Understanding causes of hospitalization and access to care among newly diagnosed HIV patients in Houston, TX

More than a quarter of patients with HIV in the United States are diagnosed in hospital settings most often with advanced HIV related conditions.(1) There has been little research done on the causes of hospitalization when the patients are first diagnosed with HIV. The aim of this study was to determine if the patients are hospitalized due to an HIV related cause or due to some other co-morbidity. Reduced access to care could be one possible reason why patients are diagnosed late in the course of the disease. This study compared the access to care of patients diagnosed with HIV in hospital and outpatient setting. The data used for the study was a part of the ongoing study “Attitudes and Beliefs and Steps of HIV Care”. The participants in the study were newly diagnosed with HIV and recruited from both inpatient and outpatient settings. The primary and the secondary diagnoses from hospital discharge reports were extracted and a primary reason for hospitalization was ascertained. These were classified as HIV-related, other infectious causes, non–infectious causes, other systemic causes, and miscellaneous causes. Access to care was determined by a score based on responses to a set of questions derived from the HIV Cost and Services Utilization Study (HCSUS) on a 6 point scale. The mean score of the hospitalized patients and mean score of the patients diagnosed in an outpatient setting was compared. We used multiple linear regressions to compare mean differences in the two groups after adjusting for age, sex, race, household income educational level and health insurance at the time of diagnosis. There were 185 participants in the study, including 78 who were diagnosed in hospital settings and 107 who were diagnosed in outpatient settings. We found that HIV-related conditions were the leading cause of hospitalization, accounting for 60% of admissions, followed by non-infectious causes (20%) and then other infectious causes (17%). The inpatient diagnosed group did not have greater perceived access-to-care as compared to the outpatient group. Regression analysis demonstrated a statistically significant improvement in access-to-care with advancing education level (p=0.04) and with better health insurance (p=0.004). HIV-related causes account for many hospitalizations when patients are first diagnosed with HIV. Many of these HIV-related hospitalizations could have been prevented if patients were diagnosed early and linked to medical care. Programs to increase HIV awareness need to be an integral part of activities aimed at control of spread of HIV in the community. Routine testing for HIV infection to promote early HIV diagnosis can prevent significant morbidity and mortality.^

Statistical and methodological challenges for disaster preparedness and medical needs assessment in Rio Grande Valley of Texas

In recent years, disaster preparedness through assessment of medical and special needs persons (MSNP) has taken a center place in public eye in effect of frequent natural disasters such as hurricanes, storm surge or tsunami due to climate change and increased human activity on our planet. Statistical methods complex survey design and analysis have equally gained significance as a consequence. However, there exist many challenges still, to infer such assessments over the target population for policy level advocacy and implementation. ^ Objective. This study discusses the use of some of the statistical methods for disaster preparedness and medical needs assessment to facilitate local and state governments for its policy level decision making and logistic support to avoid any loss of life and property in future calamities. ^ Methods. In order to obtain precise and unbiased estimates for Medical Special Needs Persons (MSNP) and disaster preparedness for evacuation in Rio Grande Valley (RGV) of Texas, a stratified and cluster-randomized multi-stage sampling design was implemented. US School of Public Health, Brownsville surveyed 3088 households in three counties namely Cameron, Hidalgo, and Willacy. Multiple statistical methods were implemented and estimates were obtained taking into count probability of selection and clustering effects. Statistical methods for data analysis discussed were Multivariate Linear Regression (MLR), Survey Linear Regression (Svy-Reg), Generalized Estimation Equation (GEE) and Multilevel Mixed Models (MLM) all with and without sampling weights. ^ Results. Estimated population for RGV was 1,146,796. There were 51.5% female, 90% Hispanic, 73% married, 56% unemployed and 37% with their personal transport. 40% people attained education up to elementary school, another 42% reaching high school and only 18% went to college. Median household income is less than $15,000/year. MSNP estimated to be 44,196 (3.98%) [95% CI: 39,029; 51,123]. All statistical models are in concordance with MSNP estimates ranging from 44,000 to 48,000. MSNP estimates for statistical methods are: MLR (47,707; 95% CI: 42,462; 52,999), MLR with weights (45,882; 95% CI: 39,792; 51,972), Bootstrap Regression (47,730; 95% CI: 41,629; 53,785), GEE (47,649; 95% CI: 41,629; 53,670), GEE with weights (45,076; 95% CI: 39,029; 51,123), Svy-Reg (44,196; 95% CI: 40,004; 48,390) and MLM (46,513; 95% CI: 39,869; 53,157). ^ Conclusion. RGV is a flood zone, most susceptible to hurricanes and other natural disasters. People in the region are mostly Hispanic, under-educated with least income levels in the U.S. In case of any disaster people in large are incapacitated with only 37% have their personal transport to take care of MSNP. Local and state government’s intervention in terms of planning, preparation and support for evacuation is necessary in any such disaster to avoid loss of precious human life. ^ Key words: Complex Surveys, statistical methods, multilevel models, cluster randomized, sampling weights, raking, survey regression, generalized estimation equations (GEE), random effects, Intracluster correlation coefficient (ICC).^

