Infectious disease models with switching are constructed and investigated in detail. Modelling infectious diseases as switched systems, which are systems that combine continuous dynamics with discrete logic, allows for the use of methods from switched systems theory. These methods are used to analyze the stability and long-term behaviour of the proposed switched epidemiological models. Switching is first incorporated into epidemiological models by assuming the contact rate to be time-dependent and better approximated by a piecewise constant. Epidemiological models with switched incidence rates are also investigated. Threshold criteria are established that are sufficient for the eradication of the disease, and, hence, the stability of the disease-free solution. In the case of an endemic disease, some criteria are developed that establish the persistence of the disease. Lyapunov function techniques, as well as techniques for stability of impulsive or non-impulsive switched systems with both stable and unstable modes are used. These methods are first applied to switched epidemiological models which are intrinsically one-dimensional. Multi-dimensional disease models with switching are then investigated in detail. An important part of studying epidemiology is to construct control strategies in order to eradicate a disease, which would otherwise be persistent. Hence, the application of controls schemes to switched epidemiological models are investigated. Finally, epidemiological models with switched general nonlinear incidence rates are considered. Simulations are given throughout to illustrate our results, as well as to make some conjectures. Some conclusions are made and future directions are given.

Infectious disease models with switching are constructed and investigated in detail. Modelling infectious diseases as switched systems, which are systems that combine continuous dynamics with discrete logic, allows for the use of methods from switched systems theory. These methods are used to analyze the stability and long-term behaviour of the proposed switched epidemiological models. Switching is first incorporated into epidemiological models by assuming the contact rate to be time-dependent and better approximated by a piecewise constant. Epidemiological models with switched incidence rates are also investigated. Threshold criteria are established that are sufficient for the eradication of the disease, and, hence, the stability of the disease-free solution. In the case of an endemic disease, some criteria are developed that establish the persistence of the disease. Lyapunov function techniques, as well as techniques for stability of impulsive or non-impulsive switched systems with both stable and unstable modes are used. These methods are first applied to switched epidemiological models which are intrinsically one-dimensional. Multi-dimensional disease models with switching are then investigated in detail. An important part of studying epidemiology is to construct control strategies in order to eradicate a disease, which would otherwise be persistent. Hence, the application of controls schemes to switched epidemiological models are investigated. Finally, epidemiological models with switched general nonlinear incidence rates are considered. Simulations are given throughout to illustrate our results, as well as to make some conjectures. Some conclusions are made and future directions are given.

Made available in DSpace on 2014-12-17T15:55:06Z (GMT). No. of bitstreams: 1 MariaCSS.pdf: 226872 bytes, checksum: 5198c11c0d1b74ef4c431b468f17ac87 (MD5) Previous issue date: 2006-08-29 / This study sprang from the hypothesis that spatial variations in the morbidity rate for dengue fever within the municipality of Natal are related to intra-city socioeconomic and environmental variations. The objective of the project was to classify the different suburbs of Natal according to their living conditions and establish if there was any correlation between this classification and the incidence rate for dengue fever, with the aim of enabling public health planners to better control this disease. Data on population density, access to safe drinking water, rubbish collection, sewage disposal facilities, income level, education and the incidence of dengue fever during the years 2001 and 2003 was drawn from the Brazilian Demographic Census 2000 and from the Reportable Disease Notification System -SINAN. The study is presented here in the form of two papers, corresponding to the types of analysis performed: a classification of the urban districts into quartiles according to the living conditions which exist there, in the first article; and the incidence of dengue fever in each of these quartiles, in the second. By applying factorial analysis to the chosen socioeconomic and environmental indicators for the year 2000, a compound index of living condition (ICV) was obtained. On the basis of this index, it was possible to classify the urban districts into quartiles. On undertaking this grouping (paper 1), a heterogeneous distribution of living conditions was found across the city. As to the incidence rate for dengue fever (paper 2), it was discovered that the quartile identified as having the best living conditions presented incidence rates of 15.62 and 15.24 per 1000 inhabitants respectively in the years 2001 and 2003; whereas the quartile representing worst living conditions showed incidence rates of 25.10 and 10.32 for the comparable periods. The results suggest that dengue fever occurs in all social classes, and that its incidence is not related in any evident way to the chosen formula for living conditions / Este estudo partiu da hip?tese de que as varia??es espaciais na magnitude da morbidade por dengue em Natal s?o influenciadas pelas desigualdades intra-urbanas de fatores socioecon?micos e ambientais. Visando identificar as diferen?as intr?nsecas de cada um deles, no sentido de fornecer subs?dios para as decis?es dos gestores p?blicos no que se refere ao controle da dengue, esta disserta??o teve como objetivo caracterizar os bairros da cidade do Natal de acordo com as condi??es de vida. Foram utilizados dados populacionais sobre densidade demogr?fica, abastecimento de ?gua, coleta de lixo, esgotamento sanit?rio, renda, escolaridade e incid?ncia da dengue no per?odo de 2001 e 2003. Esses dados foram provenientes do Censo Demogr?fico 2000 e do Sistema de Informa??es de Agravos Notific?veis -SINAN. O estudo foi apresentado sob a forma de dois artigos, de acordo com o recorte espacial utilizado: quatro estratos de condi??o de vida, no primeiro; e incid?ncia da dengue em cada estrato de condi??o de vida, no segundo. A partir dos indicadores referentes ?s condi??es socioecon?micas e ambientais obteve-se, por meio de an?lise fatorial, um indicador sint?tico da condi??o de vida (ICV). Com base no ICV, os bairros foram agrupados, para a constitui??o de quatro estratos de condi??o de vida. Na categoriza??o dos bairros segundo condi??es de vida (artigo 1) identificou-se uma distribui??o heterog?nea entre os bairros. Nos estratos (artigo 2), o de melhor condi??o de vida apresentou coeficiente de incid?ncia igual a 15,62 e 15,24 nos per?odos de 2001 e 2003, para cada 1000 habitantes, respectivamente; o estrato de pior condi??o de vida mostrou os coeficientes de incid?ncia de 25,10 e 10,32 para os per?odos de 2001 e 2003, respectivamente. Os resultados sugerem que este agravo ocorre em todas as classes sociais, a sua incid?ncia n?o est? relacionada de alguma forma evidente com o ICV

