Journal of Health & Medical Informatics

An extensive literature review on HIV/AIDS Prevention and Care Strategies was done from February to June, 2003 at Diakonhjemmet College, International Department in Oslo, Norway. Information from both developed and developing countries was examined. The main source of information included HIV/AIDS publications and text books extracted from the already existing resources. Furthermore, the author’s experience from Malawian context acted as a catalyst throughout the entire write up of this document.

The exercise aimed at documenting HIV/AIDS management strategies on various levels of intervention developments. It sought to unveil possible obstacles experienced by aid agencies in HIV/AIDS programs and the application of their policies/priorities to more diverse cultural settings. The paper seeks to address factors that are responsible for HIV transmission and how to deal with them in a more acceptable and an interdisciplinary manner. Consequently, examples of success stories that have emanated from effective HIV/AIDS management have been cited to influence the readers’ intuition in fighting against HIV/AIDS.

It is estimated that 40 million of adults and children are living with HIV/AIDS worldwide. Many of these people are in developing countries. In sub-Saharan Africa, over 21 million people are living with AIDS. In 13 countries in the region at least 10% of all adults are HIV infected and the prevalence rates in many capital cities are 35% or more. Hospitals cannot cope up with admissions of HIV/AIDS related conditions. It is estimated that by 1998, 800,000 people in Malawi had already died of AIDS since the epidemic started. Many of them were adults in their most productive years (15-45). This is very unfortunate because the young adults are the ones upon whom the development of the country depends.

Currently, 16% of the Malawian population is living with HIV/AIDS. It is therefore necessary to utilize every effort that will help to reduce HIV transmission and improve the quality of life for those who are already infected. It is in the light of this that the document seeks to provide added possibilities to those who are committed and dedicated in alleviating human suffering worldwide.

This paper is motivated in modeling a joint mixed effect model incorporating random effects with independent measurement error for both end points. Both the association in the evolution (AOE) for two or possibly multiple outcomes and evolution in the association (EOA) are expected to be assessed by joint mixed effect model. The proposed model is further trustful of grasping the problem of nonlinearity and absence of normality assumption and in turn is to predict the effect of associated covariates in the progressive evolution of longitudinal outcomes throughout the given time interval. As case study the two outcomes Systolic Blood Pressure (SBP) and Diastolic Blood Pleasure (DBP) of hypertensive patients are considered. The summary statistics of the two end points are included in this context. Thus, the average follow-up is 4.21(0.088) months, the average SBP and DBP of hypertensive patients are 136.12(0.367) and 85.13(0.273) respectively and the standard deviation of SBP and DBP are 16.21 and 12.06 respectively. Moreover, the average age of the hypertensive patients is 50.63(0.315) years old. The values inside the brackets refer the standard errors. Finally, the straight lines on the two plots indicate the normality of the two outcomes. This study suggests for the further work to the extended non-linear mixed effect model for correlated multivariate repeated measure data usually called longitudinal data. Moreover, the study can incorporate the joint model of multivariate longitudinal outcome with time to event outcomes. In order to come up with flexible and robust models, the authors can further extend these models to non-parametric smoothing models of longitudinal endpoints and survival times.

Depression is a severe mental health disorder with high societal costs. Despite its high prevalence, its diagnostic rate is very low. To assist clinicians to better diagnose depression, researchers in recent years have been looking at the problem of automatic detection of depression from speech signals. In this study, a novel multi-classifier system for depression detection in speech was developed and tested. We collected speech data in different ways, and we examined the discriminative power of different speech types (such as reading, interview, picture description, and video description). Considering that different speech types may elicit different levels of cognitive effort and provide complementary information for the classification of depression, we can utilize various speech data sets to gain a better result for depression recognition. All individual learners formed a pool of classifiers, and some individual learners with a high diversity and accuracy in the pool were selected. In the process, the kappa-error diagram helped us make decisions. Finally, a multi-classifier system with a parallel topology was built, and each individual learner in this system used different speech data types and speech features. In our experiment, a sample of 74 subjects (37 depressed patients and 37 healthy controls) was tested and a leave-one-out cross-validation scheme was used. The experiment result showed that this new approach had a higher accuracy (89.19%) than that of single classifier methods (the best is 72.97%). In addition, we also found that the overall recognition rate using interview speech was higher than those employing picture description, video description, and reading speech. Furthermore, neutral speech showed better performance than positive and negative speech.

Background: Malaria is a major public health problem with about 3.4 billion people at risk worldwide. Community health workers (CHWs) are being used to control and prevent malaria in Cameroonian communities. This study’s objectives were to assess CHWs’ knowledge, attitudes and practices and the correlates of CHWs’ knowledge regarding malaria prevention and control.

Methods: A community based cross sectional survey involving 135 CHWs was conducted at the Bamenda Health District from March - May 2015. Data was collected using a structured questionnaire and analysed using Epi Info version 3.5.4.

Result: All the participants had heard of malaria, 97.8% of them knew malaria was transmitted by mosquitos, 95.5% of them knew the most common sign/symptom of malaria was high temperature/fever and 98.5% of the CHWs knew that sleeping under bed nets protects against mosquito bites. The attitudes of CHWs regarding malaria prevention and control were somewhat good with 97.0% of them believing malaria is a life threatening disease and 92.6% of them believing it is best treated in a hospital. Few CHWs (4.0%) believed that malaria is caused by witchcraft and 3.3% of the CHWs think malaria is best treated by traditional doctors with herbs. CHWs’ practices regarding malaria were also good with most CHWs involved in distribution of nets (82.7%). Only being single had a statistically significant association with having knowledge on malaria prevention and control.

Conclusion: Participants knowledge on malaria was high and their attitudes and practices regarding malaria control were also good. Only being single had a statistically significant association to having knowledge on malaria prevention and control. CHWs still need some education and training on malaria prevention/control to provide missing knowledge. Further studies involving larger samples of CHWs for longer periods of time need to be conducted to find other correlates of CHWs’ knowledge on malaria prevention and control.

Patient cohort identification across heterogeneous data sources is a challenging task, which may involve a complicated process of data loading, harmonization and querying. Most existing cohort identification tools use a relational database model implemented in SQL for storing patient data. However, SQL databases have restrictions on the maximum number of columns in a table, which necessitates the breaking down of high dimensional data into multiple tables and as a consequence affects query performance. In this paper, we developed two NoSQL-based patient cohort query systems based on an existing SQL-based system for the cross-cohort query in the National Sleep Resource Research (NSRR). We used eight NSRR datasets in our experiment to evaluate the performance of the NoSQLbased and SQL-based systems in data loading, harmonization and query. Our experiment showed that NoSQL-based approaches outperformed the SQL-based and are rather promising for developing patient cohort query systems across heterogeneous data sources.