| 1. |
- Asif, Muhammad, et al.
(författare)
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Diagnostic Performance and Appropriate Cut-Offs of Different Anthropometric Indicators for Detecting Children with Overweight and Obesity
- 2021
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Ingår i: BioMed Research International. - : Hindawi Publishing Corporation. - 2314-6133 .- 2314-6141.
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Tidskriftsartikel (refereegranskat)abstract
- In the clinical settings, different anthropometric indicators like neck circumference (NC), waist circumference (WC), midupper arm circumference (MUAC), waist-to-height ratio (WHtR), and arm-to-height ratio (AHtR) have been suggested for evaluating overweight and obesity in children. The comparative ability of these indicators in Pakistan is yet unknown. This study is aimed at examining the validity of different anthropometric indicators of overweight and obesity simultaneously and at determining their superlative cut-off values that would correctly detect overweight and obesity in children. For this purpose, the dataset of anthropometric measurements height, weight, WC, MUAC, and NC of 5,964 Pakistani children, aged 5-12 years collected in a cross-sectional multiethnic anthropometric survey (MEAS), was used. Receiver operating characteristic (ROC) curve analysis was performed to assess the validity of different anthropometric indicators. The most sensitive and specific cut-off points, positive and negative predictive values of each indicator were also calculated. The results of the ROC curve indicated that all the studied indicators had a good performance but the indicators AHtR and WHtR had the highest value of the area under the curve (AUC) for the screening of children with overweight and obesity (AUC > 0.80). In the overall sample, AHtR, WHtR, MUAC, WC, and NC cut-off points indicative of overweight, in both boys and girls, were 0.14, 0.46, 18.41 cm, 62.86 cm, and 26.36 cm and 0.14, 0.47, 18.16 cm, 64.39 cm, and 26.54 cm, respectively; the corresponding values for obesity were 0.14, 0.47, 18.67 cm, 62.10 cm, and 26.36 cm and 0.14, 0.48, 20.19 cm, 64.39 cm, and 25.27 cm. We concluded that the sex-specific cut-off points for AHtR, WHtR, MUAC, WC, and NC can be used to diagnose overweight and obesity in Pakistani children.
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| 2. |
- Jabbar, Abdul, et al.
(författare)
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Epidemiology and antibiogram of common mastitis-causing bacteria in Beetal goats
- 2020
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Ingår i: Veterinary World. - : Veterinary World. - 0972-8988 .- 2231-0916. ; 13:12, s. 2596-2607
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Tidskriftsartikel (refereegranskat)abstract
- Background and Aim: Mastitis has been identified as the most prevalent and economically imperative disease among dairy animals. Thus, understanding its common bacterial pathogens and risk factors is necessary to improve udder health at herd, region, or country level. However, scientific research on caprine mastitis, especially on Beetal breed, has remained to be insufficient in Pakistan. Therefore, this study aimed to evaluate the epidemiology and antibiogram assay of common mastitis-causing bacterial agents, that is, Staphylococcus, Streptococcus, and Escherichia coli, in dairy goats.Materials and Methods: In total, 500 Beetal goats, irrespective of age and those that were not treated with any kind of antimicrobial agents during the past 120 h, were screened using California Mastitis Test in Pattoki, Kasur District, whereas epidemiological factors were recorded. The milk samples of mastitic goats were then collected and processed using standard methods. Each sample was primarily cultured on nutrient agar. Using a specific medium, each bacterial colony was separated using several streak methods. Six antibiotic disks belonging to different antibiotic groups were used for antibiogram profiling of bacterial isolates. Chi-square test was used to assess the association of baseline characteristics and mastitis occurrence. Meanwhile, multivariable logistic regression (p<0.001) was utilized to determine the risk factors associated with positive and negative dichotomous outcome of mastitis.Results: The results revealed that the overall prevalence of goat mastitis was 309 (61.8%), in which 260 (52%) and 49 (9.8%) cases were positive for subclinical mastitis (SCM) and clinical mastitis (CM), respectively. Streptococcus and E. coli were found to be the predominant isolates causing SCM and CM, respectively (p<0.001). It was observed that amoxicillin+clavulanic acid was highly sensitive to isolates of Staphylococcus and Streptococcus and ceftiofur sodium to isolates of Streptococcus and E. coli, while enrofloxacin was found to be sensitive to isolates of Streptococcus and E. coli. Risk factors such as herd structure, deworming, vaccination, presence of ticks, use of teat dip and mineral supplements, feeding type, age, parity, housing, blood in the milk, milk leakage, milk taste, and milk yield were found to have the strongest association with mastitis occurrence, while ease of milking has moderate association.Conclusion: In the area examined, cases of SCM were found to be higher compared with that of CM, and ceftiofur sodium has been identified as the preferred treatment in both clinical and subclinical forms of caprine mastitis in Beetal goats. Risk factors for mastitis that was identified in this study can form the basis for the creation of an udder health control program specific for dairy goats. We hope our findings could raise awareness of the risk factors and treatment approaches for common mastitis-causing bacterial agents.
