Income inequality and elderly self-rated health in Sao Paulo, Brazil

Purpose: To test the association between income inequality and elderly self-rated health and to propose a pathway to explain the relationship. Methods: We analyzed a sample of 2143 older individuals (60 years of age and over) from 49 distritos of the Municipality of Sao Paulo, Brazil. Bayesian multilevel logistic models were performed with poor self-rated health as the outcome variable. Results: Income inequality (measured by the Gini coefficient) was found to be associated with poor self-rated health after controlling for age, sex, income and education (odds ratio, 1.19; 95% credible interval, 1.01-1.38). When the practice of physical exercise and homicide rate were added to the model, the Gini coefficient lost its statistical significance (P>.05). We fitted a structural equation model in which income inequality affects elderly health by a pathway mediated by violence and practice of physical exercise. Conclusions: The health of older individuals may be highly susceptible to the socioeconomic environment of residence, specifically to the local distribution of income. We propose that this association may be mediated by fear of violence and lack of physical activity. (C) 2012 Elsevier Inc. All rights reserved.

Are prescribing doctors sensitive to the price that their patients have to pay in the Spanish National Health System?

[EN] Background This study aims to design an empirical test on the sensitivity of the prescribing doctors to the price afforded for the patient, and to apply it to the population data of primary care dispensations for cardiovascular disease and mental illness in the Spanish National Health System (NHS). Implications for drug policies are discussed. Methods We used population data of 17 therapeutic groups of cardiovascular and mental illness drugs aggregated by health areas to obtain 1424 observations ((8 cardiovascular groups * 70 areas) + (9 psychotropics groups * 96 areas)). All drugs are free for pensioners. For non-pensioner patients 10 of the 17 therapeutic groups have a reduced copayment (RC) status of only 10% of the price with a ceiling of €2.64 per pack, while the remaining 7 groups have a full copayment (FC) rate of 40%. Differences in the average price among dispensations for pensioners and non-pensioners were modelled with multilevel regression models to test the following hypothesis: 1) in FC drugs there is a significant positive difference between the average prices of drugs prescribed to pensioners and non-pensioners; 2) in RC drugs there is no significant price differential between pensioner and non-pensioner patients; 3) the price differential of FC drugs prescribed to pensioners and non-pensioners is greater the higher the price of the drugs. Results The average monthly price of dispensations to pensioners and non-pensioners does not differ for RC drugs, but for FC drugs pensioners get more expensive dispensations than non-pensioners (estimated difference of €9.74 by DDD and month). There is a positive and significant effect of the drug price on the differential price between pensioners and non-pensioners. For FC drugs, each additional euro of the drug price increases the differential by nearly half a euro (0.492). We did not find any significant differences in the intensity of the price effect among FC therapeutic groups. Conclusions Doctors working in the Spanish NHS seem to be sensitive to the price that can be afforded by patients when they fill in prescriptions, although alternative hypothesis could also explain the results found.

Biomimese biologischer Membranen: Konzeptionierung, Synthese und biophysikalische Charakterisierung einer Hierarchie festkörpergestützter Mimikry biologischer Membranen