The general objectives of this thesis were: (i) to determine the etiological and molecular profile of clinical mastitis (CM) in 20 dairy herds of Southeast, Brazil; and (ii) to quantify antimicrobial used for treatment of CM in the study population. To achieve this goals, four studies were performed. In the Study 1, we characterized the pathogen frequency and severity of CM in dairy herds. In addition, we determined the incidence rate of clinical mastitis (IRCM) and its association with the following herd-level descriptors: bulk milk somatic cell count (BMSCC), bulk milk total bacterial count (BMTBC), herd size (number of lactating cows), milk yield, housing system and season. The association between herd-level descriptors and IRCM were determined by two groups of mixed regression models: one based on the overall IRCM, and five based on the following specific-pathogen groups: contagious, other Gram-positive, Gram-negative, other (composed of yeast and Prototheca spp), and negative culture. A total of 5,957 quarter-cases of CM were recorded and the most frequently isolated pathogens were Escherichia coli (6.6% of total cultures), Streptococcus uberis (6.1%), and Streptococcus agalactiae (5.9%). The majority of CM cases were mild (60.3%), while 34.1% were moderate and 5.6% severe. Overall, the IRCM was 9.7 quarter-cases per 10,000 quarter-days at risk (QDAR), and the only herd-level parameter associated with overall IRCM was BMSCC, in which the highest IRCM was observed for herds with BMSCC >600.000 × 103 cells/mL. In the models evaluating the specific-pathogen groups, IRCM with isolation of major contagious pathogens was associated with BMSCC, milk yield and housing system. For the evaluation of other Gram-positive pathogens, the IRCM was higher in the rainy season of 2015 in comparison with the other seasonal categories. In addition, for the model evaluating the Gram-negative group, the IRCM was highest in herds with BMTBC >30 × 103 cfu/mL. The Study 2 aimed to characterize the treatment profile and quantify the antimicrobial consumption for treatment of CM in dairy herds; and to determine the association of antimicrobial use (AMU) and the same herd-level descriptors as described in the Study 1. Data on treatment practices and AMU were obtained from 19 dairy herds for a period of 12 months per herd. The AMU for treatment of CM was quantified monthly in units of defined daily dose (DDD) and expressed as antimicrobial treatment incidence (ATI; number of DDD per 1,000 lactating cows-day). The overall monthly mean ATI was 17.7 DDD per 1,000 lactating cow-days (15.4 for intramammary compounds, and 2.2 for systematically administered antimicrobials). Among intramammary drugs, aminoglycosides had the highest ATI (11.7 DDD per 1,000 lactating cow-days), while for systematically administrated antimicrobials, fluoroquinolones (4.2 DDD per 1,000 lactating cow-days) were the most frequently used antimicrobials. Herd size and BMSCC were positively associated with ATI. In addition, herd-level ATI was higher in freestall herds than in compost bedded-pack barns. In the Study 3, we determined the phylogeny of E. coli strains isolated from CM in dairy cows and the association of most frequent phylogroups with antimicrobial susceptibility. A total of 100 E. coli isolates recovered from CM cases described in the Study 1 were categorized according to their phylogenetic group using a quadruplex PCR method; antimicrobial susceptibility pattern was also evaluated. Most isolates were assigned to phylogenetic group A (52%), followed by B1 (38%), B2 (2%), C (4%), D (3%), and E (1%). Resistant isolates were observed for all evaluated antimicrobials. Overall, more than 96% of E. coli isolates were resistant to ampicillin, and more than 23% were resistant to cephalothin, sulphadimethoxine or tetracycline. High levels of resistance (>70%) were also found to erythromycin, oxacillin, penicillin, penicillin associated with novobiocin, and pirlimycin. In contrary, high susceptibility was observed to ceftiofur (96.8%) among E. coli isolates. Difference in the antimicrobial susceptibility among phylogenetic groups was observed only for cephalothin, in which E. coli strains belonging to the phylogroup A were inhibited at lower antimicrobial concentrations than strains assigned to the phylogroup B1. In Study 4, we evaluated the genotypic diversity among Strep. agalactiae and Strep. uberis isolates recovered from CM in dairy cows; in addition, the study evaluated the association of genotypes clustered by genetic similarity with antimicrobial susceptibility pattern. Isolates were subtyped using randomly amplified polymorphic DNA (RAPD) analysis. A great genotypic diversity was found for both Strep. agalactiae (45 subtypes out of 89 isolates) and Strep. uberis (56 subtypes out of 88 isolates). For evaluation of antimicrobial susceptibility, subtypes of Strep. agalactiae were clustered into three groups (Ia, Ib and II), while Strep. uberis subtypes were clustered into two groups (I and II) according to their genetic similarity. Overall, Strep. agalactiae isolates showed high susceptibility to most antimicrobials, except to tetracycline and erythromycin. Differences in the antimicrobial susceptibility among clusters of Strep. agalactiae were observed for ampicillin, ceftiofur, erythromycin, pirlimycin, sulphadimethoxine and tetracycline. In contrary, Strep. uberis isolates were categorized as resistant to most antimicrobials, except to cephalothin and penicillin+novobiocin. No differences were observed among clusters for all antimicrobials in the analysis of Strep. uberis. In conclusion, the results of this thesis indicated a high IRCM in the evaluated herds, and although environmental pathogens were the most common cause of CM in these herds, contagious pathogens such as Strep. agalactiae and Staph. aureus, are still a concern in some dairy herds of Brazil. Furthermore, high frequencies of AMU and off-label protocols were observed among the evaluated herds. The non-judicious use of antimicrobials can become a risk factor for the development of antimicrobial resistance, which was even observed for isolates belonging to the three most prevalent bacterial species identified from CM cases in our study (E. coli, Strep. agalactiae and Strep. uberis). Finally, because there were some herd-level descriptors associated with the IRCM and AMU in our study, there may be opportunity for management strategies aiming to improve the control of CM in dairy herds of southeastern Brazil. / Os objetivos gerais desta tese foram: (i) determinar o perfil etiológico