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
CCFI | Components | Age Group Scoring | Remarks | ||
---|---|---|---|---|---|
6–8 | 9–11 | 12–23 | |||
CCFI 5 | 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13 | Same as CCFI 3 plus ACF (yes = 1, no = 0); CoF (yes = 1, no = 0), PLF α(Yes = 0, No = 2), β(Yes = 0, No = 3); 13TIBF/EIBF (Yes = 1, No = 0) | Same as CCFI 3 plus ACF (yes = 1, no = 0); CoF (yes = 1, no = 0), PLF α(Yes = 0, No = 2), β(Yes = 0, No = 3); 13TIBF/EIBF (Yes = 1, No = 0) | Same as CCFI 3 plus ACF (yes = 1, no = 0); CoF (yes = 1, no = 0), PLF α(Yes = 0, No = 2), β(Yes = 0, No = 3); 13TIBF/EIBF (Yes = 1, No = 0) | Child food intake components that exhibited collinearity with other components were excluded. |
Maximum Total Score | Eigenvalues | Eigenvalues | Eigenvalues |
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Reference | CCFI Formulation Components | Age Range (Months)/Sample Size | CCFI Scoring Age Groups (Months) | Geography | Study Design/Statistical Analysis | Multivariable Study Findings | Remarks: Predictive Utility |
---|---|---|---|---|---|---|---|
Haq et al., 2020 [25] | 1, 2, 4, 5 | 6–59/n = 800 | 6–8, 9–11, 12–36, 37–59 | SEA/Rural/ Maldives | Cross-sectional/φ Multiple Linear Regression | HAZ/LAZ *, WAZ *, WLZ ** | Yes |
Chaudhary et al., 2019 [26] | 1 π, 2 π, 3, 4 π | 6–36/n = 210 | 6–9, 10–12, 13–36 | SEA/Urban Slum/India | Cross-sectional/φ Multiple Linear Regression | HAZ/LAZ *, WAZ *, WLZ * | Yes |
Qu et al., 2017 [27] | 1, 2, 3, 5, 9 | 6–35/ n = 12, 146 | 6–8.99, 9–11.99, 12–35.99 | SEA/Rural/ China | Cross-sectional/φφ Quantile Regression (Generalized Estimation Equation) and φφ Multiple Linear Regression | HAZ/LAZ *, WAZ * | Yes |
Wondafrash et al., 2017 [12] | 2, 3, 4, 5 | 6–18/n = 320 | 6–8, 9–12 | SSA/Rural/ Ethiopia | Repeated Cross-sectional and Longitudinal/φφ Multiple Linear Regression and ANCOVA | HAZ/LAZ **, WAZ **, WLZ ** | No |
Chowdhury, Rahman, and Khan, 2016 [28] | 10a | 6–23/n = 2373 | 6–11,12–17, 18–23 | SEA/Urban and Rural/ Bangladesh | Cross-sectional/φφ Multiple Binary Logistic Regression/ φφ Multivariable Multinomial Logistic Regression | Association with undernutrition not examined | Not examined |
Saaka et al., 2016 [29] | 10b | 6–23/n = 778 | 6–11, 12–17, 18–23 | SSA/Rural and Urban/Ghana | Cross-sectional/φφ Multiple Binary Logistic Regression | Association with undernutrition not examined | Not examined |
Kassa et al., 2016 [30] | 10b | 6–23/n = 611 | 6–11, 12–17, 18–23 | SSA/Rural/ Ethiopia | Cross-sectional/φφ Multiple Binary Logistic Regression | Association with undernutrition not examined | Not examined |
Reinbott et al., 2015 [31] | 1 π, 2, 3, 4 π, 5 π | 6–23/n = 803 | 6–8, 9–11, 12–23 | SEA/Rural/ Cambodia | Cross-sectional/φ Multiple Linear Regression and Non-linear Regression (Quadratic model) | HAZ/LAZ * | Yes, but weak |
