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ORIGINAL ARTICLE
Year : 2015  |  Volume : 18  |  Issue : 7  |  Page : 62-70

Impact of a short biostatistics course on knowledge and performance of postgraduate scholars: Implications for training of African doctors and biomedical researchers


Programme of Bio and Research Ethics and Medical Law, School of Nursing and Public Health, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa

Date of Web Publication1-Dec-2015

Correspondence Address:
S C Chima
Programme of Bio and Research Ethics and Medical Law, School of Nursing and Public Health, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban
South Africa
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1119-3077.170818

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   Abstract 


Background and Objectives: This study was designed to evaluate the impact of a short biostatistics course on knowledge and performance of statistical analysis by biomedical researchers in Africa. It is recognized that knowledge of biostatistics is essential for understanding and interpretation of modern scientific literature and active participation in the global research enterprise. Unfortunately, it has been observed that basic education of African scholars may be deficient in applied mathematics including biostatistics.
Materials and Methods: Forty university affiliated biomedical researchers from South Africa volunteered for a 4-day short-course where participants were exposed to lectures on descriptive and inferential biostatistics and practical training on using a statistical software package for data analysis. A quantitative questionnaire was used to evaluate participants' statistical knowledge and performance pre- and post-course. Changes in knowledge and performance were measured using objective and subjective criteria. Data from completed questionnaires were captured and analyzed using Statistical Package for Social Sciences. Participants' pre- and post-course data were compared using nonparametric Wilcoxon signed ranks tests for nonnormally distributed variables. A P < 0.05 was considered statistically significant.
Results: Baseline testing of statistical knowledge showed a median score of 0, with 75th percentile at 28.6%, and a maximum score of 71.4%. Postcourse evaluation revealed improvement in participants' core knowledge with the median score increasing to 28.5%; and the 75th percentile score to 85.7%; signifying improved understanding of statistical concepts and ability to carry out data analyses.
Conclusions: This study just showed poor baseline knowledge of biostatistics among postgraduate scholars and health science researchers in this cohort and highlights the potential benefits of short-courses in biostatistics to improve the knowledge and skills of biomedical researchers and scholars in Africa.

Keywords: Africa, biostatistics, doctors, medical ethics, research, researchers, students


How to cite this article:
Chima S C, Nkwanyana N M, Esterhuizen T M. Impact of a short biostatistics course on knowledge and performance of postgraduate scholars: Implications for training of African doctors and biomedical researchers. Niger J Clin Pract 2015;18, Suppl S1:62-70

How to cite this URL:
Chima S C, Nkwanyana N M, Esterhuizen T M. Impact of a short biostatistics course on knowledge and performance of postgraduate scholars: Implications for training of African doctors and biomedical researchers. Niger J Clin Pract [serial online] 2015 [cited 2020 Aug 10];18, Suppl S1:62-70. Available from: http://www.njcponline.com/text.asp?2015/18/7/62/170818




   Introduction Top


Statistics may be defined as that branch of mathematics that involves the collection, analysis, and interpretation of data.[1],[2] In this context, statistics has been described as the science of learning from data, and measuring, controlling, and communicating uncertainty; therefore it represents an essential tool for controlling the course of scientific and societal advances.[3] Based on this concept, statistical methodology is applied to almost all fields of human endeavor ranging from astronomy to biology, education, medicine, psychology, public health, etc.[3],[4] It has been argued that expertise in statistics will become even more important and critical in the future as the fields of academia, business, and governments come to rely on and demand more data driven evidence to inform decision-making,[3] supporting the assertion by Samuel Wicks [5] that perhaps H. G. Wells was right when he said "statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write!"

