scholarly journals County-level Analysis of Perinatal Health Indicators Within a Single Health System Catchment

2021 ◽  
Author(s):  
Dominick J. Lemas ◽  
Claire Layton ◽  
Hailey Ballard ◽  
Ke Xu ◽  
John C. Smulian ◽  
...  
Keyword(s):  
Preterm Birth ◽  
Rural Communities ◽  
Health Behaviors ◽  
Health Outcomes ◽  
Health System ◽  
Perinatal Health ◽  
County Level ◽  
Socioeconomic Vulnerability ◽  
Rural Counties ◽  
Alachua County

Abstract Background: Adverse perinatal health outcomes are disproportionally impacted in rural communities. Social determinants of health (SDoH) defined by nonclinical social, behavioral, and economic factors may impact up to 90% of health outcomes in rural communities. Objective: To evaluate county-level perinatal patterns in health outcomes, health behaviors, socioeconomic vulnerability, and healthcare providers across rural and non-rural Florida counties within a single health system catchment. Methods: Socioeconomic vulnerability metrics, digital connectivity, licensed provider metrics, and behavioral data and were obtained from Floridahealthcharts.com and the County Health Rankings. County-level birth and perinatal health outcome data were obtained from the Florida Department of Health. The University of Florida Health Perinatal Catchment Area (UFHPCA) was defined as all Florida counties where ≥5% of all infants were delivered at Shands Hospital in Alachua county between June 2011 and April 2017. County-level rurality was determined by Florida Statutes 288.0656 rurality designations. Results: The UFHPCA included three non-rural and ten rural counties that represented more than 64,000 deliveries over a 5-year 9-month period. We found that nearly 1 in 3 infants resided in a rural county (n=20,899), and 7 out of 13 counties did not have a licensed obstetrician gynecologist. Nine counties reported maternal death rates that were between 1 and 4-fold higher than the statewide rate, and rural counties generally reported neonatal mortality and preterm birth rates that were higher than the statewide averages. We found maternal smoking rates (range 6.8% – 24.8%) were above the statewide rate (6.2%) for all counties in the catchment. Except for Alachua county, breastfeeding initiation rates (range 54.9% - 81.4%) and access to household computing devices (range 72.8% - 86.4%) were below the statewide rate (82.9% and 87.9%, respectively). Finally, we found that childhood poverty rates (range 16.3% - 36.9%) in our catchment was above the statewide rate (18.5%), except for Suwanee and Columbia counties.Conclusions: The health burden of the UFHPCA is characterized by both rural and non-rural counties with increased maternal and neonatal death and preterm birth, as well as adverse health behaviors that include smoking during pregnancy and lower levels of breastfeeding.

Effect of a Health System Sponsored Mobile App on Perinatal Health Behaviors [13E]

2020 ◽  
Vol 135 ◽  
pp. 52S-53S
Author(s):  
Keri Vartanian ◽  
J.B. Rinaldi ◽  
Kevin Pieper ◽  
Trina Jellison
Keyword(s):  
Health Behaviors ◽  
Health System ◽  
Mobile App ◽  
Perinatal Health

scholarly journals Non-medical determinants of perinatal health: protocol for a systematic review with meta-analysis

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e031437
Author(s):  
Leonie A Daalderop ◽  
Marjolein W de Groot ◽  
Lindsey van der Meer ◽  
Eric A P Steegers ◽  
Loes C M Bertens
Keyword(s):  
Systematic Review ◽  
Preterm Birth ◽  
Health Outcomes ◽  
Perinatal Mortality ◽  
Data Extraction ◽  
Meta Analysis ◽  
Extraction Process ◽  
Perinatal Health ◽  
Reference List ◽  
Social Environmental

IntroductionResearch focusing on the associations between non-medical determinants and unfavourable perinatal health outcomes is increasing. Despite increasing knowledge on this theme, it still remains unclear to what extent social, environmental and lifestyle factors contribute to these unfavourable outcomes. Therefore, we aim to provide a systematic review, preferably with meta-analysis, in order to provide insight into the associations between non-medical determinants and perinatal mortality, preterm birth and being small for gestational age (SGA).Methods and analysisObservational studies performed in European countries studying the associations between non-medical determinants and unfavourable perinatal health outcomes will be included. Primary outcomes of interest are perinatal mortality, preterm birth and SGA. To retrieve potential eligible articles, a systematic literature search was performed in the following online databases on 5 October 2018: MEDLINE, Embase, Web of Science, Cochrane and Google Scholar. Additionally, a reference list check and citation search will be performed. Data of the included articles will be extracted using a standardised and piloted data extraction form. Risk of bias will be assessed using the Newcastle-Ottawa Scale. The study selection and data extraction process will be performed by two reviewers independently. Disagreements will be resolved through discussion with a third reviewer. The pooled effects will be calculated separately for each association found between one of the outcome measures and the non-medical determinants using a random effects model. Heterogeneity of the studies will be assessed using the I2statistic.Ethics and disseminationNo ethical approval is necessary for a systematic review with meta-analysis. The findings will be published in a peer-reviewed journal.PROSPERO registration numberCRD42018056105.