Im Rahmen dieser Arbeit wurden drei neue Modelle zur funktionellen Mimiese biologischer Membranen im Bereich der Bionanotechnologie entwickelt. Um den Rahmen der notwendigen Faktoren und Komponenten für biomimetische Membranmodelle abzustecken, wurde das biologische Vorbild im Bezug auf Zusammensetzung, Organisation und Funktion analysiert. Die daraus abgeleiteten Erkenntnisse erlauben das Erreichen von biologisch relevanten Membranwiderständen im Bereich von mehreren MOhm cm2 und eine gute lokale Fluidität. Ein weiteres Ziel dieser Arbeit war die Entwicklung einer Hierachie unterschiedlich stark von der Festkörperoberfläche entkoppelter Membranen zur Vergrößerung des submembranen Raumes. Diese Ziele konnten realisiert werden. Das auf archaealen Etherlipiden basierende DPTL-System wurde analog dem biologischen Vorbild stereoselektiv synthetisiert und ist in der Lage die Membran bei maximaler Elongation des TEG-Spacers mit mehr als 2 nm von der Oberfläche zu entkoppeln. Die erzielten Wiederstände liegen im hohen ein- bis zweistelligen MOhm-Bereich, die Kapazität entspricht mit 0,5 µF cm-2 ebenfalls dem Wert biologischer Membranen. Die Membraneigenschaften wurden mit Hilfe von SPS, EIS, IR-Spektroskopie, QCM, AFM und Kontaktwinkelmessungen charakterisiert. Die Funktionalität und lokale Fluidität der DPTL-Membran konnte anhand des Valinomycin vermittelten K+-Transports über die Membran gezeigt werden. Fluide Elektroden oder laterale Verdünnung mit TEGL erlauben den Einbau größerer Ionenkanäle. Lipo-Glycopolymere (LGP) mit unterschiedlichen Kettenlängen wurden mit Hilfe der kontrollierten radikalischen Polymerisation mit einer PD < 1.2 synthetisiert. Es zeigte sich, daß die Vororientierung der LGPs auf dem LB-Trog, gefolgt von einem LB-Übertrag auf einen funktionalisierten Träger mit photoreaktivem SAM, nach Belichten des Systems zu einer verlässlichen kovalenten Anbindung der supramolekularen LGP-Architektur führt. Da die Lipo-Glycopolymerketten am Glycopolymerterminus nur mit oberflächennahen Repetiereinheiten an die photoaktivierte Oberfläche binden, sind sie in der Lage Oberflächenrauhigkeiten des Festkörpersubstrates auszugleichen. Die photochemische Immobilisierung von funktionell orientierten supramolekularen LGP-Architekturen auf Goldoberflächen resultiert in tBLMs mit großen vertikalen Enkopplungen der Membran von der Festkörperoberfläche (>8 nm). Der funktionelle Ionentransport von Kaliumionen durch Valinomycin zeigt eine ausreichende lokale Fluidität der Membran die mit einem guten Membranwiderstand (mehrere MOhm) kombiniert ist. Große Membran-Oberflächenentkopplungen konnten mit Hilfe plasmapolymerisierter elektrophiler Polymere erreicht werden. Filmdicken von 50 nm sind mit homogener Oberfläche und Rauhigkeiten im Bereich von Nanometern möglich. Das System zeigt interessante fluide Eigenschaften mit guten Erholungsraten bei FRAP-Experimenten (Diffusionskonstanten von etwa 17 mikro m2 s-1). Die elektrischen Eigenschaften liegen mit Widerständen von wenigen kOhm unterhalb der für gute Membranmimikrie notwendigen Werte. Erstmalig konnte gezeigt werden, daß mit Hilfe dieser Methode inerte Polymere/Plastikträger (zum Beispiel Polypropylen und TOPAS) in effizienter Weise kovalent mit reaktiven Polymeroberflächen modifiziert werden können (Anwendung als DNA-Chip ist beschrieben).

