Logistic Regression Model on Nutritional Status Based on Body Mass Index (BMI) in Women of Reproductive Age (WRA) in Indonesia

Chumairoh; Muhammad Nur Aidi; Anang Kurnia; Efriwati

doi:10.32628/IJSRSET24115127

Authors

Chumairoh Department of Statistics, IPB University, Bogor, Indonesia Author
Muhammad Nur Aidi Department of Statistics, IPB University, Bogor, Indonesia Author
Anang Kurnia Department of Statistics, IPB University, Bogor, Indonesia Author
Efriwati Department of Statistics, IPB University, Bogor, Indonesia Author

DOI:

https://doi.org/10.32628/IJSRSET24115127

Keywords:

Riskesdas Data, Nutritional Status, BMI, WRA, Logistic Regression

Abstract

The aggregate analysis of Riskesdas data in Indonesia revealed that nearly all provinces face a serious double burden of malnutrition. Both undernutrition and overnutrition negatively affect the quality of human resources, especially when they occur in adolescent girls, Women of Reproductive Age (WRA), and pregnant and breastfeeding women, as this can lead to intergenerational nutritional problems. Body Mass Index (BMI) is a nutritional status measurement technique recommended by WHO. Nutritional problems occur when BMI is abnormal or not ideal, categorized as underweight (<18.5 kg/m²), pre-obesity (25-29.9 kg/m²), obesity class I (30-34.9 kg/m²), obesity class II (35-39.9 kg/m²), and obesity class III (>40 kg/m²). Data from laboratory results completed up to 2017, based on selected samples from the 2013 Basic Health Research (Riskesdas), were used to assess the nutritional status of the Indonesian population. This study used 9,418 respondent data samples from 33 provinces in Indonesia. Logistic regression analysis was applied because the response variable in this study is binary, representing ideal BMI (1) and non-ideal BMI (0). The independent variables used in this study include Hemoglobin level, Ferritin level, CRP level, age group, physical activity, living location, marital status, education, and risky food consumption patterns. All independent variables are categorical. The study found that the factors influencing nutritional status based on BMI in WRA are Hemoglobin level, Ferritin level, CRP level, age, and location. The resulting logit model is logit (π)=0.27695-0.1491 (Non-anemic Hemoglobin level)-0.1721 (Normal Ferritin level)+0.741 (Non-infectious CRP level)-0.2933 (Adult age group)-0.0976 (rural location). logit (π)=0.27695-0.1491 (Non-anemic Hemoglobin level)-0.1721 (Normal Ferritin level)+0.741 (Non-infectious CRP level)-0.2933 (Adult age group)-0.0976 (rural location). The largest odds ratio The largest odds ratio was produced by the CRP level variable, indicating that CRP level is the most influential factor affecting nutritional status based on BMI in WRA, where non infectious CRP compared to infectious CRP increases the likelihood of WRA having an ideal BMI.

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References

Balitbangkes. 2018. Hasil utama riskesdas 2018. Jakarta: Badan Penelitian dan Pengembangan Kesehatan Kemenkes RI.

Diana R, Tanziha I. 2020. Double duty actions to reduce the double burden of malnutrition in Indonesia. Amerta Nutrition. 4(4): 326-334.

Coulston AM, Rock CL, Monsen E. 2001. Nutrition in the prevention and treatment of disease. New York : Academic Press.

Sovina M, Harahap FA. 2022. Penentuan status gizi dengan indeks massa tubuh (IMT) menggunakan logika fuzzy. InfoSys Journal. 7(1):105-116.

Sofiantin N. 2021. Analisis kadar feritin, tibc, dan fe serum pada obesitas sentral dan non obesitas sentral. Tesis Prodi Ilmu Biomedik Universitas Hasanuddin.

Tuomisto K, Jousilahti P, Havulinna AS, Borodulin K, Mannisto S, Salomaa V. 2019. Role of inflammation markers in the prediction of weight gain and development of obesity in adults-A prospective study. Metabolism Open. 3:100016. doi: 10.1016/j.metop.2019.100016.

