Summary points
What was already known about the topic?
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Cardiovascular disease (CVD) is the leading cause of death worldwide; with developing countries affected the worst [1], [2], [3]. Screening for the risk of developing CVD is a well-recognised primary prevention strategy. This is usually done by calculating a risk score based on assessing a combination of risk factors, including, age, gender, tobacco use, blood pressure levels, blood cholesterol levels, diabetes or family history of CVD [4], [5], [6]. The human resource requirements, laboratory costs as well as inconvenience to the individual of risk scores that depend on biochemical tests has led to the development of a non-laboratory based CVD risk assessment model. This simplified model substitutes the body-mass index for blood lipid level to calculate the absolute CVD risk score thus making CVD risk screening far more feasible and potentially cost effective in both high and low resource settings [7]. The model uses data from a clinical history and physical examination, making a number of basic arithmetic calculations and decision support charts to calculate the CVD risk score (Fig. 1). This method has been found to perform as well as the common laboratory-based risk score in identifying people at high CVD risk in a South African setting [8].
Given the limited work force of nurses and doctors across all resource settings, the concept of task shifting is gaining increasing traction. Community health workers (CHWs) defined by the World Health Organization (WHO) as community members that have shorter training than professional workers, have been identified as potential candidates for task shifting in the health sector in general. CHWs have been used to provide a wide range of basic health services and it is well established that they play a crucial role in improving access to health services in under resourced settings [9]. There are, however, a number of challenges in using CHWs as they tend to have a limited amount of formal education and training. Mobile health tools are increasingly being used to assist and enable lay health workers in performing basic tasks. These interventions are thought to strengthen health systems by enabling a wide range of activities including data collection, disease surveillance, monitoring and evaluation and supporting clinic based health workers [10], [11], [12], [13].
Development of a mobile phone application that automatically calculates a CVD risk score further simplifies the task of risk assessment in the community because it allows for the risk assessment tool to be carried into the community and because it can potentially limit errors due to manual calculations. Finally, it can be used by health workers with limited formal education who may be less skilled and numerate.
The aim of this study was to develop a mobile phone CVD risk assessment application, based on a non-laboratory CVD risk assessment model and to evaluate its impacts on the training of CHWs and the screening for CVD in the community by CHWs compared to them using the paper-based chart tool.
This pilot study used quantitative and qualitative research methods. A mobile phone CVD risk assessment application was developed based on the non-laboratory paper-based CVD risk assessment model developed and validated by Gaziano et al. [7].The online CommCareHQ platform was used to develop the mobile phone version of this tool. CommCareHQ is an open-source software application with mobile phone and cloud infrastructure designed to enable creation of mobile phone job aids for CHWs. Relevant
The mean age of the CHWs was 33 years (range 21–52 years) with 21 being female and 3 male. The level of basic education was Grade 12 (twelve years of schooling; completion of high school), n = 8; Grade 11, n = 14 and Grade 10, n = 2. In addition, most CHWs also had some basic healthcare training in the form of Home-Based Community Care skills (n = 17) or Chronic Diseases of lifestyle skills (n = 4). Every participating CHW owned and was familiar with using a mobile phone with 71% (n = 17) owning feature
A number of themes were identified during the focus group discussion and are summarized in Table 2. CHWs felt the mobile phone application was easier, faster and more accurate to use, but noted that it was inferior to the chart as a visual aid when explaining risk.
The major findings of this study were that the mobile phone application of a non-blood based CVD risk tool used by CHWs was associated with a major reduction in the time taken for training, reaching adequate proficiency, screening for CVD risk and an elimination of errors in calculating a CVD risk score. Further that a quarter (25.4%) of screened participants had moderate to high risk of having a CVD event in the next five years. These are individuals that were previously unaware of having any
This study has found that when CHWs were trained to use a non-blood based CVD risk tool, compared to the paper-based chart tool, the mobile phone application was associated with a major reduction in the time taken for training and reaching adequate proficiency, in the time taken to screen individuals and there was elimination of errors in calculating a CVD risk score. The increased efficiency with reduced screening times and faster and easier training could have cost saving implications and the
Dr Sam Surka is involved in the conception and design of the study, (2) acquisition of data, analysis and interpretation of data, and (3) drafting the article, revising it critically for important intellectual content. Dr Sisira Edirippulige is involved in (1) interpretation of data, (2) revising it critically for important intellectual content, and (3) final approval of the version to be submitted. Prof Krisela Steyn is involved in (1) the conception and design of the study, (2) interpretation
None declared by any of the authors.
This Seed Grant has been funded in whole with Federal funds by the United States National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Purchase Order No. UCT HHSN268200900030C. Dr Surka was also supported by the Discovery Foundation Academic Fellowship Award and the South African Medical Research Council. Funding for this research project is gratefully acknowledged. Summary points What was already known about the topic? A
We are grateful to the following institutions for their support: CDIA at the University of Cape Town, School of Public Health at the University of the Western Cape and the Division of Telemedicine and mHealth at the South African Medical Research Council, Ms Jabu Zulu's contribution in the training and field work is also gratefully acknowledged. Dimagi provided technical support and use of their Commcare platform.
Emphasis should be laid on the benefits of risk stratification and effective communication that go beyond the identification of individuals at high-risk, but encompasses the motivation for and promotion of adherence to risk mitigation [14]. Combining risk assessment with innovative approaches like the use of community health workers to screen, identify and follow up high risk individuals [41] and use of mobile phone health technology to promote messages to motivate risk mitigation [42] can deliver impressive results in CVD risk prevention in under-resourced settings, and can help to lower the incidence and the burden of CVD [14]. FMW received the Global Health Support Award for PhD from University Medical Center, Utrecht.