Dataset Information

Prolonged postpartum length of hospital stay among women with disabilities.

ABSTRACT:

Background

Prior studies have found higher proportions of cesarean deliveries and longer postpartum hospital stays among women with disabilities compared to women without disabilities. However, no research has assessed how length of stay may differ for women with different types of disability while also considering mode of delivery.

Objective

To examine the association of disability status and disability type with length of stay, taking into account disability-related differences in mode of delivery.

Methods

We conducted a retrospective cohort study using linked maternal and infant hospital discharge and vital records data for all births in California between 2000 and 2012 (n = 6,745,201). We used multivariable regression analyses to assess association of disability status and type with prolonged length of stay (>2 days for vaginal delivery or >4 days for cesarean) while controlling for covariates.

Results

Women with disabilities had significantly elevated adjusted odds of prolonged length of stay compared to women without disabilities (aOR = 1.40, 95% CI = 1.32-1.49). Adjusted odds were highest for women with vision disabilities (aOR = 1.67, 95% CI = 1.46-1.90), followed by women with IDD (aOR = 1.53, 95% CI = 1.30-1.80), and women with physical disabilities (aOR = 1.41, 95% CI = 1.32-1.50). Women with hearing disability had the lowest adjusted odds of prolonged length of stay (aOR = 1.17, 95% CI = 1.03-1.33).

Conclusions

Prolonged length of stay did not appear to be due solely to the higher proportion of cesarean deliveries in this population. Further research is needed to better understand the reasons for prolonged length of stay among women with disabilities and develop strategies to assist women with disabilities in preparing for and recovering from childbirth.

SUBMITTER: Horner-Johnson W

PROVIDER: S-EPMC7775680 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Objective: How hospital length of stay after delivery for women with preeclampsia is associated with risk for readmission is unknown. The objective of this study was to evaluate risk for 60-day hypertension-related postpartum readmission based on length of stay after delivery.Methods: The 2014 Healthcare Cost and Utilization Project's (HCUP) Nationwide Readmissions Database was used to analyze risk for readmission for a hypertension-related diagnosis within 60 days from cesarean delivery hospitalization for women with preeclampsia who underwent cesarean delivery. Risk for readmission was evaluated based on postoperative length of stay as well as demographic, hospital, and other obstetric factors. Population weights were applied to create national estimates. Multivariable analyses were performed with adjusted risk ratios (aRR) and corresponding 95% confidence intervals as measures of effect. Mean and median hospital charges based upon postoperative length of stay were also evaluated. Time from delivery hospitalization to readmission was calculated.Results: In 2014, 65 401 women with preeclampsia underwent cesarean delivery. Of these, 1016 women (1.6%) were readmitted for a hypertension-related diagnosis. 921 of the 1016 readmissions occurred within 10 days of discharge (90.6%). In adjusted analyses, postoperative LOS 5-7 days and >7 days compared to LOS <3 days were associated with decreased risk of 60-day hypertension-related readmission (aRR 0.59 95% CI 0.45, 0.78; aRR 0.53 95% CI 0.29, 1.00, respectively). When the cohort was restricted to women with severe preeclampsia or eclampsia, LOS 5-7 days was associated with decreased risk of 60-day hypertension-related readmission in both unadjusted and adjusted analyses compared to LOS <3 days (risk ratios (RR) 0.34, 95% CI 0.18, 0.65; aRR 0.29, 95% 0.18, 0.46, respectively). Median delivery hospitalization charges were $26 512. Compared to LOS <3 days, mean and median charges increased significantly for patients with LOS 4, 5-7, and >7 days.Conclusion: Longer postoperative length of stay during cesarean delivery hospitalizations was associated with decreased risk for postpartum hypertension-related readmission. Most readmissions occurred soon after discharge. These findings support that post-delivery management may play a role in likelihood of women requiring subsequent readmission for complications related to preeclampsia after discharge.

Project description:BackgroundThe epidemic of Coronavirus Disease 2019 (COVID-19) has become a global health emergency, but the clinical characteristics of COVID-19 are not fully described. We aimed to describe the clinical characteristics of COVID-19 outside of Wuhan city; and to develop a multivariate model to predict the risk of prolonged length of stay in hospital (ProLOS).MethodsThe study was conducted in a tertiary care hospital in Zhejiang province from January to February 20, 2020. Medical records of all confirmed cases of COVID-19 were retrospectively reviewed. Patients were categorized into the ProLOS and non-ProLOS groups by hospital length of stay greater and less than 14 days, respectively. Conventional descriptive statistics were applied. Multivariate regression model was built to predict the risk of ProLOS, with variables selected using stepwise approach.ResultsA total of 75 patients with confirmed COVID-19 were included for quantitative analysis, including 25 (33%) patients in the ProLOS group. ProLOS patients were more likely to have history of traveling to Wuhan (68% vs. 28%; P=0.002). Patients in the ProLOS group showed lower neutrophil counts [median (IQR): 2.50 (1.77–3.23) ×109/L vs. 2.90 (2.21–4.19) ×109/L; P=0.048], higher partial thrombin time (PT) (13.42±0.63 vs. 13.10±0.48 s; P=0.029), lower D-Dimer [0.26 (0.22–0.46) vs. 0.44 (0.32–0.84) mg/L; P=0.012]. There was no patient died and no severe case in our cohort. The overall LOS was 11 days (IQR, 5–15 days). The median cost for a hospital stay was 7,388.19 RMB (IQR, 5,085.39–11,145.44). The prediction model included five variables of procalcitonin, heart rate, epidemiological history, lymphocyte count and cough. The discrimination of the model was 84.8% (95% CI: 75.3% to 94.4%).ConclusionsOur study described clinical characteristics of COVID-19 outside of Wuhan city and found that the illness was less severe than that in the core epidemic region. A multivariate model was developed to predict ProLOS, which showed good discrimination.