Lohia and Udipi, 2014 [15] | 1 π, 2, 3, 4, 5 | 6–24/n = 446 | 6–8.99, 9–11.99, 12–17.99, 18–24 | SEA/Urban slum/India | Cross-sectional/φ Multiple Linear Regression | HAZ/LAZ *, WAZ **, WLZ **, BAZ *, MUAC ** | Yes |
Ma et al., 2012 [16] | 1, 4, 5, 11, 12 | 5–7/n = 180 | 6 (5–7), 12 (10–14), 18 (16–20) | SEA/Urban affluent city/ China | Longitudinal/φφ Multiple Linear Regression and Stability Analysis | HAZ/LAZ *, WAZ *, WLZ ** | Yes |
Bork et al., 2012 [32] | 1 π, 3 π, 4 π, 14 π | 6–36/n = 1060 | 6–9, 9–12, 12–18, 24–36 | SSA/Rural/ Senegal | Longitudinal/φφ Multiple Linear Regression (Mixed Model) | HAZ/LAZ * | Yes |
Khatoon et al., 2011 [33] | 1, 2, 3, 4, 5 | 6–23/n = 259 | 6–8, 9–11, 12–23 | SEA/Urban Hospital/ Bangladesh | Cross-sectional/φφ Multiple Linear Regression | HAZ/LAZ *, WAZ **, WLZ ** | Yes |
Zhang et al., 2009 [34] | 1, 2 π, 3 π, 4 π, 5 | 6–11/n = 501 | 6–8, 9–11 | SEA/Rural/ China | Cross-sectional/φφ Multiple Linear Regression | HAZ/LAZ **, WAZ *, WLZ * | Yes |
Garg et al., 2009 [35] | 1, 2, 3 π, 4 π, 5, 9 | 6–12/n = 151 | 6–8, 9–12 | SEA/Rural/ India | Cross-sectional/φφ Multiple Linear Regression | HAZ/LAZ *, WAZ **, WLZ ** | Yes |
Moursi et al., 2009 [36] | 1 π, 2, 3 π, 4 π, 5 | 6–23/n = 1589 | 6–8, 9–11, 12–23 | SSA/Urban/ Madagascar | Cross-sectional/φφ Multiple Linear Regression | HAZ/LAZ **,WAZ **, WLZ ** | No |
Moursi et al., 2008 [37] | 1 π, 2, 3, 4, 5 π | 6–17/n = 363 | 6–8, 9–11, 12–17 | SSA/Urban/ Madagascar | Longitudinal/φφ Multiple Linear Regression | HAZ/LAZ **, WLZ ** | No |
Sawadogo et al., 2006 [17] | 1 π, 2 π, 3 π, 4 π, 13 π, 14 π | 6–35/n = 2466 | 6–11, 12–23, 24–35 | SSA/Rural/ Burkina Faso | Cross-sectional/φφ Multiple Linear Regression | HAZ/LAZ *, WLZ * | Yes |
Ntab et al., 2005 [38] | 1, 2, 3, 4, 15, 16, 17 | 12–42/n = 500 | 12–42 | SSA/Rural/ Senegal | Cross-sectional/φφ Multiple Linear Regression | HAZ/LAZ ** | No |
Ruel and Menon, 2002 [14] | 1, 2, 3, 4, 5 | 6–36/n = (257, 341, 234, 459, 203, 303, 709) | 6–9, 9–12, 12–36 | LA/Rural and Urban/Bolivia, Colombia, Guatemala, Nicaragua, Peru | Cross-sectional/φφ Multiple Linear Regression | HAZ/LAZ * | Yes, first study to construct such an index |
CCFIs | Components | Age Group Scoring | Remarks | ||
---|---|---|---|---|---|
6–8 | 9–11 | 12–23 | |||
CCFI 1 | 1, 2, 3, 4, 5 | 1 (Yes = 2, No = 0), 2 (Yes = 0, No = 1), 3 (0 fdg = 0, 1–3 fdgs = 1, ≥4 fdgs = 2), 4 (0 meal/day = 0, 1 meal/day = 1, 2+/day = 2), 5 (ASP, Yes = 2, No = 0; PSP, Yes = 1, No = 0) | 1 (Yes = 2, No = 0), 2 (Yes = 0, No = 1), 3 (0 fdg = 0, 1–3 fdgs = 1, ≥4 fdgs = 2), 4 (0 meal/day = 0, 1–2 meals/day = 1, 3+/day = 2), 5 (ASP, Yes = 2, No = 0; PSP, Yes = 1, No = 0) | 1 (Yes = 1, No = 0), 2 (Yes = 0, No = 1), 3 (0 fdg = 0, 1–3 fdgs = 1, ≥4 fdgs = 2),4 (0–1 meal/day = 0, 2–3 meals/day = 1, 4+/day = 2), 5 (ASP, Yes = 1, No = 0; PSP, Yes = 3, No = 0) | FFQ I (7-day recall: diverse food intake) was substituted with 5 FFQ II instead, unlike the classical formula used by Ruel and Menon. |
