What Drives Abdominal Obesity in Peru? A Multilevel Analysis Approach Using a Nationally Representative Survey
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data and Sampling Design
2.2. Population
2.3. Data Collection
2.4. Outcome of Interest
2.5. Individual-Household Level Characteristics
2.6. Contextual-Level Characteristics
2.7. Statistical Analysis
2.8. Ethical Considerations
3. Results
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Characteristics | n (%) | Abdominal Obesity | p-Value | |
---|---|---|---|---|
No | Yes | |||
% (95% CI) | % (95% CI) | |||
Overall | 30,585 (100) | 43.5 (42.6–44.4) | 56.5 (55.6–57.4) | |
Individuals | ||||
Sex | ||||
Man | 13,199 (48.8) | 51.9 (50.5–53.2) | 48.1 (46.8–49.5) | <0.001 |
Woman | 17,386 (51.2) | 35.5 (34.4–36.7) | 64.5 (63.3–65.6) | |
Age | ||||
18–29 years | 8436 (26.8) | 67.5 (65.8–69.1) | 32.5 (30.9–34.2) | <0.001 |
30–59 years | 17,096 (54.8) | 34.8 (33.7–36.0) | 65.2 (64.0–66.3) | |
60 or more | 5053 (18.3) | 34.3 (32.3–36.4) | 65.7 (63.6–67.7) | |
Education level | ||||
No formal schooling | 1624 (3.9) | 45.8 (42.1–49.7) | 54.2 (50.3–57.9) | 0.021 |
Primary | 7762 (20.6) | 41.5 (39.8–43.3) | 58.5 (56.7–60.2) | |
Secondary | 12,048 (39.6) | 44.9 (43.4–46.3) | 55.1 (53.7–56.6) | |
Higher | 9151 (35.9) | 42.8 (41.2–44.5) | 57.2 (55.5–58.8) | |
Wealth Index | ||||
Poorest | 9797 (18.3) | 62.1 (60.6–63.6) | 37.9 (36.4–39.4) | <0.001 |
Poor | 7787 (21.2) | 48.0 (46.3–49.7) | 52.0 (50.3–53.7) | |
Medium | 5530 (20.6) | 40.8 (38.9–42.7) | 59.2 (57.3–61.1) | |
Rich | 4222 (19.9) | 34.4 (32.3–36.5) | 65.6 (63.5–67.7) | |
Richest | 3249 (20.0) | 33.5 (31.3–35.9) | 66.5 (64.1–68.7) | |
Area of residence | ||||
Urban | 19,822 (80.9) | 39.9 (38.8–40.9) | 60.1 (59.1–61.2) | <0.001 |
Rural | 10,763 (19.1) | 58.8 (57.2–60.3) | 41.2 (39.7–42.8) | |
Contextual Factors | ||||
Department HDI | ||||
Low | 10,670 (17.6) | 57.4 (56.0–58.7) | 42.6 (41.3–44.0) | <0.001 |
Medium | 9936 (32.2) | 45.4 (44.1–46.7) | 54.6 (53.3–55.9) | |
High | 9979 (50.3) | 37.4 (35.9–38.9) | 62.6 (61.1–64.1) | |
Natural Region | ||||
Jungle | 5636 (8.3) | 51.8 (50.1–53.5) | 48.2 (46.5–49.9) | <0.001 |
Highlands | 12,629 (28.8) | 51.8 (50.5–53.0) | 48.2 (47.0–49.5) | |
Coast | 12,320 (62.9) | 38.6 (37.3–39.9) | 61.4 (60.1–62.7) | |
Food vulnerability index | ||||
Low | 11,091 (51.7) | 37.7 (36.3–39.2) | 62.3 (60.8–63.7) | <0.001 |
Medium | 10,127 (27.1) | 47.4 (45.9–48.9) | 52.6 (51.1–54.1) | |
High | 9367 (21.3) | 52.6 (51.3–53.9) | 47.4 (46.1–48.7) |
Empty Model | Model 1 | Model 2 | Model 3 | |||||
---|---|---|---|---|---|---|---|---|
Variables | OR | p Value | OR (95% CI) | p Value | OR (95% CI) | p Value | OR | p Value |
Individual-level variables | ||||||||
Sex | ||||||||
Man | Reference | Reference | Reference | |||||
Woman | 2.48 (2.14–2.87) | <0.001 | 2.74 (2.33–3.23) | <0.001 | 2.74 (2.33–3.23) | <0.001 | ||
Age | ||||||||
18–29 years | Reference | Reference | Reference | |||||
30–59 years | 4.13 (3.91–4.36) | <0.001 | 4.34 (3.94–4.79) | <0.001 | 4.35 (3.95–4.79) | <0.001 | ||
60 o more | 4.01 (3.51–4.58) | <0.001 | 4.64 (3.95–5.46) | <0.001 | 4.64 (3.95–5.45) | <0.001 | ||
Education Level | ||||||||
