Background: Achieving definitive care within the Golden Hour by minimizing response times is a consistent goal of regional trauma systems . How to cite this article:Swaroop M, Straus DC, Agubuzu O, Esposito TJ, Schermer CR, Crandall ML. How to cite this URL:Swaroop M, Straus DC, Agubuzu O, Esposito TJ, Schermer CR, Crandall ML. Accurate estimates of expected survival times, survival rates of AIDS patients and variables that influence survival are important for projecting future numbers of AIDS cases, increasing understanding of the patho-physiology of the disease, clinical decision making and planning health service interventions [3, 7].
However, most of the studies in Ethiopia focused on the prevention and factors that increase the chance of contracting the disease.
Next, we carried out sample size allocation to each Hospital and health center with proportional allocation. Descriptive analysis of survival data utilizes non-parametric methods to compare the survival functions of two or more groups.
The multivariable model used was the semi-parametric regression model known as the proportional hazards regression (PHR) model [11]. Table 2 gives the mean survival time and the results based on the log-rank test for each covariate. Married patients lived for an average of six months longer than widowed patients whereas they live for an average of four months longer than single and divorce patients.
HIV Patients, who developed TB, had shorter survival time than not developed TB: 12 months shorter than patients didn’t develop TB. Patients, who had ages 15 to 29 years and 30 to 40 years, respectively, lived 3 and 5 months longer on average compared with those people who had ages greater than 40 years. The covariates in Table 2.3 were further investigated in the final model and seven were statistically significant at 5 % level of significant. In this study baseline CD4 count, baseline weight and baseline lymphocyte count were determined as risk factors for the survival of HIV patients. Residence has a significance influence on survival, meaning that HIV patients living urban or near town had lower risk of mortality compared to far from town or rural [18, 19, 20]. The studies [4, 9, 16, 18, 19, 21] showed that CD4 count is a laboratory predictor of mortality in the sense that higher CD4 counts are associated with longer survival time. Mortality due to AIDS was strongly associated with small amount or count of clinical characteristics, people living with HIV were often diagnosed at a late stage and delayed initiation of treatment until they were severely ill or the clinical laboratory predictors’ reduced to small count. AcknowledgementsWe are grateful to the Wollo University for their collaboration and support the required fund in implementing HIV?AIDS related activities. This study hypothesizes that in urban Level I Trauma Centers, shorter pre-hospital times would predict outcomes in penetrating thoracic injuries. Pre-hospital transport times and survival for Hypotensive patients with penetrating thoracic trauma.
Changing patterns in the management of penetrating abdominal trauma: The more things change, the more they stay the same.
The effect of urban trauma system hospital bypass on pre-hospital transport times and Level 1 trauma patient survival. The relationship between total pre-hospital time and outcome in hypotensive victims of penetrating injuries. Emergency medical services intervals and survival in trauma: Assessment of the "golden hour" in a North American prospective cohort. HIV infection has changed from a fatal condition to a manageable chronic illness mainly due to the development of antiretroviral therapy (ART).
IntroductionAcquired Immune Deficiency Syndrome (AIDS) which is believed to be caused by the Human Immunodeficiency Virus (HIV) has been the major problem worldwide. During these years HIV infection has changed from a fatal condition to a manageable chronic illness mainly due to the development of antiretroviral therapy (ART). It can be said that limited works were conducted on the survival of HIV positives taking ART [9].
Data The target populations for this study were patients under the follow up of ART at Boru Meda and Dessie Referral Hospitals and Kombolcha Health Center from 1 January, 2008 to 31 December, 2011.
Then, patients were selected from each Hospital and Health Center using systematic sampling based on their Medical Registration Number (MRN) which is given to each patient who is taking ART. Variables of the StudyThe response variable in this research that was the “survival time” was defined as the number of months from the month of enrollment of a patient in the HIV-care till one of the events “death”, “lost to follow up”, “dropped out”, “stopped”, “transferred out to other health centers or hospitals” occurred. Statistical Analysis The statistical analytic method used in this study is known as Survival Analysis. The Kaplan-Meier estimator (product-limit-estimator) of the survival function [10] was employed for this purpose.
When a study involves multiple characteristics, appropriate statistical techniques must be used to select variables that have significant effects on survival and which are judged to be clinically meaningful for inclusion in a PHR model. Patients who were from rural residence, lived on average three months shorter than those from urban residence. HIV-positive patients, with WHO clinical stage IV had an average of 8, 6 and 5 months shorter survival time than those in stages II, III and I, respectively. Results of the PHR Model The Cox model procedure that includes model selection, tests, diagnosis and fit confirmed that there were no problems with regard to interactions of main effects and confounding (see appendix B). Socio-Demographic CharacteristicsThe referent category for age was the age group greater than 40 years. It was observed that the survival time of patients under ART varied along differences in employment status.
