An actuarial investigation into maternal out-of-hospital cost risk factors

2018 ◽  
Vol 13 (1) ◽  
pp. 1-35
Author(s):  
Jananie William ◽  
Catherine Chojenta ◽  
Michael A. Martin ◽  
Deborah Loxton
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Linear Models ◽  
Hospital Costs ◽  
Significant Risk ◽  
In Vitro Fertilisation ◽  
Two Phase ◽  
Data Set ◽  
Cost Risk

AbstractThis paper adopts an actuarial approach to identify the risk factors of government-funded maternal out-of-hospital costs in Australia, with a focus on women who experience adverse birth outcomes. We use a two-phase modelling methodology incorporating both classification and regression trees and generalised linear models on a data set that links administrative and longitudinal survey data from a large sample of women, to address maternal out-of-hospital costs. We find that adverse births are a statistically significant risk factor of out-of-hospital costs in both the delivery and postnatal periods. Furthermore, other significant cost risk factors are in-vitro fertilisation, specialist use, general practitioner use, area of residence and mental health factors (including anxiety, intense anxiety, postnatal depression and stress about own health) and the results vary by perinatal sub-period and the patient’s private health insurance status. We highlight these differences and use the results as an evidence base to inform public policy. Mental health policy is identified as a priority area for further investigation due to the dominance of these factors in many of the fitted models.

An actuarial investigation into maternal hospital cost risk factors for public patients

2017 ◽  
Vol 12 (1) ◽  
pp. 106-129 ◽  
Author(s):  
Jananie William ◽  
Michael A. Martin ◽  
Catherine Chojenta ◽  
Deborah Loxton
Keyword(s):  
Risk Factors ◽  
Smoking Status ◽  
Mode Of Delivery ◽  
Hospital Costs ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Two Phase ◽  
Data Set ◽  
Health Insurance Status ◽  
Cost Risk

AbstractWe investigate an actuarial approach to identifying the factors impacting government-funded maternal hospital costs in Australia, with a focus on women who experience adverse birth outcomes. We propose a two-phase modelling methodology that adopts actuarial methods from typical insurance claim cost modelling and extends to other statistical techniques to account for the large volume of covariates available for modelling. Specifically, Classification and Regression Trees and generalised linear mixed models are employed to analyse a data set that links longitudinal survey and administrative data from a large sample of women. The results show that adverse births are a statistically significant risk factor affecting maternal hospital costs in the antenatal and delivery periods. Other significant cost risk factors in the delivery period include mode of delivery, private health insurance status, diabetes, smoking status, area of residence and onset of labour. We demonstrate the efficacy of using actuarial techniques in non-traditional areas and highlight how the results can be used to inform public policy.

scholarly journals Significant Risk Factors Associated With Stunting for Children Under the Age of 5-years in Malawi: the Application of Proportional Odds Model Using DHS Datasets.

2021 ◽  
Author(s):  
Mzwakhe Magagula ◽  
Shaun Ramroop ◽  
Faustin Habyarimana
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Models ◽  
Linear Models ◽  
Medical Condition ◽  
Significant Risk ◽  
Proportional Odds ◽  
Data Set ◽  
Logistic Regression Models ◽  
Factors Associated

Abstract BackgroundChild malnutrition is perhaps the one of the main medical condition influencing general human wellbeing, mainly in non-industrial nations. The improvement of legitimate evaluations of malnutrition is one of the difficulties encountered by policymakers in numerous countries worldwide. In this manner, the current study was embraced with the essential goal of evaluating and determining all potential determinants of childhood malnutrition in Malawi, using the Demographic and Health Survey (DHS) data 2015/16. The study seeks to reveal some of the significant factors that are perpetuating the incidence of malnutrition in children of Malawi. It also designed to offer deeper insights on how the probability of being diagnosed with this medical condition (malnutrition) evolves across the different levels of the found significant factors.Methods The proportional odds (PO) model was the best model to utilize, motivated by the design of the current study's data set. The PO model is an alternative to conceptualize how the ordinal designed data can be sequentially into dichotomous groups without losing the ordinal nature of response variables. The model is an extension of logistic regression models with two outcomes, it is one of the best models to deal with ordinal response variable comprising of more than two categories. The PO model, as well as the logistic regression models are common classes of generalised linear models (GLMs) mostly used to model association between dependent variable and independent variables. ResultsThe observations derived from fitting the PO model on the Malawi DHS data to investigate risk factors associated with malnutrition (stunting) suggested that: the age of the child; birth type (singleton/multiple births), parents' level of education, household's type of resident; mother's age at the time of birth, mother's BMI, incident of diarrhoea in the last two weeks before the survey, are the most significant independent risk factors of malnutrition (stunting). ConclusionsAll the aforementioned risk factors are controllable, and they can be improved through intervention strategies. The policies that undergird the country are required to counteract this condition, as the majority of the risk factors need the coherent actions of several governing authorities.

