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Dataset on body weight, carapace width increment and growth band count of mud crabs, Scylla olivacea

Ikhwanuddin M, Amin-Safwan A, Hasyima-Ismail N, Azra MN

Data Brief, 2019 Oct;26:104477.
PMID: 31667242 DOI: 10.1016/j.dib.2019.104477

The present paper contains two datasets; i) the growth band count (GBC) of mud crab, Scylla olivacea collected from Setiu Wetlands, Terengganu coastal water, East coast of Peninsular Malaysia and ii) the increment sizes of body weight (BW) and carapace width (CW) of immature S. olivace after molting. The datasets presented here were associated with the research articles entitled i) "Study on carapace width growth band counts relationship of orange mud crab, S. olivacea (Herbst, 1796) from Terengganu Coastal Waters, Malaysia" (Hasyima-Ismail et al. 2017) [1] and ii) "Relationship between the carapace width and body weight increments and the confirmation of Stage 1 ovary after the molting of immature orange mud crabs, S. olivacea (Herbst, 1796), in captivity" (Amin-Safwan et al. 2019-2020) [2], and provided here as raw data of Supplementary materials. Raw datasets for GBC in the wild were generated by examination of the thin cross sectioning process of the gastric mill of S. olivacea. The GBC were measured for each individual crab wherein band counts ranged from 1 to 3. The analysis provides evidence that the GBC of the crabs can be determined through both mesocardiac and zygocardiac ossicles. This data is of importance to researchers for estimation of stock assessment and improvement of fisheries management to further improve policy. For the BW-CW increment data, a total of 135 immature crabs were sampled from Setiu Wetlands, Terengganu, Malaysia, and were introduced to limb autotomy technique in order to induced molt. Crabs were reared until successful molting and immediately prior to hardened shell, before final measurement of body weight and carapace width determination. Recorded data was analyzed by calculating the increment sizes, along with correlation and regression analysis between body weight and carapace width of mud crabs.

Matched MeSH terms: Regression Analysis
Effects of agricultural development policies on migration in peninsular Malaysia

Baydar N, White MJ, Simkins C, Babakol O

Demography, 1990 Feb;27(1):97-109.
PMID: 2303144

State planning plays a central role in Malaysia's social and economic development. The government's rural development policies are designed to promote agricultural incomes and help counterbalance ethnic inequalities. The Federal Land Development Authority (FELDA) implements one of the internationally most successful land development and resettlement programs. In this article, we quantify the impact of FELDA settlements on local out-migration rates, linking macro and micro approaches and using data from the Malaysian Family Life Survey, national censuses, and other sources. A model of instantaneous migration rates specifies an individual's migration rate as a function of individual-level sociodemographic characteristics, the level of urbanization of the origin and destination, and the extent of rural development at the district of current residence. Our results show that in the late 1960s and early 1970s, the existence of rural development centers in a district reduced the levels of out-migration to pre-1965 levels.

Matched MeSH terms: Regression Analysis
The effect of female education on fertility: a simple explanation

Jain AK

Demography, 1981 Nov;18(4):577-95.
PMID: 7308537

This paper investigates the structure of the relationship between female education and fertility. It is based on data published in First Country Reports of the World Fertility Surveys for eleven countries--Costa Rica, Colombia, Dominican Republic, Panama, Fiji, Korea, Malaysia, Pakistan, Sri Lanka, Thailand, and Indonesia. The cumulative marital fertility of educated women is shown to be similar in different settings. A lack of uniformity in the education and fertility relationship including the curvilinear nature of this relationship observed across countries is shown to be attributable to marked differences between countries in the average fertility of women with no education rather than to the presumed differences in the average fertility of the educated women. The structure of the relationship is shown to be similar across several developing countries. This analysis suggests that advancement in female education can be expected to influence fertility behavior even without simultaneous changes in other factors such as increasing opportunity for participation in the paid labor force in the modern sector.