Thus, as the hazard rate of death risk are high for those with lower baseline weight, CD4 and lymphocyte count, the lower the danger of being at risk of HIV death. The current study also shows that patients living rural experienced higher mortality rate than urban. Conclusions The study revealed that after initiation of the treatment, HIV-positive people had estimated nearly 4 years median survival time. Promotion of testing services, which are widely available in Ethiopia, is needed for earlier diagnosis and enrollment in treatment services and earlier initiation of ART with improved adherence counseling is in advance. We are particularly grateful to the Boru Meda, Dessie Referral Hospitals and Kombolcha Health Center administrative and database teams and all the health workers, patient support groups and associations. Materials and Methods: A retrospective cohort study was performed using a statewide trauma registry for the years 1999-2003 . Even if ART treatment has shown significant clinical importance by meeting the goal of therapy, we are still facing a number of deaths due to certain socio-economic, demographic, behavioral risk and health factors. ART for the treatment of HIV infection has been shown to profoundly alter HIV disease progression, including incidence of opportunistic infections in people living with HIV [4]. Study Area The study was conducted in selected governmental health institutions in South Wollo, Amhara region, Ethiopia from January 01 to December 31, 2012.

At the Hospital’s ART clinic the data were recorded using the standardized data collection formats and registers prepared by the Ministry of Health. Some of the issues are: objective of the research, design of the research, cost constraint, degree of precision required for generalization and etc. Survival data analysis involves the modeling and analyses of data that have a principal end point the time until an event occurs (time-to-event data). Hence, the model development process identifies the relevant variables following model scrutiny as discussed in [12].
Results of the Descriptive StatisticsA sample of 654 of the 7163 patients was selected that were followed during 1 January, 2008 to 31 December, 2011 using an appropriate sample size determination formula. The p-values in Table 2 show differences in survival experience between two or more levels of predictors. Patients who are on job and unemployed lived on average 18 and 17 months longer than those didn’t work due to illness, respectively.
Those patients in clinical stage II had an average of 3 and 2 months longer survival time than stage I and III, respectively. Therefore, the results in Table 3 and Table 4 are based on the main effects and the following elaboration details survival experience based on estimated hazard ratios (HR). Not working due to illness patients had the least survival time compared to patients who are on job.
Results in [9, 17, 20] showed that the survival time was reduced among individuals infected at older ages.
From among the variables included in the study, four of them (Education level, marital status, study area and gender) did not have significant impact on survival.
Improvement of the clinical characteristics has strong association with longer survival of HIV patients, furthermore, further study required on the implication of ART service that brings improvement on clinical factors thorough the study period. The expansion of the epidemic has now become a burning issue globally and this is particularly so more in developing countries; specially, in Sub Saharan Africa [1].
The goal of this therapy is to improve survival; to reduce HIV associated morbidity and mortality, to increase the quality of life, to restore immune function and to achieve maximal and sustained suppression of viral replication [5].
It is believed that, in resource poor countries like Ethiopia the survival of patients with AIDS treated with ART depends on a variety of factors, which may also vary greatly with economic, demographic, behavioral risk and health factors.
P = 0.10 is used in this study obtained from previous study at Tikur Anbessa Specialized Hospital, Addis Ababa [9]. Survival Analysis considers conditional information on the remaining time of a subject’s survival given current survival time. Of those, about 87% were right-censored (dropout, transferred, loss and alive till the study period) and the remaining 13% uncensored (Died).
All predictors with the exception of gender and education level manifest differences in levels of survival functions. Patients were having weights 45 and above kilograms lived four months longer on average than those having weight below 45 kilograms. It should be pointed out that variables with p- values below 0.05 were considered as statistically significant. Those patients aging in between 30 and 40 years had on average better survival experience than patients aging greater than 40 years. Residence, CD4 count, age, employment status, weight, WHO clinical stage, lymphocyte count and HIV-TB co-infection status had significant impact on the survival experience of patients.
Crude and adjusted mortality rates were compared by pre-hospital time using STATA statistical software. These patients are most likely to benefit from expedited transport if definitive surgical management is required. The data for this research were collected during the follow-up time from January 1, 2008 to December 31, 2011.
In fact, Sub-Saharan Africa accounts for 22.4 million infections, which is about 67% of the total HIV burden.
