Showing 6 results for Distribution
M Osooli, Aa Haghdoost, Sh Yarahmadi, Mh Foruzanfar, M Dini, K Holakouie Naieni,
Volume 5, Issue 1 (6-2009)
Abstract
Background and Objectives: The aim of this study was to assess the geographical distribution of Congenital Hypothyroidism (CH) in Iran using Geographic Information System
Methods: The incidence of Congenital Hypothyroidism in each city and province calculated based on national CH screening program and then the map of its distribution was depicted. The spatial distribution of CH was assessed in each city by employing binominal test and Hotspot Analysis. The map of distribution of CH was drawn by ArcGIS version 9.2 software.
Results: The national incidence of CH (including both transient and permanent types) has been estimated 2.2/1000 in screened new borne babies. The distribution of CH seems more or less equally around the country and its spatial variation was not statistically significant. We did not find any specific CH Hot Spot in Iran.
Conclusions: We did not find any particular explanation for high incidence of CH is Iran geographically therefore other explanations for such a high risk in screened neonates should be investigated including the non-environmental factors and factors related to quality of screening program in Iran.
Hr Khalkhali, E Hajizadeh, A Kazemnezad, A Ghafari,
Volume 6, Issue 2 (9-2010)
Abstract
Background & Objective: Clinically Chronic Allograft Dysfunction (CAD) is characterized by a progressive decline in Glomerular Filtration Rate (GFR) over time, the pattern of disease progression determined by the five-stage model. In this paper, we used Erlang and Hypo-exponential distributions as Phase- Type distributions to describe hazard of kidney failure at over time in RTR with CAD.
Methods: In a single-center retrospective study, 214 patients with RTR with CAD were investigated at the Emam Hospital of Urmia University of Medical Sciences from 1997 to 2005. Kidney function at each visit assessed with GFR and categorized based on NKF and KCOQI staging system.
Results: The estimated hazard rates of disease progression from stage 1 to 2 , 0.0378 from stage 2 to 3 ,0.04 from stage 3 to 4 , 0.0458 and from stage 4 to 5 0.0541 were respectively based on each expected month . This estimates yield a mean waiting time of disease progression from stage 1 to Kidney failure or dialysis 91.63 month. The 18th, 58th, 118th and 155th months of death-censored graft survival were 0.99, 0.75, 0.25 and 0.10 respectively.
Conclusions: The findings of this study are compatible with hyperfiltration theory in chronic kidney disease and give us more detailed information about the daynamic process of disease which would help to manage it effictevliy.
S Hamzeh, Ar Soltanian, J Faradmal,
Volume 12, Issue 4 (2-2017)
Abstract
Background and Objectives: When computing a confidence interval for a binomial proportion p, one must choose an exact interval that has a coverage probability of at least 1-α for all values of p. In this study, we compared the confidence intervals of Clopper-Pearson, Wald, Wilson, and double ArcSin transformation in terms of maintaining a constant nominal type I error.
Methods: Simulations were used to compare four methods of estimating a confidence interval, including the Clopper-Pearson, Wald, Wilson, and double ArcSic. The data were generated from the binomial and Poison distribution with parameters p, n and µ=np, 1000 were produced . Type I error of each method was calculated per simulation. The above methods were used to estimate confidence intervals in a meta-analysis study.
Results: The results of the simulation study showed that double ArcSin keep confidence interval at [0,1], but for some proportion has high type I error or low coverage probability. The Clopper–Pearson interval guarantees that the coverage probability is always equal to or above the nominal confidence level for any fixed p.
Conclusion: This study showed that confidence interval estimations the Clopper-Pearson than other methods of calculating the type I error fixed and smaller.