Matched MeSH terms: Regression Analysis
The impact of child mortality on spacing by parity: a Cox-regression analysis

Lehrer E

Demography, 1984 Aug;21(3):323-37.
PMID: 6479392

Child mortality may affect spacing through biological and behavioral channels. The death of a child may elicit a desire to have another one soon; further, it may interrupt breastfeeding and shorten the sterile period following childbirth. The hypothesis that the child mortality-spacing linkage varies across parities, being strongest in the middle parities, is examined using microdata from Malaysia and the Cox-regression technique. The empirical results lend support to the hypothesis.

Matched MeSH terms: Regression Analysis
Fulltext Sleep disturbances in Malaysian children with cerebral palsy

Atmawidjaja RW, Wong SW, Yang WW, Ong LC

Dev Med Child Neurol, 2014 Jul;56(7):681-5.
PMID: 24528212 DOI: 10.1111/dmcn.12399

The aim of the study was to compare the frequency and type of sleep disturbances in a group of Malaysian children aged 4 to 18 years with cerebral palsy (CP) with their nearest-age, able-bodied siblings and to identify factors associated with sleep disturbances.

Matched MeSH terms: Regression Analysis
Comparison studies on the percolation thresholds of binary mixture tablets containing excipients of plastic/brittle and plastic/plastic deformation properties

Amin MC, Fell JT

Drug Dev Ind Pharm, 2004;30(9):937-45.
PMID: 15554218

Percolation theory has been used with great interest in understanding the design and characterization of dosage forms. In this study, work has been carried out to investigate the behavior of binary mixture tablets containing excipients of similar and different deformation properties. The binary mixture tablets were prepared by direct compression using lactose, polyvinyl chloride (PVC), Eudragit RS 100, and microcrystalline cellulose (MCC). The application of percolation theory on the relationships between compactibility, Pmax, or compression susceptibility (compressibility), gamma, and mixture compositions reveals the presence of percolation thresholds even for mixtures of similar deformation properties. The results showed that all mixture compositions exhibited at least one discreet change in the slope, which was referred to as the percolation threshold. The PVC/Eudragit RS100 mixture compositions showed significant percolation threshold at 80% (w/w) PVC loading. Two percolation thresholds were observed from a series of binary mixtures containing similar plastic deformation materials (PVC/MCC). The percolation thresholds were determined at 20% (w/w) and 80% (w/w) PVC loading. These are areas where one of the components percolates throughout the system and the properties of the tablets are expected to experience a sudden change. Experimental results, however, showed that total disruption of the tablet physical properties at the specified percolation thresholds can be observed for PVC/lactose mixtures at 20-30% (w/w) loading while only minor changes in the tablets' strength for PVC/MCC or PVC/Eudragit RS 100 mixtures were observed.

Matched MeSH terms: Regression Analysis
Acquisition of Malay word recognition skills: lessons from low-progress early readers

Lee LW, Wheldall K

Dyslexia, 2011 Feb;17(1):19-37.
PMID: 21241030 DOI: 10.1002/dys.421

Malay is a consistent alphabetic orthography with complex syllable structures. The focus of this research was to investigate word recognition performance in order to inform reading interventions for low-progress early readers. Forty-six Grade 1 students were sampled and 11 were identified as low-progress readers. The results indicated that both syllable awareness and phoneme blending were significant predictors of word recognition, suggesting that both syllable and phonemic grain-sizes are important in Malay word recognition. Item analysis revealed a hierarchical pattern of difficulty based on the syllable and the phonic structure of the words. Error analysis identified the sources of errors to be errors due to inefficient syllable segmentation, oversimplification of syllables, insufficient grapheme-phoneme knowledge and inefficient phonemic code assembly. Evidence also suggests that direct instruction in syllable segmentation, phonemic awareness and grapheme-phoneme correspondence is necessary for low-progress readers to acquire word recognition skills. Finally, a logical sequence to teach grapheme-phoneme decoding in Malay is suggested.