Importance of biostatistics knowledge for medical students, doctors, and researchers

The importance of teaching statistics to medical students and future doctors has been recognized by the United Kingdom General Medical Council since recommending reform of the undergraduate medical curriculum in tomorrow's doctors, 1993.[6],[7] Further, it has been observed that because of varying background and heterogeneity of the recent medical student recruits in terms of their exposure and knowledge of mathematics, it is likely that many students will arrive at medical school inadequately prepared in mathematics.[7],[8],[9] This suggests that courses in biostatistics must become an important component of the medical curriculum, where the goal would be to train medical students and future doctors about making intelligent application of statistical methodology. Such training should be innovative and integrated into the context of epidemiological analysis, medical decision–making, and computer applications, thereby making statistics more people-oriented and relevant to clinical practice.[7],[8],[9],[10],[11] Biomedical researchers have also recognized the importance of statistics knowledge in analyzing research data, prompting requests for more courses in biostatistics to improve core knowledge of clinical academics.[12] From the foregoing, one can conclude that knowledge of biostatistics is essential for medical doctors not only for designing scientific experiments, e.g., clinical trials, but also for accurate analysis and interpretation of results. It could be argued that knowledge of basic biostatistics and common statistical software packages is essential for all biomedical researchers and doctors in the 21st century.[6],[7],[8],[9],[10],[11],[12],[13],[14],[15] Altman argued previously that lack of proper biostatistical knowledge may be responsible for the publication of unethical research, including research articles with statistical errors, leading to misinterpretation of scientific results, which could ultimately have a negative impact on the practice of evidence-based medicine and global healthcare.[16] Such erroneous publications may lead to derogation from the ethical responsibilities of academic medicine which are ultimately aimed at improvement of global health.[17],[18]

Apparent problems with mathematics and statistics education for African scholars

It has been observed that training in biostatistics provided to allied health professionals and medical students in South Africa may be deficient because of overemphasis on general purpose and descriptive statistics.[19] It has also been reported that courses in biostatistics and epidemiology are the subjects most despised by South African medical students in the medical curriculum.[20] This is partly blamed on the teaching approach by biostatistics lecturers who are not appropriately trained in biostatistics.[7],[8],[9],[11],[21] Similar aversion to biostatistics has been reported in students from Turkey,[9] England,[11] and Pakistan,[12] based on the misconception by students that they will not require knowledge of biostatistics during future clinical practice. This perceived aversion to biostatistics by South African medical students may not be unrelated to reported deficiency in mathematics and science education of students prior to entry into tertiary institutions and medical schools.[22],[23],[24],[25] Recent studies on South African mathematics education suggest that the content knowledge of mathematics by high school and primary school teachers was so deficient, that it would be impossible for them to be able to impart the appropriate level of knowledge to high school students, thereby leading to a sometimes insurmountable level of knowledge deficit, which may be carried over to higher education and future professional endeavors.[22],[23],[24],[25] A recent report by the World Economic Forum on South African mathematics and science education, ranked South Africa 143 out of 144 countries globally in 2013,[26] and 146 out of 148 countries in 2014.[27] These purported deficiencies in mathematics education could be partly blamed on the deficiency of content knowledge and teaching methodology applied by teachers among other sociocultural factors.[28],[29] Other studies have suggested that South African science and mathematics teachers may not be properly trained in basic statistics or applied mathematics when compared to teachers from neighboring African countries such as Botswana.[30] The sum total of these observations is that based on analyses of many international comparative datasets on educational achievement, it appears that most South African students graduate from high school with an almost insurmountable knowledge deficit in mathematics and science.[25],[31] After the apartheid era, South Africa introduced a quota system for admission of students to medical schools in an effort to address the injustices of the past, and increase the available pool of healthcare professionals to service the majority black population. It has been reported that since 1998, at least 50% of medical school intakes were expected come from historically disadvantaged population groups.[20] According to Mostert, medical students admitted to Stellenbosch University in 2005 showed a large variation in their science and mathematics scores at high school.[20] This is consistent with reports from England that the heterogeneous population of students admitted to English medical schools in recent times, with differing backgrounds in mathematics education, required some form of remedial education in biostatistics in order to function effectively as medical doctors in the new millennium.[7] Therefore, in view of the large proportion of students with different science and cultural backgrounds and different skill sets being accepted into medical schools globally, there may be need for remedial courses in biostatistics and applied mathematics to bring such students up to the required standard. It has been argued that the background characteristics of students are a very important predictor of success in American higher education.[32] Thus, knowledge deficits in mathematics and science maybe carried forward to tertiary or postgraduate studies in the universities, resulting in an aversion to mathematics and biostatistics as reported among medical students,[9],[10],[11],[12],[20],[21],[33],[34],[35] requiring some form of remedial action such as continuous professional development or short-courses or special study modules in biostatistics for remedy.

