Forecasting the Number of Low Birth Weight Infants in Chumphon Province
Keywords:
Forecasting, Low Birth Weight, Grey System Theory, BOX and Jenkins MethodAbstract
The objective of this quantitative predictive research was to forecast the number of low birth weight (LBW) infants in Chumphon Province for the fiscal year 2024. Data were collected from the Ministry of Public Health, focusing on standard reports from maternal and child health statistics. The study analyzed the percentage of newborns weighing less than 2,500 grams within Health Region 11, using monthly data from the fiscal years 2013–2023 and partial data for fiscal year 2024 (October 2023–April 2024). The analysis employed methods suitable for the dataset: the Box-Jenkins method for monthly data and Grey System Theory for annual data. The accuracy of the forecasting models was evaluated using the Mean Absolute Percentage Error (MAPE), with computations performed using R and Microsoft Excel.
Using monthly data from fiscal years 2013–2023, the ARIMA (0,1,1)(2,0,0)12 model was identified for forecasting LBW infants for fiscal year 2024. The model predicted 12–15 cases per month, averaging 13 cases monthly or 156 cases annually. When compared with the GM(1,1)EPC model, which yielded the lowest MAPE at 5.14%, the forecast predicted 148 LBW cases for fiscal year 2024. This number is 8 cases (or 5.13%) lower than the ARIMA forecast, aligning with the observed downward trend in annual LBW cases.
Per findings, both methods yielded similar predictions, providing useful data for public health planning. These forecasts can help local health authorities develop budgets for maternal health campaigns to promote and improve maternal and child health outcomes.
References
Abu Hasan, N.I., Abdul Aziz, A., Ganggayah, M., Jamal, N.F., & Abdul Ghafar, N.M. (2022). Projection of infant mortality rate in malaysia using R. Jurnal Sains Kesihatan Malaysia, 20(1), 23-36. doi: 10.17576/JSKM-2022-2001-03.
Agarwal, M., Tripathi, P.K., & Pareek, S. (2021). Forecasting infant mortality rate of india using ARIMA model: A comparison of bayesian and classical approaches. Statistics and Applications, 19(2), 101 –114.
Andrés, D. (2023). Machine Learning Pills: Error Metrics for Time Series Forecasting. Retrieved May 5, 2024 from https://mlpills.dev/time-series/error-metrics-for-time-series-forecasting/
Box, G.E.P., & Jenkins, G.M. (1976). Time Series Analysis, forecasting and Control. San Francisco: Holden-Day.
Çetin, T., & Çilengiroglo, Ö.V. (2023). New approaches in time series analysis: Health data application. International Journal of New Horizons in the Sciences, 1(1), 1-11.
Chakreyavanich, R. (2020). Factors related to low birth weight in health region 5. Journal of Public Health Nursing, 34(3), 1-17. (in Thai)
Chumphon Provincial Public Health Office. (2024). Medical and Health Data Warehouse System, Chumphon Province. Retrieved November 15, 2024 from https://cpn.hdc.moph.go.th/hdc/main/index_pk.php (in Thai)
Deng, J. (1982). Grey control system. Journal of Huazhong University of Science and Technology, 1, 9–18.
Hales, D. (2010). An Introduction to Triangulation. Switzerland: UNAIDS.
Hao, J., Peng, L., Cheng, P., Li, S., Zhang, C., Fu, W., et al. (2022). A time series analysis of ambient air pollution and low birth weight in Xuzhou, China. International Journal of Environmental Health Research. 32(6), 1238–1247.
Harnsomboon, T. (2022). Evaluation of antenatal care, maternal and Infant health,Lang Suan district, Chumporn province. Mahasarakham Hospital Journal. 19(1), 23-33. (in Thai)
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice, 2nd Edition (Online version last updated on 26 October 2023). Melbourne: OTexts
Hyndman, R. J. & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3), 1-12.
Hyndman, R. J., & Kostenko, A. V. (2007). Minimum sample size requirements for seasonal forecasting models. Foresight, 6, 12-15.
Kotu, V., & Deshpande, B. (2019). Chapter 12 - Time Series Forecasting. In Data Science. (2nd Edition). (pp. 395-445). Massachusetts: Morgan Kaufmann.
Lewis, C.D. (1982). Industrial and Business Forecasting Methods. London: Butterworths.