scholarly journals Men report good mental health 20 to 23 years after in vitro fertilisation treatment

2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Gunilla Sydsjö ◽  
Josefin Vikström ◽  
Marie Bladh ◽  
Barbara Jablonowska ◽  
Agneta Skoog Svanberg
Keyword(s):  
Mental Health ◽  
In Vitro Fertilisation ◽  
Good Mental Health ◽  
Fertilisation Treatment

scholarly journals Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
O. Karasch ◽  
M. Schmitz-Buhl ◽  
R. Mennicken ◽  
J. Zielasek ◽  
E. Gouzoulis-Mayfrank
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Risk Factors ◽  
Prediction Model ◽  
Psychiatric Hospitalization ◽  
Previous Analysis ◽  
Psychiatric Hospitals ◽  
Data Set ◽  
Interaction Detection ◽  
Socioeconomic Data

Abstract Background The purpose of this study was to identify factors associated with a high risk of involuntary psychiatric in-patient hospitalization both on the individual level and on the level of mental health services and the socioeconomic environment that patients live in. Methods The present study expands on a previous analysis of the health records of 5764 cases admitted as in-patients in the four psychiatric hospitals of the Metropolitan City of Cologne, Germany, in the year 2011 (1773 cases treated under the Mental Health Act and 3991 cases treated voluntarily). Our previous analysis had included medical, sociodemographic and socioeconomic data of every case and used a machine learning-based prediction model employing chi-squared automatic interaction detection (CHAID). Our current analysis attempts to improve the previous one through (1) optimizing the machine learning procedures (use of a different type of decision-tree prediction model (Classification and Regression Trees (CART) and application of hyperparameter tuning (HT)), and (2) the addition of patients’ environmental socioeconomic data (ESED) to the data set. Results Compared to our previous analysis, model fit was improved. Main diagnoses of an organic mental or a psychotic disorder (ICD-10 groups F0 and F2), suicidal behavior upon admission, admission outside of regular service hours and absence of outpatient treatment prior to admission were confirmed as powerful predictors of detention. Particularly high risks were shown for (1) patients with an organic mental disorder, specifically if they were retired, admitted outside of regular service hours and lived in assisted housing, (2) patients with suicidal tendencies upon admission who did not suffer from an affective disorder, specifically if it was unclear whether there had been previous suicide attempts, or if the affected person lived in areas with high unemployment rates, and (3) patients with psychosis, specifically those who lived in densely built areas with a large proportion of small or one-person households. Conclusions Certain psychiatric diagnoses and suicidal tendencies are major risk factors for involuntary psychiatric hospitalization. In addition, service-related and environmental socioeconomic factors contribute to the risk for detention. Identifying modifiable risk factors and particularly vulnerable risk groups should help to develop suitable preventive measures.

A prospective analysis of youth violence in adolescents: influence of depression, personality, social environment and media violence

2011 ◽  
Vol 26 (S2) ◽  
pp. 530-530
Author(s):  
C. Ferguson
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Protective Factors ◽  
Family Environment ◽  
Video Game ◽  
Youth Violence ◽  
Significant Risk ◽  
Risk And Protective Factors ◽  
Media Violence ◽  
Positive Effects

