Temesgen Zewotir

Prevalence and risk factors of malaria in Ethiopia

BackgroundMore than 75% of the total area of Ethiopia is malarious, making malaria the leading public health problem in Ethiopia. The aim of this study was to investigate the prevalence rate and the associated socio-economic, geographic and demographic factors of malaria based on the rapid diagnosis test (RDT) survey results.MethodsFrom December 2006 to January 2007, a baseline malaria indicator survey in Amhara, Oromiya and Southern Nation Nationalities and People (SNNP) regions of Ethiopia was conducted by The Carter Center. This study uses this data. The method of generalized linear model was used to analyse the data and the response variable was the presence or absence of malaria using the rapid diagnosis test (RDT).ResultsThe analyses show that the RDT result was significantly associated with age and gender. Other significant covariates confounding variables are source of water, trip to obtain water, toilet facility, total number of rooms, material used for walls, and material used for roofing. The prevalence of malaria for households with clean water found to be less. Malaria rapid diagnosis found to be higher for thatch and stick/mud roof and earth/local dung plaster floor. Moreover, spraying anti-malaria to the house was found to be one means of reducing the risk of malaria. Furthermore, the housing condition, source of water and its distance, gender, and ages in the households were identified in order to have two-way interaction effects.ConclusionIndividuals with poor socio-economic conditions are positively associated with malaria infection. Improving the housing condition of the household is one of the means of reducing the risk of malaria. Children and female household members are the most vulnerable to the risk of malaria. Such information is essential to design improved strategic intervention for the reduction of malaria epidemic in Ethiopia.

Factors affecting first-month adherence to antiretroviral therapy among HIV-positive adults in South Africa.

This study explores the influence of baseline factors on first-month adherence to highly active antiretroviral therapy (HAART) among adults. The study design involved a review of routinely collected patient information in the CAPRISA AIDS Treatment (CAT) programme, at a rural and an urban clinic in KwaZulu-Natal Province, South Africa. The records of 688 patients enrolled in the CAT programme between June 2004 and September 2006 were analysed. Adherence was calculated from pharmacy records (pill counts) and patients were considered adherent if they had taken at least 95% of their prescribed drugs. Logistic regression was used to analyse the data and account for confounding factors. During the first month of therapy, 79% of the patients were adherent to HAART. HAART adherence was negatively associated with a higher baseline CD4 count. Women had better adherence if they attended voluntarily testing and counselling or if they had taken an HIV test because they were unwell, while men had higher adherence if they were tested due to perceived risk of HIV infection. HAART adherence was positively associated with higher age among patients who possessed cell phones and among patients who provided a source of income in the urban setting, but not in the rural setting. Though long-term data from this cohort is required to fully evaluate the impact of non-adherence in the first month of treatment, this study identifies specific groups of patients at higher risk for whom adherence counselling should be targeted and tailored. For example, first-month HAART adherence can be improved by targeting patients initiated on treatment with a high CD4 count.

Determinants of optimal adherence over time to antiretroviral therapy amongst HIV positive adults in South Africa: A longitudinal study

Highly active antiretroviral therapy (HAART) requires strict adherence to achieve optimal clinical and survival benefits. A study was done to explore the factors affecting HAART adherence among HIV positive adults by reviewing routinely collected patient information in the Centre for the AIDS Programme of Research in South Africa’s (CAPRISA) AIDS Treatment Programme. Records of 688 patients enrolled between 2004 and 2006 were analysed. Patients were considered adherent if they had taken at least 95% of their prescribed drugs. Generalized estimating equations were used to analyse the data. The results showed that HAART adherence increased over time, however, the rate of increase differed by some of the socio-demographic and behavioural characteristics of the patients. For instance, HAART adherence increased in both urban and rural treatment sites over time, but the rate of increase was higher in the rural site. This helped identify sub-populations, such as the urban population, that required ongoing adherence counseling.

Multiple correspondence analysis as a tool for analysis of large health surveys in African settings

BACKGROUND More than two thirds of the total population of Ethiopia is estimated to be at risk of malaria. Therefore, malaria is the leading public health problem in Ethiopia. OBJECTIVE To investigate the determinants of malaria Rapid Diagnosis Test (RDT) result and the association between socio-economic, demographic and geographic factors. METHOD The study used data from household cluster malaria survey which was conducted from December 2006 to January 2007. A total of 224 clusters of about 25 households each were selected from the Amhara, Oromiya and Southern Nation Nationalities and People (SNNP) regions of Ethiopia. A multiple correspondence analysis was used to jointly analyse malaria RDT result, socio-economic, demographic and geographic factors. RESULTS The result from multiple correspondence analysis shows that there is association between malaria RDT result and different socio-economic, demographic and geographic variables. CONCLUSION There is an indication that some socio-economic, demographic and geographic factors have joint effects. It is important to confirm the association between socio-economic, demographic and geographic factors using advanced statistical techniques.

Multiple Cases Deletion Diagnostics for Linear Mixed Models

The influence of observations on statistical inference is of importance in statistical data analysis. A practical and well-established approach to influence analysis is based on case deletion. We provide computationally inexpensive diagnostic tools for linear mixed models. The diagnostics are a function of basic building blocks, computed only once from the complete data analysis, and provide information on the influence of the data on different aspects of the model fit. To demonstrate the utility of the diagnostic tools, we analyze the aerosol penetration data and the data on the perceptions of the health effects of nicotine and cigarettes in South Africa.

