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ObjectiveTo elucidate the principles and methods of the Bayesian probabilistic linkage model， and to demonstrate the effect of applying the model in linking birth and death data. MethodsThrough the Shanghai birth and death registration system， data of 199 025 infants born in 2017 and 1 512 infants who died in 2017 and 2018 were collected. After cleaning the data， the data were divided into monthly blocks and fully linked. The Jaro-Winkler algorithm and Euclidean distance were employed to measure the similarity of fields for matching. A Bayesian probabilistic linkage model was constructed and the linking effect was evaluated using a confusion matrix. ResultsUsing the Bayesian probabilistic linkage model， the birth and death data of infants were effectively linked， revealing that 36.71% of infants who died in Shanghai were born outside the city， and the probability of infant death was 2.6‰. The confusion matrix of the test set showed a recall rate of 0.86， precision of 0.76， and an F-score of 0.81. ConclusionThe practical application of Bayesian probabilistic linkage demonstrates a good model performance， enabling the establishment of birth-death cohorts that more accurately reflect the true levels of infant mortality. Utilizing this technique to integrate data from different departments can effectively improve research efficiency in the field of public health.
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ObjectiveTo introduce the basic design， development plan and objectives of a population-based birth cohort in Shanghai， and further present the main data and baseline characteristics of enrolled participants in the cohort， and to provide key information for reproductive health-related studies. MethodsThe Shanghai population-based birth cohort initiated on January 1， 2005， included newborns born in Shanghai every year and their parents， and collected information on reproductive health， reproductive treatment， birth characteristics， growth and development status， as well as the incidence， treatment and death of diseases by employing data linkage technology and investigations. This formed a birth cohort spanning the entire life cycle. ResultsAs of October 2022， a total of 2 978 538 newborns and their parents were included in the cohort. Among them， 2 905 135 （97.54%） were naturally conceived （NC）， and 73 403 （2.46%） were born through assisted reproductive technologies （ART）. The average age of parents was （32.56±4.12） years old for females and （34.62±5.34） years old for males in the ART group， which was higher than （28.02±4.71） years and （30.07±5.54） years for parents in the NC group. Among parents， females and males aged 30 and above accounted for 77.12% and 85.08%， respectively， which were higher than that of parents （35.28% for females and 49.66% for males） in the NC group. Furthermore， the percentage of parents with a college degree or above in the ART group was 73.23% for females and 73.66% for males， which were higher than those in the NC group （49.98% and 50.91%， respectively）. The multiple births rate in the ART group was 33.81%， which was higher than that in the NC group （1.88%）. The incidence of premature birth and low birth weight in the ART group were 24.47% and 19.08%， respectively， which was higher than that in the NC group （5.47% and 3.73%）. ConclusionThe comprehensive collection of reproductive health-related information in the birth cohort in Shanghai can provide essential resources to determine the influence of genetics， environment， reproductive treatment and other related factors on the health of offspring after birth.
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ObjectiveTo analyze the dynamic response relationship between urban development and mortality rate in Shanghai， and to predict the trend of mortality rate changes. MethodsBy analyzing the total mortality rate （TMR）， gross domestic product （GDP） and socio-demographic index （SDI） in Shanghai from 1978 to 2017， a vector autoregressive （VAR） model was constructed to evaluate the impact of urban development on the mortality rate. ResultsThe fitted R2 of the VAR model was 0.92. The short-term effect of GDP on the improvement of death level was negative， while the long-term effect was positive， and the SDI was negative regardless of the short-term and long-term effects. By the tenth year， GDP and SDI contributed 10.61% and 27.25% to TMR changes， respectively. The model predicted that the mortality rate in Shanghai would be 9.17 per thousand by 2030. ConclusionLong-term economic growth can effectively promote a decline in population mortality. However， as the economy develops vigorously， the adverse effects of declining birth rates and population aging on population health during the era of high-level population development should not be ignored.