Psychological and neighborhood factors associated with urban women’s preventive care use

Veldhuis, Cindy B.; Maki, Pauline M.; Molina, Kristine M.

doi:10.1007/s10865-019-00122-4

Cited by 7 publications

(9 citation statements)

References 101 publications

(105 reference statements)

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“…For instance, the first pandemic surge resulted in a decline in hospitalization rates and lengths of hospital stay associated with acute cardiovascular conditions as well as other common conditions, such as acute appendicitis, bone fractures, cancer, and live births. Given the evidence on differences based on sex [ 40 ], race and ethnicity [ 41 ], sexual and gender identity [ 42 , 43 ], and other key factors, we need to investigate sociodemographic differences in delayed or interrupted care. Further, the significant economic effects of the COVID-19 pandemic as well as the high rates of job loss may result in the loss of insurance.…”

Section: Discussionmentioning

confidence: 99%

Characterizing Health Care Delays and Interruptions in the United States During the COVID-19 Pandemic: Internet-Based, Cross-sectional Survey Study

Papautsky¹,

Rice²,

Ghoneima³

et al. 2021

J Med Internet Res

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Background The COVID-19 pandemic has broader geographic spread and potentially longer lasting effects than those of previous disasters. Necessary preventive precautions for the transmission of COVID-19 has resulted in delays for in-person health care services, especially at the outset of the pandemic. Objective Among a US sample, we examined the rates of delays (defined as cancellations and postponements) in health care at the outset of the pandemic and characterized the reasons for such delays. Methods As part of an internet-based survey that was distributed on social media in April 2020, we asked a US–based convenience sample of 2570 participants about delays in their health care resulting from the COVID-19 pandemic. Participant demographics and self-reported worries about general health and the COVID-19 pandemic were explored as potent determinants of health care delays. In addition to all delays, we focused on the following three main types of delays, which were the primary outcomes in this study: dental, preventive, and diagnostic care delays. For each outcome, we used bivariate statistical tests (t tests and chi-square tests) and multiple logistic regression models to determine which factors were associated with health care delays. Results The top reported barrier to receiving health care was the fear of SARS-CoV-2 infection (126/374, 33.6%). Almost half (1227/2570, 47.7%) of the participants reported experiencing health care delays. Among those who experienced health care delays and further clarified the type of delay they experienced (921/1227, 75.1%), the top three reported types of care that were affected by delays included dental (351/921, 38.1%), preventive (269/921, 29.2%), and diagnostic (151/921, 16.4%) care. The logistic regression models showed that age (P<.001), gender identity (P<.001), education (P=.007), and self-reported worry about general health (P<.001) were significantly associated with experiencing health care delays. Self-reported worry about general health was negatively related to experiencing delays in dental care. However, this predictor was positively associated with delays in diagnostic testing based on the logistic regression model. Additionally, age was positively associated with delays in diagnostic testing. No factors remained significant in the multiple logistic regression for delays in preventive care, and although there was trend between race and delays (people of color experienced fewer delays than White participants), it was not significant (P=.06). Conclusions The lessons learned from the initial surge of COVID-19 cases can inform systemic mitigation strategies for potential future disruptions. This study addresses the demand side of health care delays by exploring the determinants of such delays. More research on health care delays during the pandemic is needed, including research on their short- and long-term impacts on patient-level outcomes such as mortality, morbidity, mental health, people’s quality of life, and the experience of pain.

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Section: Discussionmentioning

confidence: 99%

Characterizing Health Care Delays and Interruptions in the United States During the COVID-19 Pandemic: Internet-Based, Cross-sectional Survey Study

Papautsky¹,

Rice²,

Ghoneima³

et al. 2021

J Med Internet Res

Self Cite

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“…Similarly, community socioeconomic deprivation is theorized to pose barriers to preventive care, such as diabetes screening, through a range of potential pathways, including poor access to transportation services, lack of social networks that share health information, and shared medical mistrust ( Benkert et al, 2019 ; Veldius et al, 2020 ). Consistent with this hypothesis, we did not observe an association of community socioeconomic deprivation with hyperfiltration at the time of type 2 diabetes detection.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, GFR at time of type 2 diabetes diagnosis could vary depending upon when during the course of disease an individual is diagnosed. Hyperglycemia may go undetected for a longer time among individuals living in socioeconomically deprived communities due to poor transportation access; lack of social support that encourages utilization of preventive health services (e.g., screenings); and lack of social networks that exchange health information ( Fernandes et al, 2015 ; Veldhius et al, 2020 ). Little is known regarding the association between community socioeconomic deprivation and type 2 diabetes diagnosis delays.…”

Section: Introductionmentioning

confidence: 99%

Association of community socioeconomic deprivation with evidence of reduced kidney function at time of type 2 diabetes diagnosis