By 2010, WHO has planned to put 9.8 million people on ART with the goal of providing universal access to HIV care and ART [6]. In other words, even if ART treatment has shown significant clinical importance by meeting the goal of therapy, we are still facing a number of deaths that can otherwise be avoided by appropriate interventions on certain socio-economic, demographic, behavioral risk and health factors. The selected institutions are Dessie referral Hospital, Boru Meda Hospital and Kombolcha Health Center.
Survival data were censored in the sense that they did not provide complete information since, for a variety of reasons, subjects of the study may not have experienced the event of interest. A resume emanating from descriptive analysis with reference to the seven predictors (leaving out sex, study site and education level) that manifest differences in survival are provided as follows. The relationship between each covariates and survival time of AIDS patients which are significant using a modest level of significance 25% to be included for further investigation in the multiple covariates model are presented in Table 3. The findings of the study [4, 17, 19] showed that WHO clinical stage remained significantly associated with survival of patients taking ART. Results: During the study period, 908 patients presented to the hospital after penetrating thoracic trauma, with 79% surviving . Out of a population of HIV-patients who were taking antiretroviral therapy in the hospitals and health center in that period, 654 patients were selected based on simple random sampling technique for this study.
The number of people estimated to acquire new infections is around 1.9 million accounting for 68 % of the total number of new infections [2]. Thus this study was undertaken with objectives to identify predictors that have impact on the survival of HIV AIDS patients with the hope that the results would contribute to existing knowledge. Based on this record of the patients who had complete information, the variables which are important for the study was collected using the patients’ medical registration number (MIR) without any direct contact with the patients, instead by communicating with the nurses and counselors to get the medical record and other information important for the study. 10% of the sample size, which is 60, is added to the determined sample size 594 to compensate for the random errors and the sample size, with population size N=7165 for the current study become 654. The existence of variables that change over time is also a distinguishing feature in survival analysis.
In order to investigate if there is significant difference between the survivals of a patient between categories of covariates, Kaplan-Meier survivor estimates all significant covariates in the log-rank test. P-value in Table 3 indicates survival of the patients is significantly related with all the proposed covariates except gender, study area and education level of the patient. Similarly, based on 564 sampled HIV patients who are taking ART, the mean and median survival time was almost 42 and 48 months.
Patients who were in WHO clinical Stage IV had the highest risk of mortality than stage II and III.
The studies that have examined the urban setting have yielded contradictory conclusions; Sloan and Petri in Chicago evaluated blunt and penetrating trauma patients and did not find a relationship between transport time and outcome.
The median survival time was similar with [9] a four year period study but larger than [16] a five year follow up study whereas the mean survival time in the current study was smaller than [9] and larger than [4] a three year follow up retrospective cohort study.

The current study comes up with the same conclusion [4, 17, 19] except with stage II didn’t statistical significant from Stage IV. Higher body weight did indicate longer survival, whereas low weight was associated with shorter survival. The Kaplan-Meier Method was employed to estimate survival; the Cox Proportional Hazards Regression Method was used to identify determinants of survival. Results in [18] showed that HIV-TB co-infected patients had short survival time than negative in HIV-TB. Higher CD4 cell counts were observed to have an association with better survival experience. Living rural, order baseline age, not working due to illness, smaller CD4 count, weight, and lymphocyte count, HIV-TB co-infection developing and being in WHO clinical Stage IV were identified as a documented risk factors for shortened the survival experience of the HIV-patients who are in care of ART.
Likewise higher lymphocyte counts were observed to have an association with better survival experience.
Conclusion: In victims of penetrating thoracic trauma, more severely injured patients arrive at urban trauma centers sooner . Trauma centers in many high-volume locations do not have significant number of penetrating trauma patients. The mortality among our patients was comparable to that reported from other low-income countries. Mortality is strongly predicted by injury severity, although shorter pre-hospital times are associated with improved survival . This begs further investigation to define the influence of pre-hospital time factors on trauma outcomes in urban environments and in particular, in patients with penetrating trauma.Our study explores the impact of pre-hospital times on penetrating trauma outcomes within urban centers of Illinois. We hypothesized that there would be a relationship between total pre-hospital time and outcomes in patients with penetrating thoracic trauma, an injury that is potentially lethal without emergent intervention .