Project description:BackgroundThe Blue Book (2005), recommended guidelines for patients care with fragility fractures. Together with introduction of a National Hip Fracture Database Audit and Best Practice Tariff model to financially incentivise hospitals by payment of a supplement for patients whose care satisfied six clinical standards), have improved hip fracture after-care. However, there is a lack of data-driven evidence to support its effectiveness. We aimed to verify the impact of an orthogeriatric service on hospital length of stay (LOS)-duration from admission to discharge.MethodsWe conducted a repeated cross-sectional study over a 10 year period of older individuals aged ≥ 60 years admitted with hip fractures to a hospital.ResultsAltogether 2798 patients, 741 men and 2057 women (respective mean ages; 80.5 ± 10.6 and 83.2 ± 8.9 years) were admitted from their own homes with a hip fracture and survived to discharge. Compared to 2009-2014, LOS during 2015-2019, when the orthogeriatric service was fully implemented, was shorter for all discharge destinations: 10.4 vs 17.5 days (P < 0.001). Each discharge destination showed reductions: back to own homes, 9.7 vs 17.7 days (P < 0.001); to rehabilitation units: 10.8 vs 13.1 days (P < 0.001); to residential care: 15.4 vs 26.2 days (P = 0.001); or nursing care, 24.4 vs 53.1 days (P < 0.001). During 2009-2014, the risk of staying > 3 weeks in hospital was greater by six-fold and pressure ulcers by three-fold. The number of bed days for every thousand patients per year was also shortened during 2015-2019 by: 1665 days for discharge back to own homes; 469 days with transfer to rehabilitation units; 1258 days for discharge to residential care, and 5465 days to nursing care. Estimated annual savings (2017 costs) per thousand patients after complete establishment of the service was about £2.7 m.ConclusionsImplementation of an orthogeriatric service generated significant reductions in hospital LOS for all patients, with associated cost-savings, especially for those discharged to nursing care.

Project description:BackgroundThe increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Therefore, physicians need an effective pLOS prediction model for PD-treated patients.ObjectiveThis study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data.MethodsPatient data collected using the Hospital Quality Monitoring System (HQMS) in China were used to develop pLOS prediction models. A stacking model was constructed with support vector machine, random forest (RF), and K-nearest neighbor algorithms as its base models and traditional logistic regression (LR) as its meta-model. The meta-model used the outputs of all 3 base models as input and generated the output of the stacking model. Another LR-based pLOS prediction model was built as the benchmark model. The prediction performance of the stacking model was compared with that of its base models and the benchmark model. Five-fold cross-validation was employed to develop and validate the models. Performance measures included the Brier score, area under the receiver operating characteristic curve (AUROC), estimated calibration index (ECI), accuracy, sensitivity, specificity, and geometric mean (Gm). In addition, a calibration plot was employed to visually demonstrate the calibration power of each model.ResultsThe final cohort extracted from the HQMS database consisted of 23,992 eligible PD-treated patients, among whom 30.3% had a pLOS (ie, longer than the average LOS, which was 16 days in our study). Among the models, the stacking model achieved the best calibration (ECI 8.691), balanced accuracy (Gm 0.690), accuracy (0.695), and specificity (0.701). Meanwhile, the stacking and RF models had the best overall performance (Brier score 0.174 for both) and discrimination (AUROC 0.757 for the stacking model and 0.756 for the RF model). Compared with the benchmark LR model, the stacking model was superior in all performance measures except sensitivity, but there was no significant difference in sensitivity between the 2 models. The 2-sided t tests revealed significant performance differences between the stacking and LR models in overall performance, discrimination, calibration, balanced accuracy, and accuracy.ConclusionsThis study is the first to develop data-driven pLOS prediction models for PD-treated patients using basic admission data from a national database. The results indicate the feasibility of utilizing a stacking-based pLOS prediction model for PD-treated patients. The pLOS prediction tools developed in this study have the potential to assist clinicians in identifying patients at a high risk of pLOS and to allocate resources optimally for PD-treated patients.