Spatial distribution of malaria problem in three regions of Ethiopia

BackgroundThe transmission of malaria is the leading public health problem in Ethiopia. From the total area of Ethiopia, more than 75% is malarious. The aim of this study was to identify socio-economic, geographic and demographic risk factors of malaria based on the rapid diagnosis test (RDT) survey results and produce the prevalence map of the area illustrating variation in malaria risk.MethodsThis study accounts for spatial correlation in assessing the effects of socio- economic, demographic and geographic factors on the prevalence of malaria in Ethiopia. A total of 224 clusters of about 25 households each were selected from the Amhara, Oromiya and Southern Nation Nationalities and People’s (SNNP) regions of Ethiopia. A generalized linear mixed model with spatial covariance structure was used to analyse the data where the response variable was the presence or absence of malaria using the RDT.ResultsThe results showed that households in the SNNP region were found to be at more risk than Amhara and Oromiya regions. Moreover, households which have toilet facilities clean drinking water, and a greater number of rooms and mosquito nets in the rooms, have less chance of having household members testing positive for RDT. Moreover, from this study, it can be suggested that incorporating spatial variability is necessary for understanding and devising the most appropriate strategies to reduce the risk of malaria.

Structured additive regression models with spatial correlation to estimate under-five mortality risk factors in Ethiopia

BackgroundThe risk of a child dying before reaching five years of age is highest in Sub-Saharan African countries. But Child mortality rates have shown substantial decline in Ethiopia. It is important to identify factors affecting under-five mortality.MethodsA structured additive logistic regression model which accounts the spatial correlation was adopted to estimate under-five mortality risk factors. The 2011 Ethiopian Demographic and Health Survey data was used for this study.ResultsThe analysis showed that the risk of under-five mortality increases as the family size approaches seven and keeps increasing. With respect to socio-economic factors, the greater the household wealth, the lower the mortality. Moreover, for older mothers, the chance of their child to dying before reaching five is diminishes.ConclusionThe model enables simultaneous modeling of possible nonlinear effects of covariates, spatial correlation and heterogeneity. Our findings are relevant because the identified risk factors can be used to provide priority areas for intervention activities by the government to combat under-five mortality in Ethiopia.

Semiparametric models for malaria rapid diagnosis test result

BackgroundMore than 75% of the total of Ethiopia is malarious. Therefore, malaria is a leading public health problem in Ethiopia. This study aims to identify socio-economic, geographic and demographic factors contributing to the spread of malaria and is based on the results of a malaria Rapid Diagnosis Test survey.MethodsThe data used in this study originates from the baseline malaria indicator survey, conducted in the Amhara, Oromiya and Southern Nation Nationalities and People (SNNP) regions of Ethiopia from December 2006 to January 2007. The study applies the method of generalized additive mixed model (GAMM) to analyse data. The response variable is the presence or absence of malaria, using the malaria Rapid Diagnosis Test (RDT).ResultsThe results provide an improved insight into the distribution of malaria in relation to the age of affected people, the altitude, the total number of rooms, the total number of mosquito nets, family size, and the number of months that their rooms have been sprayed. The results confirm that positive malaria RDT test results are high for children under 15 years and for older persons. Gender, source of drinking water, time needed to fetch water, toilet facilities, main materials used for the construction of walls, floors and roofs, and use of mosquito nets were all found to have a significant impact on the results of the malaria rapid diagnosis test.ConclusionThe result of the analysis identifies poor socio-economic conditions as a major contributing factor or determinant for the spread of malaria. With the correct use of mosquito nets, indoor residual spraying with insecticide and other preventative measures, the incidence of malaria could be decreased. In addition, improving housing conditions is a means to reduce the risk of malaria. Other measures such as creating awareness of the use of mosquito nets, indoor residual spraying with insecticide, and malaria transmission, can lead to a further reduction in the number of malaria cases.

Optimal HAART adherence over time and time interval between successive visits: their association and determinants

Abstract We aimed to investigate the determinants of optimal highly active antiretroviral therapy (HAART) adherence and time interval between successive clinic visits, and the association between these two processes. This was done by reviewing routinely collected patient information in the Centre for the AIDS Programme of Research in South Africa (CAPRISA). Records of 688 patients enrolled in the CAPRISA AIDS treatment (CAT) programme between 2004 and 2006 were analysed. Patients were considered adherent if they had taken at least 95% of their prescribed drugs. The adherence has been measured using the pill counts data. A multivariate generalised mixed random effects approach was used to jointly analyse optimal HAART adherence and time interval between successive visits. The results showed that on the overall, the association between optimal HAART adherence and time interval between successive visits was negative. The results further showed that the interaction between time and treatment site had a significant joint effect on optimal HAART adherence and time interval between successive visits. The interaction revealed that as the number of follow-up visits increased, the interval between successive visits also increased while at the same time high levels of optimal adherence were maintained in the rural treatment site. Moreover, after accounting for the time interval between successive visits, the results showed that optimal HAART adherence was significantly associated with having a cell phone, living with a partner as well as interactions that include time and gender, time and treatment site, age and gender and age and education. The findings provide evidence of a negative association between optimal HAART adherence and the time interval between successive clinic visits on the overall, which therefore indicates that longer time interval between successive clinic visits is undesirable if optimal HAART adherence is to be maintained. This notwithstanding, rural patients were able to maintain HAART adherence for longer time interval between successive clinic visits. Furthermore, the findings indicated that optimal HAART adherence was low for some sub-populations, such as the urban and male populations, thus vigorous ongoing adherence counseling is required.

Evaluation of Linear Mixed Model Case Deletion Diagnostic Tools by Monte Carlo Simulation

A large number of statistics have been proposed to study the influence of individual observations in the linear mixed model. An extensive Monte Carlo simulation study is used to evaluate the appropriateness of these influence diagnostic measures. The sensitivity of the diagnostic measures to outliers and leverages is examined, and helpful results are obtained.