Estimating the tuberculosis incidence using a SARIMAX-NNARX hybrid model by integrating meteorological factors in Qinghai Province, China

Liang, Wenjuan; Hu, Ailing; Pan, Hongge; Zhu, Jinqin; Wang, Yongbin

doi:10.1007/s00484-022-02385-0

Cited by 6 publications

(6 citation statements)

References 37 publications

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“…The results of this study showed that the overall patient delay rate of patients with pulmonary tuberculosis in Yantai City from 2013 to 2022 was 67.8%, which was higher than that in Yinchuan City (46.54%), [21] Dongguan City (60.3%), [22] Qinghai Province(59.60%), [6] Jinshan District, Shanghai (37.89%) [7] and the national level (47.38%). [23] The median patient delay was 28 ± 52 days, which was higher than that in Qinghai Province (21 days), [6] Hanzhong City (26 days), [24] and lower than that in Heilongjiang (35 days), [25] similar to that in Mongolia (28 days), [26] and shorter than that in Montenegro (30 days) [27] and Switzerland (36.4 days), [28] when compared with foreign studies. It can be seen that the level of patient delay in Yantai, China was at a high level in the provinces and cities in China, which should be taken into account by the relevant authorities.…”

Section: Discussionmentioning

confidence: 99%

“…[30] Some related studies have shown that farmers are prone to patient delays. [6,22,24] Farmers’ low literacy and economic incomes, poor knowledge of tuberculosis prevention and treatment, and the national policy of free treatment, [31] fear of losing their jobs and incomes, and poor access to health care in rural areas contribute to this group vulnerability to patient delay. Compared to farmers, workers have a better working environment, are less labor-intensive and more educated, and formal factories organize regular medical checkups for their workers; therefore, workers are less likely to have patient delays.…”

Section: Discussionmentioning

confidence: 99%

“…Owing to the large differences in economic development between different regions in China, there are differences in patient delays between groups in various provinces and cities; for example, the patient delay rate of pulmonary tuberculosis patients was 59.60% in Qinghai Province, [6] 37.89% in Jinshan District, Shanghai, [7] and 68.06% in Urumqi City. [8] At present, the commonly studied factors for the delay of tuberculosis patients are sex, age, region of residence, literacy, treatment category, etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Patient delay of patients with pulmonary tuberculosis in Yantai, China, 2013 to 2022: Analysis of a case series

Zhu,

Wang,

Chu

et al. 2024

Medicine

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Patient delay increases the morbidity and mortality due to tuberculosis (TB). This study aimed to assess patient delay among patients with pulmonary tuberculosis in Yantai from 2013 to 2022, and to analyze factors related to patient delay. Data of patients with pulmonary tuberculosis in Yantai City from 2013 to 2022 were obtained from the Tuberculosis Management Information System of the Chinese Disease Prevention and Control System. Statistical analyses were performed using the SPSS.26.0 software. The trend in patient delay rate was tested using the chi-square trend test. Univariate analyses were performed using the chi-square test, and factors with statistically significant differences in the univariate analysis were included in the binary logistic regression analysis to identify the factors affecting patient delay. Patient delay was defined as an interval of more than 14 days between the onset of clinical symptoms and the patient first visit to a healthcare facility. From 2013 to 2022, the median delay time for patients with pulmonary tuberculosis in Yantai was 28 ± 52 days and the patient delay rate was 69.5%. There was an overall increasing trend in the rate of patient delay as the number of years increased. Univariate analyses revealed statistically significant differences in patient delay in terms of age, occupation, patient source, domicile, pathogenetic results, and the presence of comorbidities (all P < .05). The results of logistic regression analysis showed that the age was 20 to 39, 40 to 59, and ≥ 60 years (OR = 1.365, 95%CI: 1.156–1.612; OR = 1.978, 95%CI: 1.660–2.356; OR = 1.767, 95%CI: 1.480–2.110), occupation was domestic and un-employed (OR = 1.188, 95%CI: 1.071–1.317), domicile as mobile population (OR = 1.212, 95%CI: 1.099–1.337), and positive pathogenic results (OR = 1.242, 95%CI: 1.015–1.520) were risk factors for patient delay. Patient delays were serious among pulmonary tuberculosis patients in Yantai City, 2013 to 2022, and patient delay was related to factors such as age, occupation, domicile, patient source, and pathogenetic results.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Patient delay of patients with pulmonary tuberculosis in Yantai, China, 2013 to 2022: Analysis of a case series