Matched MeSH terms: Regression Analysis
Fulltext Statistical modelling of awareness on risk factors of gastric cancer : a study on youngsters in Melaka

Izni Syamsina Saari, Nor Adila Kedin, Ainon Syazana Ab Hamid, Mohd Amirul Atan

ESTEEM Academic Journal, 2019;15(1):33-43.
MyJurnal

In Malaysia, gastric cancer (also known as stomach cancer) is diagnosed at a complex stage and has poor prognosis. Most mortality cases due to gastric cancer diseases are potentially preventable. Thus greater efforts are needed for gastric cancer awareness to give adequate knowledge for Malaysians in order to be more cautious or alert in diagnosing the ailment much earlier. After all, early detection is the key towards improving the survival rate in gastric cancer patients. Hence, the aim of this study is to assess the youngsters’ awareness on the risk factors of gastric cancer. A survey using online forms was conducted on 350 students of UiTM Melaka, Alor Gajah Campus, to assess their awareness of risk factors regarding gastric cancer. Based on the regression analysis conducted, it can be highlighted that attitudes, knowledge, medication, education level and faculty (dummy) are some of the variables that were found to be the risk factors of the awareness on gastric cancer among these selected youngsters in the 21st century.

Matched MeSH terms: Regression Analysis
Fulltext Development of Linear Regression Model for tile waste generation in Malaysian construction industry

Siti Hafizan Hassan, Hamidi Abdul Aziz, Mohd Samsudin Abdul Hamid, Siti Rashidah Mohd Nasir, Suhailah Mohamed Noor

ESTEEM Academic Journal, 2019;15(2):11-23.
MyJurnal

The effect of unmanageable construction waste is an unstable land settlement and groundwater pollution. In addition to environmental pollution, construction waste could incur construction cost. The most construction waste is the material used at sites and tile is also a part of the waste generated in construction. The objectives of this study are to determine the tile waste generated in construction stages and linear regression analysis for the amount of tile waste generated. The method used in this study was the Linear Regression Model. The regression model established in the sample data reported an R2 value of 0.793; therefore, the model can predict approximately 79.3% of the factor (area) of tile waste generation. The linear regressions can be applied as tools to predict the tile waste generated at construction sites and help the contractor to track the sources of missing waste.

Matched MeSH terms: Regression Analysis
Fulltext Costs associated with tuberculosis diagnosis and treatment in Yemen for patients and public health services

Othman GQ, Ibrahim MI, Raja'a YA

East Mediterr Health J, 2012 Apr;18(4):393-8.
PMID: 22768704

This study determined the costs associated with tuberculosis (TB) diagnosis and treatment for the public health services and patients in Sana'a, Yemen. Data were collected prospectively from 320 pulmonary and extrapulmonary TB patients (160 each) who were followed until completion of treatment. Direct medical and nonmedical costs and indirect costs were calculated. The proportionate cost to the patients for pulmonary TB and extrapulmonary TB was 76.1% arid 89.4% respectively of the total for treatment. The mean cost to patients for pulmonary and extrapulmonary TB treatment was US$ 108.4 and US$ 328.0 respectively. The mean cost per patient to the health services for pulmonary and extrapulmonary TB treatment was US$ 34.0 and US$ 38.8 respectively. For pulmonary and extrapulmonary TB, drug treatment represented 59.3% and 77.9% respectively of the total cost to the health services. The greatest proportionate cost to patients for pulmonary TB treatment was time away from work (67.5% of the total cost), and for extrapulmonary TB was laboratory and X-ray costs (55.5%) followed by transportation (28.6%).

Matched MeSH terms: Regression Analysis
Intrinsic vs. extrinsic influences on life history expression: metabolism and parentally induced temperature influences on embryo development rate

Martin TE, Ton R, Niklison A

Ecol Lett, 2013 Jun;16(6):738-45.
PMID: 23473270 DOI: 10.1111/ele.12103

Intrinsic processes are assumed to underlie life history expression and trade-offs, but extrinsic inputs are theorised to shift trait expression and mask trade-offs within species. Here, we explore application of this theory across species. We do this based on parentally induced embryo temperature as an extrinsic input, and mass-specific embryo metabolism as an intrinsic process, underlying embryonic development rate. We found that embryonic metabolism followed intrinsic allometry rules among 49 songbird species from temperate and tropical sites. Extrinsic inputs via parentally induced temperatures explained the majority of variation in development rates and masked a relationship with metabolism; metabolism explained a minor proportion of the variation in development rates among species, and only after accounting for temperature effects. We discuss evidence that temperature further obscures the expected interspecific trade-off between development rate and offspring quality. These results demonstrate the importance of considering extrinsic inputs to trait expression and trade-offs across species.