e molecular da mastite clínica (MC) em 20 rebanhos leiteiros do Sudeste do Brasil; e, (ii) quantificar os antimicrobianos usados para tratamento da MC na população estudada. Para alcançar esses objetivos, quatro estudos foram realizados. No Estudo 1, foi caracterizada a frequência de patógenos causadores de MC e a gravidade das infecções nos rebanhos leiteiros. Além disso, foi determinada a taxa de incidência de mastite clínica (TIMC) e sua associação com as seguintes variáveis em nível de rebanho: contagem de células somáticas em leite de tanque (CCSLT), contagem bacteriana total em leite de tanque (CBTLT), tamanho (número de vacas em lactação), produção de leite, sistema de alojamento e estação do ano. A associação entre as variáveis em nível de rebanho e a TIMC foi determinada por dois grupos de modelos de regressão logística multivariada: um baseado na TIMC geral, e cinco baseados nos seguintes grupos específicos de patógenos: contagiosos, outros Gram-positivos, Gram-negativos, outros patógenos (composto de leveduras e Prototheca spp.), e cultura negativa. Um total de 5.957 casos de MC em nível de quarto mamário foi registrado e os patógenos mais prevalentes foram Escherichia coli (6,6% de todas as culturas), Streptococcus uberis (6,1%), e Streptococcus agalactiae (5,9%). A maioria dos casos de MC foi de gravidade leve (60,3%), enquanto 34,1% dos casos foram moderados e 5,6% foram graves. A TIMC geral foi de 9,7 casos por 10.000 quartos-dia em risco (QDR), e o único parâmetro em nível de rebanho associado com a TIMC geral foi a CCSLT, em que a TIMC mais alta foi observada em rebanhos com CCSLT >600.000 × 103 células/mL. Nos modelos que avaliaram os grupos específicos de patógenos, a TIMC de patógenos contagiosos foi associada com a CCSLT, produção de leite e sistema de alojamento. Na avaliação de outros patógenos Gram-positivos, a TIMC foi maior na estação chuvosa de 2015 em comparação com as outras categorias referentes à estação do ano. Adicionalmente, para o modelo avaliando o grupo de patógenos Gram-negativos, a TIMC foi mais alta em rebanhos com CBTLT >30.000 × 103 ufc/mL. O Estudo 2 teve como objetivo caracterizar o perfil de tratamento e o consumo de antimicrobianos em rebanhos leiteiros; e determinar a associação de uso de antimicrobianos (UAM) e as mesmas variáveis em nível de rebanho descritas no Estudo 1. Dados sobre as práticas terapêuticas e UAM foram obtidos de 19 rebanhos leiteiros durante um período de 12 meses por rebanho. A frequência de UAM para tratamento da MC foi quantificada mensalmente em unidades de doses definidas diárias (DDD) e expressa como incidência de tratamento antimicrobiano (ITA: número de DDD por 1.000 vacas em lactação-dia). A média de ITA mensal foi de 17,7 DDD por 1.000 vacas em lactação-dia (15,4 para compostos intramamários, e 2,2 para compostos sistêmicos). Entre os produtos intramamários, os aminoglicosídeos tiveram a ITA mais alta (11,7 DDD por 1.000 vacas em lactação-dia), enquanto que para os compostos administrados pela via sistêmica, as fluoroquinolonas (4,2 DDD por 1.000 vacas em lactação-dia) foram os antimicrobianos mais frequentemente usados. O tamanho do rebanho e CCSLT foram positivamente associados com a ITA. Além disso, a ITA foi mais alta em rebanhos com freestall do que em rebanhos com sistema tipo compost barn. No Estudo 3, determinou-se a filogenia de cepas de E. coli isoladas de casos de MC em vacas leiteiras, e a associação dos filogrupos mais frequentes com a susceptibilidade aos antimicrobianos. Um total de 100 isolados de E. coli identificados nos casos de MC descritos no Estudo 1 foram categorizados de acordo com os grupos filogenéticos por meio de um método de PCR quadruplex; o perfil de susceptibilidade aos antimicrobianos também foi avaliado. A maioria dos isolados pertenceram ao grupo A (52%), seguido dos grupos B1 (38%), B2 (2%), C (4%), D (3%), e E (1%). Foram encontrados isolados resistentes para todos os antimicrobianos avaliados. De forma geral, mais de 96% dos isolados de E. coli foram resistentes a ampicilina, e mais de 23% foram resistentes a cefalotina, sulfadimetoxina ou tetraciclina. Altos níveis de resistência (>70%) foram encontrados também para eritromicina, oxacilina, penicilina e penicilina associada a novobiocina. Ao contrário, foi observado alta susceptibilidade ao ceftiofur (96.8%) entre os isolados de E. coli. Diferenças na susceptibilidade entre os grupos filogenéticos foi observada apenas para a cefalotina, em que os isolados de E. coli pertencentes ao filogrupo A foram inibidos em concentrações de antimicrobianas mais baixas que isolados pertencentes ao filogrupo B1. No Estudo 4, avaliou-se a diversidade genotípica entre isolados de Strep. agalactiae e Strep. uberis identificados em casos de MC em vacas leiteiras; adicionalmente, o estudo avaliou a associação dos genótipos agrupados de acordo com a similaridade genética com o perfil de susceptibilidade aos antimicrobianos. Os isolados foram genotipados por meio do método de amplificação randômica de DNA polimórfico (RAPD). Grande diversidade genotípica foi observada tanto para o Strep. agalactiae (45 subtipos de 89 isolados) quanto para Strep. uberis (56 subtipos de 89 isolados). Para a avaliação de susceptibilidade aos antimicrobianos, os subtipos de Strep. agalactiae foram agrupados em três clusters (Ia, Ib e II), enquanto que os subtipos de Strep. uberis foram agrupados em dois clusters (I e II) de acordo com a similaridade genética. De forma geral, os isolados de Strep. agalactiae apresentaram alta susceptibilidade à maioria dos antimicrobianos, exceto para tetraciclina e eritromicina. Diferenças na susceptibilidade aos antimicrobianos entre os clusters de Strep. agalactiae foram observadas para ampicilina, ceftiofur, eritromicina, pirlimicina, sulfadimetoxina e tetraciclina. Por outro lado, os isolados de Strep. uberis foram resistentes à maioria dos antimicrobianos, exceto para cefalotina e penicilina + novobiocina. Não foram encontradas diferenças entre os clusters para todos os antimicrobianos na análise de Strep. uberis. Em conclusão, os resultados desta tese indicaram alta TIMC nos rebanhos avaliados, e apesar de os patógenos ambientais serem a causa mais comum de MC nestes rebanhos, patógenos contagiosos como Strep. agalactiae e Staph. aureus, ainda são uma preocupação em alguns rebanhos do Brasil. Além disso, observaram-se altas frequências de UAM e de terapias não recomendadas em bula entre os rebanhos avaliados. O uso não judicioso de antimicrobianos pode se tornar um fator de risco para o desenvolvimento da resistência bacteriana aos antimicrobianos, o que foi inclusive observado para isolados pertencentes as três espécies bacterianas mais prevalentes nos casos de MC no nosso estudo (E. coli, Strep. agalactiae e Strep. uberis). Finalmente, pelo fato de algumas variáveis em nível de rebanho terem sido associadas com a TIMC e com o UAM em nosso estudo, é possível que hajam oportunidades para implementação de estratégias de manejo com o objetivo de melhorar o controle da MC em rebanhos leiteiros do sudeste do Brasil.