Similar studies in the future need to consider predictors in addition to those considered in this study and the clinical advantage of ART that brings improvement on the clinical characteristics in the follow up period. Since injury severity is strongly associated with mortality, we specifically studied the patients at highest risk, hypotensive patients who would be most likely to have hemorrhage that would benefit from rapid surgical control. This registry contains data from all traumatically injured patients brought to the 67 Level I and Level II Trauma Centers in Illinois . The data are entered by a registrar at each trauma center and uploaded to the Illinois Department of Public Health, who maintains the data set and website . Not included in this registry are patients who are declared dead-on-scene and not transported to a trauma center and patients not treated at or transferred to a Level I or Level II Trauma Center . The Emergency Medical System (EMS) in Illinois is a tiered system with paramedics trained in both Basic Life Support and Advanced Life Support techniques . The Chicago Committee on Trauma works with the state of Illinois and pre-hospital providers to ensure safe, standardized care and undergoes rigorous quality improvement scrutiny . This study was approved by the Institutional Review Board of Northwestern University.The Illinois State Trauma Registry (ISTR) includes approximately 45,000 records per year.
To limit our study to urban trauma patients, we selected patients of all ages injured in the major urban areas of Chicago, East St.
Because our question of interest was the effect of transport time on mortality, we excluded all transfer patients, as well as walk-in and self-transport patients (10% of cases annually) . We identified patients with penetrating injuries using information regarding the primary mechanism of injury recorded in the database (approximately 2,500 cases per year) and then classified those patients with thoracic trauma using ICD-9CM codes .
The final list of ICD-9 codes was limited to penetrating thoracic injuries (codes 860-862, approximately 150 cases per year), consistent with the Center for Medicaid and Medicare Services (CMS) designations. Each of these variables was evaluated separately and in aggregate as total pre-hospital time.Patient demographics along with injury details and outcomes were obtained. Student's t-test was used as the test statistic for the comparisons of mean pre-hospital time between cohorts . The injury severity score (ISS) was used as an anatomic marker of injury severity and was highly conserved in the data set (<3% missing data) . Logistic regression statistics were performed to determine the independent effect of transport time on mortality when controlling for age, race, and severity of injury for both hypotensive patients with penetrating thoracic injuries (HPTI) and non-hypotensive patients with penetrating thoracic injuries (NPTI) . For the models, transport times were categorized into 15 min intervals to determine the independent effect of successively longer transport times. One-hundred forty-three patients (16%) were hypotensive on presentation, with a systolic blood pressure less than 90mmHg (hypotensive PTI group = HPTI), while 765 patients (84%) had systolic blood pressures greater than 90 mmHg (normotensive PTI group = NPTI) . The mean age was approximately 27 years (age range 11-78, 99% between ages 15 and 55) and the majority of patients were African American, male and uninsured [Table 1] .
Hypotensive patients with penetrating thoracic injuries (HPTI) patients showed increased mortality with increased total pre-hospital time [Figure 1]. The odds ratio (OR) of death for HPTI patients, similarly, increased with increasing total pre-hospital time [Table 3], [Figure 2]. Non-hypotensive patients with penetrating thoracic injuries (NPTI) regression results are not included, but the covariates for age, race, gender, and ISS were of similar direction and magnitude .
The analysis indicates that more severely injured patients may survive devastating injuries with rapid transit: The sicker the patient, the quicker they should be in the trauma bay . A significantly positive finding of this study was that patients with higher injury severity experienced shorter pre-hospital transport time .
This finding in our study is not unique; Newgard demonstrated this in a multi-institutional study mixed population of blunt and penetrating trauma. This study is one of the first to show a consistent association between pre-hospital time and survival in an urban trauma system with uniform professional pre-hospital EMS care .
Variations in transport times might be due to travel distances to the nearest trauma center from incident scenes, traffic congestion or road conditions, or weather conditions . It is possible with new global positioning systems (GPS) data that incorporates traffic congestion and travel distances, pre-hospital times may be shortened. Some rural trauma systems have augmented their pre-hospital services with aeromedical transporter provided increased trauma training for first responders [16] to decrease the impact of longer transport times on injury mortality . However, comprehensive changes in a system that is working well for most patients may not be indicated or cost-effective. It is likely that many, less severely injured will not experience a survival benefit associated with decreased pre-hospital time; as notably seen in the NPTI group, in whom survival was not adversely affected by increased transport time, and was actually improved .
It is important to recognize that the majority of the overall trauma patient population with penetrating thoracic injuries did not benefit from shorter transport times . Though no systematic changes have occurred in urban pre-hospital transport during that time, additional years of data should be accessed when available to validate these results.Despite these limitations, these results suggest that careful planning to optimize transport time-encompassing hospital capacity and existing resources, traffic patterns, and trauma incident densities-may be beneficial in areas with a high burden of penetrating trauma.

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