ObjectiveUnderstanding youth violence remains a pressing issue of social concern. Identifying key risk factors for youth violence may help guide prevention and intervention efforts.AimsTo examine the relative impact of varying social influences related to family, community, mental health, television and video games on youth violence in a multivariate format.Study designThe current study involved a one-year prospective study of multiple risk and protective factors for youth violence in a Hispanic-majority sample of early adolescents. Multiple regression was used to examine risk factors from a multivariate format.ResultsResults find that current depression level was the most significant risk factor for youth violence. The influence of depression was most pronounced in individuals with preexisting antisocial personality traits. Risk and protective factors related to schools, neighborhoods, family environment or video game or television violence use were not predictive of youth violence.ConclusionsPotentially, prevention efforts which focus on mental health issues may demonstrate the most positive effects. The degree of resources and rhetoric spent on other factors, particularly television and video game violence may conversely proove unhelpful in reducing youth violence.

scholarly journals Identification of risk factors for involuntary psychiatric hospitalization: Using environmental socioeconomic data and methods of machine learning to improve prediction

2020 ◽  
Author(s):  
Olaf Karasch ◽  
Mario Schmitz-Buhl ◽  
R Roman Mennicken ◽  
Jürgen Zielasek ◽  
Euphrosyne Gouzoulis-Mayfrank
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Risk Factors ◽  
Prediction Model ◽  
Suicide Attempts ◽  
Psychiatric Hospitalization ◽  
Previous Analysis ◽  
Data Set ◽  
Interaction Detection ◽  
Socioeconomic Data

Abstract Background: The purpose of this study was to identify factors associated with a high risk of involuntary psychiatric in-patient hospitalization both on the individual level and on the level of mental health services and the socioeconomic environment that patients live in. Methods: The present study expands on a previous analysis of the health records of 5764 cases admitted as in-patients in the four psy­chiat­ric hospitals of the Metropolitan City of Cologne, Germany, in the year 2011 (1773 cases treated under the Mental Health Act and 3991 cases trea­ted voluntarily). Our previous analysis had included medical, socio­demographic and socioeconomic data of every case and used a machine learning-based prediction model employing chi-squared automatic interaction detection (CHAID). Our current analysis attempts to improve the previous one through (1) optimizing the machine learning procedures (use of a different type of decision-tree prediction model (CART) and application of hyperparameter tuning), and (2) the addition of socioeconomic data on the patients’ environment to the data set. Results: Compared to our previous analysis, model fit was improved. Main diagnoses of an organic mental or a psychotic disorder (ICD-10 groups F0 and F2), suicidal behavior upon admission, admission outside of regular service hours and absence of outpatient treatment prior to admission were confirmed as powerful predictors of detention. Particularly high risks were shown for (1) patients with an organic mental disorder, specifically if they were retired, admitted outside of regular service hours and lived in assisted housing, (2) patients with suicidal tendencies upon admission who did not suffer from an affective disorder, specifically if it was unclear whether there had been previous suicide attempts, or if the affected person lived in areas with high unemployment rates, and (3) patients with psychosis, specifically those who lived in densely built areas with a large proportion of small or one-person households. Conclusions: Certain psychiatric diagnoses and suicidal tendencies are major risk factors for involuntary psychiatric hospitalization. In addition, service-related and environmental socioeconomic factors contribute to the risk for detention. Identifying modifiable risk factors and particularly vulnerable risk groups should help to develop suitable preventive measures.

scholarly journals Psychological Stress Risk Factors, Concerns and Mental Health Support Among Health Care Workers in Vietnam During the Coronavirus Disease 2019 (COVID-19) Outbreak

2021 ◽  
Vol 9 ◽  
Author(s):  
Phuong Thi Lan Nguyen ◽  
Tien Bao Le Nguyen ◽  
Anh Gia Pham ◽  
Khanh Ngoc Cong Duong ◽  
Mac Ardy Junio Gloria ◽  
...  
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Psychological Stress ◽  
Health Care Workers ◽  
Significant Risk ◽  
Web Based ◽  
Consulting Services ◽  
Care Workers ◽  
Mental Health Support ◽  
Health Support

