Does neighborhood socioeconomic status predict the risk of preterm birth? A community-based Canadian cohort study

Association Between Time to Treatment With Endovascular Thrombectomy and Home-Time After Acute Ischemic Stroke

BACKGROUND AND OBJECTIVES

Home-time is a patient-prioritized stroke outcome that can be derived from administrative data linkages. The effect of faster time-to-treatment with endovascular thrombectomy (EVT) on home-time after acute stroke is unknown.

METHODS

We used the Quality Improvement and Clinical Research registry to identify a cohort of patients who received EVT for acute ischemic stroke between 2015 and 2022 in Alberta, Canada. We calculated days at home in the first 90 days after stroke. We used ordinal regression across 6 ordered categories of home-time to evaluate the association between onset-to-arterial puncture and higher home-time, adjusting for age, sex, rural residence, NIH Stroke Scale, comorbidities, intravenous thrombolysis, and year of treatment. We used restricted cubic splines to assess the nonlinear relationship between continuous variation in time metrics and higher home-time, and also reported the adjusted odds ratios within time categories. We additionally evaluated door-to-puncture and reperfusion times. Finally, we analyzed home-time with zero-inflated models to determine the minutes of earlier treatment required to gain 1 day of home-time.

RESULTS

We had 1,885 individuals in our final analytic sample. There was a nonlinear increase in home-time with faster treatment when EVT was within 4 hours of stroke onset or 2 hours of hospital arrival. There was a higher odds of achieving more days at home when onset-to-puncture time was <2 hours (adjusted odds ratio 2.36, 95% CI 1.77-3.16) and 2 to <4 hours (1.37, 95% CI 1.11-1.71) compared with ≥6 hours, and when door-to-puncture time was <1 hour (aOR 2.25, 95% CI 1.74-2.90), 1 to <1.5 hours (aOR 1.89, 95% CI 1.47-2.41), and 1.5 to <2 hours (1.35, 95% CI 1.04-1.76) compared with ≥2 hours. Results were consistent for reperfusion times. For every hour of faster treatment within 6 hours of stroke onset, there was an estimated increase in home-time of 4.7 days, meaning that approximately 1 day of home-time was gained for each 12.8 minutes of faster treatment.

DISCUSSION

Faster time-to-treatment with EVT for acute stroke was associated with greater home-time, particularly within 4 hours of onset-to-puncture and 2 hours of door-to-puncture time. Within 6 hours of stroke onset, each 13 minutes of faster treatment is associated with a gain of 1 day of home-time.

The effect of socioeconomic status on adverse obstetric and perinatal outcomes in women with polycystic ovary syndrome-An evaluation of a population database

OBJECTIVE

To evaluate the modifying effect of low socioeconomic status (SES) on polycystic ovary syndrome (PCOS) women's pregnancy and neonatal complications.

METHODS

A retrospective population-based cohort study including all women with an ICD-9 diagnosis of PCOS in the US between 2004 and 2014, who delivered in the third trimester or had a maternal death. SES was defined according to the total annual family income quartile for the entire population studied. We compared women in the lowest income quartile (<$39 000 annually) to those in the higher income quartiles combined (≥$39 000 annually). Pregnancy, delivery, and neonatal outcomes were compared between the two groups.

RESULTS

Overall, 9 096 788 women delivered between 2004 and 2014, of which 12 322 had a PCOS diagnosis and evidence of SES classification. Of these, 2117 (17.2%) were in the lowest SES group, and 10 205 (82.8%) were in the higher SES group. PCOS patients in the lowest SES group, compared to the higher SES group, were more likely to be younger, obese (body mass index ≥30 kg/m2 ), to have smoked tobacco during pregnancy, and to have chronic hypertension and pregestational diabetes mellitus (DM) (P < 0.05). In a multivariate logistic regression, women in the lowest SES group, compared to the higher SES group, had increased odds of pregnancy-induced hypertension (aOR 1.27, 95% CI: 1.12-1.46, P < 0.001), pre-eclampsia (aOR 1.37, 95% CI: 1.14-1.65, P < 0.001), and cesarean delivery (aOR 1.21, 95% CI: 1.09-1.34, P < 0.001), with other comparable pregnancy, delivery and neonatal outcomes.

CONCLUSION

In PCOS patients, low SES increases the risk for pregnancy-induced hypertension, pre-eclampsia and CD, highlighting the importance of diligent pregnancy follow-up and pre-eclampsia prevention in these patients.