This study was designed to answer the question of whether a short-course aimed at postgraduate biomedical researchers will achieve the objective of increasing participants' theoretical knowledge of biostatistics as well as improving their competency in using a computer-based statistical software package (Statistical Package for Social Sciences (SPSS), IBM Corp. Armonk, NY)[36] for data analysis. Specific objectives of this study were to measure baseline knowledge of biostatistics and also to evaluate using subjective and objective measures whether the course has brought about a change in these outcomes among biomedical researchers practicing at a local tertiary institution in KwaZulu-Natal Province, South Africa.


   Materials and Methods Top


Study design

This was an evaluation of a teaching and learning program using pre- and post-course self-administered questionnaires. The course was offered within the College of Health (CHS), University of KwaZulu-Natal (UKZN) during and after office hours at a campus computer laboratory. The target populations were postgraduate students and biomedical researchers at the university and medical school, including specialist doctors and allied health professionals from affiliated institutions.

Participants

There were 40 course participants in this cohort. All participants who attended the 4-day short course in basic biostatistics and the use of SPSS [36] statistical software package for data analysis during the month of February 2011 were eligible to participate in this study. Those who did not complete either the baseline or follow-up questionnaires were excluded from analysis.

Measurement instruments

A questionnaire designed to measure participants' level of knowledge on biostatistical theory and practice, was administered at 2 times points, before the course and 2 months after the course. The questionnaire included objective questions addressing knowledge of which statistical test to use in a given situation and questions on self-reported understanding of statistical theory (measured on a 5-point Likert scale). Questions regarding self-reported competence with selected computer-based statistical software packages were also included [Table 1],[Table 2],[Table 3]. Baseline questionnaires were completed by participants before initial lectures. These were identified with unique study numbers which the participants were asked to memorize or record. Two months after the course, participants were emailed the same questionnaire and asked to compete it using the same study number which they were allocated previously. Repeated follow-up attempts were made to encourage completion of post course questionnaires; however, completion was entirely voluntary.
Table 1: Responses at baseline to objective knowledge questions

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Table 2: Baseline responses to questions on understanding of statistical theory

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Table 3: Baseline responses to questions on ability to conduct analyses using statistical software

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Course presentation

The course was conducted as a 4-day training course in February 2011 facilitated by two qualified biostatisticians. The format consisted of 45 min theory lectures followed immediately by 1 h facilitator guided practical sessions in the mornings. Another 1 h practical session was allocated in the afternoons for students to work through exercises covering the content from that day, with facilitators available to answer any questions. Participants were encouraged to bring their own research datasets for practice and assistance with analysis during the practical sessions. Participants received 10 theoretical lectures covering topics from hypothesis testing to descriptive and inferential biostatistics, including correlation and linear regression. The practical training focused on the use of SPSS [36] to manage data, (for instance, labeling, coding variables, and creating new variables), as well as applying each of the theoretical tests covered, in the analysis of raw data.

Data analysis

Quantitative data from evaluation questionnaires were used to assess objective and subjective knowledge and competency of participants. Data from completed questionnaires were captured and analyzed using SPSS version 20.[36] Responses to objective knowledge questions were scored by allocating a point to each correct response, summing-up the scores, and expressing the score as a percentage of the total number of questions. Participants' pre- and post-course data, linked via unique study numbers, were compared using nonparametric Wilcoxon signed ranks tests for nonnormally distributed variables. A P < 0.05 was considered statistically significant.

Ethical issues

Ethical approval was obtained from the Biomedical Research Ethics Committee, UKZN. Written informed consent was obtained from all participants after full information disclosure.


   Results Top


Thirty-four of 40 participants (85%) completed the baseline questionnaires. Sixty-five percent of participants were female, while 55% were qualified researchers of varying academic designations including research supervisors. The rest were postgraduate students, allied health professionals, and medical doctors. Follow-up response was poor, with only six respondents (17.6%), completing the postcourse questionnaires despite repeated attempts by researchers. However, participation was entirely voluntary.