Introduction: Coronavirus disease 2019 (COVID-19) has significantly affected health care workers (HCWs), including their mental health. However, there has been limited evidence on this topic in the Vietnamese context. Therefore, this study aimed to explore COVID-19-related, psychological stress risk factors among HCWs, their concerns and demands for mental health support during the pandemic period.Methods: We employed a cross-sectional study design with convenience sampling. An online, self-administered questionnaire was used and distributed through social media among medical and non-medical HCWs from April 22 to May 12, 2020. HCWs were categorized either as frontline or non-frontline. We measured the prevalence of psychological stress using the Impact of Event Scale-Revised (IES-R) instrument. Multivariate binary logistic regression analysis was performed to identify risk factors associated with psychological stress among HCWs.Results: Among the 774 enrolled participants, 761 (98.3%) eligible subjects were included in the analysis. Most respondents were females (58.2%), between 31 and 40 years of age (37.1%), lived in areas where confirmed COVID-19 cases had been reported (61.9%), medical HCWs (59.9%) and practiced being at the frontline (46.3%). The prevalence of stress was 34.3%. We identified significant risk factors such as being frontline HCWs (odds ratio [OR] = 1.77 [95% confidence interval [CI]: 1.17–2.67]), perceiving worse well-being as compared to those before the COVID-19 outbreak [OR = 4.06 (95% CI: 2.15–7.67)], and experiencing chronic diseases [OR = 1.67 (95% CI: (1.01–2.77)]. Majority (73.9%) were concerned about testing positive for COVID-19 and exposing the infection to their families. Web-based psychological interventions that could provide knowledge on managing mental distress and consulting services were highly demanded among HCWs.Conclusion: The prevalence of psychological stress among HCWs in Vietnam during the COVID-19 pandemic was high. There were also significant risk factors associated with it. Psychological interventions involving web-based consulting services are highly recommended to provide mental health support among HCWs.

scholarly journals Early indicators and risk factors associated with mental health problems during COVID-19 quarantine: Is there a relationship with the number of confirmed cases and deaths?

2020 ◽  
pp. 002076402096602
Author(s):  
Héctor Badellino ◽  
María Emilia Gobbo ◽  
Eduardo Torres ◽  
María Emilia Aschieri
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Sleep Quality ◽  
Perceived Stress ◽  
Mental Health Problems ◽  
Significant Risk ◽  
Health Problems ◽  
Poor Sleep ◽  
Strong Impact ◽  
Anxiety Depression

Background: On March 20 2020, the Argentine Ministry of Health enforced a mandatory quarantine throughout the country in response to the COVID-19 pandemic. Aims: The object of this study is to determine the initial impact on mental health of Argentine population, by measuring the prevalence of anxiety, depression, insomnia, and self-perceived stress and by determining the associated risk factors, and to analyze that impact in relation to the number of confirmed cases and deaths. Method: A cross-sectional survey was conducted through a digital questionnaire, which was completed by 1,985 respondents between March 29 and April 12. The prevalence of anxiety, depression, stress and insomnia was measured with the Generalized Anxiety Disorder-7 Scale (GAD-7), the 9-Item Patients Health Questionnaire (PHQ-9); the Perceived Stress Scale (PSS-10) and the Pittsburgh Sleep Quality Index (PSQI), respectively. Results: The 62.4% of the surveyed population reported signs of psychological distress. It was found that being a woman, being 18 to 27 years old, living with family members or a partner, smoking, and having a poor sleep quality were the significant risk factors. Conclusion: Despite the low number of COVID-19 confirmed cases and deaths at that time, a strong impact on mental health indicators was revealed. The authors of this study recommend the monitoring of the population at risk over time and early interventions in order to avoid long-lasting mental health problems.

scholarly journals The Clinical and Economic Burden of Colorectal Anastomotic Leaks: Middle-Income Country Perspective

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Ulysses Ribeiro Jr. ◽  
Daiane O. Tayar ◽  
Rodrigo A. Ribeiro ◽  
Priscila Andrade ◽  
Silvio M. Junqueira Jr.
Keyword(s):  
Risk Factors ◽  
Blood Transfusion ◽  
Clinical Outcomes ◽  
Cancer Diagnosis ◽  
Economic Burden ◽  
Emergency Surgery ◽  
Linear Models ◽  
Hospital Costs ◽  
Anastomotic Leaks ◽  
Total Hospital