Influence of Neighborhood Socioeconomic Status on Adverse Outcomes in Pregnancy

PURPOSE

To evaluate whether ZIP-code level neighborhood socioeconomic status (SES) is associated with adverse pregnancy outcomes.

METHODS

A retrospective study of 2009-2014 Oregon Health and Science University (OHSU) births with maternal ZIP codes in one of 89 Portland metropolitan area ZIP codes. Deliveries with ZIP codes outside of the Portland metro area were excluded. Deliveries were stratified by SES based on ZIP code median household income: low (below 10th percentile), medium (11th-89th percentile), and high (above 90th percentile). Univariate analysis and multivariable logistic regression with medium SES as the reference group evaluated perinatal outcomes and strength of association between SES and adverse events.

RESULTS

This study included 8118 deliveries with 1654 (20%) classified as low SES, 5856 (72%) medium SES, and 608 (8%) high SES. The low SES group was more likely to be younger, have a higher maternal BMI, have increased tobacco use, identify as Hispanic or Black, and less likely to have private insurance. Low SES was associated with a significantly increased risk of preeclampsia (RR 1.23 95% CI 1.01-1.49), but this was no longer significant after adjusting for confounders (aRR 1.23 95% CI .971-1.55). High SES was negatively associated with gestational diabetes mellitus (GDM), even after adjusting for confounders (aRR 0.710, 95% CI 0.507-0.995).

CONCLUSION

In the Portland metropolitan area, high SES was associated with a lower risk of GDM. Low SES was associated with a higher risk of preeclampsia before accounting for covariates. ZIP code-based risk assessment may be a useful indicator in detecting healthcare disparities.

Unsupervised Learning Applied to the Stratification of Preterm Birth Risk in Brazil with Socioeconomic Data

Preterm birth (PTB) is a phenomenon that brings risks and challenges for the survival of the newborn child. Despite many advances in research, not all the causes of PTB are already clear. It is understood that PTB risk is multi-factorial and can also be associated with socioeconomic factors. Thereby, this article seeks to use unsupervised learning techniques to stratify PTB risk in Brazil using only socioeconomic data. Through the use of datasets made publicly available by the Federal Government of Brazil, a new dataset was generated with municipality-level socioeconomic data and a PTB occurrence rate. This dataset was processed using various unsupervised learning techniques, such as k-means, principal component analysis (PCA), and density-based spatial clustering of applications with noise (DBSCAN). After validation, four clusters with high levels of PTB occurrence were discovered, as well as three with low levels. The clusters with high PTB were comprised mostly of municipalities with lower levels of education, worse quality of public services-such as basic sanitation and garbage collection-and a less white population. The regional distribution of the clusters was also observed, with clusters of high PTB located mostly in the North and Northeast regions of Brazil. The results indicate a positive influence of the quality of life and the offer of public services on the reduction in PTB risk.

Machine Learning-Based Prediction Model of Preterm Birth Using Electronic Health Record

Objective

Preterm birth (PTB) was one of the leading causes of neonatal death. Predicting PTB in the first trimester and second trimester will help improve pregnancy outcomes. The aim of this study is to propose a prediction model based on machine learning algorithms for PTB.

Method

Data for this study were reviewed from 2008 to 2018, and all the participants included were selected from a hospital in China. Six algorisms, including Naive Bayesian (NBM), support vector machine (SVM), random forest tree (RF), artificial neural networks (ANN), K-means, and logistic regression, were used to predict PTB. The receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity were used to assess the performance of the model.

Results

A total of 9550 pregnant women were included in the study, of which 4775 women had PTB. A total of 4775 people were randomly selected as controls. Based on 27 weeks of gestation, the area under the curve (AUC) and the accuracy of the RF model were the highest compared with other algorithms (accuracy: 0.816; AUC = 0.885, 95% confidence interval (CI): 0.873–0.897). Meanwhile, there was positive association between the accuracy and AUC of the RF model and gestational age. Age, magnesium, fundal height, serum inorganic phosphorus, mean platelet volume, waist size, total cholesterol, triglycerides, globulins, and total bilirubin were the main influence factors of PTB.

Conclusion

The results indicated that the prediction model based on the RF algorithm had a potential value to predict preterm birth in the early stage of pregnancy. The important analysis of the RF model suggested that intervention for main factors of PTB in the early stages of pregnancy would reduce the risk of PTB.