Type 2 diabetes mellitus (T2DM) is a global health concern affecting six to ten percent of adults worldwide, with the number of diagnosed individuals projected to double in the next 25 years. However, effective public health strategies can help mitigate its impact on quality of life, morbidity, mortality and the associated social and economic burden. In Sweden, screening programs classify individuals with Impaired fasting glucose (IFG) as high-risk individuals, however scholars argue that preventative measures should also include those at lower fasting plasma glucose (FPG) concentrations as some evidence suggests that the disease begins developing several years before the current threshold. A systematic review and meta-analysis of cohort and case-control studies was conducted with the purpose of exploring the IFG threshold from a longitudinal perspective. Eleven studies on the incidence rates of T2DM in normoglycemic individuals and seven studies on pre-diagnostic trajectories of FPG concentrations were analyzed. Incidence rates increased progressively across the normoglycemic range, with a twelve-fold difference between the low and high end. FPG concentrations in eventual progressors and non-diabetic controls were significantly different twelve years prior to diagnosis. Mildly increasing FPG concentrations were observed in eventual progressors until three years before diagnosis, at which a larger increase followed. In the last year before diagnosis, FPG concentrations rose dramatically in this group. The findings imply that a lowering of the threshold would extend the time spent in the IFG state, allowing for earlier identification of at-risk individuals. In addition, further stratification of normoglycemic range may enhance the accuracy of T2DM risk assessment.

Background Type 2 diabetes mellitus (T2DM) is a globally prevalent disease anticipated to double from 500 million diagnosed cases in 2021 to more than one billion by 2050. The investigation of the potentially protective effects of skeletal muscle mass on glucose intolerance may lead to the development of more precise screening protocols. Purpose This thesis aimed to address the lack of clear consensus in existing literature by exploring the relationship between skeletal muscle mass and glucose intolerance. Methods Fifteen healthy young adults were recruited to partake in a prospective correlational study. The participants underwent anthropometric measurements and an oral glucose tolerance test (OGTT). Anthropometric data was collected using an bioelectrical impedance analysis (BIA) scale. Blood glucose levels were measured using capillary sampling before and after ingestion of a 75 g/200 mL glucose solution. Statistical analysis included Spearman’s rank correlation test and Pearson’s correlation coefficient test. Results All associations between skeletal muscle mass and glucose intolerance were of moderate strength. Skeletal muscle mass (SMM) correlated significantly with glucose concentrations two hours (2hPG) following ingestion of the glucose solution and an adjusted measure of SMM was significantly associated with glucose area under the curve (AUC). Statistical significance was also found between Sex and incremental glucose area under the curve (iAUC). Conclusion This thesis suggests an inverse relationship between skeletal muscle mass and glucose intolerance in a group of healthy young adults. The results imply the potential usefulness of incorporating muscle mass when determining the glucose load during an OGTT, especially in preventive contexts. Nevertheless, further research with larger samples is crucial to establish precise cutoff levels for clinical applications. / Bakgrund Typ 2 diabetes mellitus (T2DM) är en global sjukdom och antalet diagnostiserade individer förväntas fördubblas från 500 miljoner fall 2021 till över en miljard år 2050. En undersökning av muskelmassans potentiellt skyddande effekt på glukostolerans kan leda till utvecklandet av noggrannare screeningmetoder. Syfte Syftet med denna uppsats var att bemöta den bristfälliga konsensus som råder bland befintlig forskning genom att undersöka sambandet mellan muskelmassa och glukosintolerans. Metod Femton friska yngre vuxna rekryterades för att delta i en prospektiv korrelationsstudie. Deltagarna genomgick antropometriska mätningar samt ett oralt glukostoleranstest (OGTT). Antropometriska värden mättes med hjälp av en bioelektrisk impedansanalysvåg. Blodglukosnivåer mättes kapillärt före och efter intag av en 75 g/200 mL glukoslösning. Statistisk analys inkluderade Spearmans rangkorrelationstest och Pearsons korrelationskoefficientstest. Resultat Alla samband mellan muskelmassa och glukosintolerans var av måttlig styrka. Muskelmassa (SMM) korrelerade signifikant med blodglukos två timmar (2hPG) efter intag av glukoslösningen och ett justerat SMM-mått (adjSMM) var signifikant associerat med arean under glukoskurvan (AUC). Statistisk signifikans hittades även mellan kön och den inkrementella arean under glukoskurvan (iAUC). Slutsats Resultaten från denna uppsats antyder att ett omvänt samband existerar mellan muskelmassa och glukosintolerans hos en grupp friska yngre vuxna. Resultatet innebär en potentiell möjlighet att använda muskelmassan vid bestämmandet av mängden glukos som administreras vid ett OGTT, särskilt i preventiva syften. Ytterligare forskning med fler studiedeltagare är avgörande för att fastställa exakta gränsvärden för klinisk tillämpning.