Purpose. Anastomotic leaks (AL) present a significant source of clinical and economic burden on patients undergoing colorectal surgeries. This study was aimed at evaluating the clinical and economic consequences of AL and its risk factors. Methods. A retrospective cohort study was conducted between 2012 and 2013 based on the billing information of 337 patients who underwent low anterior resection (LAR). The outcomes evaluated were the development of AL, use of antibiotics, 30-day readmission and mortality, and total hospital costs, including readmissions and length of stay (LOS). The risk factors for AL, as well as the relationship between AL and clinical outcomes, were analyzed using multivariable Poisson regression. Generalized linear models (GLM) were employed to evaluate the association between AL and continuous outcomes (LOS and costs). Results. AL was detected in 6.8% of the patients. Emergency surgery (aRR 2.56; 95% CI: 1.15–5.71, p=0.021), blood transfusion (aRR 4.44; 95% CI: 1.86–10.64, p=0.001), and cancer diagnosis (aRR 2.51; 95% CI: 1.27–4.98, p=0.008) were found to be independent predictors of AL. Patients with AL showed higher antibiotic usage (aRR 1.69; 95% CI: 1.37–2.09, p<0.001), 30-day readmission (aRR 3.34; 95% CI: 1.53–7.32, p=0.003) and mortality (aRR 13.49; 95% CI: 4.10–44.35, p<0.001), and longer LOS (39.6 days, as opposed to 7.5 days for patients without AL, p<0.001). Total hospital costs amounted to R$210,105 for patients with AL in comparison with R$34,270 for patients without AL (p<0.001). In multivariable GLM, the total hospital costs for AL patients were 4.66 (95% CI: 3.38–6.23, p<0.001) times higher than those for patients without AL. Conclusions. AL leads to worse clinical outcomes and increases hospital costs by 4.66 times. The risk factors for AL were found to be emergency surgery, blood transfusion, and cancer diagnosis.

scholarly journals Risk Factors for Crop Health Under Global Change and Agricultural Shifts: A Framework of Analyses Using Rice in Tropical and Subtropical Asia as a Model

2011 ◽  
Vol 101 (6) ◽  
pp. 696-709 ◽  
Author(s):  
S. Savary ◽  
A. Mila ◽  
L. Willocquet ◽  
P. D. Esker ◽  
O. Carisse ◽  
...  
Keyword(s):  
Risk Factors ◽  
Health Risks ◽  
Large Scale ◽  
Plant Protection ◽  
Significant Risk ◽  
Global Changes ◽  
Agricultural Change ◽  
Data Set ◽  
Holistic View ◽  
Crop Health

Plant disease epidemiology requires expansion of its current methodological and theoretical underpinnings in order to produce full contributions to global food security and global changes. Here, we outline a framework which we applied to farmers' field survey data set on rice diseases in the tropical and subtropical lowlands of Asia. Crop health risks arise from individual diseases, as well as their combinations in syndromes. Four key drivers of agricultural change were examined: labor, water, fertilizer, and land availability that translate into crop establishment method, water shortage, fertilizer input, and fallow period duration, respectively, as well as their combinations in production situations. Various statistical approaches, within a hierarchical structure, proceeding from higher levels of hierarchy (production situations and disease syndromes) to lower ones (individual components of production situations and individual diseases) were used. These analyses showed that (i) production situations, as wholes, represent very large risk factors (positive or negative) for occurrence of disease syndromes; (ii) production situations are strong risk factors for individual diseases; (iii) drivers of agricultural change represent strong risk factors of disease syndromes; and (iv) drivers of change, taken individually, represent small but significant risk factors for individual diseases. The latter analysis indicates that different diseases are positively or negatively associated with shifts in these drivers. We also report scenario analyses, in which drivers of agricultural change are varied in response to possible climate and global changes, generating predictions of shifts in rice health risks. The overall set of analyses emphasizes the need for large-scale ground data to define research priorities for plant protection in rapidly evolving contexts. They illustrate how a structured theoretical framework can be used to analyze emergent features of agronomic and socioecological systems. We suggest that the concept of “disease syndrome” can be borrowed in botanical epidemiology from public health to emphasize a holistic view of disease in shifting production situations in combination with the conventional, individual disease-centered perspective.

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