Data harmonization and data pooling from cohort studies: a practical approach for data management

Data pooling from pre-existing datasets can be useful to increase study sample size and statistical power in order to answer a research question. However, individual datasets may contain variables that measure the same construct differently, posing challenges for data pooling. Variable harmonization, an approach that can generate comparable datasets from heterogeneous sources, can address this issue in some circumstances. As an illustrative example, this paper describes the data harmonization strategies that helped generate comparable datasets across two Canadian pregnancy cohort studies: All Our Families; and the Alberta Pregnancy Outcomes and Nutrition. Variables were harmonized considering multiple features across the datasets: the construct measured; question asked/response options; the measurement scale used; the frequency of measurement; timing of measurement, and the data structure. Completely matching, partially matching, and completely un-matching variables across the datasets were determined based on these features. Variables that were an exact match were pooled as is. Partially matching variables were harmonized or processed under a common format across the datasets considering the frequency of measurement, the timing of measurement, the measurement scale used, and response options. Variables that were completely unmatching could not be harmonized into a single variable. The variable harmonization strategies that were used to generate comparable cohort datasets for data pooling are applicable to other data sources. Future studies may employ or evaluate these strategies, which permit researchers to answer novel research questions in a statistically efficient, timely, and cost-efficient manner that could not be achieved using a single data source.

Can the adverse childhood experiences (ACEs) checklist be utilized to predict emergency department visits among children and adolescents?

Background

Extensive literature has shown an association of Adverse Childhood Experiences (ACEs) with adverse health outcomes; however, its ability to predict events or stratify risks is less known. Individuals with mental illness and ACE exposure have been shown to visit emergency departments (ED) more often than those in the general population. This study thus examined the ability of the ACEs checklist to predict ED visits within the subsequent year among children and adolescents presenting to mental health clinics with pre-existing mental health issues.

Methods

The study analyzed linked data (n = 6100) from two databases provided by Alberta Health Services (AHS). The Regional Access and Intake System (RAIS 2016–2018) database provided data on the predictors (ACE items, age, sex, residence, mental health program type, and primary diagnosis) regarding children and adolescents (aged 0–17 years) accessing addiction and mental health services within Calgary Zone, and the National Ambulatory Care Reporting System (NACRS 2016–2019) database provided data on ED visits. A 25% random sample of the data was reserved for validation purposes. Two Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression models, each employing a different method to tune the shrinkage parameter lambda (namely cross-validated and adaptive) and performing 10-fold cross-validation for a set of 100 lambdas in each model were examined.

Results

The adaptive LASSO model had a slightly better fit in the validation dataset than the cross-validated model; however, it still demonstrated poor discrimination (AUC 0.60, sensitivity 37.8%, PPV 49.6%) and poor calibration (over-triaged in low-risk and under-triaged in high-risk subgroups). The model’s poor performance was evident from an out-of-sample deviance ratio of − 0.044.

Conclusion

The ACEs checklist did not perform well in predicting ED visits among children and adolescents with existing mental health concerns. The diverse causes of ED visits may have hindered accurate predictions, requiring more advanced statistical procedures. Future studies exploring other machine learning approaches and including a more extensive set of childhood adversities and other important predictors may produce better predictions. Furthermore, despite highly significant associations being observed, ACEs may not be deterministic in predicting health-related events at the individual level, such as general ED use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01392-w.

Data-driven risk stratification for preterm birth in Brazil: a population-based study to develop of a machine learning risk assessment approach

Background

Preterm birth (PTB) is a growing health issue worldwide, currently considered the leading cause of newborn deaths. To address this challenge, the present work aims to develop an algorithm capable of accurately predicting the week of delivery supporting the identification of a PTB in Brazil.

Methods

This a population-based study analyzing data from 3,876,666 mothers with live births distributed across the 3,929 Brazilian municipalities. Using indicators comprising delivery characteristics, primary care work processes, and physical infrastructure, and sociodemographic data we applied a machine learning-based approach to estimate the week of delivery at the point of care level. We tested six algorithms: eXtreme Gradient Boosting, Elastic Net, Quantile Ordinal Regression - LASSO, Linear Regression, Ridge Regression and Decision Tree. We used the root-mean-square error (RMSE) as a precision.

