Knowledge of healthcare providers on Down syndrome in children: a cross-sectional study in Rwanda

¹University of Rwanda, College of Medicine and Health Sciences, School of Nursing and Midwifery, Kigali, Rwanda, ²University of Rwanda, College of Medicine and Health Sciences, School of Public, Health, Kigali, Rwanda, ³Partners in Health, Kigali, Rwanda, ⁴University of Rwanda, College of Medicine and Health Sciences, School of Medicine and Pharmacy, Kigali, Rwanda, ⁵Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland, ⁶University of Rwanda, College of Medicine and Health Sciences, Center of Human Genetics, Kigali, Rwanda, ⁷Rwanda Military Teaching and Referral Hospital, Kigali, Rwanda

^&Corresponding author
Joselyne Rugema, University of Rwanda, College of Medicine and Health Sciences, School of Nursing and Midwifery, Kigali, Rwanda

Introduction    

Down syndrome (DS) is a group of birth defects caused by an extra copy of chromosome 21. It is the most common chromosomal abnormality in live-born infants, with an occurrence rate of approximately 1 in 700-1500 births [1]. First described in 1866 by British physician John Langdon Down, the condition was named after him [2]. The presence of an extra chromosome 21 (trisomy 21) results in a range of defects, diseases, and dysfunctions associated with the genetic mutation. Key symptoms of DS include cognitive impairment, early-onset Alzheimer's disease, and distinctive physical traits such as narrow, slanted eyes, a flat nose, and short stature. Down syndrome (DS) is one of the most common genetic disorders, characterized by a combination of intellectual disability and nutritional issues [3]. Individuals with DS also experience various health problems, including heart defects, pulmonary abnormalities, developmental delays, gastrointestinal problems, thyroid abnormalities, and nutritional disorders like overweight, obesity, hypercholesterolemia, and vitamin and mineral deficiencies [3]. Those with DS often have skeletal abnormalities and impaired brain development, which contribute to intellectual disabilities [3].

Individuals with DS are more prone to the increased risk of overweight and obesity compared to the general population [4]. A suitable dietary therapy is often essential to prevent additional health issues in DS children [4]. Clinical research has shown that children and adolescents with DS often present with mild to moderate obesity [5-8]. They tend to have higher overall body fat, with fat accumulation concentrated in particular regions of the body, in comparison to their peers without DS [9,10]. Studies show that individuals with DS frequently have imbalances in nutrient intake due to poor food choices, food intolerances (e.g., celiac disease), or malabsorption [11]. People with DS who are overweight or obese often exhibit a slow metabolic rate, abnormal leptin levels, and low physical activity [11]. Deficiencies in B vitamins and abnormal homocysteine levels further hinder cognitive development, while zinc deficiencies can lead to short stature, thyroid dysfunction, and increased appetite from over-supplementation [11]. Early dietary interventions in children with DS can help reduce the risk or delay the onset of some associated conditions, thereby improving their quality of life [11]. In other countries, numerous studies on knowledge of Down syndrome have primarily focused on mothers and the general community [12-16]. However, no study has assessed the knowledge of DS in children among healthcare providers in Rwanda. Therefore, this study aimed to assess the knowledge of DS among health professionals.

Methods     

Study design: we used a cross-sectional design with a quantitative approach to assess the knowledge of children with Down syndrome. This study is a part of my doctoral thesis, which aims to assess knowledge of DS among professional caregivers, measure nutritional status, and develop nutrition interventional guidelines for children with Down syndrome in Rwanda.

Settings: the study was conducted in 9 hospitals and 70 health Centers of catchment areas of each hospital. Our study setting was all public health settings from Kigali city either referral and district hospitals and all health centers and selected public health hospitals and from the Eastern province of Rwanda and all health Centers of their catchment areas. The hospitals from Kigali city were 3 referrals and teaching hospitals; and Rwanda Military Hospital. We selected one level II teaching hospital 4 district hospitals: Kacyiru District Hospital, Kibagabaga Level II Teaching Hospital, Muhima District Hospital, Nyarugenge District Hospital, Masaka District Hospital. We selected 2 hospitals from the Eastern province; Kirehe District Hospital, and Rwinkwavu District Hospital. We collected data on Neonatal, pediatric, maternity, and Nutrition services.