No formal schooling | Reference | Reference | Reference | |||||
Primary | 1.52 (1.28–1.80) | <0.001 | 1.44 (1.24–1.68) | <0.001 | 1.43 (1.23–1.67) | <0.001 | ||
Secondary | 1.55 (1.26–1.91) | <0.001 | 1.47 (1.22–1.77) | <0.001 | 1.45 (1.21–1.75) | <0.001 | ||
Higher | 1.28 (1.01–1.61) | 0.031 | 1.20 (0.97–1.49) | 0.068 | 1.20 (0.97–1.48) | 0.065 | ||
Wealth Index | ||||||||
Poorest | Reference | Reference | Reference | |||||
Poor | 1.83 (1.66–2.02) | <0.001 | 1.83 (1.66–2.02) | <0.001 | 1.82 (1.65–2.00) | <0.001 | ||
Medium | 2.31 (2.07–2.58) | <0.001 | 2.33 (2.09–2.59) | <0.001 | 2.28 (2.05–2.53) | <0.001 | ||
Rich | 2.87 (2.44–3.38) | <0.001 | 2.89 (2.46–3.41) | <0.001 | 2.81 (2.40–3.30) | <0.001 | ||
Richest | 2.74 (2.30–3.27) | <0.001 | 2.76 (2.32–3.30) | <0.001 | 2.65 (2.22–3.17) | <0.001 | ||
Area of residence | ||||||||
Urban | Reference | Reference | Reference | |||||
Rural | 0.83 (0.76–0.92) | 0.83 (0.76–0.92) | 0.85 (0.77–0.94) | |||||
Sex#Age | ||||||||
Woman # 18–29 years | Reference | Reference | ||||||
Woman # 30–59 years | 0.92 (0.80–1.06) | 0.232 | 0.92 (0.80–1.06) | 0.236 | ||||
Woman # 60 o more | 0.73 (0.63–0.85) | <0.001 | 0.73 (0.63–0.85) | <0.001 | ||||
Contextual Factors | ||||||||
Department HDI | ||||||||
Low | Reference | |||||||
Medium | 1.22 (0.99–1.49) | 0.056 | ||||||
High | 1.59 (1.17–2.16) | 0.003 | ||||||
Natural Region | ||||||||
Jungle | Reference | |||||||
Highlands | 0.81 (0.69–0.95) | 0.011 | ||||||
Coast | 0.98 (0.79–1.21) | 0.852 | ||||||
Food Vulnerability Index | ||||||||
Low | Reference | |||||||
Medium | 1.13 (0.93–1.38) | 0.214 | ||||||
High | 1.03 (0.87–1.22) | 0.751 | ||||||
N | 30,585 | 30,585 | 30,585 | 30,585 | ||||
Community-level variance (SE) | 0.15 (0.03) | 0.06 (0.02) | 0.06 (0.02) | 0.01 (0.01) | ||||
Fitness model statistics (AIC) | 16,652.88 | 14,825.01 | 14,822.55 | 14,805.4 | ||||
ICC (%) | 0.0444855 | 0.0188607 | 0.018835 | 0.0046789 | ||||
Log-likelihood | −8324.44 | −7399.5073 | −7396.276 | −7382.7019 | ||||
LR Test | ||||||||
PCV (%) | 0.59 | 0.00 | 0.76 | |||||
Median odds ratio | 1.45 | 1.27 | 1.27 | 1.13 | ||||
−2 log likelihood | 16,648.879 | 14,807.12 | 14,792.553 | 14,765.404 |
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Hernández-Vásquez, A.; Olazo-Cardenas, K.M.; Visconti-Lopez, F.J.; Barrenechea-Pulache, A. What Drives Abdominal Obesity in Peru? A Multilevel Analysis Approach Using a Nationally Representative Survey. Int. J. Environ. Res. Public Health 2022, 19, 10333. https://doi.org/10.3390/ijerph191610333
Hernández-Vásquez A, Olazo-Cardenas KM, Visconti-Lopez FJ, Barrenechea-Pulache A. What Drives Abdominal Obesity in Peru? A Multilevel Analysis Approach Using a Nationally Representative Survey. International Journal of Environmental Research and Public Health. 2022; 19(16):10333. https://doi.org/10.3390/ijerph191610333
Chicago/Turabian StyleHernández-Vásquez, Akram, Kamyla M. Olazo-Cardenas, Fabriccio J. Visconti-Lopez, and Antonio Barrenechea-Pulache. 2022. "What Drives Abdominal Obesity in Peru? A Multilevel Analysis Approach Using a Nationally Representative Survey" International Journal of Environmental Research and Public Health 19, no. 16: 10333. https://doi.org/10.3390/ijerph191610333