Zhu,

Wang,

Chu

et al. 2024

Medicine

View full text Add to dashboard Cite

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“…A long-term symmetric negative correlation between MAP and HFRS was observed (MAP = − 1.727, P = 0.006), inconsistent with recent studies 16 , 40 . Plausible explanations are that 16 , 28 , 40 , 41 : (1) changes in MAP might influence rodent behavior, potentially impacting their interaction with humans or with each other. This could affect the transmission dynamics of the hantaviruses responsible for HFRS.…”

Section: Discussionmentioning

confidence: 99%

“…(2) MAP can affect weather patterns and conditions, potentially impacting the habitats or breeding conditions of the rodents that are vectors for HFRS. A study indicated that elevated MAP levels are often linked to lower humidity 28 , 41 , these conditions are unfavorable for the survival and transmission of hantaviruses. (3) Changes in MAP often come with weather changes, which might alter human outdoor activities, influencing the likelihood of contact with infected rodents.…”

Section: Discussionmentioning

confidence: 99%

Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model

Wang,

Liang,

Qing

et al. 2024

Sci Rep

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Hemorrhagic fever with renal syndrome (HFRS) poses a major threat in Shandong. This study aimed to investigate the long- and short-term asymmetric effects of meteorological factors on HFRS and establish an early forecasting system using autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Between 2004 and 2019, HFRS exhibited a declining trend (average annual percentage change = − 9.568%, 95% CI − 16.165 to − 2.451%) with a bimodal seasonality. A long-term asymmetric influence of aggregate precipitation (AP) (Wald long-run asymmetry [WLR] = − 2.697, P = 0.008) and aggregate sunshine hours (ASH) (WLR = 2.561, P = 0.011) on HFRS was observed. Additionally, a short-term asymmetric impact of AP (Wald short-run symmetry [WSR] = − 2.419, P = 0.017), ASH (WSR = 2.075, P = 0.04), mean wind velocity (MWV) (WSR = − 4.594, P < 0.001), and mean relative humidity (MRH) (WSR = − 2.515, P = 0.013) on HFRS was identified. Also, HFRS demonstrated notable variations in response to positive and negative changes in ∆MRH(−), ∆AP(+), ∆MWV(+), and ∆ASH(−) at 0–2 month delays over the short term. In terms of forecasting, the NARDL model demonstrated lower error rates compared to ARDL. Meteorological parameters have substantial long- and short-term asymmetric and/or symmetric impacts on HFRS. Merging NARDL model with meteorological factors can enhance early warning systems and support proactive measures to mitigate the disease's impact.

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Long- and short-run asymmetric impacts of climate variation on tuberculosis based on a time series study

Wang,

Xue,

Xue

et al. 2024

Sci Rep

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Distinguishing between long-term and short-term effects allows for the identification of different response mechanisms. This study investigated the long- and short-run asymmetric impacts of climate variation on tuberculosis (TB) and constructed forecasting models using the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL). TB showed a downward trend, peaking in March-May per year. A 1 h increment or decrement in aggregate sunshine hours resulted in an increase of 32 TB cases. A 1 m/s increment and decrement in average wind velocity contributed to a decrement of 3600 and 5021 TB cases, respectively (Wald long-run asymmetry test [WLR] = 13.275, P < 0.001). A 1% increment and decrement in average relative humidity contributed to an increase of 115 and 153 TB cases, respectively. A 1 hPa increment and decrement in average air pressure contributed to a decrease of 318 and 91 TB cases, respectively (WLR = 7.966, P = 0.005). ∆temperature(−), ∆(sunshine hours)( −), ∆(wind velocity)(+) and ∆(wind velocity)(−) at different lags had a meaningful short-run effect on TB. The NARDL outperformed the ARDL in forecasting. Climate variation has significant long- and short-run asymmetric impacts on TB. By incorporating both dimensions of effects into the NARDL, the accuracy of the forecasts and policy recommendations for TB can be enhanced. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-73370-3.

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Estimating the tuberculosis incidence using a SARIMAX-NNARX hybrid model by integrating meteorological factors in Qinghai Province, China

Cited by 6 publications

References 37 publications

Patient delay of patients with pulmonary tuberculosis in Yantai, China, 2013 to 2022: Analysis of a case series

Patient delay of patients with pulmonary tuberculosis in Yantai, China, 2013 to 2022: Analysis of a case series

Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model

Long- and short-run asymmetric impacts of climate variation on tuberculosis based on a time series study

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