Lin, Y. H., Chiu, C. C., Lin, Y. J., & Lee P. C. (2013). Rainfall prediction using innovative grey model with the dynamic index. Journal of Marine Science and Technology, 21(1), 63-75. doi:10.61 19/JMST-011-1116-1.
Liu, S. (2021). Grey System Theory and its Application. (9th Ed.). Beijing: Science Press.
Liu, S., & Lin, Y. (2010). Grey System Theory and its Application. Berlin, Heidelberg: Springer.
Lu, M. (2015). Grey System: Theory, Methods, Applications and Challenges. Retrieved November 17, 2024 from https://www.dmu.ac.uk/documents/technology-documents/research-faculties/cci/lu-grey-system-2015.pdf
Mahidol University. (2023). Announcement of Mahidol University Regarding Guidelines for Research Projects that Do Not Qualify as Human Research, 2022. Retrieved May 5, 2024 from https://sp.mahidol.ac.th/th/LAW/policy/2565-MU-Non-Human.pdf (in Thai)
Mahidol University Central Institutional Review Board (MU-CIRB). (2022). Self-Assessment form Whether an Activity is Human Subject Research Which Requires Ethical Approval. Retrieved May 5, 2024 from https://sp.mahidol.ac.th/th/ethics-human/forms/checklist/2022-Human%20Research%20Checklist-researcher.pdf. (in Thai)
Ministry of Public Health. (2024). Standard Reporting Group Maternal and Child Health Percentage of Infants with Birth Weight Less than 2,500 grams, Health Zone 11. Retrieved May 5, 2024 from https://hdcservice.moph.go.th. (in Thai)
Mishra, A. K., Sahanaa, C., & Manikandan, M. (2019). Forecasting Indian infant mortality rate: An application of autoregressive integrated moving average model, Journal of Family Community Med., 26(2), 123-126.
NCSS Statistical Software. (n.d.). Chapter 470 The Box-Jenkins Method. Retrieved November 17, 2024 from https://www.ncss.com/wp-content/themes/ncss/pdf/Procedures/NCSS/The_Box-Jenkins_ Method. pdf
Nwokike, C, Offorha, B. C., Obubu, M., & Uche-Ikonne, O. (2020). ARIMA modelling of neonatal mortality in Abia State of Nigeria. Asian Journal of Probability and Statistics, 6(2), 54-62.
Oseni, B. M., & Igboroodowo, O. J. (2022). Modelling of infant mortality rate in Nigeria using autoregressive moving average and neural network autoregression. Rattanakosin Journal of Science and Technology: RJST, 4(2), 2-9.
Rattanahon, J. & Jayathavaj, V. (2024). Predicting the number of neonatal deaths in Thailand using grey system theory. Journal of Medicine and Public Health, Ubon Ratchathani University, 7(2), 154-163. (in Thai)
Statistical Modeling and Forecasting. (n.d.). 3.3 Regression Models for Counts Data Sets. Retrieved August 11, 2024 from https://timeseriesreasoning.com/.
Talirongan, F. J. B., Talirongan, H., & Orong, M. Y. (2020). Modeling national trends on health in the Philippines using ARIMA. Journal of Health & Medical Informatics, 11(1), 1-6.
Tangsakul, P. (2011). Related factors of low birth weight infant. Journal of Yala Rajabhat University, 6(2), 113-122. (in Thai)
United Nations, Department of Economic and Social Affairs, Population Division. (2024). World Population Prospects: The 2024 Revision, Custom Data Acquired Via Website. Retrieved August 11, 2024 from https://population.un.org/dataportal/data/indicators/22/locations/ 458/start/1990/end/2024/table/pivotbylocation?df=0e3fb2a0-f89f-49c2-bd7a-69de13232f98
World Bank. (n.d.). Outlier Detection and Treatment LECTURE 12. Retrieved May 5, 2024 from https://thedocs.worldbank.org/en/doc/20f02031de132cc3d76b91b5ed8737d0-0050012017/related/ lecture-12-1.pdf
World Health Organization (WHO). (2024). Low Birth weight. Retrieved May 5, 2024 from https://www.who.int/data/nutrition/nlis/info/low-birth-weight
World Health Organization (WHO). (2014). Global Nutrition Targets 2025: Low Birth Weight Policy Brief (WHO/NMH/NHD/14.5). Geneva: World Health Organization.
Xie, N. (2022). A summary of grey forecasting models. Grey Systems: Theory and Application, 12(4), 703–722. doi:10.1108/GS-06-2022-0066
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