Study population: data were collected from healthcare providers, nurses, midwives, and nutritionists working in services, exposing them to caring for or crossing children from all selected health settings. Those services are Integrated Management Childhood Illness (IMCI), an integrated consultation that focuses on the child's health and well-being, immunization, nutrition service, maternity if available from the health center, neonatology, and pediatric wards in both district and referral hospitals.

Sample size: the sample size of this study was calculated using Slovin's Formula, the sample size was calculated as follows: N is the total population size, and the margin of error (e) represents the permitted probability of committing an error when selecting a small representative of the population. The confidence level of 95% gives a margin error of 0.05 [17,18], representing the sample size to be calculated.

The sample size of this study was based on the estimated total population of 2140 healthcare providers from selected health facilities with a desired margin error of 5%, which equals 337 healthcare providers.

Inclusion criteria: we included nurses, midwives, and nutritionists currently working at the selected health facility. Nurses or midwives currently working in IMCI, immunization, maternity, neonatology, pediatric services, and nutrition services from study sites. Nutritionists working in nutrition service for selected health facility.

Exclusion criteria: we excluded nurses or midwives and nutritionists who were not working in selected services.

The tool: data was collected using a structured validated questionnaire, adapted from the study conducted by Rabbani and colleagues in 2023 entitled "Down syndrome: knowledge and attitudes among future healthcare providers [19]. Our questionnaire included a section of socio-demographic information containing 6 variables: profession, gender, age categories, level of education, working area, and working experience. We had a section of eleven questions related to the knowledge of healthcare providers regarding Down syndrome. Those questions were the best way the participant to define Down syndrome (DS), classification of DS, the biggest risk factor of DS if DS can be curable in developed countries, the most disorders associated with DS, clinical features for DS, whether all children with the Down syndrome die prematurely, the most common congenital malformation among children with DS, management approach would help children of DS to achieve near-normal development milestone, special diet for children with DS, types of malnutrition frequently found in children with DS.

Validity: to ensure the validity of the questionnaire, we ensured face validity, content validity, and construct validity. For face validity, the questionnaire was translated into Kinyarwanda, which is a local language understood by the participants, and the questionnaire was adapted to the Rwandan context. For content validity, the researcher ensured that the questionnaire measured what was intended to measure related to the objectives of the study [20,21]. It reflects the extent to which a test evaluates all facets of the concept it is designed to measure [20,21]. In this study, content validity was ensured from the research proposal stage, where the researcher and the supervisory team defined the key concepts and set the study objective. The supervisory team comprised of expert geneticists, nutritionists, and senior researchers reviewed and verified questionnaire items for their significance and completeness [20,21]. The construct validity focuses on the level at which the questionnaire assesses the concept or abstract to be measured [20,21]. To ensure this, the researcher with expert several tested procedures for consistency and conducted a pilot study (n=35) to confirm that the questionnaire exactly measures the quality of the concept of knowledge of healthcare providers related to Down syndrome in children.

Reliability: to assure reliability, research assistants underwent training to gather data, receiving a comprehensive overview of the research objectives and ethical considerations. The training of the research assistants was harmonized to maximize reliability. Each question was thoroughly discussed and clarified during the training, and the assistants practised with dummy data collection, followed by constructive feedback. We conducted a pilot test on 10% (n=35) of participants from postgraduate students doing their masters in neonatology, pediatrics, and midwifery in the school of nursing to assess the internal consistency of the research tool, identifying any ambiguous, unnecessary, or sensitive questions.