scholarly journals Educational disparities in perinatal health in Denmark in the first decade of the 21st century: a register-based cohort study

BMJ Open ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. e023531 ◽  
Author(s):  
Josephine Funck Bilsteen ◽  
Josefine Bernhard Andresen ◽  
Laust Hvas Mortensen ◽  
Anne Vinkel Hansen ◽  
Anne-Marie Nybo Andersen
Keyword(s):  
Cohort Study ◽  
Preterm Birth ◽  
Health Outcomes ◽  
21St Century ◽  
Gestational Age ◽  
Small For Gestational Age ◽  
Congenital Anomalies ◽  
Perinatal Health ◽  
Postneonatal Mortality ◽  
Adjusted Risk

ObjectiveTo investigate socioeconomic differences in six perinatal health outcomes in Denmark in the first decade of the 21st century.DesignA population-based cohort study.SettingDanish national registries.ParticipantsA total of 646 829 live born children and 3076 stillborn children (≥22+0 weeks of gestation) born in Denmark from 2000 to 2009. We excluded children with implausible relations between birth weight and gestational age (n=644), children without information on maternal country of origin (n=138) and implausible values of maternal year of birth (n=36).Main outcome measuresWe investigated the following perinatal health outcomes: stillbirth, neonatal and postneonatal mortality, small-for-gestational age, preterm birth grated into moderate preterm, very preterm and extremely preterm, and congenital anomalies registered in the first year of life.ResultsMaternal educational level was inversely associated with all adverse perinatal outcomes. For all examined outcomes, the risk association displayed a clear gradient across the educational levels. The associations remained after adjustment for maternal age, maternal country of origin and maternal year of birth. Compared with mothers with vocational education, mothers with more than 15 years of education had an adjusted risk ratio for stillbirth of 0.64(95% CI 0.56 to 0.72). The corresponding adjusted risk ratios for neonatal mortality, postneonatal mortality, congenital anomalies, moderate preterm birth and small-for-gestational age were, respectively, 0.79(95% CI 0.67 to 0.93), 0.57(95% CI 0.42 to 0.78), 0.87(95% CI 0.83 to 0.91), 0.80(95% CI 0.77 to 0.83) and 0.83(95% CI 0.81 to 0.85).ConclusionSubstantial educational inequalities in perinatal health were still present in Denmark in the first decade of the 21st century.

scholarly journals The impact of economic sanctions on health and health systems in low- and middle-income countries

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Suhrcke ◽  
M Pinna Pintor ◽  
C Hamelmann
Keyword(s):  
Health Outcomes ◽  
Health System ◽  
Health Systems ◽  
Grey Literature ◽  
Economic Sanctions ◽  
Political Crisis ◽  
Middle Income ◽  
Financial Flows ◽  
Adverse Health ◽  
The Impact

Abstract Background Economic sanctions, understood as measures taken by one state or a group of states to coerce another into a desired conduct (eg by restricting trade and financial flows) do not primarily seek to adversely affect the health or health system of the target country's population. Yet, there may be indirect or unintended health and health system consequences that ought to be borne in mind when assessing the full set of effects of sanctions. We take stock of the evidence to date in terms of whether - and if so, how - economic sanctions impact health and health systems in LMICs. Methods We undertook a structured literature review (using MEDLINE and Google Scholar), covering the peer-reviewed and grey literature published from 1970-2019, with a specific focus on quantitative assessments. Results Most studies (23/27) that met our inclusion criteria focus on the relationship between sanctions and health outcomes, ranging from infant or child mortality as the most frequent case over viral hepatitis to diabetes and HIV, among others. Fewer studies (9/27) examined health system related indicators, either as a sole focus or jointly with health outcomes. A minority of studies explicitly addressed some of the methodological challenges, incl. control for relevant confounders and the endogeneity of sanctions. Taking the results at face value, the evidence is almost unanimous in highlighting the adverse health and health system effects of economic sanctions. Conclusions Quantitatively assessing the impact of economic sanctions on health or health systems is a challenging task, not least as it is persistently difficult to disentangle the effect of sanctions from many other, potentially major factors at work that matter for health (as, for instance, war). In addition, in times of severe economic and political crisis (which often coincide with sanctions), the collection of accurate and comprehensive data that could allow appropriate measurement is typically not a priority. Key messages The existing evidence is almost unanimous in highlighting the adverse health and health system effects of economic sanctions. There is preciously little good quality evidence on the health (system) impact of economic sanctions.

