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Lee, Andy H.; Ng, Shu‐Kay; Yau, Kelvin K. W.

doi:10.1023/a:1011810326113

Cited by 30 publications

(3 citation statements)

References 11 publications

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“…The direct effect of maternal complications on LoS was observed in our results and the results of several prior studies. 5 , 9 , 11 , 22 Perinatal complications, such as LBW, were associated with a longer LoS in our study. A similar finding was reported in a previous Italian study.…”

Section: Discussionsupporting

confidence: 51%

Length of Hospital Stay After Cesarean Delivery and Its Determinants Among Women in Eastern Sudan

Hassan¹,

Mandar²,

Alhabardi³

et al. 2022

IJWH

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Background There is an increasing caesarean delivery (CD) rate globally. Length of hospital stay (LoS) is longer in CD compared with vaginal delivery. There are few published data on LoS following CD in Africa, including Sudan. We aimed to investigate LoS after CD in eastern Sudan and its associated risk factors. Methods A cross-sectional study was conducted at Gadarif hospital in eastern Sudan from May to December 2020. Sociodemographic, clinical and obstetrical data were gathered through questionnaires. Poisson regressions were used to model the LoS and provide relative risk (RR) and a 95.0% confidence interval (CI). Results We enrolled 544 women with CD. The median (interquartile range, IQR) of their age and parity was 28.0 (24.0 ‒32.0) years and 3(2‒3), respectively. The LoS range was 1.0–9.0 days (mean = 2.7 days) and its median (IRQ) was 3.0 (2.0‒3.0) days. The median (IQR) of the LoS was significantly higher in women who had emergency CD vs elective CD, [3 (3.0‒3.0) vs 3 (2.0‒3.0) days, P < 0.001] and in women with maternal complications vs women who had no maternal complications [3 (2.0‒3.0) vs 3 (2.0‒3.0) days, P < 0.001]. Poisson regression showed that women with emergency CD stayed for 13.0% longer than women with elective CD (RR=1.13, 95% CI=1.01‒1.29). Women with maternal complications stayed 24.0% longer than women who had no maternal complications (RR=1.24, 95% CI=1.07‒1.43). Women who had neonatal complications stayed for 21.0% longer than women who had no neonatal complications (RR=1.21, 95% CI=1.05‒1.40). Age, parity, residence, education, occupation and postoperative haemoglobin were not associated with LoS. Conclusion The mean LoS in this study was 2.7 days, and women with emergency CD and maternal and neonatal complications had longer LoS.

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Section: Discussionsupporting

confidence: 51%

Length of Hospital Stay After Cesarean Delivery and Its Determinants Among Women in Eastern Sudan

Hassan¹,

Mandar²,

Alhabardi³

et al. 2022

IJWH

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“…The current literature focuses on applying the methodology of finite mixture models to analyzing LOS and developing algorithms for model estimation [17,19,20,[23][24][25][26][27][28]. Finite mixture models can incorporate patient level and hospital level characteristics (covariates) that influence LOS for different subpopulations (components).…”

Section: Statistical Literature Reviewmentioning

confidence: 99%

Dealing with highly skewed hospital length of stay distributions: The use of Gamma mixture models to study delivery hospitalizations

et al. 2020

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The increased focus on addressing severe maternal morbidity and maternal mortality has led to studies investigating patient and hospital characteristics associated with longer hospital stays. Length of stay (LOS) for delivery hospitalizations has a strongly skewed distribution with the vast majority of LOS lasting two to three days in the United States. Prior studies typically focused on common LOSs and dealt with the long LOS distribution tail in ways to fit conventional statistical analyses (e.g., log transformation, trimming). This study demonstrates the use of Gamma mixture models to analyze the skewed LOS distribution. Gamma mixture models are flexible and, do not require data transformation or removal of outliers to accommodate many outcome distribution shapes, these models allow for the analysis of patients staying in the hospital for a longer time, which often includes those women experiencing worse outcomes. Random effects are included in the model to account for patients being treated within the same hospitals. Further, the role and influence of differing placements of covariates on the results is discussed in the context of distinct model specifications of the Gamma mixture regression model. The application of these models shows that they are robust to the placement of covariates and random effects. Using New York State data, the models showed that longer LOS for childbirth hospitalizations were more common in hospitals designated to accept more complicated deliveries, across hospital types, and among Black women. Primary insurance also was associated with LOS. Substantial variation between hospitals suggests the need to investigate protocols to standardize evidence-based medical care.

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“…Therefore, the heterogeneous LOS observations can be modeled by a two-component model, with components corresponding to the short-stay sub-population and the long-stay sub-population [21,22].…”

Section: Introductionmentioning

confidence: 99%

Inpatient length of stay: a finite mixture modeling analysis

Chungkham

Ladusingh

2009

Eur J Health Econ

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Length of stay (LOS) in hospital for inpatient treatment is a measure of crucial recovery time. Using nationwide data on inpatient healthcare in India, a three-component finite mixture negative binomial model was found to provide a reasonable fit to the heterogeneous LOS distribution. Associated risk factors for short-stay, medium-stay and long-stay subgroups were identified from the respective negative binomial components. In addition, significant heterogeneities within each group were also found.

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Cited by 30 publications

References 11 publications

Length of Hospital Stay After Cesarean Delivery and Its Determinants Among Women in Eastern Sudan

Length of Hospital Stay After Cesarean Delivery and Its Determinants Among Women in Eastern Sudan

Dealing with highly skewed hospital length of stay distributions: The use of Gamma mixture models to study delivery hospitalizations

Inpatient length of stay: a finite mixture modeling analysis

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