Longitudinal Projection of Herd Prevalence of Influenza A(H1N1)pdm09 Virus Infection in the Norwegian Pig Population by Discrete-Time Markov Chain Modelling

Er, Chiek

doi:10.3390/idr13030070

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Additionally, machine-learning techniques have been utilized to enhance predictive accuracy beyond that of parametric methods such as logistic and linear regression 23,24 . Furthermore, very few studies utilize multiple states as their outcome, often choosing to rely on binary outcomes (obesity, not obesity), losing signi cant information in the process 25 .…”

Section: Introductionmentioning

confidence: 99%

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends

Huang¹,

Huang

2023

Preprint

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Importance: The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020. However, despite the recognition of long-term weight gain as an important public health issue, there is a paucity of studies studying the long-term weight gain and building models for long-term projection. Methods: A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017–2020) was conducted in patients who completed the weight questionnaire and had accurate data for both weight at time of survey and weight ten years ago. Multistate gradient boost modeling classifiers were used to generate covariate dependent transition matrices and Markov chains were utilized for multistate modeling. Results: Of the 6,146 patients that met the inclusion criteria, 3,024 (49%) of patients were male and 3,122 (51%) of patients were female. There were 2,252 (37%) White patients, 1,257 (20%) Hispanic patients, 1,636 (37%) Black patients, and 739 (12%) Asian patients. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight (Fig. 1). A total of 2,411 (39%) patients lost weight, and 3,735 (61%) patients gained weight (Table 1). We observed that 87 (1%) of patients were underweight (BMI < 18.5), 2,058 (33%) were normal weight (18.5 ≤ BMI < 25), 1,376 (22%) were overweight (25 ≤ BMI < 30) and 2,625 (43%) were obese (BMI > 30). From analysis of the transitions between normal/underweight, overweight, and obese, we observed that after 10 years, of the patients who were underweight, 65% stayed underweight, 32% became normal weight, 2% became overweight, and 2% became obese. After 10 years, of the patients who were normal weight, 3% became underweight, 78% stayed normal weight, 17% became overweight, and 2% became obese. Of the patients who were overweight, 71% stayed overweight, 0% became underweight, 14% became normal weight, and 15% became obese. Of the patients who were obese, 84% stayed obese, 0% became underweight, 1% became normal weight, and 14% became overweight. Conclusions: United States adults are at risk of transitioning from normal weight to becoming overweight or obese. Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions.

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Section: Introductionmentioning

confidence: 99%

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends

Huang¹,

Huang

2023

Preprint

View full text Add to dashboard Cite

show abstract

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends

Huang,

Huang

2023

BMC Res Notes

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Importance The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020. However, despite the recognition of long-term weight gain as an important public health issue, there is a paucity of studies studying the long-term weight gain and building models for long-term projection. Methods A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017–2020) was conducted in patients who completed the weight questionnaire and had accurate data for both weight at time of survey and weight ten years ago. Multistate gradient boost modeling classifiers were used to generate covariate dependent transition matrices and Markov chains were utilized for multistate modeling. Results Of the 6146 patients that met the inclusion criteria, 3024 (49%) of patients were male and 3122 (51%) of patients were female. There were 2252 (37%) White patients, 1257 (20%) Hispanic patients, 1636 (37%) Black patients, and 739 (12%) Asian patients. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight (Fig. 1). A total of 2411 (39%) patients lost weight, and 3735 (61%) patients gained weight (Table 1). We observed that 87 (1%) of patients were underweight (BMI < 18.5), 2058 (33%) were normal weight (18.5 ≤ BMI < 25), 1376 (22%) were overweight (25 ≤ BMI < 30) and 2625 (43%) were obese (BMI > 30). From analysis of the transitions between normal/underweight, overweight, and obese, we observed that after 10 years, of the patients who were underweight, 65% stayed underweight, 32% became normal weight, 2% became overweight, and 2% became obese. After 10 years, of the patients who were normal weight, 3% became underweight, 78% stayed normal weight, 17% became overweight, and 2% became obese. Of the patients who were overweight, 71% stayed overweight, 0% became underweight, 14% became normal weight, and 15% became obese. Of the patients who were obese, 84% stayed obese, 0% became underweight, 1% became normal weight, and 14% became overweight. Conclusions United States adults are at risk of transitioning from normal weight to becoming overweight or obese. Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions.

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Longitudinal Projection of Herd Prevalence of Influenza A(H1N1)pdm09 Virus Infection in the Norwegian Pig Population by Discrete-Time Markov Chain Modelling

Cited by 2 publications

References 22 publications

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends

Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends

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