Matched MeSH terms: Regression Analysis
Fulltext Effects of a DVD-delivered mindfulness-based intervention for stress reduction in medical students: a randomized controlled study

Phang, Cheng Kar, Firdaus Mukhtar, Normala Ibrahim, Keng, Shian-Ling, Sherina Mohd Sidik

Education in Medicine Journal, 2015;7(3):8-20.
MyJurnal

The aim of this study was to evaluate the effects of a DVDdelivered mindfulness-based intervention for reducing stress among medical students in a Malaysian university. Methods: A total of 76 medical students participated in the intervention program (Mindful-Gym DVD). They were stratified according to year of studies and randomly allocated to intervention (N = 38) and control groups (N = 38). The following outcome variables were measured at pre- and post-intervention: mindfulness (with Mindful Attention Awareness Scale, MAAS); perceived stress (with Perceived Stress Scale, PSS); mental distress (with Depression, Anxiety and Stress Scale, DASS); and self-efficacy (with General Selfefficacy Scale, GSE). Hierarchical multiple regression analyses were used to analyse the effect of group (Intervention vs. Control) on changes in the outcome variables. Results: One week after the intervention, participants in the intervention group reported significantly greater improvements with small to medium effect sizes in self-reported levels of mindfulness (β =0.19, ∆R2= 0.04, p = 0.028, f2= 0.07), perceived stress (β = -0.25, ∆R2=0.06, p = 0.006, f2= 0.11), mental distress (β = -0.27, ∆R2= 0.07, p =0.003, f2= 0.13), and self-efficacy (β = 0.27, ∆R2= 0.07, p = 0.003, f2= 0.13), compared to participants in the control group. The number needed to treat (NNT) to achieve ‘normal’ levels of depressive, anxiety, and stress symptoms (as indicated by the DASS scores) were 5, 8, and 6 respectively. Conclusions: This study found that the DVD-delivered mindfulness-based program is potentially a labour- and cost-effective and easily accessible stress reduction program for medical students.

Matched MeSH terms: Regression Analysis
Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS

Golkarian A, Naghibi SA, Kalantar B, Pradhan B

Environ Monit Assess, 2018 Feb 17;190(3):149.
PMID: 29455381 DOI: 10.1007/s10661-018-6507-8

Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.

Matched MeSH terms: Regression Analysis
Regression models for sediment transport in tropical rivers

Harun MA, Safari MJS, Gul E, Ab Ghani A

Environ Sci Pollut Res Int, 2021 Oct;28(38):53097-53115.
PMID: 34023993 DOI: 10.1007/s11356-021-14479-0

The investigation of sediment transport in tropical rivers is essential for planning effective integrated river basin management to predict the changes in rivers. The characteristics of rivers and sediment in the tropical region are different compared to those of the rivers in Europe and the USA, where the median sediment size tends to be much more refined. The origins of the rivers are mainly tropical forests. Due to the complexity of determining sediment transport, many sediment transport equations were recommended in the literature. However, the accuracy of the prediction results remains low, particularly for the tropical rivers. The majority of the existing equations were developed using multiple non-linear regression (MNLR). Machine learning has recently been the method of choice to increase model prediction accuracy in complex hydrological problems. Compared to the conventional MNLR method, machine learning algorithms have advanced and can produce a useful prediction model. In this research, three machine learning models, namely evolutionary polynomial regression (EPR), multi-gene genetic programming (MGGP) and M5 tree model (M5P), were implemented to model sediment transport for rivers in Malaysia. The formulated variables for the prediction model were originated from the revised equations reported in the relevant literature for Malaysian rivers. Among the three machine learning models, in terms of different statistical measurement criteria, EPR gives the best prediction model, followed by MGGP and M5P. Machine learning is excellent at improving the prediction distribution of high data values but lacks accuracy compared to observations of lower data values. These results indicate that further study needs to be done to improve the machine learning model's accuracy to predict sediment transport.