Prediction of Psychosis by Mismatch Negativity

BACKGROUND: To develop risk-adapted prevention of psychosis, an accurate estimation of the individual risk of psychosis at a given time is needed. Inclusion of biological parameters into multilevel prediction models is thought to improve predictive accuracy of models on the basis of clinical variables. To this aim, mismatch negativity (MMN) was investigated in a sample clinically at high risk, comparing individuals with and without subsequent conversion to psychosis. METHODS: At baseline, an auditory oddball paradigm was used in 62 subjects meeting criteria of a late risk at-state who remained antipsychotic-naive throughout the study. Median follow-up period was 32 months (minimum of 24 months in nonconverters, n = 37). Repeated-measures analysis of covariance was employed to analyze the MMN recorded at frontocentral electrodes; additional comparisons with healthy controls (HC, n = 67) and first-episode schizophrenia patients (FES, n = 33) were performed. Predictive value was evaluated by a Cox regression model. RESULTS: Compared with nonconverters, duration MMN in converters (n = 25) showed significantly reduced amplitudes across the six frontocentral electrodes; the same applied in comparison with HC, but not FES, whereas the duration MMN in in nonconverters was comparable to HC and larger than in FES. A prognostic score was calculated based on a Cox regression model and stratified into two risk classes, which showed significantly different survival curves. CONCLUSIONS: Our findings demonstrate the duration MMN is significantly reduced in at-risk subjects converting to first-episode psychosis compared with nonconverters and may contribute not only to the prediction of conversion but also to a more individualized risk estimation and thus risk-adapted prevention.

Influence of reported study design characteristics on intervention effect estimates from randomized, controlled trials

Published evidence suggests that aspects of trial design lead to biased intervention effect estimates, but findings from different studies are inconsistent. This study combined data from 7 meta-epidemiologic studies and removed overlaps to derive a final data set of 234 unique meta-analyses containing 1973 trials. Outcome measures were classified as "mortality," "other objective," "or subjective," and Bayesian hierarchical models were used to estimate associations of trial characteristics with average bias and between-trial heterogeneity. Intervention effect estimates seemed to be exaggerated in trials with inadequate or unclear (vs. adequate) random-sequence generation (ratio of odds ratios, 0.89 [95% credible interval {CrI}, 0.82 to 0.96]) and with inadequate or unclear (vs. adequate) allocation concealment (ratio of odds ratios, 0.93 [CrI, 0.87 to 0.99]). Lack of or unclear double-blinding (vs. double-blinding) was associated with an average of 13% exaggeration of intervention effects (ratio of odds ratios, 0.87 [CrI, 0.79 to 0.96]), and between-trial heterogeneity was increased for such studies (SD increase in heterogeneity, 0.14 [CrI, 0.02 to 0.30]). For each characteristic, average bias and increases in between-trial heterogeneity were driven primarily by trials with subjective outcomes, with little evidence of bias in trials with objective and mortality outcomes. This study is limited by incomplete trial reporting, and findings may be confounded by other study design characteristics. Bias associated with study design characteristics may lead to exaggeration of intervention effect estimates and increases in between-trial heterogeneity in trials reporting subjectively assessed outcomes.

A multi-level decomposition of variance in somatic symptom reporting in families with adolescent children

OBJECTIVES: This paper examines four different levels of possible variation in symptom reporting: occasion, day, person and family. DESIGN: In order to rule out effects of retrospection, concurrent symptom reporting was assessed prospectively using a computer-assisted self-report method. METHODS: A decomposition of variance in symptom reporting was conducted using diary data from families with adolescent children. We used palmtop computers to assess concurrent somatic complaints from parents and children six times a day for seven consecutive days. In two separate studies, 314 and 254 participants from 96 and 77 families, respectively, participated. A generalized multilevel linear models approach was used to analyze the data. Symptom reports were modelled using a logistic response function, and random effects were allowed at the family, person and day level, with extra-binomial variation allowed for on the occasion level. RESULTS: Substantial variability was observed at the person, day and occasion level but not at the family level. CONCLUSIONS: To explain symptom reporting in normally healthy individuals, situational as well as person characteristics should be taken into account. Family characteristics, however, would not help to clarify symptom reporting in all family members.

Computational Techniques for Spatial Logistic Regression with Large Datasets

In epidemiological work, outcomes are frequently non-normal, sample sizes may be large, and effects are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. We focus on binary outcomes, with the risk surface a smooth function of space. We compare penalized likelihood models, including the penalized quasi-likelihood (PQL) approach, and Bayesian models based on fit, speed, and ease of implementation. A Bayesian model using a spectral basis representation of the spatial surface provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial features while limiting overfitting and being more efficient computationally than other Bayesian approaches. One of the contributions of this work is further development of this underused representation. The spectral basis model outperforms the penalized likelihood methods, which are prone to overfitting, but is slower to fit and not as easily implemented. Conclusions based on a real dataset of cancer cases in Taiwan are similar albeit less conclusive with respect to comparing the approaches. The success of the spectral basis with binary data and similar results with count data suggest that it may be generally useful in spatial models and more complicated hierarchical models.