Aa Haghdoost, H Hashemi, S Noori Hekmat , M Haji Aghajani , Gh Janbabaee, A Maher, Am Javadi, S Emadi, H Haghighi, Mr Rajabalipour, R Dehnavieh, M Ferdosi, Hr Rashidinejad, F Moeen Samadani , R Rahimisadegh,
Volume 13, Issue 0 (3-2018)
Abstract
Background and Objectives:Among health sector resources, hospital beds are the primary unit of calculation for the capacity of the health service and vital capacity in patient care. Lack of appropriate distribution in different parts of the country leads to transfer of patients and irreparable problems. The aim of this study was to provide accurate information on the number and distribution of hospital beds in the country in 2016 and to estimate the number of beds required by 2026.
Methods:This descriptive-analytic study was conducted in 2016. The population of the study comprised 439 counties covered by 46 medical universities of the country. In this study, the data of 2016 were used and information about the number and ownership of beds and the size of hospitals were obtained from the treatment deputies of medical universities.
Results:The number of active beds in the country was 117580 in 2016, and it is estimated that in order to meet the needs of the community, this number should reach 194471 beds by 2026. There were 1.47 beds for 1,000 people in 2016, which will increase to 2.9 in 2026 by implementing the NEDA project. The coefficient of variation in 2016 was 36%, which will reach 19% by 2026 according to estimates in the Iran's roadmap project.
Conclusion:The distribution of beds was differed in different regions of the country and there are not enough hospital beds in some areas. If the Iran roadmap is implemented, 2026 beds will be distributed more evenly across the country.
S Noori Hekmat, H Hashemi, Aa Haghdoost, M Haji Aghajani , Gh Janbabaee, A Maher, A Javadi, R Rahimisadegh, S Emadi, Mr Rajabalipour, R Dehnavieh, H Haghighi,
Volume 13, Issue 0 (3-2018)
Abstract
Background and Objectives: The distribution of specialists is important in two ways: geographical and specialty. In this study, we provided a description of the distribution of specialists in Iran in 2016 and its estimates in 2026.
Methods: This descriptive-analytical study was conducted in 2016 to estimate the number of specialists in 2026. Data were gathered through a census of specialists working in each of 439 cities in the country, including those in public and private sectors. Coefficient of variation and the number of specialists in 100000 populations were applied as distribution measures.
Results: In the year 2016, there were 46 specialists per 100,000 populations, and it is estimated that considering the full-time equivalent index of 1.2, 63 specialists per 100,000 populations will be required in the year 2026. The highest and lowest ratio of specialists per population in the year 2016 was reported in Tehran (89 per 100,000 populations) and Jiroft (10 per 100,000 populations), respectively. The gynecologist group and geriatric specialists group were the largest (4747 specialists) and smallest group (4 specialists), respectively.
Conclusion: There was a considerable disparity between different regions of the country in terms of access to specialists. Furthermore, the ratio of specialist per population in different specialty groups varied from one province to another. Upon implementation of the Iran Roadmap, according to 2026 estimates, this dispersion will be reduced to some extent; however, part of the dispersion related to the regionalisation pilicy.
S Ghorbani Gholiabad , M Sadeghifar, R Ghorbani Gholiabad , O Hamidi,
Volume 14, Issue 1 (6-2018)
Abstract
Background and Objectives: Delivery is one of the most important services in the health systems, and increasing its effectiveness and efficiency are a health priorities. The aim of this study was to forecast the number of deliveries in order to design plans for using all facilities to provide patients with better services.
Methods: The data used in this study were the number of deliveries per month in Hakim Jorjani Hospital, Gorgan, Iran during the years 2010 to 2016. Due to the over-dispersion of the data and non-compliance with a Poisson distribution, the Poisson hidden Markov model was applied to predict the frequency of monthly deliveries. The model parameters were estimated using the maximum likelihood method and expectation maximization algorithm.
Results: The use of the Akaike criteria revealed the frequency of delivery in different months in the hospital followed a Poisson hidden Markov models with three hidden states, and the mean Poisson distribution in each component was 193.74, 236.05, and 272.61 labors, respectively.
Conclusion: The results of this study showed that government’s encouraging policies have had short-term, limited effects on increasing fertility with minimal effects on the results of the two-year forecast.