科学研究費補助金 研究種目:一般研究(B) 課題番号:06454236 研究代表者:豊嶋 英明 研究期間:1994-1995年度

L’hémolyse post transfusionnelle retardée (HPTR) est une complication potentiellement mortelle de la transfusion dans la drépanocytose. Sa fréquence est sous-estimée et aucun facteur prédictif de sa survenue n'a encore été identifié. Nous avons mené une étude observationnelle prospective monocentrique pendant 30 mois. Nous avons inclus 694 épisodes transfusionnels (ET) chez 311 patients adultes drépanocytaires, divisés en ET ponctuels (ETP = 360) et en ET chronique dans le cadre d’un programme (ETC = 334). Au cours de ce suivi, 15 HPTR ont été enregistrées, exclusivement après un ETP. L'incidence cumulative d’HPTR sur 30 mois était de 4,2 % par ETP (IC 95 % [2,6 ; 6,9]) ou 6,8 % par drépanocytaires transfusés (IC 95 % [4,2 ; 11,3]). Le taux d’incidence était de 16,4 HPTR pour 1000 ETP-Année (IC 95 % [10,1 ; 27,2]) ou 27,1 HPTR pour 1000 Personne-Année (IC 95% [16,7 ; 45,0]). Nous avons étudié 11 HPTR supplémentaires afin de construire un score prédictif. Les variables retenues étaient un antécédent d’HPTR, le nombre d’unités précédemment transfusées et l’état d’immunisation pré transfusionnelle. L’adéquation du score était excellente (Hosmer-Lemeshow = 1,40, p = 0,71), sa capacité discriminante très satisfaisante (aire sous la courbe ROC = 0,85 ; p < 0,0001), une valeur prédictive négative de 98,4 % et une valeur prédictive positive de 50,0 %. La validité interne du score, réalisée par Bootstrap, montre une très bonne performance. Nous rapportons, pour la première fois, l'incidence de l’HPTR et montrons qu’elle survient uniquement suite à un ETP. Nous décrivons également un score simple de prédiction d’une HPTR avant un ETP afin de mieux prendre en charge cette transfusion. / Delayed hemolytic transfusion reaction (DHTR) is a life-threatening complication of transfusion in sickle cell disease (SCD). The frequency of DHTR is underestimated and no predictive fac-tors for identifying patients likely to develop DHTR have yet been defined. We conducted a prospective single-center observational study over 30 months. We included 694 transfusion epi-sodes (TE) in 311 adult patients, divided into occasional TE (OTE: 360) and TE during a chronic program (CTE: 334). During follow-up, 15 cases of DHTR were recorded, exclusively after OTEs. DHTR cumulative incidence durin the 30 months was 4.2% per OTE (95% CI [2.6, 6.9]) or 66.8% per patient (95% CI [4.2, 11.3]). The incidence rate was 16.4 DHTR per 1000 OTE-Year (95% CI [10.1, 27.2]) or 27.1 DHTR per 1000 Person-Year (95% CI [16.7 ; 45.0]). We studied 11 additional DHTR cases to construct a score for predicting DHTR after OTE. Fifteen % of the 26 DHTR patients died. The variables retained in the multivariate model were history of DHTR, number of units previously transfused and immunization status before transfusion. The score adequacy was excellent (Hosmer-Lemeshow = 1.40, p = 0.71), very satisfactory dis-criminant capacity (area under ROC curve = 0.85, p <0.0001), negative-predictive value of 98.4% and a positive-predictive value of 50%. The internal validity of the score, realized by Bootstrap, shows a very good performance.We report, for the first time, the incidence of DHTR and we show that DHTR developed only in OTE. We also describe a simple score for predicting DHTR in patients undergoing occasional transfusion, to facilitate the management of blood transfusion in SCD patients.

According to Oliver (2002) the success of turnaround maintenances is measured in terms of the cost of completion, time, safety performance and the performance of the plant afterwards. The Escravos gas plant (EGP) is a gas processing plant that converts associated gas from Chevron owned crude oil wells to liquefied petroleum gas, natural gas and gas condensate (Chevron intranet. Website assessed on September 14, 2007). According to the EGP plant operations coordinator (See interview Appendix A), the plant undergoes a turnaround maintenance exercise once every two years. The major tasks done during these turnaround maintenances are 1. Change–out of three molecular sieve beds. 2. Servicing of three compressor turbines. 3. Servicing of expander turbo–machinery. 4. Clean–out of fired gas heater tubes and burners. 5. Tie–ins for major upgrades. The EGP management does not involve the contractor personnel that carry out the tasks in the management of the turnaround maintenance. The contractor’s personnel simply follow the work plans and instructions developed by the EGP management. The EGP turnaround management team consists of the coordinator who is the head of the turnaround maintenance team, shift supervisors, maintenance supervisors (rotating equipment maintenance supervisor, instrumentation and electrical maintenance supervisor, and static equipment maintenance supervisors), safety supervisors, maintenance planners, process engineers and construction supervisors. All these listed personnel in the preceding paragraph and the supervisors of the contractor teams participate in the pre–turnaround meetings which happen once a month for the first 10 months of the 12 months leading to the turnaround. The meeting frequency increases to once every two weeks during the last two months leading to the turnaround maintenance. The meeting is held daily during the turnaround maintenance and once every two weeks for the first month after the turnaround maintenance. During the preceding months to the turnaround maintenance, the work scope is defined, the job sequence outlined and schedules are developed. Resources requirements are detailed and procured. During the turnaround maintenance the focus of the turnaround meeting is to discuss potential deviations, observe at–risk behaviors and likely challenges. Plans are then made to address these deviations, challenges and at–risk behaviors. After the turnaround maintenance, “lessons learnt” are captured and the turnaround maintenance is closed out. According to the EGP coordinator (see interview in appendix A), the success of its turnaround maintenance is measured by the time used to complete the turnaround maintenance, the total recordable incident rate during the turnaround maintenance, the days away from work, the lost time injury(LTI) and the cost incurred. Poling et al noted that it is difficult to rate turnaround maintenance projects because no two turnaround maintenances strategies are exactly the same. They iterated that the most common tactics used is benchmarking and that benchmarking enables a company to measure and compare its performance against peer companies in a constructive and confidential manner. They pointed out that the quantitative differences computed between a plant and other similar plants using detailed data taxonomy can provide invaluable information regarding improvement opportunities. This is a way of effectively extending a “lessons learned” exercise across multiple companies. According to then however a critical attribute of effective reliability and maintenance benchmarking is the ability to compare disparate assets; but even small differences for similar plants can alter the value of the comparison. Existing literature indicate that the parameters the gas plant management use to rate the safety of its turnaround maintenance (i.e. the total recordable incident rate, the days away from work and the lost time injury)are reactive in nature. They are otherwise called lagging indicators. Lagging indicators are safety performance metrics that are recorded after the accident or incidents has occurred. For example lost time injury is any work related injury or illness which prevents that person from doing any work day after accident (E&P Consultancy Associates. Website assessed on June 15, 2009). In contrast the other group of metrics called pro–active metrics or leading indicators such as at–risk behaviors, near misses and preventive maintenance not completed are parameters that measure safety performance before accident occurs. Leading indicators gained popularity in the 1930’s after Heinrich postulate his iceberg theory (Wright, 2004). Heinrich’s used the iceberg analogy to explain reactive (lagging) and proactive (leading) indicators. Heinrich likened accident and at–risk behaviors to two parts of an Iceberg; the part you see above water and the part hidden under the water. The size of the iceberg above water is relatively small compared to that under water. The iceberg starts to grow under the water and only after they reach a certain size does part of the ice begin to appear above water. Heinrich believed that accidents are the result of root causes such as at–risk behaviors, inconsistencies, wrong policies, lack of training and lack of information. When the number of accidents that occur in an endeavor is measured you get relatively smaller numerical quantities when compared to the number of at–risk behaviors. Heinrich suggested that to eliminate accidents that occur infrequently, organizations must make effort to eliminate the root causes which occur very frequently. This makes sense because imagine a member of personnel coming to work intoxicated every day. Binging intoxicated at work is an at–risk behavior. The employee is very likely to be involved in an accident at some time as a result of his drinking habit. The number of times he is intoxicated if counted will be huge when compared to the impact of the accident when it does occur. The iceberg theory is supported by work from Bird (1980) and Ludwig (1980) who both attempted to establish the correct ratio of accidents to root causes in different industries. Heinrich suggested a ratio of three hundred incidents to twenty nine minor injuries to one major injury. This researcher chose to use the number of