Baseline evaluation of knowledge

Baseline statistical knowledge and terminology was assessed using the seven questions shown in [Table 1]. Respondents showed very poor understanding and content knowledge of basic biostatistics terminology and methodology at baseline [Figure 1]. Scores for core knowledge revealed a median score of 0% (53% of baseline respondents), with a 75th percentile of 28.6%, and a maximum score of 71.4% [Table 1]. Self-reported knowledge and confidence required in carrying out simple data management and statistical procedures was limited. Knowledge and skills on general epidemiological and data management aspects was slightly better than more theoretical and applied mathematical and statistical concepts [[Table 2] and [Table 3]]. Less than 20% of participants had attempted or succeeded in carrying out simple statistical procedures prior to registration for the course.
Figure 1: Percentage of students who gave correct responses to questions assessing knowledge of statistical tests

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Postcourse evaluation of knowledge

Postcourse evaluation of the six participants who completed follow-up questionnaires showed a trend of overall improvement in statistical knowledge with the median knowledge score increasing from 7.1% to 28.5% and the 75th percentile score from 14.3% to 85.7% This change was not statistically significant; P = 0.109 [Table 4]. However, the power of the study was low as this comparison was based on six respondents. There was a trend toward improved understanding of statistical concepts and improved ability to carry out basic analyses using computer-based statistical software. For most constructs measured, there was an increase in the proportion who reported good and excellent understanding [Figure 2]. [Figure 3] also mirrors the trend regarding self-reported ability to carry out procedures using statistical software.
Table 4: Comparison of median knowledge scores between baseline and follow-up (n=6)

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Figure 2: Percentage of students who reported good or excellent level of understanding of epidemiological and statistical theory at baseline and follow-up (n=6)

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Figure 3: Percentage reporting being able to carry out data management and statistical procedures using statistical software at baseline and follow-up (n=6)

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   Discussion Top


The baseline results from this study revealed that majority of biomedical researchers and postgraduate students in this cohort have deficiency in core knowledge of basic statistical methodology and terminology. This appears to confirm recent reports that the teaching of science and mathematics in South African schools is somewhat deficient,[22],[25] due in part to socio-cultural and other factors impacting on South African education.[28],[37] Other underlying causes for this poor performance in biostatistics may include deficiency in teaching methodology such as overemphasis on general and descriptive statistics.[19] Failure to use practical exercises including relevant statistical software packages.[7],[8],[9],[10],[11],[12] Other potential causes of deficiency in biostatistical knowledge among biomedical researchers may include student apathy and lack of interest in the subject among medical students [9],[10],[11],[12],[20],[33],[35] due to misguided belief that they will not require biostatistics in future clinical practice.[11],[12] Others have suggested that because courses are usually offered in the early part of medical curriculum, this may lead to a situation where students and doctors may have forgotten what they studied during the early years of medical school during later clinical practice. All of the above factors may play a role in the observed deficiency of biostatistics knowledge among biomedical researchers and doctors, suggesting the need for reforms of medical curricula with regards to biostatistics education.[6],[7],[8] In addition suggesting the need for continuing education in biostatistics post graduation for biomedical researchers and doctors, especially in view of the reported deficiency and limitations of mathematics education at the primary and secondary level in some African countries.[31] The poor preparation in mathematics could explain students' aversion to biostatistics and epidemiology reported at South African medical schools and elsewhere.[9],[11],[20],[33],[35] Anecdotal evidence from the Nelson Mandela School of Medicine, which is a historically black institution admitting a higher percentage of medical students from the educationally disadvantaged background, suggests that students frequently struggle with basic biostatistical concepts and calculations, during courses in public health and epidemiology. It is not inconceivable that similar difficulties in mathematics and statistics could be experienced at other medical schools in Africa including Nigeria, prompting a recent call by the Nigerian medical students association, for reform of the medical curriculum in Nigerian medical schools, to meet global standards and better prepare medical students to become future physician-scientists.[38] It is arguable that such an objective cannot be accomplished without better preparation of African students in basic mathematics supplemented with short-courses and remedial training in biostatistics at medical schools and at postgraduate level. Recent studies also suggest that elementary and secondary school teachers in South Africa have a poor grasp of mathematical concepts such as fractions, ratios, odds, probabilities,[23],[24] which are building blocks for biostatistics knowledge.[39] All of this evidence suggests that many African students especially those from disadvantaged backgrounds, may enter tertiary institutions with an unwarranted fear of mathematics and statistics and maybe poorly prepared for courses in applied mathematics such as biostatistics, and future training in medicine and other scientific professions.[27],[40],[41] Even where such students have to learn statistics at tertiary level, the infrastructural deficits in form of few trained biostatisticians,[20],[21] lack of computers and appropriate software,[37] may lead to a situation where students graduate from university or tertiary education still plagued by knowledge deficits.[40],[41] This study was designed to evaluate whether health science researchers working in Local Healthcare and Tertiary Institutions in South Africa had adequate knowledge and essential biostatical skills to function effectively in their role as biomedical researchers and physician-scientists. Additional reasons for this study include the fact that the few available biostatisticians at CHS had to take on the role of analyzing most of the data derived from student and professional research, because the researchers could not analyze these data themselves. We hypothesized, that if we could teach biomedical researchers and postgraduate students' basic biostatistical skills, they could then design their own studies and analyze their own data using readily available statistical software. This would then free up the few available professionally qualified biostatisticians, to focus on teaching, and analyzing more challenging data. The postcourse results from this study suggested improvement in core knowledge of basic statistics methodology and terminology as well as ability to perform basic data analysis using computer-based statistical software by participants. Though the number of postcourse responders was low, leading to reduced statistical power. These results are comparable to results obtained from a similar sized cohort of biomedical researchers from Iran [34] The results reported from this study are also somewhat comparable to the Kirkpatrick model of training evaluation.[42] If we apply level 2 of the Kirkpatrick model [42] to this teaching evaluation exercise, one can argue that there was an improvement in knowledge and capability of the participants, based on the postcourse evaluation and analysis of responses. Therefore this training exercise will provide effective return on investment if the participants are followed up in later years, consistent with level 4 of Kirkpatrick's model for training evaluation.[42] Any discrepancies in results could be due to methodological variation.

