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- Zhou, QJ, et al.
(författare)
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Seroprevalence of Cytomegalovirus and Associated Factors Among Preconception Women: A Cross-Sectional Nationwide Study in China
- 2021
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Ingår i: Frontiers in public health. - : Frontiers Media SA. - 2296-2565. ; 9, s. 631411-
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Tidskriftsartikel (refereegranskat)abstract
- Background: Cytomegalovirus seroconversion during pregnancy is common and has a substantial risk of congenital infection with longterm sequale. Screening during pregnancy or vaccination have not been shown to be effective for eliminating congenital infections. Preconception screening policy has not been evaluated adequately in a large scale. This nationwide study aimed to investigate epidemiological features of cytomegalovirus seropositivity and its geographic variation among Chinese women planning a pregnancy to gather epidemiological evidence as an essential for developing novel prevention strategies.Method: This cross-sectional sero-epidemiological survey enrolled women intending to become pregnant within 6 months in mainland China during 2010–2012. The primary outcomes in this study were cytomegalovirus Immunoglobulin G and M seropositivity. Secondary outcomes were the associations between Immunoglobulin G and Immunoglobulin M, with socio-demographic characteristics, including age, occupation, education level, place of residence, and ethnicity. The overall seropositivity and regional disparity was analyzed on the individual and regional level, respectively.Results: This study included data from 1,564,649 women from 31 provinces in mainland China. Among participants, 38.6% (n = 603,511) were cytomegalovirus immunoglobulin G+, 0.4% (n = 6,747) were immunoglobulin M+, and 0.2% (n = 2,879) were immunoglobulin M+ and immunoglobulin G+. On individual level, participant's age, ethnicity, and residing region were significantly associated with IgG+, IgM+, and IgM+IgG+ (P < 0.001), while occupation, education level, and place of residence were not statistically significant (P > 0.05). On regional level, cytomegalovirus immunoglobulin G and immunoglobulin M seropositivity was highest in the eastern region (49.5 and 0.5%, respectively), and lowest in the western region (26.9 and 0.4%, respectively). This geographic variation was also noted at the provincial level, characterized by higher provincial immunoglobulin M+ and immunoglobulin G+ rates associated with higher immunoglobulin G seropositivity. In the subgroup analysis of immunoglobulin G seropositivity, areas of higher immunoglobulin G positivity had a higher rate of immunoglobulin M+, indicating an expected increased risk of reinfection and primary infection.Conclusions: A substantial proportion of women (>60%) were susceptible to cytomegalovirus in preconception period in China, and immunoglobulin G seropositivity was seen at a low-medium level with substantial geographic variation. Integration of cytomegalovirus antibody testing in preconception screening program based on regional immunoglobulin G seropositivity, should be considered to promote strategies directed toward preventing sero-conversion during pregnancy to reduce the risk of this congenital infection.
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- Mei, J, et al.
(författare)
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Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
- 2020
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 10:12, s. e044028-
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Tidskriftsartikel (refereegranskat)abstract
- This study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.DesignRetrospective cohort study.SettingFive designated tertiary hospitals for COVID-19 in Hubei province, China.ParticipantsWe routinely collected medical data of 1364 confirmed adult patients with COVID-19 between 8 January and 19 March 2020. Among them, 1088 patients from two designated hospitals in Wuhan were used to develop the prognostic model, and 276 patients from three hospitals outside Wuhan were used for external validation. All patients were followed up for a maximal of 60 days after the diagnosis of COVID-19.MethodsThe model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and Somers’ D test, and calibration was examined by the calibration plot. Decision curve analysis was conducted.Main outcome measuresThe primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19.ResultsThe full model included seven predictors of age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer and lactate dehydrogenase. The simple model contained five indicators of age, respiratory failure, coronary heart disease, renal failure and heart failure. After cross-validation, the AUC statistics based on derivation cohort were 0.96 (95% CI, 0.96 to 0.97) for the full model and 0.92 (95% CI, 0.89 to 0.95) for the simple model. The AUC statistics based on the external validation cohort were 0.97 (95% CI, 0.96 to 0.98) for the full model and 0.88 (95% CI, 0.80 to 0.96) for the simple model. Good calibration accuracy of these two models was found in the derivation and validation cohort.ConclusionThe prediction models showed good model performance in identifying patients with COVID-19 with a high risk of death in 60 days. It may be useful for acute risk classification.
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