at–risk behavior exhibited by the turnaround maintenance teams to rate the safety performance of tasks despite criticism from individuals like Robotham (2004) who said that from his experience minor incidents do not have the potential to become major accidents and Wright et al (2004). Leading indicators are convenient to analysis because of their relative large quantity. In a turnaround environment, the numbers of accidents that occur are relatively few unlike the number of near misses (Bird, 1980). It is easy to statistically analyze thirty at–risk behaviors than four accidents. In addition Fleming et al (2001) noted that data from industry show much success by companies in the reduction of accidents by efforts at reducing the number of at–risk behaviors, increase the number of safety audits, and reduce the number of closed items from audits etc. Phimister et al made similar claims when they said Near miss programs improve corporate environmental, health and safety performance through the identification of near misses. Existing literature also reveals many theories about management styles and their possible impact on performance. The theories are grouped into trait theories, situational theories and behavioral theories. The trait theories tries to explain management styles by traits of the managers like initiative, wisdom, compassion and ambitious. Situational theories suggest that there is no best management style and managers will need to determine which management style best suit the situation. Behavioral theories explain management success by what successful managers do. Behavioral theorists identify autocratic, benevolent, consultative and participatory management styles. Vroom and Yetton (1973) identified variables that will determine the best management style for any given situation. The variables are; 1. Nature of the problem. Is it simple, hard, complex or clear? 2. Requirements for accuracy. What is the consequence of mistakes? 3. Acceptance of an initiative. Do you want people to use their initiative or not? 4. Time–constraints. How much time do we have to finish the task? 5. Cost constraints. Do we have enough or excess to achieve the objective? A decision model was developed by Vroom and Yago (1988)to help managers determine the best management style for different situations based on the variables listed above (See figure six). They also defined five management style could adopt, namely the; 1. Autocratic I style 2. Autocratic II style. 3. Consultative I style 4. Consultative II style 5. Group II style The autocratic I management style is a management style where the leader solves the problem alone using information that is readily available to him/her, is the normal management style of the Escravos gas plant management in all turnarounds prior to 2009. However the Vroom and Yago model recommends the Consultative II management style for the type of work done during the Escravos gas plant turnaround maintenance. According to Coye et al (1995), participatory management or consultative style II creates a sense of ownership in organization. In this management style the leader shares problem with group members individually, and asks for information and evaluation. Group members do not meet collectively, and leader makes decision alone (Vroom and Yago, 1988). Coye et al believe that this management styles instills a sense of pride and motivate employees to increase productivity. In addition they stated that employees who participate in the decisions of the organization feel like they are a part of a team with a common goal, and find their sense of self–esteem and creative fulfillment heightened. According to Filley et al (1961), Spector and Suttle did not find any significant difference in the output of employees under autocratic and participatory management style. This research studies if and how the Escravos gas plant turnaround maintenance can be improved by changing the management style from autocratic I style to consultative II style. Two tasks in the turnaround were studied; namely the change out of the molecular sieve catalyst beds and the servicing of the turbine engines. The turnaround contractor Techint Nigeria Limited divides the work group into teams responsible for specific tasks. Six teams (team A, B, C, D, E and F) were studied. EGP management will not allow the researcher to study more than these six teams for fear of the research disrupting the work. The tasks completed by these teams are amongst those not on the projects critical path so delays caused by the research will not impact the entire turnaround project provided the float on these activities were not exceeded. They also had the fewest number of personnel, so cost impact of the research work could be easier to manager. Teams A, B and C are different maintenance teams comprising of eight personnel each. They were responsible for changing the EGP molecular sieve beds A, B and C respectively in the 2007 and 2009 turnaround. Their tasks are identical because the molecular sieve beds are identical. Teams E, D and F are also maintenance teams comprising of six personnel each. They were responsible for servicing the EGP turbine engines A, B and C during the 2007 and 2009 turnaround maintenance. Their tasks are also identical because the turbine engines are identical. Consultative management style II is exercised by involving team A and team D in the development of the procedures, processes and job safety analysis of all tasks that they were assigned to complete during the 2009 turnaround maintenance. They were also permitted to participate in the turnaround maintenance meetings and to make contributions in the meetings. In the 2007 turnaround maintenance team A and team D only carried out their tasks. They did not participate in the development of procedures and job safety analysis neither did they participate in the turnaround maintenance meetings. The other four teams; team B, team C, team E and team F are used as experimental controls for the research. They did not participate in the development of the procedures, processes nor the job safety analysis for the tasks in either of the turnaround maintenance. They were also not permitted to attend the daily turnaround meetings. They only completed their tasks based on instructions given to them during the 2007 and 2009 turnaround maintenance. It was necessary to study the experimental control teams as the researcher was not sure whether task repetition, increased knowledge or improved team cohesion would lead to a reduced time or a reduced numbers of at–risk behavior. ix The research tested the hypothesis 1H0 and 1H1 and 2H0and 2H1 at the 0.025 and 0.05 level of significance as follows; Null hypothesis, 1H0: There is no significant difference in the time spent by team A and team Din 2007 when they did not participate in the development of the procedures and processes with the time in 2009 when they did(u1-u2=0). Alternate hypothesis, 1H1: There is a significant difference in the time spent by the team A and Din 2007 when they did not participate in the development of the procedures and processes with the time in 2009 when they did (u1-u2!=0). Null hypothesis, 2H0: There is no significant difference in the number of at–risk behaviors observed to have been exhibited by the team A and team D in 2007 when they did not participate in the development of the procedures and processes with the number in 2009 when they did (u1-u2=0). Alternate hypothesis, 2H1: There is a significant difference in the number of at–risk behaviors observed to have been exhibited by the team A and team D in 2007 when they did not participate in the development of the procedures and processes with the number in 2009 when they did (u1-u2!=0). The student t test was used to analyze these times and number of at–risk behavior. At the 0.025 and the 0.05 level of significance, the data show that there is no difference in the times all the teams used to complete their task in 2007 and in 2009. The researcher concludes that a change in the management style from autocratic I style to consultative II style did not lead to a reduction in the time used by any team to complete their task. However at the 0.025 and the 0.05 level of significance, there is a significant difference in the number of at–risk behaviors of the research team A and team D. There is however no significant difference in the number of at–risk behavior of the control team B, team C, team E and team F at the same level of significance. The researcher concludes that a change in the management style from autocratic I style to consultative II style lead to a reduction in the number of at–risk behavior of team A and team D. In addition the reduction in the number of at–risk behavior of team A and team D could not have been because of task repetition, increased knowledge or improved team cohesion since there is no significant difference in the number of at–risk behavior exhibited by team B, team C, team E and team F. The research can be used by the Escravos gas plant management and the management of any similar process plant to fashion out more cost effective, time effective and safer methods for carrying out their turnaround maintenance. A change in management styles may just be a better approach to improving productivity than giving financial incentives to contractors and personnel. Changes in management style will have to be managed. The change must be gradual because sudden change can be detrimental as people may just need to understand and adapt to the change. The turnaround personnel must also understand the intent so as to prevent conflicts. / Thesis (M.Ing. (Development and Management Engineering))--North-West University, Potchefstroom Campus, 2012.