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

AN ATTEMPT TO MEASURE FOOD SAFETY STATUS

The main objective of this study was to attempt to develop some indicators for measuring the food safety status of a country. A conceptual model was put forth by the investigator. The assumption was that food safety status was multifactorily influenced by medico-health levels, food-nutrition programs, and consumer protection activities. However, all these in turn depended upon socio-economic status of the country.^ Twenty-six indicators were reviewed and examined. Seventeen were first screened and three were finally selected, by the stepwise multiple regression analysis, to reflect the food safety status. Sixty-one countries/areas were included in this study.^ The three indicators were life expectancy at birth with multiple correlation coefficient (R2 = 34.62%), adult literacy rate (R2 = 29.66%), and child mortality rate for ages 1-4 (R2 = 9.99%). They showed a cumulative R2 of 57.79%. ^

FEASIBILITY OF CORRELATING INCIDENCE OF DIARRHEA WITH CHARACTERISTICS OF RURAL EGYPTIAN HOUSEHOLDS BY MEANS OF AN ENVIRONMENTAL SURVEY

Few, if any studies, have attempted to identify the specific environmental factors associated with the incidence of diarrheal disease and to rank these by their contribution to the total incidence of diarrheal illness. Potentially those factors with the greatest contribution are the variables on which intervention could be expected to have the greatest impact on the incidence of diarrhea.^ In 317 rural Egyptian households participating in a longitudinal study of diarrheal disease, selected environmental characteristics were observed and recorded on a questionnaire. Characteristics of the environment were classified into seven categories including water usage, proximity of animals to the house, waste management, food preparation area, toilet area, the household structure and hygiene. The variables from each of the seven major groupings most associated with the incidence of diarrhea in infants were selected through the application of stepwise multiple regression. Each area was then ranked by the portion of the incidence of diarrhea in infants that each composite group of area-specific variables alone would explain. The groups of household structure and water usage variables were found to be more associated with the incidence of diarrhea in infants than variables describing the toilet area, proximity to animals or others. It was also found that 24.7% of the total variance in incidence of diarrheal illness was explained by environmental variables. ^

HEALTH MAINTENANCE ORGANIZATION ENVIRONMENT: AN EMPIRICAL STUDY OF THE RELATIONSHIP OF EXTERNAL CONTEXT AND PERFORMANCE

The research project is an extension of a series of administrative science and health care research projects evaluating the influence of external context, organizational strategy, and organizational structure upon organizational success or performance. The research will rely on the assumption that there is not one single best approach to the management of organizations (the contingency theory). As organizational effectiveness is dependent on an appropriate mix of factors, organizations may be equally effective based on differing combinations of factors. The external context of the organization is expected to influence internal organizational strategy and structure and in turn the internal measures affect performance (discriminant theory). The research considers the relationship of external context and organization performance.^ The unit of study for the research will be the health maintenance organization (HMO); an organization the accepts in exchange for a fixed, advance capitation payment, contractual responsibility to assure the delivery of a stated range of health sevices to a voluntary enrolled population. With the current Federal resurgence of interest in the Health Maintenance Organization (HMO) as a major component in the health care system, attention must be directed at maximizing development of HMOs from the limited resources available. Increased skills are needed in both Federal and private evaluation of HMO feasibility in order to prevent resource investment and in projects that will fail while concurrently identifying potentially successful projects that will not be considered using current standards.^ The research considers 192 factors measuring contextual milieu (social, educational, economic, legal, demographic, health and technological factors). Through intercorrelation and principle components data reduction techniques this was reduced to 12 variables. Two measures of HMO performance were identified, they are (1) HMO status (operational or defunct), and (2) a principle components factor score considering eight measures of performance. The relationship between HMO context and performance was analysed using correlation and stepwise multiple regression methods. In each case it has been concluded that the external contextual variables are not predictive of success or failure of study Health Maintenance Organizations. This suggests that performance of an HMO may rely on internal organizational factors. These findings have policy implications as contextual measures are used as a major determinant in HMO feasibility analysis, and as a factor in the allocation of limited Federal funds. ^

Identification of micro-RNAs targeting genes of PI3K/AKT pathway in ovarian cancer

Ovarian cancer is the leading cause of cancer-related death for females due to lack of specific early detection method. It is of great interest to find molecular-based biomarkers which are sensitive and specific to ovarian cancer for early diagnosis, prognosis and therapeutics. miRNAs have been proposed to be potential biomarkers that could be used in cancer prevention and therapeutics. The current study analyzed the miRNA and mRNA expression data extracted from the Cancer Genome Atlas (TCGA) database. Using simple linear regression and multiple regression models, we found 71 miRNA-mRNA pairs which were negatively associated between 56 miRNAs and 24 genes of PI3K/AKT pathway. Among these miRNA and mRNA target pairs, 9 of them were in agreement with the predictions from the most commonly used target prediction programs including miRGen, miRDB, miRTarbase and miR2Disease. These shared miRNA-mRNA pairs were considered to be the most potential genes that were involved in ovarian cancer. Furthermore, 4 of the 9 target genes encode cell cycle or apoptosis related proteins including Cyclin D1, p21, FOXO1 and Bcl2, suggesting that their regulator miRNAs including miR-16, miR-96 and miR-21 most likely played important roles in promoting tumor growth through dysregulated cell cycle or apoptosis. miR-96 was also found to directly target IRS-1. In addition, the results showed that miR-17 and miR-9 may be involved in ovarian cancer through targeting JAK1. This study might provide evidence for using miRNA or miRNA profile as biomarker.^