Underestimation of Standard Errors in Multi-Site Time Series Studies

Multi-site time series studies of air pollution and mortality and morbidity have figured prominently in the literature as comprehensive approaches for estimating acute effects of air pollution on health. Hierarchical models are generally used to combine site-specific information and estimate pooled air pollution effects taking into account both within-site statistical uncertainty, and across-site heterogeneity. Within a site, characteristics of time series data of air pollution and health (small pollution effects, missing data, highly correlated predictors, non linear confounding etc.) make modelling all sources of uncertainty challenging. One potential consequence is underestimation of the statistical variance of the site-specific effects to be combined. In this paper we investigate the impact of variance underestimation on the pooled relative rate estimate. We focus on two-stage normal-normal hierarchical models and on under- estimation of the statistical variance at the first stage. By mathematical considerations and simulation studies, we found that variance underestimation does not affect the pooled estimate substantially. However, some sensitivity of the pooled estimate to variance underestimation is observed when the number of sites is small and underestimation is severe. These simulation results are applicable to any two-stage normal-normal hierarchical model for combining information of site-specific results, and they can be easily extended to more general hierarchical formulations. We also examined the impact of variance underestimation on the national average relative rate estimate from the National Morbidity Mortality Air Pollution Study and we found that variance underestimation as much as 40% has little effect on the national average.

LEARNING FROM NEAR MISSES IN MEDICATION ERRORS: A BAYESIAN APPROACH

Medical errors originating in health care facilities are a significant source of preventable morbidity, mortality, and healthcare costs. Voluntary error report systems that collect information on the causes and contributing factors of medi- cal errors regardless of the resulting harm may be useful for developing effective harm prevention strategies. Some patient safety experts question the utility of data from errors that did not lead to harm to the patient, also called near misses. A near miss (a.k.a. close call) is an unplanned event that did not result in injury to the patient. Only a fortunate break in the chain of events prevented injury. We use data from a large voluntary reporting system of 836,174 medication errors from 1999 to 2005 to provide evidence that the causes and contributing factors of errors that result in harm are similar to the causes and contributing factors of near misses. We develop Bayesian hierarchical models for estimating the log odds of selecting a given cause (or contributing factor) of error given harm has occurred and the log odds of selecting the same cause given that harm did not occur. The posterior distribution of the correlation between these two vectors of log-odds is used as a measure of the evidence supporting the use of data from near misses and their causes and contributing factors to prevent medical errors. In addition, we identify the causes and contributing factors that have the highest or lowest log-odds ratio of harm versus no harm. These causes and contributing factors should also be a focus in the design of prevention strategies. This paper provides important evidence on the utility of data from near misses, which constitute the vast majority of errors in our data.

Seasonal Analyses of Air Pollution and Mortality in 100 U.S. Cities

Time series models relating short-term changes in air pollution levels to daily mortality counts typically assume that the effects of air pollution on the log relative rate of mortality do not vary with time. However, these short-term effects might plausibly vary by season. Changes in the sources of air pollution and meteorology can result in changes in characteristics of the air pollution mixture across seasons. The authors develop Bayesian semi-parametric hierarchical models for estimating time-varying effects of pollution on mortality in multi-site time series studies. The methods are applied to the updated National Morbidity and Mortality Air Pollution Study database for the period 1987--2000, which includes data for 100 U.S. cities. At the national level, a 10 micro-gram/m3 increase in PM(10) at lag 1 is associated with a 0.15 (95% posterior interval: -0.08, 0.39),0.14 (-0.14, 0.42), 0.36 (0.11, 0.61), and 0.14 (-0.06, 0.34) percent increase in mortality for winter, spring, summer, and fall, respectively. An analysis by geographical regions finds a strong seasonal pattern in the northeast (with a peak in summer) and little seasonal variation in the southern regions of the country. These results provide useful information for understanding particle toxicity and guiding future analyses of particle constituent data.

