Risk Factors of Post Partum Haemorrhage in Indonesia

Background: Post-partum haemorrhage (PPH) is one of the classic triad of causes of maternal death. This analysis aimed to evaluate several risk factors of PPH.Methods: This analysis using a cross-sectional Basic Health Research (Riskesdas) 2010 data. For this analysis, the subjects consisted of married women aged 13-49 years, who gave birth of their last child between January 1, 2005 and August 2010, who had a probability of PPH history. The PPH was defi ned as bleeding more than two wet pieces materials, 1.5 m each, during giving birth.Results: This analysis noted 601 subjects had PPH and 19,583 subjects did not have PPH. Post-partum haemorrhage related to demographic (education level, and economic level), gynecologic (parity) as well as obstetric factors. Those who had than did not have eclampsia had 3.5-fold PPH [95% confi dence interval (CI) = 2.53–4.69]. Those who had than did not have premature rupture of the membranes had 2.2-fold PPH (95% CI) = 2.53–4.69). Those who had than did not have placenta previa had 2.1-fold PPH (95% CI) =1.29–3.31). In term of uterine rupture, those who had than did not uterine rupture had 65% increase PPH (95% CI) = 1.11–2.46). Compared to women with 1-2 parity, women with 3-5 and 5 or more parity had an increased PPH risk for 24% and 81% respectively.Conclusion: Eclampsia was the strongest risk factor of PPH. Other risk factors of PPH include premature rupture of the membranes, placenta previa, premature or post-term pregnancies, and high parity. (Health Science Indones 2011;2:66-70)

A Prognostic Model for the Thirty-day Mortality Risk after Adult Heart Transplantation

Objective: To develop a prognostic model for the thirty-day mortality risk after adult heart transplantation. Methods: In this report we developed a prediction model for the 30-day mortality risk after adult heart transplantation. Logistic regression analysis was used to develop the model in 1,262 adult patients undergoing primary heart transplantation. We evaluated the accuracy of the prediction model; the agreement between the predicted probability and the observed mortality (calibration); and the ability of the model to correctly discriminate between the discordant survival pairs (discrimination). The internal validity of the prediction model was evaluated using the bootstrapping procedures. Results: Recipients age and sex, pre-transplant diagnosis, transplant status, waiting time, cardiopulmonary bypass time, donors age and sex, donor-recipient mismatch for BMI and blood type were independent predictors for 30-day mortality risk after adult heart transplantation. The model showed a good calibration and reasonable discrimination (the corrected area under the receiver operating characteristic curve was 0.71). The internal validity of the prediction model was acceptable. For practical use, we converted the prediction model to score chart. Conclusion: The accuracy and the validity of the prediction model were acceptable. This easy-to-use instrument for predicting the 30-day mortality risk after adult heart transplantation would benefit decision-making by classifying recipients according to their mortality risk and allowing optimal allocation of a donor to a recipient for heart transplantation

Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>Coal workers' pneumoconiosis (CWP) is a preventable, but not fully curable occupational lung disease. More and more coal miners are likely to be at risk of developing CWP owing to an increase in coal production and utilization, especially in developing countries. Coal miners with different occupational categories and durations of dust exposure may be at different levels of risk for CWP. It is necessary to identify and classify different levels of risk for CWP in coal miners with different work histories. In this way, we can recommend different intervals for medical examinations according to different levels of risk for CWP. Our findings may provide a basis for further emending the measures of CWP prevention and control.</p> <p>Methods</p> <p>The study was performed using longitudinal retrospective data in the Tiefa Colliery in China. A three-layer artificial neural network with 6 input variables, 15 neurons in the hidden layer, and 1 output neuron was developed in conjunction with coal miners' occupational exposure data. Sensitivity and ROC analyses were adapted to explain the importance of input variables and the performance of the neural network. The occupational characteristics and the probability values predicted were used to categorize coal miners for their levels of risk for CWP.</p> <p>Results</p> <p>The sensitivity analysis showed that influence of the duration of dust exposure and occupational category on CWP was 65% and 67%, respectively. The area under the ROC in 3 sets was 0.981, 0.969, and 0.992. There were 7959 coal miners with a probability value < 0.001. The average duration of dust exposure was 15.35 years. The average duration of ex-dust exposure was 0.69 years. Of the coal miners, 79.27% worked in helping and mining. Most of the coal miners were born after 1950 and were first exposed to dust after 1970. One hundred forty-four coal miners had a probability value ≥0.1. The average durations of dust exposure and ex-dust exposure were 25.70 and 16.30 years, respectively. Most of the coal miners were born before 1950 and began to be exposed to dust before 1980. Of the coal miners, 90.28% worked in tunneling.</p> <p>Conclusion</p> <p>The duration of dust exposure and occupational category were the two most important factors for CWP. Coal miners at different levels of risk for CWP could be classified by the three-layer neural network analysis based on occupational history.</p