According to Oliver (2002) the success of turnaround maintenances is measured in terms of the cost of completion, time, safety performance and the performance of the plant afterwards. The Escravos gas plant (EGP) is a gas processing plant that converts associated gas from Chevron owned crude oil wells to liquefied petroleum gas, natural gas and gas condensate (Chevron intranet. Website assessed on September 14, 2007). According to the EGP plant operations coordinator (See interview Appendix A), the plant undergoes a turnaround maintenance exercise once every two years. The major tasks done during these turnaround maintenances are 1. Change–out of three molecular sieve beds. 2. Servicing of three compressor turbines. 3. Servicing of expander turbo–machinery. 4. Clean–out of fired gas heater tubes and burners. 5. Tie–ins for major upgrades. The EGP management does not involve the contractor personnel that carry out the tasks in the management of the turnaround maintenance. The contractor’s personnel simply follow the work plans and instructions developed by the EGP management. The EGP turnaround management team consists of the coordinator who is the head of the turnaround maintenance team, shift supervisors, maintenance supervisors (rotating equipment maintenance supervisor, instrumentation and electrical maintenance supervisor, and static equipment maintenance supervisors), safety supervisors, maintenance planners, process engineers and construction supervisors. All these listed personnel in the preceding paragraph and the supervisors of the contractor teams participate in the pre–turnaround meetings which happen once a month for the first 10 months of the 12 months leading to the turnaround. The meeting frequency increases to once every two weeks during the last two months leading to the turnaround maintenance. The meeting is held daily during the turnaround maintenance and once every two weeks for the first month after the turnaround maintenance. During the preceding months to the turnaround maintenance, the work scope is defined, the job sequence outlined and schedules are developed. Resources requirements are detailed and procured. During the turnaround maintenance the focus of the turnaround meeting is to discuss potential deviations, observe at–risk behaviors and likely challenges. Plans are then made to address these deviations, challenges and at–risk behaviors. After the turnaround maintenance, “lessons learnt” are captured and the turnaround maintenance is closed out. According to the EGP coordinator (see interview in appendix A), the success of its turnaround maintenance is measured by the time used to complete the turnaround maintenance, the total recordable incident rate during the turnaround maintenance, the days away from work, the lost time injury(LTI) and the cost incurred. Poling et al noted that it is difficult to rate turnaround maintenance projects because no two turnaround maintenances strategies are exactly the same. They iterated that the most common tactics used is benchmarking and that benchmarking enables a company to measure and compare its performance against peer companies in a constructive and confidential manner. They pointed out that the quantitative differences computed between a plant and other similar plants using detailed data taxonomy can provide invaluable information regarding improvement opportunities. This is a way of effectively extending a “lessons learned” exercise across multiple companies. According to then however a critical attribute of effective reliability and maintenance benchmarking is the ability to compare disparate assets; but even small differences for similar plants can alter the value of the comparison. Existing literature indicate that the parameters the gas plant management use to rate the safety of its turnaround maintenance (i.e. the total recordable incident rate, the days away from work and the lost time injury)are reactive in nature. They are otherwise called lagging indicators. Lagging indicators are safety performance metrics that are recorded after the accident or incidents has occurred. For example lost time injury is any work related injury or illness which prevents that person from doing any work day after accident (E&P Consultancy Associates. Website assessed on June 15, 2009). In contrast the other group of metrics called pro–active metrics or leading indicators such as at–risk behaviors, near misses and preventive maintenance not completed are parameters that measure safety performance before accident occurs. Leading indicators gained popularity in the 1930’s after Heinrich postulate his iceberg theory (Wright, 2004). Heinrich’s used the iceberg analogy to explain reactive (lagging) and proactive (leading) indicators. Heinrich likened accident and at–risk behaviors to two parts of an Iceberg; the part you see above water and the part hidden under the water. The size of the iceberg above water is relatively small compared to that under water. The iceberg starts to grow under the water and only after they reach a certain size does part of the ice begin to appear above water. Heinrich believed that accidents are the result of root causes such as at–risk behaviors, inconsistencies, wrong policies, lack of training and lack of information. When the number of accidents that occur in an endeavor is measured you get relatively smaller numerical quantities when compared to the number of at–risk behaviors. Heinrich suggested that to eliminate accidents that occur infrequently, organizations must make effort to eliminate the root causes which occur very frequently. This makes sense because imagine a member of personnel coming to work intoxicated every day. Binging intoxicated at work is an at–risk behavior. The employee is very likely to be involved in an accident at some time as a result of his drinking habit. The number of times he is intoxicated if counted will be huge when compared to the impact of the accident when it does occur. The iceberg theory is supported by work from Bird (1980) and Ludwig (1980) who both attempted to establish the correct ratio of accidents to root causes in different industries. Heinrich suggested a ratio of three hundred incidents to twenty nine minor injuries to one major injury. This researcher chose to use the number of at–risk behavior exhibited by the turnaround maintenance teams to rate the safety performance of tasks despite criticism from individuals like Robotham (2004) who said that from his experience minor incidents do not have the potential to become major accidents and Wright et al (2004). Leading indicators are convenient to analysis because of their relative large quantity. In a turnaround environment, the numbers of accidents that occur are relatively few unlike the number of near misses (Bird, 1980). It is easy to statistically analyze thirty at–risk behaviors than four accidents. In addition Fleming et al (2001) noted that data from industry show much success by companies in the reduction of accidents by efforts at reducing the number of at–risk behaviors, increase the number of safety audits, and reduce the number of closed items from audits etc. Phimister et al made similar claims when they said Near miss programs improve corporate environmental, health and safety performance through the identification of near misses. Existing literature also reveals many theories about management styles and their possible impact on performance. The theories are grouped into trait theories, situational theories and behavioral theories. The trait theories tries to explain management styles by traits of the managers like initiative, wisdom, compassion and ambitious. Situational theories suggest that there is no best management style and managers will need to determine which management style best suit the situation. Behavioral theories explain management success by what successful managers do. Behavioral theorists identify autocratic, benevolent, consultative and participatory management styles. Vroom and Yetton (1973) identified variables that will determine the best management style for any given situation. The variables are; 1. Nature of the problem. Is it simple, hard, complex or clear? 