Hirsch

Nordberg

Chang

et al. 2021

SSM - Population Health

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Background While there are known individual-level risk factors for kidney disease at time of type 2 diabetes diagnosis, little is known regarding the role of community context. We evaluated the association of community socioeconomic deprivation (CSD) and community type with estimated glomerular filtration rate (eGFR) when type 2 diabetes is diagnosed. Methods This was a retrospective cohort study of 13,144 adults with newly diagnosed type 2 diabetes in Pennsylvania. The outcome was the closest eGFR measurement within one year prior to and two weeks after type 2 diabetes diagnosis, calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equation. We used adjusted multinomial regression models to estimate associations of CSD (quartile 1, least deprivation) and community type (township, borough, city) with eGFR and used adjusted generalized estimating equation models to evaluate whether community features were associated with the absence of diabetes screening in the years prior to type 2 diabetes diagnosis. Results Of the participants, 1279 (9.7%) had hyperfiltration and 1377 (10.5%) had reduced eGFR. Women were less likely to have hyperfiltration and more likely to have reduced eGFR. Black (versus White) race was positively associated with hyperfiltration when the eGFR calculation was corrected for race but inversely associated without the correction. Medical Assistance (ever versus never) was positively associated with reduced eGFR. Higher CSD and living in a city were each positively associated (odds ratio [95% confidence interval]) with reduced eGFR (CSD quartiles 3 and 4 versus quartile 1, 1.23 [1.04, 1.46], 1.32 [1.11, 1.58], respectively; city versus township, 1.38 [1.15, 1.65]). These features were also positively associated with the absence of a type 2 diabetes screening measure. Conclusions In a population-based sample, more than twenty percent had hyperfiltration or reduced eGFR at time of type 2 diabetes diagnosis. Individual- and community-level factors were associated with these outcomes.

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“…(6) Furthermore, the Chicago Community Adult Health Study found the women's use of preventive care to be associated with objective and perceived neighborhood support and stressors such as odors, presence of trees, and noise levels. (7) The existing literature motivated this study to examine the impact of the built environment on health and healthcare utilization among women, and particularly, the pregnant population. (8)(9)(10) Levels of prenatal care vary across the United States.…”

Section: Introductionmentioning

confidence: 99%

“…Existing studies have commonly relied on the subjective perceived measures obtained from interviews and questionnaires. (4,7,18) However, relying on subjective measurements may increase recall bias which occurs when some participants recall the exposure differently than others.…”

Section: Introductionmentioning

confidence: 99%

Identifying Urban Built Environment Factors in Pregnancy Care and Maternal Mental Health Outcomes

Zhang

Tayarani

Wang

et al. 2021

Preprint

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Backgrounds: Environmental risk factors related to the built environment have been associated with women’s mental health and preventive care. This study sought to identify built environment factors that are associated with variations in prenatal care and subsequent pregnancy-related outcomes in an urban setting. Methods: In a retrospective observational study using machine learning, we characterized the types and frequency of events in prenatal care that are associated with the various built environment factors of the patients’ residing neighborhoods. We hypothesize that, in comparison to women living in high-quality built environments, women who reside in low-quality built environments experience a different pattern of clinical events that may increase the risk for adverse outcomes. Using machine learning, we performed pattern detection to characterize the variability in prenatal care with respect to encounter types, clinical problems, and medication prescriptions. Structural equation modeling was used to test the associations among built environment, prenatal care variation, and pregnancy outcome. The main outcome is postpartum depression (PPD) diagnosis within 1 year following childbirth. The exposures were the quality of the built environment in the patients’ residing neighborhoods. Electronic health records (EHR) data of pregnant women (n=8,949) who had live delivery at an urban academic medical center in 2015 to 2017 were included in the study. Results: We discovered prenatal care patterns that were summarized into three common types. Women who experienced the prenatal care pattern with the highest rates of PPD were more likely to reside in neighborhoods with homogeneous land use, lower walkability, lower air pollutant concentration, and lower accessibility to retail stores after adjusting for age, neighborhood average education level, marital status, and income inequality. Conclusions: In an urban setting, multi-purpose and walkable communities were found to be associated with a lower risk of PPD. Findings may inform urban design policies and provide awareness for care providers on the association of patients’ residing neighborhoods and healthy pregnancy.

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Psychological and neighborhood factors associated with urban women’s preventive care use

Cited by 7 publications

References 101 publications

Characterizing Health Care Delays and Interruptions in the United States During the COVID-19 Pandemic: Internet-Based, Cross-sectional Survey Study

Characterizing Health Care Delays and Interruptions in the United States During the COVID-19 Pandemic: Internet-Based, Cross-sectional Survey Study

Association of community socioeconomic deprivation with evidence of reduced kidney function at time of type 2 diabetes diagnosis

Identifying Urban Built Environment Factors in Pregnancy Care and Maternal Mental Health Outcomes

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