Primary care physician supply and other key determinants of health care utilisation: The case of Switzerland

Background The Swiss government decided to freeze new accreditations for physicians in private practice in Switzerland based on the assumption that demand-induced health care spending may be cut by limiting care offers. This legislation initiated an ongoing controversial public debate in Switzerland. The aim of this study is therefore the determination of socio-demographic and health system-related factors of per capita consultation rates with primary care physicians in the multicultural population of Switzerland. Methods The data were derived from the complete claims data of Swiss health insurers for 2004 and included 21.4 million consultations provided by 6564 Swiss primary care physicians on a fee-for-service basis. Socio-demographic data were obtained from the Swiss Federal Statistical Office. Utilisation-based health service areas were created and were used as observational units for statistical procedures. Multivariate and hierarchical models were applied to analyze the data. Results Models within the study allowed the definition of 1018 primary care service areas with a median population of 3754 and an average per capita consultation rate of 2.95 per year. Statistical models yielded significant effects for various geographical, socio-demographic and cultural factors. The regional density of physicians in independent practice was also significantly associated with annual consultation rates and indicated an associated increase 0.10 for each additional primary care physician in a population of 10,000 inhabitants. Considerable differences across Swiss language regions were observed with reference to the supply of ambulatory health resources provided either by primary care physicians, specialists, or hospital-based ambulatory care. Conclusion The study documents a large small-area variation in utilisation and provision of health care resources in Switzerland. Effects of physician density appeared to be strongly related to Swiss language regions and may be rooted in the different cultural backgrounds of the served populations.

Chronic NSAID use and long-term decline of renal function in a prospective rheumatoid arthritis cohort study

OBJECTIVES Non-steroidal anti-inflammatory drugs (NSAIDs) may cause kidney damage. This study assessed the impact of prolonged NSAID exposure on renal function in a large rheumatoid arthritis (RA) patient cohort. METHODS Renal function was prospectively followed between 1996 and 2007 in 4101 RA patients with multilevel mixed models for longitudinal data over a mean period of 3.2 years. Among the 2739 'NSAID users' were 1290 patients treated with cyclooxygenase type 2 selective NSAIDs, while 1362 subjects were 'NSAID naive'. Primary outcome was the estimated glomerular filtration rate according to the Cockroft-Gault formula (eGFRCG), and secondary the Modification of Diet in Renal Disease and Chronic Kidney Disease Epidemiology Collaboration formula equations and serum creatinine concentrations. In sensitivity analyses, NSAID dosing effects were compared for patients with NSAID registration in ≤/>50%, ≤/>80% or ≤/>90% of assessments. FINDINGS In patients with baseline eGFRCG >30 mL/min, eGFRCG evolved without significant differences over time between 'NSAID users' (mean change in eGFRCG -0.87 mL/min/year, 95% CI -1.15 to -0.59) and 'NSAID naive' (-0.67 mL/min/year, 95% CI -1.26 to -0.09, p=0.63). In a multivariate Cox regression analysis adjusted for significant confounders age, sex, body mass index, arterial hypertension, heart disease and for other insignificant factors, NSAIDs were an independent predictor for accelerated renal function decline only in patients with advanced baseline renal impairment (eGFRCG <30 mL/min). Analyses with secondary outcomes and sensitivity analyses confirmed these results. CONCLUSIONS NSAIDs had no negative impact on renal function estimates but in patients with advanced renal impairment.

