Spatio-temporal modelling of extreme low birth rates in U.S. counties
Abstract The continuous declining rate of birth may cause severe worldwide problems, including population ageing and demographic imbalance. This paper aims to identify the geographic disparities and potential influential factors of the extreme downward excess of birth rates in the U.S. at the county...
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| Language: | English |
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BMC
2025-02-01
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| Series: | BMC Public Health |
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| Online Access: | https://doi.org/10.1186/s12889-025-21686-8 |
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| author | Kai Wang Yingqing Zhang Long Bai Ying Chen Chengxiu Ling |
| author_facet | Kai Wang Yingqing Zhang Long Bai Ying Chen Chengxiu Ling |
| author_sort | Kai Wang |
| collection | DOAJ |
| description | Abstract The continuous declining rate of birth may cause severe worldwide problems, including population ageing and demographic imbalance. This paper aims to identify the geographic disparities and potential influential factors of the extreme downward excess of birth rates in the U.S. at the county level from 2003 to 2020. The innovative Bayesian generalized Pareto (GP) quantile regression model is employed for analysing the spatiotemporal pattern of stressful low birth rate and its association with socioeconomic, demographic, and meteorological factors. The optimal stochastic partial differential equation and autoregressive model of order one (SPDE-AR(1)) spatiotemporal structured model is selected based on common Bayesian criteria and scaled threshold weighted continuous ranked probability score, indicating apparent spatial unbalance and deteriorating tendency of falling birth rate. The findings show that elderly, highly educated population structure, city-developed situation, average birth weight, and average age of mother have a significant impact on the stressful downward excess of birth rate, particularly in examples of Charlotte, Sarasota, Collier counties in Florida, and Marin, Santa, Cruuz, San Mateo in California. Conversely, the increasing female proportion and GDP per capita can alleviate the declining birth rate pressure. Our methodology is productive for analysing extreme population stress including but not limited to birth weight and cognitive impairment. |
| format | Article |
| id | doaj-art-4e8281de85954f91acace9c3e4f2fc10 |
| institution | Kabale University |
| issn | 1471-2458 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
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| series | BMC Public Health |
| spelling | doaj-art-4e8281de85954f91acace9c3e4f2fc102025-02-09T12:58:39ZengBMCBMC Public Health1471-24582025-02-0125111110.1186/s12889-025-21686-8Spatio-temporal modelling of extreme low birth rates in U.S. countiesKai Wang0Yingqing Zhang1Long Bai2Ying Chen3Chengxiu Ling4Wisdom Lake Academy of Pharmacy, Xi’an Jiaotong-Liverpool UniversityWisdom Lake Academy of Pharmacy, Xi’an Jiaotong-Liverpool UniversityDepartment of Financial and Actuarial Mathematics, Xi’an Jiaotong-Liverpool UniversityWisdom Lake Academy of Pharmacy, Xi’an Jiaotong-Liverpool UniversityWisdom Lake Academy of Pharmacy, Xi’an Jiaotong-Liverpool UniversityAbstract The continuous declining rate of birth may cause severe worldwide problems, including population ageing and demographic imbalance. This paper aims to identify the geographic disparities and potential influential factors of the extreme downward excess of birth rates in the U.S. at the county level from 2003 to 2020. The innovative Bayesian generalized Pareto (GP) quantile regression model is employed for analysing the spatiotemporal pattern of stressful low birth rate and its association with socioeconomic, demographic, and meteorological factors. The optimal stochastic partial differential equation and autoregressive model of order one (SPDE-AR(1)) spatiotemporal structured model is selected based on common Bayesian criteria and scaled threshold weighted continuous ranked probability score, indicating apparent spatial unbalance and deteriorating tendency of falling birth rate. The findings show that elderly, highly educated population structure, city-developed situation, average birth weight, and average age of mother have a significant impact on the stressful downward excess of birth rate, particularly in examples of Charlotte, Sarasota, Collier counties in Florida, and Marin, Santa, Cruuz, San Mateo in California. Conversely, the increasing female proportion and GDP per capita can alleviate the declining birth rate pressure. Our methodology is productive for analysing extreme population stress including but not limited to birth weight and cognitive impairment.https://doi.org/10.1186/s12889-025-21686-8Low birth rateGeneralized Pareto distributionStochastic partial differential equation (SPDE)Integrated nested Laplace approximationAuto-regressive modelBayesian quantile regression |
| spellingShingle | Kai Wang Yingqing Zhang Long Bai Ying Chen Chengxiu Ling Spatio-temporal modelling of extreme low birth rates in U.S. counties BMC Public Health Low birth rate Generalized Pareto distribution Stochastic partial differential equation (SPDE) Integrated nested Laplace approximation Auto-regressive model Bayesian quantile regression |
| title | Spatio-temporal modelling of extreme low birth rates in U.S. counties |
| title_full | Spatio-temporal modelling of extreme low birth rates in U.S. counties |
| title_fullStr | Spatio-temporal modelling of extreme low birth rates in U.S. counties |
| title_full_unstemmed | Spatio-temporal modelling of extreme low birth rates in U.S. counties |
| title_short | Spatio-temporal modelling of extreme low birth rates in U.S. counties |
| title_sort | spatio temporal modelling of extreme low birth rates in u s counties |
| topic | Low birth rate Generalized Pareto distribution Stochastic partial differential equation (SPDE) Integrated nested Laplace approximation Auto-regressive model Bayesian quantile regression |
| url | https://doi.org/10.1186/s12889-025-21686-8 |
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