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| 3. |
- Amin, Muhammad, et al.
(författare)
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Influence diagnostics in gamma ridge regression model
- 2019
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Ingår i: Journal of Statistical Computation and Simulation. - : Taylor & Francis. - 0094-9655 .- 1563-5163. ; 89:3, s. 536-556
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we proposed some influence diagnostics for the gamma regression model (GRM) and the gamma ridge regression model (GRRM). We assess the impact of influential observations on the GRM and GRRM estimates by extending the work of Pregibon [Logistic regression diagnostics. Ann Stat. 1981;9:705–724] and Walker and Birch [Influence measures in ridge regression. Technometrics. 1988;30:221–227]. Comparison of both models is made and demonstrated with the help of a simulation study and a real data set. We report some momentous results in detecting the influential observations and their effects on the GRM and GRRM estimates.
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| 4. |
- Khan, Kifayatullah, et al.
(författare)
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Heavy metals in five commonly consumed fish species from River Swat, Pakistan, and their implications for human health using multiple risk assessment approaches
- 2023
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Ingår i: Marine Pollution Bulletin. - 0025-326X .- 1879-3363. ; 195
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Tidskriftsartikel (refereegranskat)abstract
- This study analyzed the levels of heavy metals bioaccumulation in commonly consumed riverine fish species, including G. cavia, T. macrolepis, G. gotyla, S. plagiostomus, and M. armatus from River Swat in Pakistan, and quantify their potential risk to children and adults in general and fisherfolk communities using multiple pollution and risk assessment approaches. The highest metal detected by inductive coupled plasma mass spectrometry (ICP-MS) was Zn, which ranged from 49.61 to 116.83 mg/kg, followed by Fe (19.25–101.33 mg/kg) > Mn (5.25–40.35 mg/kg) > Cr (3.05–14.59 mg/kg) > Ni (4.26–11.80 mg/kg) > Al (1.59–12.25 mg/kg) > Cu (1.24–8.59 mg/kg) > Pb (0.29–1.95 mg/kg) > Co (0.08–0.46 mg/kg) > Cd (0.01–0.29 mg/kg), demonstrating consistent fluctuation with the safe recommendations of global regulatory bodies. The average bioaccumulation factor (BAF) values in the examined fish species were high (BAF > 5000) for Pb, Zn, Mn, Cu, Cr, Ni, and Cd, bioaccumulate (1000 > BAF < 5000) for Co, and probable accumulative (BAF <1000) for Fe, and Al, while the overall ∑heavy metals pollution index (MPI) values were greater than one (MPI > 1) indicating sever heavy metals toxicity in G. cavia, followed by S. plagiostomus, M. armatus, G. gotyla, and T. macrolepis. The multivariate Pearson's correlation analysis identified the correlation coefficients between heavy metal pairs (Ni Cr, Cu Cr, Pb Cr, Al Co, Cu Ni, and Pb Ni), the hierarchical cluster analysis (CA) determined the origin by categorizing heavy metal accumulation into Cluster-A, Cluster-B, and Cluster-C, and the principal component analysis (PCA) discerned nearby weathering, mining, industrial, municipal, and agricultural activities as the potential sources of heavy metals bioaccumulation in riverine fish. As per human risk perspective, S. plagiostomus contributed significantly to the estimated daily intake (EDI) of heavy metals, followed by G.cavia > M. armatus > G. gotyla > T. macrolepis in dependent children and adults of the fisherfolk followed by the general population. The non-carcinogenic target hazard quotient (THQ) and hazard index (HI) values for heavy metal intake through fish exposure were < 1, while the carcinogenic risk (CR) for individual metal intake and the total carcinogenic risk (TCR) for cumulative Cr, Cd, and Pb intake were within the risk threshold of 10−6–10−4, suggesting an acceptable to high non-carcinogenic and carcinogenic risk for both children and adults in the fisherfolk, followed by the general population.
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| 5. |
- Akram, Muhammad N., et al.
(författare)
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A new biased estimator for the gamma regression model : Some applications in medical sciences
- 2023
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Ingår i: Communications in Statistics - Theory and Methods. - : Taylor & Francis. - 0361-0926 .- 1532-415X. ; 52:11, s. 3612-3632
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Tidskriftsartikel (refereegranskat)abstract
- The Gamma Regression Model (GRM) has a variety of applications in medical sciences and other disciplines. The results of the GRM may be misleading in the presence of multicollinearity. In this article, a new biased estimator called James-Stein estimator is proposed to reduce the impact of correlated regressors for the GRM. The mean squared error (MSE) properties of the proposed estimator are derived and compared with the existing estimators. We conducted a simulation study and employed the MSE and bias evaluation criterion to judge the proposed estimator’s performance. Finally, two medical dataset are considered to show the benefit of the proposed estimator over existing estimators.
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| 6. |
- Akram, Muhammad Nauman, et al.