Data collection procedure: after getting ethical clearance. We recruited 20 data collectors who were experienced in data collection, preferably those with health backgrounds, especially pediatric nurses, neonatal nurses, and midwives. We trained data collectors on the Down syndrome concept, tools, and study process. After the data collectors' training, we conducted a pilot study to ensure the validity and reliability of the tools and early preparedness for any fluctuation and for data collectors to be familiar with the tool. The research team went to the selected study sites for field preparation. This study utilized a simple random sampling strategy for selecting study participants. This random sampling strategy was selected due to its usefulness in giving an equal chance to all participants to be included in the selection, it helped to minimize biases, and it is a good choice for the representative of the whole population [17]. After having a total population of healthcare providers from health settings by asking the total number of nurses, midwives, and nutritionists in the health center and the hospital, we focused on only selected services (neonatal service, pediatric service, maternity service, and nutrition service). Participants in each service had an equal chance to be involved in the study.

We used the translated tool for the Kinyarwanda version because Kinyarwanda is the language better understood by the participants. The anonymity and confidentiality of participants were respected. Completed questionnaires were uploaded and checked daily, collected data were reviewed at the end of every day, a daily report was compiled, and every observation, challenge, or issue was reported daily by the field team to ensure prompt reliability and any discrepancies. Data were securely entered into Google Forms, strictly accessed by only the research team at the end, and exported in Excel for cleaning by checking for missing data, inconsistency, etc. Cleaned data were imported into SPSS 25 for analysis.

Statistical analysis

Outcome variable: in this study, the outcome variable of interest was the knowledge score. This variable was measured from the summation of 11 maximum items that we used to assess the knowledge among professional caregivers towards children with Down syndrome in Rwanda. Each reported item was based on binary responses and was scored 1 for yes and 0 for no. We did the score summation of the self-reported 11 knowledge items. The total knowledge score was the sum of responses to the 11 knowledge questions, and the total score ranged from 0 to 11. The knowledge score was grouped into three categories. Participants with scores ranging from 7 to 10 are categorized as having a high knowledge score. This grouping indicates that these individuals possessed a significant level of understanding about the subject. Conversely, participants with scores ranging from 5 to 6.9 were considered to have a moderate knowledge score. Participants with scores from 0 to 5 were considered to have poor knowledge. The mean score was calculated and converted to percentage scores; the mean of Knowledge was 57.57. This approach was used in other similar studies, such as a study conducted in the Al Ahsa district of Saudi Arabia by Rabbani in 2023 [19].

Explanatory variables and covariates: explanatory variables were participants' socio-demographics. There were 6 variables, namely profession, gender, age categories, level of education, working area, and working experience. We categorized these variables where necessary and summarized them by frequencies and percentages. The profession was classified into three categories: nurse, midwife, and nutritionist. The gender was binary (male and female), and age was classified into four categories; those who were less than 30 years old, between 30 and 39, between 40 and 49, and 50 and above. The level of education was classified into secondary certificate, advanced diploma, bachelor's degree, and master's degree. The working area was classified into six categories. Those are working in various health care services that take care of children with Down syndrome; those working in Integrated Management of Childhood Illness (IMCI), immunization, maternity, neonatology, nutritional department, and pediatric department. The working experience was categorized into 4 categories: those who had experience working in the above services of less than 1 year, between one year and 2 years, between 2 years and 5 years and above.

Statistical methods: data was entered into Google Forms, and then the data were cleaned and imported into SPSS version software version 25 for analysis. Descriptive and inferential statistics were used. We calculated socio-demographics using percentages and frequencies. We calculated the percentage of the percentage of questions regarding Down syndrome that were correctly answered. We evaluated the association between explanatory variables and the knowledge score in chi-square analysis. We created a multivariate logistic regression model that included all factors to discover the factors associated with the knowledge score (outcome variable) and find predictors of high knowledge scores. We reported the crude and adjusted odds ratios at 95% confidence intervals, and p-values of less than 0.05 were considered significant.

Ethical consideration: all methods were carried out per relevant guidelines and regulations. The study protocols were approved by the Institutional Review Board of the College of Medicine and Health Sciences of the University of Rwanda Institutional Review Board (approval notice: No 482/CMHS IRB/2023). We sought permission from study sites. Participants were explained all about the study. Informed consent was obtained from all participants involved in the study. Confidentiality and privacy were respected. None of the participants were minors.