scholarly journals Genome Sequences of 15 Gardnerella vaginalis Strains Isolated from the Vaginas of Women with and without Bacterial Vaginosis: TABLE 1 

2016 ◽  
Vol 4 (5) ◽  
Author(s):  
Lloyd S. Robinson ◽  
Justin Perry ◽  
Sai Lek ◽  
Aye Wollam ◽  
Erica Sodergren ◽  
...  
Keyword(s):  
Preterm Birth ◽  
Health Outcomes ◽  
Bacterial Vaginosis ◽  
Draft Genome ◽  
Gardnerella Vaginalis ◽  
Genome Sequences ◽  
Predominant Species ◽  
Adverse Health ◽  
Adverse Health Outcomes

Gardnerella vaginalis is a predominant species in bacterial vaginosis, a dysbiosis of the vagina that is associated with adverse health outcomes, including preterm birth. Here, we present the draft genome sequences of 15 Gardnerella vaginalis strains (now available through BEI Resources) isolated from women with and without bacterial vaginosis.

scholarly journals 694 Insufficient sleep linked with higher COVID-19 infection cases and deaths in the United States

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A271-A271
Author(s):  
Azizi Seixas ◽  
Nicholas Pantaleo ◽  
Samrachana Adhikari ◽  
Michael Grandner ◽  
Giardin Jean-Louis
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
The United States ◽  
Poor Sleep ◽  
County Level ◽  
Infection Rates ◽  
Insufficient Sleep ◽  
Death Rates ◽  
Rural Counties ◽  
The Relationship

Abstract Introduction Causes of COVID-19 burden in urban, suburban, and rural counties are unclear, as early studies provide mixed results implicating high prevalence of pre-existing health risks and chronic diseases. However, poor sleep health that has been linked to infection-based pandemics may provide additional insight for place-based burden. To address this gap, we investigated the relationship between habitual insufficient sleep (sleep <7 hrs./24 hr. period) and COVID-19 cases and deaths across urban, suburban, and rural counties in the US. Methods County-level variables were obtained from the 2014–2018 American community survey five-year estimates and the Center for Disease Control and Prevention. These included percent with insufficient sleep, percent uninsured, percent obese, and social vulnerability index. County level COVID-19 infection and death data through September 12, 2020 were obtained from USA Facts. Cumulative COVID-19 infections and deaths for urban (n=68), suburban (n=740), and rural (n=2331) counties were modeled using separate negative binomial mixed effects regression models with logarithmic link and random state-level intercepts. Zero-inflated models were considered for deaths among suburban and rural counties to account for excess zeros. Results Multivariate regression models indicated positive associations between cumulative COVID-19 infection rates and insufficient sleep in urban, suburban and rural counties. The incidence rate ratio (IRR) for urban counties was 1.03 (95% CI: 1.01 – 1.05), 1.04 (95% CI: 1.02 – 1.05) for suburban, and 1.02 (95% CI: 1.00 – 1.03) rural counties.. Similar positive associations were observed with county-level COVID-19 death rates, IRR = 1.11 (95% CI: 1.07 – 1.16) for urban counties, IRR = 1.04 (95% CI: 1.01 – 1.06) for suburban counties, and IRR = 1.03 (95% CI: 1.01 – 1.05) for rural counties. Level of urbanicity moderated the association between insufficient sleep and COVID deaths, but not for the association between insufficient sleep and COVID infection rates. Conclusion Insufficient sleep was associated with COVID-19 infection cases and mortality rates in urban, suburban and rural counties. Level of urbanicity only moderated the relationship between insufficient sleep and COVID death rates. Future studies should investigate individual-level analysis to understand the role of sleep mitigating COVID-19 infection and death rates. Support (if any) NIH (K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453

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