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1.
Am J Perinatol ; 2023 Sep 29.
Article in English | MEDLINE | ID: mdl-37604202

ABSTRACT

Glycated hemoglobin is an adjunct tool in early pregnancy to assess glycemic control. We examined trends and maternal predictors for those who had A1c screening in early pregnancy using hospital discharge and vital registry data between 2009 and 2017 linked with the New York City A1C Registry (N = 798,312). First-trimester A1c screening increased from 2.3% in 2009 to 7.7% in 2017. The likelihood of screening became less targeted to high-risk patients over time, with a decrease in mean A1c values from 5.8% (95% confidence interval [CI]: 5.8, 5.9) to 5.3 (95% CI: 5.3, 5.4). The prevalence of gestational diabetes mellitus increased while testing became less discriminate for those with high-risk factors, including pregestational type 2 diabetes, chronic hypertension, obesity, age over 40 years, as well as Asian or Black non-Hispanic race/ethnicity. KEY POINTS: · First-trimester A1c screening increased from 2.3% in 2009 to 7.7% in 2017 in New York City.. · The likelihood of screening became less targeted to high-risk patients over time.. · The prevalence of gestational diabetes mellitus increased, while testing became less discriminate..

2.
Diabetes Care ; 46(8): 1483-1491, 2023 08 01.
Article in English | MEDLINE | ID: mdl-37341505

ABSTRACT

OBJECTIVE: Racial/ethnic-specific estimates of the influence of gestational diabetes mellitus (GDM) on type 2 diabetes remain underexplored in large population-based cohorts. We estimated racial/ethnic differences in the influence of GDM on diabetes risk and glycemic control in a multiethnic, population-based cohort of postpartum women. RESEARCH DESIGN AND METHODS: Hospital discharge and vital registry data for New York City (NYC) births between 2009 and 2011 were linked with NYC A1C Registry data between 2009 and 2017. Women with baseline diabetes (n = 2,810) were excluded for a final birth cohort of 336,276. GDM on time to diabetes onset (two A1C tests of ≥6.5% from 12 weeks postpartum onward) or glucose control (first test of A1C <7.0% following diagnosis) was assessed using Cox regression with a time-varying exposure. Models were adjusted for sociodemographic and clinical factors and stratified by race/ethnicity. RESULTS: The cumulative incidence for diabetes was 11.8% and 0.6% among women with and without GDM, respectively. The adjusted hazard ratio (aHR) of GDM status on diabetes risk was 11.5 (95% CI 10.8, 12.3) overall, with slight differences by race/ethnicity. GDM was associated with a lower likelihood of glycemic control (aHR 0.85; 95% CI 0.79, 0.92), with the largest negative influence among Black (aHR 0.77; 95% CI 0.68, 0.88) and Hispanic (aHR 0.84; 95% CI 0.74, 0.95) women. Adjustment for screening bias and loss to follow-up modestly attenuated racial/ethnic differences in diabetes risk but had little influence on glycemic control. CONCLUSIONS: Understanding racial/ethnic differences in the influence of GDM on diabetes progression is critical to disrupt life course cardiometabolic disparities.


Subject(s)
Diabetes Mellitus, Type 2 , Diabetes, Gestational , Pregnancy , Female , Humans , Diabetes, Gestational/etiology , Retrospective Studies , Diabetes Mellitus, Type 2/complications , Glycated Hemoglobin , Glycemic Control/adverse effects , White
3.
Lancet Infect Dis ; 21(2): 203-212, 2021 02.
Article in English | MEDLINE | ID: mdl-33091374

ABSTRACT

BACKGROUND: As the COVID-19 pandemic continues to unfold, the infection-fatality risk (ie, risk of death among all infected individuals including those with asymptomatic and mild infections) is crucial for gauging the burden of death due to COVID-19 in the coming months or years. Here, we estimate the infection-fatality risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in New York City, NY, USA, the first epidemic centre in the USA, where the infection-fatality risk remains unclear. METHODS: In this model-based analysis, we developed a meta-population network model-inference system to estimate the underlying SARS-CoV-2 infection rate in New York City during the 2020 spring pandemic wave using available case, mortality, and mobility data. Based on these estimates, we further estimated the infection-fatality risk for all ages overall and for five age groups (<25, 25-44, 45-64, 65-74, and ≥75 years) separately, during the period March 1 to June 6, 2020 (ie, before the city began a phased reopening). FINDINGS: During the period March 1 to June 6, 2020, 205 639 people had a laboratory-confirmed infection with SARS-CoV-2 and 21 447 confirmed and probable COVID-19-related deaths occurred among residents of New York City. We estimated an overall infection-fatality risk of 1·39% (95% credible interval 1·04-1·77) in New York City. Our estimated infection-fatality risk for the two oldest age groups (65-74 and ≥75 years) was much higher than the younger age groups, with a cumulative estimated infection-fatality risk of 0·116% (0·0729-0·148) for those aged 25-44 years and 0·939% (0·729-1·19) for those aged 45-64 years versus 4·87% (3·37-6·89) for those aged 65-74 years and 14·2% (10·2-18·1) for those aged 75 years and older. In particular, weekly infection-fatality risk was estimated to be as high as 6·72% (5·52-8·01) for those aged 65-74 years and 19·1% (14·7-21·9) for those aged 75 years and older. INTERPRETATION: Our results are based on more complete ascertainment of COVID-19-related deaths in New York City than other places and thus probably reflect the true higher burden of death due to COVID-19 than that previously reported elsewhere. Given the high infection-fatality risk of SARS-CoV-2, governments must account for and closely monitor the infection rate and population health outcomes and enact prompt public health responses accordingly as the COVID-19 pandemic unfolds. FUNDING: National Institute of Allergy and Infectious Diseases, National Science Foundation Rapid Response Research Program, and New York City Department of Health and Mental Hygiene.


Subject(s)
COVID-19/mortality , Pandemics , SARS-CoV-2 , Adolescent , Adult , Aged , Algorithms , COVID-19/epidemiology , COVID-19/transmission , COVID-19/virology , Child , Child, Preschool , Humans , Infant , Infant, Newborn , Male , Middle Aged , Models, Theoretical , Mortality , New York City/epidemiology , Public Health Surveillance , Young Adult
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