Matched MeSH terms: Regression Analysis
Urbanization and carbon dioxide emissions in Singapore: evidence from the ARDL approach

Ali HS, Abdul-Rahim AS, Ribadu MB

Environ Sci Pollut Res Int, 2017 Jan;24(2):1967-1974.
PMID: 27798805 DOI: 10.1007/s11356-016-7935-z

The main aim of this article is to examine empirically the impact of urbanization on carbon dioxide emissions in Singapore from 1970 to 2015. The autoregressive distributed lags (ARDL) approach is applied within the analysis. The main finding reveals a negative and significant impact of urbanization on carbon emissions in Singapore, which means that urban development in Singapore is not a barrier to the improvement of environmental quality. Thus, urbanization enhances environmental quality by reducing carbon emissions in the sample country. The result also highlighted that economic growth has a positive and significant impact on carbon emissions, which suggests that economic growth reduces environmental quality through its direct effect of increasing carbon emissions in the country. Despite the high level of urbanization in Singapore, which shows that 100 % of the populace is living in the urban center, it does not lead to more environmental degradation. Hence, urbanization will not be considered an obstacle when initiating policies that will be used to reduce environmental degradation in the country. Policy makers should consider the country's level of economic growth instead of urbanization when formulating policies to reduce environmental degradation, due to its direct impact on increasing carbon dioxide emissions.

Matched MeSH terms: Regression Analysis
Regression and multivariate models for predicting particulate matter concentration level

Nazif A, Mohammed NI, Malakahmad A, Abualqumboz MS

Environ Sci Pollut Res Int, 2018 Jan;25(1):283-289.
PMID: 29032528 DOI: 10.1007/s11356-017-0407-2

The devastating health effects of particulate matter (PM10) exposure by susceptible populace has made it necessary to evaluate PM10 pollution. Meteorological parameters and seasonal variation increases PM10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM10 concentration levels. The analyses were carried out using daily average PM10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM10 concentration levels having coefficient of determination (R 2) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.

Matched MeSH terms: Regression Analysis
Estimation of aerosol optical depth at different wavelengths by multiple regression method

Tan F, Lim HS, Abdullah K, Holben B

Environ Sci Pollut Res Int, 2016 Feb;23(3):2735-48.
PMID: 26438373 DOI: 10.1007/s11356-015-5506-3

This study aims to investigate and establish a suitable model that can help to estimate aerosol optical depth (AOD) in order to monitor aerosol variations especially during non-retrieval time. The relationship between actual ground measurements (such as air pollution index, visibility, relative humidity, temperature, and pressure) and AOD obtained with a CIMEL sun photometer was determined through a series of statistical procedures to produce an AOD prediction model with reasonable accuracy. The AOD prediction model calibrated for each wavelength has a set of coefficients. The model was validated using a set of statistical tests. The validated model was then employed to calculate AOD at different wavelengths. The results show that the proposed model successfully predicted AOD at each studied wavelength ranging from 340 nm to 1020 nm. To illustrate the application of the model, the aerosol size determined using measure AOD data for Penang was compared with that determined using the model. This was done by examining the curvature in the ln [AOD]-ln [wavelength] plot. Consistency was obtained when it was concluded that Penang was dominated by fine mode aerosol in 2012 and 2013 using both measured and predicted AOD data. These results indicate that the proposed AOD prediction model using routine measurements as input is a promising tool for the regular monitoring of aerosol variation during non-retrieval time.

Matched MeSH terms: Regression Analysis
Multiple regression analysis in modeling of columnar ozone in Peninsular Malaysia

Tan KC, Lim HS, Mat Jafri MZ

Environ Sci Pollut Res Int, 2014 Jun;21(12):7567-77.
PMID: 24599658 DOI: 10.1007/s11356-014-2697-y

This study aimed to predict monthly columnar ozone (O3) in Peninsular Malaysia by using data on the concentration of environmental pollutants. Data (2003-2008) on five atmospheric pollutant gases (CO2, O3, CH4, NO2, and H2O vapor) retrieved from the satellite Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) were employed to develop a model that predicts columnar ozone through multiple linear regression. In the entire period, the pollutants were highly correlated (R = 0.811 for the southwest monsoon, R = 0.803 for the northeast monsoon) with predicted columnar ozone. The results of the validation of columnar ozone with column ozone from SCIAMACHY showed a high correlation coefficient (R = 0.752-0.802), indicating the model's accuracy and efficiency. Statistical analysis was utilized to determine the effects of each atmospheric pollutant on columnar ozone. A model that can retrieve columnar ozone in Peninsular Malaysia was developed to provide air quality information. These results are encouraging and accurate and can be used in early warning of the population to comply with air quality standards.

