A STUDY OF INFLUENZA AND OF STATISTICAL METHODS USED TO REPORT INFLUENZA

This paper defines and compares several models for describing excess influenza pneumonia mortality in Houston. First, the methodology used by the Center for Disease Control is examined and several variations of this methodology are studied. All of the models examined emphasize the difficulty of omitting epidemic weeks.^ In an attempt to find a better method of describing expected and epidemic mortality, time series methods are examined. Grouping in four-week periods, truncating the data series to adjust epidemic periods, and seasonally-adjusting the series y(,t), by:^ (DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI)^ is the best method examined. This new series w(,t) is stationary and a moving average model MA(1) gives a good fit for forecasting influenza and pneumonia mortality in Houston.^ Influenza morbidity, other causes of death, sex, race, age, climate variables, environmental factors, and school absenteeism are all examined in terms of their relationship to influenza and pneumonia mortality. Both influenza morbidity and ischemic heart disease mortality show a very high relationship that remains when seasonal trends are removed from the data. However, when jointly modeling the three series it is obvious that the simple time series MA(1) model of truncated, seasonally-adjusted four-week data gives a better forecast.^

HOUSE-TO-HOUSE SURVEY OF ACCESS TO AND USE OF PUBLIC HEALTH FACILITIES IN METROPOLITAN LAGOS, NIGERIA

One of the broad objectives of the Nigerian health service, vigorously being pursued at all levels of government, is to make comprehensive health care available and accessible to the population at the lowest possible cost, within available resources. Some state governments in the federation have already introduced free medical service as a practical way to remove financial barriers to access and in turn to encourage greater utilization of publicly funded care facilities.^ To aid health planners and decision makers in identifying a shorter corridor through which urban dwellers can gain access to comprehensive health care, a health interview survey of the metropolitan Lagos was undertaken. The primary purpose was to ascertain the magnitude of access problems which urban households face in seeking care from existing public facilities at the time of need. Six categories of illness chosen from the 1975 edition of the International Classification of Disease were used as indicators of health need.^ Choice of treatment facilities in response to illness episode was examined in relation to distance, travel time, time of use and transportation experiences. These were graphically described. The overall picture indicated that distance and travel time coexist with transportation problems in preventing a significant segment of those in need of health care from benefitting in the free medical service offered in public health facilities. Within this milieu, traditional medicine and its practitioners became the most preferred alternative. Recommendations were offered for action with regard to decentralization of general practitioner (GP) consultations in general hospitals and integration of traditional medicine and its practitioners into public health service. ^

A comparison of two statistical methods for analysis of day of the week effect on births from 2005-2007 in Harris County of Texas

The pattern of the births during the week has been reported by many studies. The births occurred in weekends are found consistently less then births occurred in weekdays. This study employed two statistical methods, two-way ANOVA and two-way Friedman's test to analyse the daily variations in amount of births of 222,735 births from 2005-2007 in Harris County, Texas. The two methods were compared on their assumptions, procedures and results. Both of the tests showed a significant result which indicated that the births through the week are not uniformly distributed. The result of multiple comparison demonstrated the births occurring on weekends were significantly different than the births occurring on weekdays with least amount on Sundays.^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

The effect of user fee exemption on the utilization of maternal health care at mission health facilities in Malawi

Acknowledgements This article was based on the first author’s PhD which was financed by the Malawi Health Research Capacity Strengthening Initiative. We thank Mr Patrick Naphini formerly of the Ministry of Health and Mrs Mafase Sesani at CHAM Secretariat for helping with the data. We also thank Mr Jacob Mazalale for useful comments on the article.

Statistical methods for characterizing similarities and differences between semantic structures

This paper describes a variety of statistical methods for obtaining precise quantitative estimates of the similarities and differences in the structures of semantic domains in different languages. The methods include comparing mean correlations within and between groups, principal components analysis of interspeaker correlations, and analysis of variance of speaker by question data. Methods for graphical displays of the results are also presented. The methods give convergent results that are mutually supportive and equivalent under suitable interpretation. The methods are illustrated on the semantic domain of emotion terms in a comparison of the semantic structures of native English and native Japanese speaking subjects. We suggest that, in comparative studies concerning the extent to which semantic structures are universally shared or culture-specific, both similarities and differences should be measured and compared rather than placing total emphasis on one or the other polar position.

Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations

We present statistical methods for analyzing replicated cDNA microarray expression data and report the results of a controlled experiment. The study was conducted to investigate inherent variability in gene expression data and the extent to which replication in an experiment produces more consistent and reliable findings. We introduce a statistical model to describe the probability that mRNA is contained in the target sample tissue, converted to probe, and ultimately detected on the slide. We also introduce a method to analyze the combined data from all replicates. Of the 288 genes considered in this controlled experiment, 32 would be expected to produce strong hybridization signals because of the known presence of repetitive sequences within them. Results based on individual replicates, however, show that there are 55, 36, and 58 highly expressed genes in replicates 1, 2, and 3, respectively. On the other hand, an analysis by using the combined data from all 3 replicates reveals that only 2 of the 288 genes are incorrectly classified as expressed. Our experiment shows that any single microarray output is subject to substantial variability. By pooling data from replicates, we can provide a more reliable analysis of gene expression data. Therefore, we conclude that designing experiments with replications will greatly reduce misclassification rates. We recommend that at least three replicates be used in designing experiments by using cDNA microarrays, particularly when gene expression data from single specimens are being analyzed.

Applying the WHO recommendations on health-sector response to violence against women to assess the Spanish health system. A mixed methods approach

This methodological note describes the development and application of a mixed-methods protocol to assess the responsiveness of Spanish health systems to violence against women in Spain, based on the World Health Organization (WHO) recommendations. Five areas for exploration were identified based on the WHO recommendations: policy environment, protocols, training, accountability/monitoring, and prevention/promotion. Two data collection instruments were developed to assess the situation of 17 Spanish regional health systems (RHS) with respect to these areas: 1) a set of indicators to guide a systematic review of secondary sources, and 2) an interview guide to be used with 26 key informants at the regional and national levels. We found differences between RHSs in the five areas assessed. The progress of RHSs on the WHO recommendations was notable at the level of policies, moderate in terms of health service delivery, and very limited in terms of preventive actions. Using a mixed-methods approach was useful for triangulation and complementarity during instrument design, data collection and interpretation.

Need to more consistently reimburse health facilities under Medicare and Medicaid, Department of Health, Education, and Welfare : report to the Congress /

"B-164031(4)."

Health hazard evaluation program /

Mode of access: Internet.

Modern statistical methods; descriptive and inductive,

Mode of access: Internet.

Health effects evaluation data file : data file design, abstraction, coding, and computerization of the 1977 and 1978 health hazard evaluation and technical assistance reports /

Mode of access: Internet.