Maximum total score | 10 points | 10 points | 10 points | ||
CCFI 2 | 9 | ACF (yes = 1, no = 0) if TICF, MDD, and MMF are all yes | ACF (yes = 1, no = 0) if TICF, MDD, and MMF are all yes | ACF (yes = 1, no = 0) if TICF, MDD, and MMF are all yes | Only CF-related core IYCF indices were used. |
Maximum total score | 1 point | 1 point | 1 point | ||
CCFI 3 | 1, 2, 3, 4, 5, 6, 7, 8, 10 | Same as CCFI 1 plus 6 Fe (AFS (yes = 2, no = 0), α IFF, PFS (yes = 1, no = 0)), 7 Fruits and Vegetables (VitA-rich (yes = 2, no = 0), Other F and V (yes = 1, no = 0)), 8 TICF (0 or 1), 10 FVI (0, 1 or 2) | Same as CCFI 1 plus 6 Fe (AFS (yes = 2, no = 0), α IFF, PFS (yes = 1, no = 0)), 7 Fruits and Vegetables (VitA-rich (yes = 2, no = 0), Other F and V (yes = 1, no = 0)), 8 TICF (0 or 1), 10 FVI (0, 1 or 2) | Same as CCFI 1 plus 6 Fe (AFS (yes = 2, no = 0), α IFF, PFS (yes = 1, no = 0)), 7 Fruits and Vegetables (VitA-rich (yes = 2, no = 0), Other F and V (yes = 1, no = 0)), 8 TICF (0 or 1), 10 FVI (0, 1 or 2) | CCFI 1 plus intake of micronutrient-rich foods (MRF), TICF, and intake of varieties of foods (FVI). |
Maximum total score | 20 points | 20 points | 20 points | ||
CCFI 4 | 1, 2, 3, 4, 5, 6 | Same as CCFI 1 plus 6 Fe (AFS (yes = 2, no = 0), PFS (yes = 1, no = 0)) | Same as CCFI 1 plus 6 Fe (AFS (yes = 2, no = 0), PFS (yes = 1, no = 0)) | Same as CCFI 1 plus 6 Fe (AFS (yes = 2, no = 0), PFS (yes = 1, no = 0)) | CCFI 1 plus predominant source of iron intake (animal or plant) |
Maximum total score | 13 points | 13 points | 13 points | ||
CCFI 5 | All possible CCFI components | CCFI 3 plus all the other possible components not exhibiting multicollinearity. | CCFI 3 plus all the other possible components not exhibiting multicollinearity. | CCFI 3 plus all the other possible components not exhibiting multicollinearity. | Excluded collinear components. 1st principal component used. |
Maximum total score | Eigenvalue (Appendix A) | Eigenvalue (Appendix A) | Eigenvalue (Appendix A) |
Characteristics | Frequency (n) | % |
---|---|---|
Maternal age ** | ||
15–24 years | 136 | 23.4 |
25–34 years | 321 | 55.2 |
35–49 years | 124 | 21.3 |
Marital status | ||
Unmarried | 16 | 2.8 |
Married | 565 | 97.2 |
Maternal height | ||
160 cm and above | 282 | 48.5 |
Below 160 cm | 299 | 51.5 |
Occupation | ||
Trader/vendor/manual laborer | 166 | 28.6 |
Farmer | 323 | 55.6 |
Vocational/skilled service worker | 48 | 8.3 |
Unemployed | 44 | 7.6 |
Currently breastfeeding | ||
Yes | 560 | 96.4 |
No | 21 | 3.6 |
Child age * | ||
6–11 months | 242 | 41.7 |
12–17 months | 185 | 31.8 |
18–23 months | 154 | 26.5 |
Child gender | ||
Male | 301 | 51.8 |
Female | 280 | 48.2 |
Child’s nutritional status | ||
Stunting | 193 | 33.2 |
Wasting | 82 | 14.1 |
Underweight | 157 | 27.0 |
Child’s birth weight (n = 274) | ||
Less than 2.5 kg | 246 | 89.8 |
More than 2.5 kg | 28 | 10.2 |
CCFIs (Continuous) | Child Nutritional Status | ||
---|---|---|---|
Stunting | Wasting | Underweight | |
