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      Rural–urban disparities in pregestational and gestational diabetes in pregnancy: Serial, cross‐sectional analysis of over 12 million pregnancies

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          Abstract

          Objective

          To compare trends in pregestational (DM) and gestational diabetes (GDM) in pregnancy in rural and urban areas in the USA, because pregnant women living in rural areas face unique challenges that contribute to rural–urban disparities in adverse pregnancy outcomes.

          Design

          Serial, cross‐sectional analysis.

          Setting

          US National Center for Health Statistics (NCHS) Natality Files from 2011 to 2019.

          Population

          A total of 12 401 888 singleton live births to nulliparous women aged 15–44 years.

          Methods

          We calculated the frequency (95% confidence interval [CI]) per 1000 live births, the mean annual percentage change (APC), and unadjusted and age‐adjusted rate ratios (aRR) of DM and GDM in rural compared with urban maternal residence (reference) per the NCHS Urban–Rural Classification Scheme overall, and by delivery year, reported race and ethnicity, and US region (effect measure modification).

          Main outcome measures

          The outcomes (modelled separately) were diagnoses of DM and GDM.

          Results

          From 2011 to 2019, there were increases in both the frequency (per 1000 live births; mean APC, 95% CI per year) of DM and GDM in rural areas (DM: 7.6 to 10.4 per 1000 live births; APC 2.8%, 95% CI 2.2%–3.4%; and GDM: 41.4 to 58.7 per 1000 live births; APC 3.1%, 95% CI 2.6%–3.6%) and urban areas (DM: 6.1 to 8.4 per 1000 live births; APC 3.3%, 95% CI 2.2%–4.4%; and GDM: 40.8 to 61.2 per 1000 live births; APC 3.9%, 95% CI 3.3%–4.6%). Individuals living in rural areas were at higher risk of DM (aRR 1.48, 95% CI 1.45%–1.51%) and GDM versus those in urban areas (aRR 1.17, 95% CI 1.16%–1.18%). The increased risk was similar each year for DM (interaction p = 0.8), but widened over time for GDM (interaction p < 0.01). The rural–urban disparity for DM was wider for individuals who identified as Hispanic race/ethnicity and in the South and West (interaction p < 0.01 for all); and for GDM the rural–urban disparity was generally wider for similar factors (i.e. Hispanic race/ethnicity, and in the South; interaction p < 0.05 for all).

          Conclusions

          The frequency of DM and GDM increased in both rural and urban areas of the USA from 2011 to 2019 among nulliparous pregnant women. Significant rural–urban disparities existed for DM and GDM, and increased over time for GDM. These rural–urban disparities were generally worse among those of Hispanic race/ethnicity and in women who lived in the South. These findings have implications for delivering equitable diabetes care in pregnancy in rural US communities.

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          Most cited references50

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          Social Determinants of Health and Diabetes: A Scientific Review

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            Progression to type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis

            Abstract Objective To estimate and compare progression rates to type 2 diabetes mellitus (T2DM) in women with gestational diabetes mellitus (GDM) and healthy controls. Design Systematic review and meta-analysis. Data sources Medline and Embase between January 2000 and December 2019, studies published in English and conducted on humans. Eligibility criteria for selecting studies Observational studies investigating progression to T2DM. Inclusion criteria were postpartum follow-up for at least 12 months, incident physician based diagnosis of diabetes, T2DM reported as a separate outcome rather than combined with impaired fasting glucose or impaired glucose tolerance, and studies with both a group of patients with GDM and a control group. Results This meta-analysis of 20 studies assessed a total of 1 332 373 individuals (67 956 women with GDM and 1 264 417 controls). Data were pooled by random effects meta-analysis models, and heterogeneity was assessed by use of the I2 statistic. The pooled relative risk for the incidence of T2DM between participants with GDM and controls was estimated. Reasons for heterogeneity between studies were investigated by prespecified subgroup and meta-regression analyses. Publication bias was assessed by funnel plots and, overall, studies were deemed to have a low risk of bias (P=0.58 and P=0.90). The overall relative risk for T2DM was almost 10 times higher in women with previous GDM than in healthy controls (9.51, 95% confidence interval 7.14 to 12.67, P<0.001). In populations of women with previous GDM, the cumulative incidence of T2DM was 16.46% (95% confidence interval 16.16% to 16.77%) in women of mixed ethnicity, 15.58% (13.30% to 17.86%) in a predominantly non-white population, and 9.91% (9.39% to 10.42%) in a white population. These differences were not statistically significant between subgroups (white v mixed populations, P=0.26; white v non-white populations, P=0.54). Meta-regression analyses showed that the study effect size was not significantly associated with mean study age, body mass index, publication year, and length of follow-up. Conclusions Women with a history of GDM appear to have a nearly 10-fold higher risk of developing T2DM than those with a normoglycaemic pregnancy. The magnitude of this risk highlights the importance of intervening to prevent the onset of T2DM, particularly in the early years after pregnancy. Systematic review registration PROSPERO CRD42019123079.
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              Estimating average annual per cent change in trend analysis

              Trends in incidence or mortality rates over a specified time interval are usually described by the conventional annual per cent change (cAPC), under the assumption of a constant rate of change. When this assumption does not hold over the entire time interval, the trend may be characterized using the annual per cent changes from segmented analysis (sAPCs). This approach assumes that the change in rates is constant over each time partition defined by the transition points, but varies among different time partitions. Different groups (e.g. racial subgroups), however, may have different transition points and thus different time partitions over which they have constant rates of change, making comparison of sAPCs problematic across groups over a common time interval of interest (e.g. the past 10 years). We propose a new measure, the average annual per cent change (AAPC), which uses sAPCs to summarize and compare trends for a specific time period. The advantage of the proposed AAPC is that it takes into account the trend transitions, whereas cAPC does not and can lead to erroneous conclusions. In addition, when the trend is constant over the entire time interval of interest, the AAPC has the advantage of reducing to both cAPC and sAPC. Moreover, because the estimated AAPC is based on the segmented analysis over the entire data series, any selected subinterval within a single time partition will yield the same AAPC estimate—that is it will be equal to the estimated sAPC for that time partition. The cAPC, however, is re-estimated using data only from that selected subinterval; thus, its estimate may be sensitive to the subinterval selected. The AAPC estimation has been incorporated into the segmented regression (free) software Joinpoint, which is used by many registries throughout the world for characterizing trends in cancer rates. Copyright © 2009 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                BJOG: An International Journal of Obstetrics & Gynaecology
                BJOG
                Wiley
                1470-0328
                1471-0528
                January 2024
                June 27 2023
                January 2024
                : 131
                : 1
                : 26-35
                Affiliations
                [1 ] Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology The Ohio State University College of Medicine Columbus Ohio USA
                [2 ] Department of Preventive Medicine Northwestern University Feinberg School of Medicine Chicago Illinois USA
                [3 ] Division of General Internal Medicine and Geriatrics, Department of Medicine Northwestern University Feinberg School of Medicine Chicago Illinois USA
                [4 ] Department of Medicine The Ohio State University College of Medicine Columbus Ohio USA
                [5 ] Division of Cardiology, Department of Medicine Northwestern University Feinberg School of Medicine Chicago Illinois USA
                Article
                10.1111/1471-0528.17587
                d386f5d8-c658-47da-bee5-f38369a32b78
                © 2024

                http://creativecommons.org/licenses/by/4.0/

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