Limitations of the study

This study was somewhat limited by the size of the sample cohort and the fact that the study was conducted at one institution. However, since the cohort consisted of a random sample of volunteer African biomedical researchers and postgraduate students with differing backgrounds and training, the majority of who exhibited poor baseline knowledge of biostatistics. This may be generally indicative of the poor level of mathematics education reported from some South African schools. The study is further limited by the postcourse cohort consisting of only six respondents. While this precluded statistically significant conclusions, there was a clear trend in improved core knowledge and skills suggesting that if the study is repeated in a larger cohort, the results obtained may show statistical significance. Further, there may have been a selection bias in the follow-up group compared to baseline. It is possible that those participants who were more familiar with biostatistics self-selected themselves to complete the postcourse evaluation. However, it is not clear whether this could have led to an under or overestimation of the impact of this training exercise.


   Conclusion Top


This study appears to confirm evidently poor baseline and residual knowledge of biostatistics among biomedical researchers and postgraduate scholars in South Africa. It further highlights the potential benefits of a short-course in biostatistics in improving the knowledge and performance of biostatistical analysis by biomedical researchers in Africa and other resource-poor settings. The study suggests that there is need for continuous professional development courses in the areas of biostatistics and applied mathematics, and that this could ultimately assist in improving the core knowledge of biomedical researchers, postgraduate scholars and medical doctors in the performance of their duties including biomedical research in Africa.

Recommendations

Although this study was done with limited cohort of postgraduate students, doctors, and researchers, we believe that the findings may apply in a wider context and have implications not only for the teaching of undergraduate students in the medical sciences. It also has implications for the continuous professional development of medical doctors and biomedical researchers in Africa. Our feeling is that similar short-courses in biostatistics and computer based statistical software packages can go a long way toward improving the knowledge base and remedy some of the inherent weaknesses in the undergraduate and postgraduate education of medical doctors students and researcher not only in South Africa, but in countries within sub-Saharan African and elsewhere.

Financial support and sponsorship

Nil.

Conflicts of interest

An earlier version of this paper was presented at the 8th Annual Teaching and Learning in Higher Education Conference, and shorter version was published in the Conference Proceedings and abstracts book.

 
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    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]


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   Results
   Discussion
   Conclusion
    References
    Article Figures
    Article Tables

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