Predicting occupational lung diseases

This thesis aims at demonstrating the development, validation, and application of prediction models for occupational lung diseases. Prediction models are developed to estimate an individual’s probability of the presence or future likelihood of occurrence of an outcome (i.e. disease of interest or its related condition). These models are used to assist clinical decision making for individuals, or to stratify individuals into risk groups with a different likelihood for developing disease or with disease severity. In this thesis the development of a diagnostic model is described to detect sensitization to wheat allergens among bakery workers. This model made use of questionnaire items only. Secondly a more generic model was developed for sensitization to high molecular weight allergens in bakery workers (exposed to wheat and or fungal alpha amylase allergens) and laboratory animal workers (exposed to rat and or mouse urinary allergens). The third diagnostic model included questionnaire items and lung function test results to predict the probability of having chest X-ray changes indicative for pneumoconiosis in Dutch construction workers exposed to silica dust. All diagnostic models were transformed into easy-to-use scoring systems to facilitate their application in practice. These diagnostic models enable objective and standardized quantification of the probability of having or developing disease without performing (invasive) advanced and costly reference test. We also developed prognostic models for occurrence of occupational sensitization and respiratory symptoms in apprentices in animal health technology. The prognostic value of questionnaire items, skin-prick tests, and bronchial challenge to methacholine, as a single test or in combination with others, was assessed. Another element in the thesis was to assess the generalizability of a prediction model. Different statistical approaches were used to externally validate a questionnaire model for sensitization to LA allergens -developed in Dutch workers- in Canadian animal health technology apprentices. A new model was eventually developed in Canadian apprentices and compared to the original Dutch model. Model revision was done to evaluate if inclusion of new predictors from the Canadian setting could improve the performance of the original model. Finally the application of a diagnostic model for occupational sensitization is described in a surveillance program for respiratory diseases in baking and flour-producing industries in the Netherlands. The diagnostic model was applied in 5,325 Dutch bakery and flour and enzyme exposed workers. This chapter illustrates how a diagnostic model may improve the efficiency of surveillance programs. After assuring that a model is valid and produces accurate predictions, it is important to determine probability cut-off points to stratify individuals into risk categories. The choice for a cut-off point must be based on the balance between the proportion of missed cases and reduction of unnecessary diagnostic tests. This thesis shows that predicting lung diseases in the context of occupational health care and practice is possible. The use of the prediction tools assists the decision making process and would hopefully reduce expenses. Application of prediction models has not been fully explored but efforts to increase the use of predictive models deserve strong support

A simple diagnostic model for ruling out pneumoconiosis among construction workers

Background: Construction workers exposed to silica-containing dust are at risk of developing silicosis even at low exposure levels. Health surveillance among these workers is commonly advised but the exact diagnostic work-up is not specified and therefore may result in unnecessary chest x ray investigations. Aim: To develop a simple diagnostic model to estimate the probability of an individual worker having pneumoconiosis from questionnaire and spirometry results, in order to accurately rule out workers without pneumoconiosis. Methods: The study was performed using cross-sectional data of 1291 Dutch natural stone and construction workers with potentially high quartz dust exposure. A multivariable logistic regression model was developed using chest x ray with ILO profusion category ⩾1/1 as the reference standard. The model’s calibration was evaluated with the Hosmer–Lemeshow test; the discriminative ability was determined by calculating the area under the receiver operating characteristic curve (ROC area). Internal validity of the final model was assessed by a bootstrapping procedure. For clinical application, the diagnostic model was transformed into an easy-to-use score chart. Results: Age 40 years or older, current smoker, high-exposure job, working 15 years or longer in the construction industry, “feeling unhealthy” and FEV1 were independent predictors in the diagnostic model. The model showed good calibration (a non-significant Hosmer–Lemeshow test) and discriminative ability (ROC area 0.81, 95% CI 0.74 to 0.85). Internal validity was reasonable; the optimism corrected ROC area was 0.76. By using a cut-off point with a high negative predictive value the occupational physician can efficiently detect a large proportion of workers with a low probability of having pneumoconiosis and exclude them from unnecessary x ray investigations. Conclusions: This diagnostic model is an efficient and effective instrument to rule out pneumoconiosis among construction workers. Its use in health surveillance among these workers can reduce the number of redundant x ray investigations

Application of Prediction Models in Occupational Health Practice

[No Abstract Available