The Relationship of Provincial Economic Level and Child Injury Mortality in Thailand: A Cross-Sectional Analysis in a Middle Income Country

The objective of this cross-sectional study was to examine the relationship of provincial economic development indices with incidences of child injury mortality in Thailand from 1999 - 2001. All injury deaths among children age 1-14 years were included. The independent variables included gross provincial product per capita (GPP/c), poverty and inequality indices, material and social deprivation indices, population in rural/ urban areas, and migration. Due to multicollinearity of such variables, the 76 provinces were categorized by GPP/c quartile, and means of overall injury, drowning, and transport-related mortality rates were compared among quartile groups. Spearman’s rho correlation between GPP/c and injury mortality rates was also performed. Finally, factor analysis was employed to create a set of factors to be treated as uncorrelated variables and stepwise multiple regression was carried out for the effects of the factors on injury mortality rates. A significant direct relationship was observed between GPP/c and overall injury mortality among children age 1-4 years, and 10-14 year-olds of both genders. Drowning was the main cause of this relationship among children age 1-4 years, and transport-related injury was the principle cause among children age 10-14 years. Conversely, provinces with lower GPP/c experienced higher injury mortality rates among school-age children 5-9 years old for both genders, mostly due to drowning. Factor analysis, and multiple regression results confirmed the relationships between economic development and injury mortality rates. These findings revealed that economic development had an adverse impact on injury-related mortality among children 1 to 4 and 10 to14 in Thailand.

Use of universal precautions in the workplace: A comparison of two fire /EMS services

The Health Belief Model (HBM) provided the theoretical framework for examining Universal Precautions (UP) compliance factors by Firefighter, EMTs and Paramedics (prehospital care providers). A convenient sample of prehospital care providers (n = 4000) from two cities (Houston and Washington DC), were surveyed to explore the factors related to their decision to comply with Universal Precautions. Eight hundred and sixty-five useable questionnaires were analyzed. The responders were primarily male (95.7%) eight hundred and twenty-eight and thirty-seven were female, prehospital based (100%), EMTs (60.0%) and paramedics (12.8%) who had a mean 13 years of prehospital care experience. ^ Linear regression was used to evaluate the four hypotheses. The first hypothesis evaluating perceived susceptibility and seriousness with reported UP use was statistically significant (p = < .05). Perceived susceptibility, when considered independently, did not make a significant contribution (t = −4.2852; p = 0.0000) to the stated use of Universal precautions. The hypothesis is not supported as stated. The data indicates the opposite effect. Supported is the premise that as perceived susceptibility and perceived seriousness increase the use of Universal Precautions decreases. Hypothesis two tested perceived benefits with internal and external barriers. Both perceived benefits and internal and external barriers as well as the overall regression were significant (F = 112.6, p = 0.0000). The contribution of internal and external barriers was statistically significant (t = 0.0175; p = 0.0000) and (t = 0.0128; p = 0.0000). Hypothesis three which tested modifying factors, cues to action, select demographic variables, and the main effects of the HBM with self reported UP compliance overall was significant. The variables gender, birth, education, job type, EMS certification, years of service, years of experience providing patient care, Universal Precautions training hours, type of apparatus assigned to and the number of EMS related incidents responded to in a month were found to have a significant contribution to the stated use of Universal Precautions. ^ The additive effects were tested by use of a stepwise regression that assessed the contribution of each of the significant variables. Three variables in the equation were statistically significant. Internal barriers (t = −8.5507; p = 0.0000), external barriers (t = −6.2862; p = 0.000) and job type 2 & 3. Job type two (t = −2.8464; p = 0.0045 is titled Engineer/Operator. Job type three (t = −2.5730; p = 0.0103) is titled captain. The overall regression was significant (F = 24.06; p = 0.000). The Hypothesis is supported in the certain demographic variables do influence the stated use of Universal precautions and that as internal and external barriers are decreased, there is an increase in the stated use of Universal Precautions. ^ In summary, this study demonstrated that internal and external barriers have a significant impact on the stated use of Universal Precautions. Internal barriers are those factors within the individual that require an internal change (i.e., forgetfulness, freedom, perception of the urgency of the patient's needs etc.) and external barriers are things in the environment that can be altered (i.e., equipment design, availability of equipment, ease of use). These two model variables explained 23%–30% of the variance. ^