(författare)
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A new Liu-type estimator for the Inverse Gaussian Regression Model
- 2020
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Ingår i: Journal of Statistical Computation and Simulation. - : Taylor & Francis. - 0094-9655 .- 1563-5163. ; 90:7, s. 1153-1172
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Tidskriftsartikel (refereegranskat)abstract
- The Inverse Gaussian Regression Model (IGRM) is used when the response variable is positively skewed and follows the inverse Gaussian distribution. In this article, we propose a Liu-type estimator to combat multicollinearity in the IGRM. The variance of the Maximum Likelihood Estimator (MLE) is overstated due to the presence of severe multicollinearity. Moreover, some estimation methods are suggested to estimate the optimal value of the shrinkage parameter. The performance of the proposed estimator is compared with the MLE and some other existing estimators in the sense of mean squared error through Monte Carlo simulation and different real-life applications. Under certain conditions, it is concluded that the proposed estimator is superior to the MLE, ridge, and Liu estimator.
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| 7. |
- Amin, Muhammad, et al.
(författare)
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Almost unbiased ridge estimator in the gamma regression model
- 2022
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Ingår i: Communications in statistics. Simulation and computation. - : Taylor & Francis. - 0361-0918 .- 1532-4141. ; 51:7, s. 3830-3850
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Tidskriftsartikel (refereegranskat)abstract
- This article introduces the almost unbiased gamma ridge regression estimator (AUGRRE) estimator based on the gamma ridge regression estimator (GRRE). Furthermore, some shrinkage parameters are proposed for the AUGRRE. The performance of the AUGRRE by using different shrinkage parameters is compared with the existing GRRE and maximum likelihood estimator. A Monte Carlo simulation is carried out to assess the performance of the estimators where the bias and mean squared error performance criteria are used. We also used a real-life dataset to demonstrate the benefit of the proposed estimators. The simulation and real-life example results show the superiority of AUGRRE over the GRRE and the maximum likelihood estimator for the gamma regression model with collinear explanatory variables.
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| 8. |
- Amin, Muhammad, et al.
(författare)
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Diagnostic techniques for the inverse Gaussian regression model
- 2022
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Ingår i: Communications in Statistics - Theory and Methods. - : Taylor & Francis. - 0361-0926 .- 1532-415X. ; 51:8, s. 2552-2564
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we propose some diagnostic techniques for the inverse Gaussian regression model (IGRM), which are appropriate for modeling the response variable that undertakes positively skewed continuous dataset. Moreover, two new diagnostic methods are mainly proposed for the IGRM, which named as covariance ratio (CVR) and Welsch?s distance (WD). The comparison of our proposed methods of influence diagnostics with the existing approaches has been made through Monte Carlo simulation under different factors. In addition, the benefit of the proposed methods is assessed using a real application. Based on the simulation and empirical application results, we observed that the performance of the proposed method is better than the existing methods for detection of influential observations.
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| 9. |
- Amin, Muhammad, et al.
(författare)
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Performance of Asar and Genç and Huang and Yang’s Two-Parameter Estimation Methods for the Gamma Regression Model
- 2019
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Ingår i: Iranian Journal of Science and Technology, Transactions A: Science. - : Springer. - 1028-6276 .- 2364-1819. ; 43:6, s. 2951-2963
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Tidskriftsartikel (refereegranskat)abstract
- This study assesses the performance of two-parameter estimation methods to combat multicollinearity in the Gamma regression model. We derived optimal values for two-parameter estimation methods in the Gamma regression model. Furthermore, we proposed some estimation methods to estimate the shrinkage parameters and these methods improve the efficiency of the two-parameter estimator. We compare the performance of these estimators by means of Monte Carlo simulation study where the mean squared error (MSE) is considered as a performance criterion. Finally, consider a reaction rate data to evaluate the performance of the estimators. The simulation and numerical example results showed that the two-parameter biased estimators have smaller MSE than the maximum likelihood estimator under certain conditions.
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| 10. |
- Amin, Muhammad, et al.
(författare)
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Performance of some ridge estimators for the gamma regression model
- 2020
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Ingår i: Statistical papers. - : Springer. - 0932-5026 .- 1613-9798. ; 61:3, s. 997-1026
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Tidskriftsartikel (refereegranskat)abstract
- In this study, we proposed some ridge estimators by considering the work of Månsson (Econ Model 29(2):178–184, 2012), Dorugade (J Assoc Arab Univ Basic Appl Sci 15:94–99, 2014) and some others for the gamma regression model (GRM). The GRM is a special form of the generalized linear model (GLM), where the response variable is positively skewed and well fitted to the gamma distribution. The commonly used method for estimation of the GRM coefficients is the maximum likelihood (ML) estimation method. The ML estimation method perform better, if the explanatory variables are uncorrelated. There are the situations, where the explanatory variables are correlated, then the ML estimation method is incapable to estimate the GRM coefficients. In this situation, some biased estimation methods are proposed and the most popular one is the ridge estimation method. The ridge estimators for the GRM are proposed and compared on the basis of mean squared error (MSE). Moreover, the outperforms of proposed ridge estimators are also calculated. The comparison has done using a Monte Carlo simulation study and two real data sets. Results show that Kibria’s and Månsson and Shukur’s methods are preferred over the ML method.
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