CCFI 1 | * HAZα, * HAZβ, * HAZπ, * HAZΣ | ** WHZα, * WHZβ, * WHZπ, * WHZΣ | * WAZα, * WAZβ, * WAZπ, * WAZΣ |
CCFI 2 | * HAZα, * HAZβ, * HAZπ, ** HAZΣ | * WHZα, * WHZβ, * WHZπ, * WHZΣ | * WAZα, * WAZβ, ** WAZπ, * WAZΣ |
CCFI 3 | * HAZα, * HAZβ, ** HAZπ, ** HAZΣ | * WHZα, * WHZβ, ** WHZπ, * WHZΣ | * WAZα, * WAZβ, ** WAZπ, * WAZΣ |
CCFI 4 | * HAZα, * HAZβ, * HAZπ, * HAZΣ | ** WHZα, * WHZβ, ** WHZπ, * WHZΣ | * WAZα, * WAZβ, ** WAZπ, * WAZΣ |
CCFI 5 | * HAZα, * HAZβ, ** HAZπ, ** HAZΣ | ** WHZα, * WHZβ, ** WHZπ, * WHZΣ | * WAZα, * WAZβ, ** WAZπ, ** WAZΣ |
CCFIs (Categorical) | Child Nutritional Status | ||
---|---|---|---|
Stunting | Wasting | Underweight | |
CCFI 1 | * HAZα, * HAZβ, ** HAZπ, ** HAZΣ | * WHZα, * WHZβ, * WHZπ, * WHZΣ | * WAZα, * WAZβ, ** WAZπ, ** WAZΣ |
CCFI 2 | * HAZα, * HAZβ, * HAZπ, * HAZΣ | * WHZα, * WHZβ, * WHZπ, * WHZΣ | * WAZα, * WAZβ, ** WAZπ, * WAZΣ |
CCFI 3 | * HAZα, * HAZβ, ** HAZπ, ** HAZΣ | * WHZα, * WHZβ, ** WHZπ, * WHZΣ | * WAZα, * WAZβ, ** WAZπ, * WAZΣ |
CCFI 4 | * HAZα, * HAZβ, ** HAZπ, ** HAZΣ | * WHZα, * WHZβ, * WHZπ, * WHZΣ | * WAZα, * WAZβ, * WAZπ, ** WAZΣ |
CCFI 5 | * HAZα, * HAZβ, ** HAZπ, ** HAZΣ | * WHZα, * WHZβ, ** WHZπ, * WHZΣ | * WAZα, * WAZβ, ** WAZπ, * WAZΣ |
CCFIs | Reliability | Validity | |||
---|---|---|---|---|---|
Cronbach’s α | α If Item # Deleted | Face | Content | Criterion @ (Wasting) | |
CCFI 1 | 0.40 | 0.56 | Good | Medium | Fairly good |
CCFI 4 | 0.60 | 0.71 | Very good | High | Good |
CCFI 5 | 0.80 | 0.86 | Excellent | Very high | Very good |
Significant CCFIs | Effect Size | F-Statistic | p-Value | 95% CI | |
---|---|---|---|---|---|
R2 | adjR2 | ||||
CCFI 1 | 0.098 | 0.067 | 3.994 | 0.046 | −0.126, −0.001 |
CCFI 4 | 0.102 | 0.075 | 6.996 | 0.008 | −0.095, −0.014 |
CCFI 5 | 0.102 | 0.075 | 7.007 | 0.008 | −0.265, −0.039 |
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Anin, S.K.; Ansong, R.S.; Fischer, F.; Kraemer, A. Predictive Utility of Composite Child Feeding Indices (CCFIs) for Child Nutritional Status: Comparative Analyses for the Most Suitable Formula for Constructing an Optimum CCFI. Int. J. Environ. Res. Public Health 2022, 19, 6621. https://doi.org/10.3390/ijerph19116621
Anin SK, Ansong RS, Fischer F, Kraemer A. Predictive Utility of Composite Child Feeding Indices (CCFIs) for Child Nutritional Status: Comparative Analyses for the Most Suitable Formula for Constructing an Optimum CCFI. International Journal of Environmental Research and Public Health. 2022; 19(11):6621. https://doi.org/10.3390/ijerph19116621
Chicago/Turabian StyleAnin, Stephen Kofi, Richard Stephen Ansong, Florian Fischer, and Alexander Kraemer. 2022. "Predictive Utility of Composite Child Feeding Indices (CCFIs) for Child Nutritional Status: Comparative Analyses for the Most Suitable Formula for Constructing an Optimum CCFI" International Journal of Environmental Research and Public Health 19, no. 11: 6621. https://doi.org/10.3390/ijerph19116621