Findings

All models obtained RMSE indexes close to each other. The lower levels of RMSE were obtained using the eXtreme Gradient Boosting approach which was able to estimate the week of delivery within a 2.09 window 95%IC (2.090-2.097). The five most important variables to predict the week of delivery were: number of previous deliveries through Cesarean-Section, number of prenatal consultations, age of the mother, existence of ultrasound exam available in the care network, and proportion of primary care teams in the municipality registering the oral care consultation.

Interpretation

Using simple data describing the prenatal care offered, as well as minimal characteristics of the pregnant, our approach was capable of achieving a relevant predictive performance regarding the week of delivery.

Funding

Bill and Melinda Gates Foundation, and National Council for Scientific and Technological Development - Brazil, (Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPQ acronym in portuguese) Support of the research project named: Data-Driven Risk Stratification for Preterm Birth in Brazil: Development of a Machine Learning-Based Innovation for Health Care- Grant: OPP1202186.

Impact of Race and Socioeconomic Status on Outcomes in Patients Hospitalized with COVID-19

BACKGROUND

The impact of race and socioeconomic status on clinical outcomes has not been quantified in patients hospitalized with coronavirus disease 2019 (COVID-19).

OBJECTIVE

To evaluate the association between patient sociodemographics and neighborhood disadvantage with frequencies of death, invasive mechanical ventilation (IMV), and intensive care unit (ICU) admission in patients hospitalized with COVID-19.

DESIGN

Retrospective cohort study.

SETTING

Four hospitals in an integrated health system serving southeast Michigan.

PARTICIPANTS

Adult patients admitted to the hospital with a COVID-19 diagnosis confirmed by polymerase chain reaction.

MAIN MEASURES

Patient sociodemographics, comorbidities, and clinical outcomes were collected. Neighborhood socioeconomic variables were obtained at the census tract level from the 2018 American Community Survey. Relationships between neighborhood median income and clinical outcomes were evaluated using multivariate logistic regression models, controlling for patient age, sex, race, Charlson Comorbidity Index, obesity, smoking status, and living environment.

KEY RESULTS

Black patients lived in significantly poorer neighborhoods than White patients (median income: $34,758 (24,531-56,095) vs. $63,317 (49,850-85,776), p < 0.001) and were more likely to have Medicaid insurance (19.4% vs. 11.2%, p < 0.001). Patients from neighborhoods with lower median income were significantly more likely to require IMV (lowest quartile: 25.4%, highest quartile: 16.0%, p < 0.001) and ICU admission (35.2%, 19.9%, p < 0.001). After adjusting for age, sex, race, and comorbidities, higher neighborhood income ($10,000 increase) remained a significant negative predictor for IMV (OR: 0.95 (95% CI 0.91, 0.99), p = 0.02) and ICU admission (OR: 0.92 (95% CI 0.89, 0.96), p < 0.001).

CONCLUSIONS

Neighborhood disadvantage, which is closely associated with race, is a predictor of poor clinical outcomes in COVID-19. Measures of neighborhood disadvantage should be used to inform policies that aim to reduce COVID-19 disparities in the Black community.

Ethnic disparity and exposure to supplements rather than adverse childhood experiences linked to preterm birth in Pakistani women

BACKGROUND

Adverse childhood experiences (ACEs) are associated with prenatal mental health and negative pregnancy outcomes in high income countries, but whether the same association exists in Pakistan, a low- to middle-income (LMI) country, remains unclear.

METHODS

Secondary data analyses of a prospective longitudinal cohort study examining biopsychosocial measures of 300 pregnant women at four sites in Karachi, Pakistan. A predictive multiple logistic regression model for preterm birth (PTB; i.e., <37 weeks' gestation) was developed from variables significantly (P < 0.05) or marginally (P < 0.10) associated with PTB in the bivariate analyses.

RESULTS

Of the 300 women, 263 (88%) returned for delivery and were included in the current analyses. The PTB rate was 11.1%. We found no association between ACE and PTB. Mother's education (P = 0.011), mother's ethnicity (P = 0.010), medications during pregnancy (P = 0.006), age at birth of first child or current age if primiparous (P = 0.049) and age at marriage (P = 0.091) emerged as significant in bivariate analyses. Mother's ethnicity and taking medications remained predictive of PTB in the multivariate model.

LIMITATIONS

Findings are limited by the relatively small sample size which precludes direct testing for possible interactive effects.

CONCLUSIONS

In sum, pathways to PTB for women in LMI countries may differ from those observed in high-income countries and may need to be modelled differently to include behavioural response to emotional distress and socio-cultural contexts.