Matched MeSH terms: Regression Analysis
A statistical model to predict and analyze air surface temperature based on remotely sensed observations

Lim HS, Rajab J, Al-Salihi A, Salih Z, MatJafri MZ

Environ Sci Pollut Res Int, 2022 Feb;29(7):9755-9765.
PMID: 34505243 DOI: 10.1007/s11356-021-16321-z

Air surface temperature (AST) is a crucial importance element for many applications such as hydrology, agriculture, and climate change studies. The aim of this study is to develop regression equation for calculating AST and to analyze and investigate the effects of atmospheric parameters (O3, CH4, CO, H2Ovapor, and outgoing longwave radiation (OLR)) on the AST value in Iraq. Dataset retrieved from the Atmospheric Infrared Sounder (AIRS) at EOS Aqua Satellite, spanning the years of 2003 to 2016, and multiple linear regression were used to achieve the objectives of the study. For the study period, the five atmospheric parameters were highly correlated (R, 0.855-0.958) with predicted AST. Statistical analyses in terms of β showed that OLR (0.310 to 1.053) contributes significantly in enhancing AST values. Comparisons among selected five stations (Mosul, Kanaqin, Rutba, Baghdad, and Basra) for the year 2010 showed a close agreement between the predicted and observed AST from AIRS, with values ranging from 0.9 to 1.5 K and for ground stations data, within 0.9 to 2.6 K. To make more complete analysis, also, comparison between predicted and observed AST from AIRS for four selected month in 2016 (January, April, July, and October) has been carried out. The result showed a high correlation coefficient (R, 0.87 and 0.95) with less variability (RMSE ≤ 1.9) for all months studied, indicating model's capability and accuracy. In general, the results indicate the advantage of using the AIRS data and the regression analysis to investigate the impact of the atmospheric parameters on AST over the study area.

Matched MeSH terms: Regression Analysis
Fulltext Intestinal obstruction: predictor of poor prognosis in colorectal carcinoma?

Mohd Suan MA, Tan WL, Soelar SA, Ismail I, Abu Hassan MR

Epidemiol Health, 2015;37:e2015017.
PMID: 25868638 DOI: 10.4178/epih/e2015017

OBJECTIVES: The goal of this study was to assess the relationship between intestinal obstruction and the prognosis of colorectal carcinoma.
METHODS: Data pertaining to 4,501 colorectal carcinoma patients were extracted from the national colorectal registry and analysed. Survival analysis was performed using the Kaplan-Meier method. The log-rank test was used to compare the survival rate between patients with intestinal obstruction and those without intestinal obstruction. The p-values<0.05 were considered to indicate statistical significance. Simple Cox proportional hazards regression analysis was used to estimate the crude hazard ratio of mortality from colorectal cancer.
RESULTS: Intestinal obstruction was reported in more than 13% of patients. The 3-year survival rate after treatment was 48.3% (95% confidence interval [CI], 43.9 to 52.8) for patients with intestinal obstruction (n=593) and 54.9% (95% CI, 53.1 to 56.6) for patients without intestinal obstruction (n=3,908). The 5-year survival rate for patients with intestinal obstruction was 37.3% (95% CI, 31.9 to 42.8), which was lower than that of patients without intestinal obstruction (45.6%; 95% CI, 43.5 to 47.7). After adjusting the hazard ratio for other prognostic variables, intestinal obstruction had a statistically significant negative correlation with the survival rate of colorectal cancer patients, with an adjusted hazard ratio of 1.22 (p=0.008).
CONCLUSIONS: The presence of intestinal obstruction is associated with a lower survival rate among colorectal cancer patients.

Matched MeSH terms: Regression Analysis

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