Trauma care capacity and performance in Texas: Regional comparisons

Objective. The study reviewed one year of Texas hospital discharge data and Trauma Registry data for the 22 trauma services regions in Texas to identify regional variations in capacity, process of care and clinical outcomes for trauma patients, and analyze the statistical associations among capacity, process of care, and outcomes. ^ Methods. Cross sectional study design covering one year of state-wide Texas data. Indicators of trauma capacity, trauma care processes, and clinical outcomes were defined and data were collected on each indicator. Descriptive analyses were conducted of regional variations in trauma capacity, process of care, and clinical outcomes at all trauma centers, at Level I and II trauma centers and at Level III and IV trauma centers. Multilevel regression models were performed to test the relations among trauma capacity, process of care, and outcome measures at all trauma centers, at Level I and II trauma centers and at Level III and IV trauma centers while controlling for confounders such as age, gender, race/ethnicity, injury severity, level of trauma centers and urbanization. ^ Results. Significant regional variation was found among the 22 trauma services regions across Texas in trauma capacity, process of care, and clinical outcomes. The regional trauma bed rate, the average staffed bed per 100,000 varied significantly by trauma service region. Pre-hospital trauma care processes were significantly variable by region---EMS time, transfer time, and triage. Clinical outcomes including mortality, hospital and intensive care unit length of stay, and hospital charges also varied significantly by region. In multilevel regression analysis, the average trauma bed rate was significantly related to trauma care processes including ambulance delivery time, transfer time, and triage after controlling for age, gender, race/ethnicity, injury severity, level of trauma centers, and urbanization at all trauma centers. Transfer time only among processes of care was significant with the average trauma bed rate by region at Level III and IV. Also trauma mortality only among outcomes measures was significantly associated with the average trauma bed rate by region at all trauma centers. Hospital charges only among outcomes measures were statistically related to trauma bed rate at Level I and II trauma centers. The effect of confounders on processes and outcomes such as age, gender, race/ethnicity, injury severity, and urbanization was found significantly variable by level of trauma centers. ^ Conclusions. Regional variation in trauma capacity, process, and outcomes in Texas was extensive. Trauma capacity, age, gender, race/ethnicity, injury severity, level of trauma centers and urbanization were significantly associated with trauma process and clinical outcomes depending on level of trauma centers. ^ Key words: regionalized trauma systems, trauma capacity, pre-hospital trauma care, process, trauma outcomes, trauma performance, evaluation measures, regional variations ^

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).^

Comparison of Bayesian and frequentist approaches in clinical outcomes between hospitals

Many public health agencies and researchers are interested in comparing hospital outcomes, for example, morbidity, mortality, and hospitalization across areas and hospitals. However, since there is variation of rates in clinical trials among hospitals because of several biases, we are interested in controlling for the bias and assessing real differences in clinical practices. In this study, we compared the variations between hospitals in rates of severe Intraventricular Haemorrhage (IVH) infant using Frequentist statistical approach vs. Bayesian hierarchical model through simulation study. The template data set for simulation study was included the number of severe IVH infants of 24 intensive care units in Australian and New Zealand Neonatal Network from 1995 to 1997 in severe IVH rate in preterm babies. We evaluated the rates of severe IVH for 24 hospitals with two hierarchical models in Bayesian approach comparing their performances with the shrunken rates in Frequentist method. Gamma-Poisson (BGP) and Beta-Binomial (BBB) were introduced into Bayesian model and the shrunken estimator of Gamma-Poisson (FGP) hierarchical model using maximum likelihood method were calculated as Frequentist approach. To simulate data, the total number of infants in each hospital was kept and we analyzed the simulated data for both Bayesian and Frequentist models with two true parameters for severe IVH rate. One was the observed rate and the other was the expected severe IVH rate by adjusting for five predictors variables for the template data. The bias in the rate of severe IVH infant estimated by both models showed that Bayesian models gave less variable estimates than Frequentist model. We also discussed and compared the results from three models to examine the variation in rate of severe IVH by 20th centile rates and avoidable number of severe IVH cases. ^