2. Requirements for accuracy. What is the consequence of mistakes? 3. Acceptance of an initiative. Do you want people to use their initiative or not? 4. Time–constraints. How much time do we have to finish the task? 5. Cost constraints. Do we have enough or excess to achieve the objective? A decision model was developed by Vroom and Yago (1988)to help managers determine the best management style for different situations based on the variables listed above (See figure six). They also defined five management style could adopt, namely the; 1. Autocratic I style 2. Autocratic II style. 3. Consultative I style 4. Consultative II style 5. Group II style The autocratic I management style is a management style where the leader solves the problem alone using information that is readily available to him/her, is the normal management style of the Escravos gas plant management in all turnarounds prior to 2009. However the Vroom and Yago model recommends the Consultative II management style for the type of work done during the Escravos gas plant turnaround maintenance. According to Coye et al (1995), participatory management or consultative style II creates a sense of ownership in organization. In this management style the leader shares problem with group members individually, and asks for information and evaluation. Group members do not meet collectively, and leader makes decision alone (Vroom and Yago, 1988). Coye et al believe that this management styles instills a sense of pride and motivate employees to increase productivity. In addition they stated that employees who participate in the decisions of the organization feel like they are a part of a team with a common goal, and find their sense of self–esteem and creative fulfillment heightened. According to Filley et al (1961), Spector and Suttle did not find any significant difference in the output of employees under autocratic and participatory management style. This research studies if and how the Escravos gas plant turnaround maintenance can be improved by changing the management style from autocratic I style to consultative II style. Two tasks in the turnaround were studied; namely the change out of the molecular sieve catalyst beds and the servicing of the turbine engines. The turnaround contractor Techint Nigeria Limited divides the work group into teams responsible for specific tasks. Six teams (team A, B, C, D, E and F) were studied. EGP management will not allow the researcher to study more than these six teams for fear of the research disrupting the work. The tasks completed by these teams are amongst those not on the projects critical path so delays caused by the research will not impact the entire turnaround project provided the float on these activities were not exceeded. They also had the fewest number of personnel, so cost impact of the research work could be easier to manager. Teams A, B and C are different maintenance teams comprising of eight personnel each. They were responsible for changing the EGP molecular sieve beds A, B and C respectively in the 2007 and 2009 turnaround. Their tasks are identical because the molecular sieve beds are identical. Teams E, D and F are also maintenance teams comprising of six personnel each. They were responsible for servicing the EGP turbine engines A, B and C during the 2007 and 2009 turnaround maintenance. Their tasks are also identical because the turbine engines are identical. Consultative management style II is exercised by involving team A and team D in the development of the procedures, processes and job safety analysis of all tasks that they were assigned to complete during the 2009 turnaround maintenance. They were also permitted to participate in the turnaround maintenance meetings and to make contributions in the meetings. In the 2007 turnaround maintenance team A and team D only carried out their tasks. They did not participate in the development of procedures and job safety analysis neither did they participate in the turnaround maintenance meetings. The other four teams; team B, team C, team E and team F are used as experimental controls for the research. They did not participate in the development of the procedures, processes nor the job safety analysis for the tasks in either of the turnaround maintenance. They were also not permitted to attend the daily turnaround meetings. They only completed their tasks based on instructions given to them during the 2007 and 2009 turnaround maintenance. It was necessary to study the experimental control teams as the researcher was not sure whether task repetition, increased knowledge or improved team cohesion would lead to a reduced time or a reduced numbers of at–risk behavior. ix The research tested the hypothesis 1H0 and 1H1 and 2H0and 2H1 at the 0.025 and 0.05 level of significance as follows; Null hypothesis, 1H0: There is no significant difference in the time spent by team A and team Din 2007 when they did not participate in the development of the procedures and processes with the time in 2009 when they did(u1-u2=0). Alternate hypothesis, 1H1: There is a significant difference in the time spent by the team A and Din 2007 when they did not participate in the development of the procedures and processes with the time in 2009 when they did (u1-u2!=0). Null hypothesis, 2H0: There is no significant difference in the number of at–risk behaviors observed to have been exhibited by the team A and team D in 2007 when they did not participate in the development of the procedures and processes with the number in 2009 when they did (u1-u2=0). Alternate hypothesis, 2H1: There is a significant difference in the number of at–risk behaviors observed to have been exhibited by the team A and team D in 2007 when they did not participate in the development of the procedures and processes with the number in 2009 when they did (u1-u2!=0). The student t test was used to analyze these times and number of at–risk behavior. At the 0.025 and the 0.05 level of significance, the data show that there is no difference in the times all the teams used to complete their task in 2007 and in 2009. The researcher concludes that a change in the management style from autocratic I style to consultative II style did not lead to a reduction in the time used by any team to complete their task. However at the 0.025 and the 0.05 level of significance, there is a significant difference in the number of at–risk behaviors of the research team A and team D. There is however no significant difference in the number of at–risk behavior of the control team B, team C, team E and team F at the same level of significance. The researcher concludes that a change in the management style from autocratic I style to consultative II style lead to a reduction in the number of at–risk behavior of team A and team D. In addition the reduction in the number of at–risk behavior of team A and team D could not have been because of task repetition, increased knowledge or improved team cohesion since there is no significant difference in the number of at–risk behavior exhibited by team B, team C, team E and team F. The research can be used by the Escravos gas plant management and the management of any similar process plant to fashion out more cost effective, time effective and safer methods for carrying out their turnaround maintenance. A change in management styles may just be a better approach to improving productivity than giving financial incentives to contractors and personnel. Changes in management style will have to be managed. The change must be gradual because sudden change can be detrimental as people may just need to understand and adapt to the change. The turnaround personnel must also understand the intent so as to prevent conflicts. / Thesis (M.Ing. (Development and Management Engineering))--North-West University, Potchefstroom Campus, 2012.