Asil E, Surucuoglu MS, Cakiroglu FP, Ucar A, Ozcelik AO, Yilmaz MV, Akan LS. 2014. Factors that affect body mass index of adults. Pakistan Journal of Nutrition. 13(5):255-260.

Sattar A, Baig S, Naveed ur Rehman, Bashir B. 2013. Factors affecting bmi; assessment of the effect of sociodemographic factors on bmi in the population of Ghulam Mohammad Abad Faisalabad. Proffesional Med J. 20(6):956-964.

Meilana AS, Bachtiar F, Condrowati, Nazhira F. 2022. Hubungan antara aktivitas fisik dengan Indeks Massa Tubuh pada remaja. Physiotherapy Health Science. 4(2).

Tuffery, S. 2011. Data mining and statistics for decision making. UK:Wiley.

[Kutner, MH, Nachtseim CJ, and Neter J. 2004. Applied Linear Statistical Models 4th Edition. McGraw-Hill: New York.

[Kemenkes] Kementrian Kesehatan RI. 2021. Profil Kesehatan Indonesia. Jakarta: Kemenkes RI.

[WHO] World Health Organization. 2010. A healthy lifestyle-WHO recommendations [internet]. [diacu 2022 Oktober 25]. Tersedia dari: https://www.who.int/europe/news-room/fact-sheets/item/a-healthy-lifestyle---who-recommendations

[WHO] World Health Organization. 2020. WHO guideline on use of Ferritin concentrations to assess iron status in individuals and populations. Geneva: World Health Organization.

Knovich MA, Storey JA, Coffman LG, Torti SV. 2009. Ferritin for the clinician. Blood Rev. 23(3):95-104. Doi:10.1016/j.blre.2008.08.001.

Dewi HNC, Paruntu ME, Tiho M. 2016. Gambaran kadar C-reactive protein (CRP) serum pada perokok aktif usia >40 tahun. Jurnal e-Biomedik (eBM). 4(2).

Johns I, Moschonas KE, Medina J, Ossei-Gerning N, Kassianos G, Halcox JP. 2018. Risk classification in primary prevention of CVD according to QRISK2 and JBS3 ‘heart age’, and prevalence of elevated high-sensitivy C reactive protein in the UK cohort of the EUREKA study. Open Heart. 5(2):e000849. doi: 10.1136/openhrt-2018-000849. PMID: 30564373; PMCID: PMC6269641.

Setyandari R. 2016. Hubungan durasi tidur dengan status gizi dan kadar hemoglobin pada pekerja shift wanita. Skrispsi. Semarang : Prodi Ilmu Gizi Fakultas Kedokteran UNDIP.

BPS. 2010. Peraturan kepala Badan Pusat Statistik nomor 37 tahun 2010 tentang klasifikasi perkotaan dan perdesaan di Indonesia cetakan II. Jakarta : BPS.

BPS. 2022. Jenjang dan jenis pendidikan tertinggi yang pernah/sedang diduduki: konsep defiinisi variabel [internet]. [diacu 2022 Agustus 8]. Tersedia dari: https://sirusa.bps.go.id/sirusa/index.php/variabel/1209

Azkia FI, Wahyono TYM. 2018. Hubungan pola konsumsi makanan beresiko dengan obesitas sentral pada wanita usia 25-65 tahun di Bogor tahun 2011-2012. Jurnal Epidemiologi Kesehatan Indonesia. 2(1).

Hosmer DW, Lemeshow JS. 2000. Applied logistic regression. Canada : John Wiley & Sons Inc.

Pasalina P E, Jurnalis Y D, Ariadi. 2019. Hubungan indeks massa tubuh dengan kejadian anemia pada wanita usia subur pranikah. Jurnal Ilmu Keperawatan dan Kebidanan. 10 (1):12-20.

Lourenco BH, Cardoso MA. 2014. C-reactive protein concentration predicts change in body mass index during childhood. PLoS One. 9(3):e90357.

Todendi PF, Possuelo LG, Klinger EI, Reuter CP, Burgos MS, Moura DJ, Fiegenbaum M, Valim ARM. 2016. Low-grade inflammation markers in children and adolescents: influence of anthropometric characteristics and crp and il6 polymorphisms. Cytokine. 88:177-183.