Results     

Participants: slightly more than half (55%) of the participants were nurses, and the majority (70%) were female. Most of our participants (43%) were aged between 30-39 years old, the majority (47%) had advanced diplomas, and the majority (27%) were working in maternity. Slightly more than half (51%) had five years and more of working experience (Table 1).

Main results

This figure showed that the majority (64.1%) of the participants responded correctly to the question asking if all children with Down syndrome will die prematurely. The second item with the big proportion (62.9%) responded correctly to the prognosis of Down syndrome. The fewest participants (12.5%) responded correctly to what Down syndrome means. The mean of knowledge was 57.57, the median was 60.06, and the mode was 70. The standard deviation was 17.39 (Figure 1). Table 2 shows that the participants with poor scores (<50%) were 128 (38%), moderate (50-69%) were 131(39%), and excellent (>70%) were 78(23%). In Table 2, the Chi-square analysis showed that profession and level of education were associated with the outcome with a P-value of 0.010 and 0.027, respectively. Most of the participants have poor and moderate knowledge of children with Down syndrome (Table 2). Table 3 indicates the association between the factors (socio-demographic characteristics) and the outcome (high knowledge). The multivariate logistic analyses revealed that gender, age groups, and experience working in service are significant predictors of a high knowledge score. Females, individuals less than 30 years old, and those with 2-5 years of experience are more likely to achieve a high knowledge score.

Being female was associated with a high knowledge score compared to males with a crude odds ratio (OR) of 2.30 (CI: 1.34 - 3.95) with a p-value of 0.003. After adjustment, the adjusted Odds Ratio (AOR) for females remains at 2.30 (CI: 1.41 - 3.76) with a p-value of 0.002. The odds ratio stays the same, and the p-value decreases slightly, reinforcing the robustness and statistical significance of the association even after controlling for other variables. Being under 30 years old was also associated with a high knowledge score compared to the ones 50 years and above, with a crude OR of 3.02 (CI: 1.08 - 8.39) with a p-value of 0.034. After adjustment, the AOR for this age group increases slightly to 3.11 (CI: 1.12 - 8.60) with a p-value of 0.030. This means that younger individuals (less than 30 years) are more likely to score high in knowledge, even after accounting for other factors. Having 2-5 years of experience was associated with a high knowledge score compared to those with one year or less of experience, with a crude OR of 2.61 (CI: 1.05 - 6.48) with a p-value of 0.037. After adjustment, the AOR was 2.55 (CI: 1.10 - 6.20) with a p-value of 0.028. Being a nutritionist was associated with a high knowledge score compared to being a midwife, with a crude OR of 0.29 (CI: 0.12 - 0.69) with a p-value of 0.006. After adjustment, the AOR was 0.29 (0.12 - 0.71) with a p-value of 0.007. Working in neonatology was associated with a high knowledge score compared to working in maternity services, with a crude OR of 2.20 (CI: 1.32 - 3.85) with a p-value of 0.021. After adjustment, the AOR was 2.00 (CI: 1.41 - 4.76) with a p-value of 0.002 (Table 3).

Discussion     

Our study reported that the majority of our participants had poor to moderate knowledge in DS. Our participants with poor scores of less than 50% were 128 (38%), and those with moderate between 50-69% were 131(39%). This is controversial to the study conducted on nursing staff working at the primary health care centers of Al Ahsa district of Saudi Arabia by Ayed and colleagues, where the majority of the participants (59.6%) had good knowledge about Down syndrome [22]. The multivariate logistic regression analysis identifies several factors that significantly predict a high knowledge score of children DS. Those predictors are namely gender, age, years of experience, professional role (nutritionist), and the department of work (neonatology). Our study showed that females have more than double the likelihood of achieving a high knowledge score compared to males. This is similar to the finding from the study conducted in Saudi Arabia, which reported that good knowledge about Down syndrome was significantly higher among female participants than among male participants (59.21% vs.41.07%, P=0.045) [22]. In addition, this finding is supported by the study conducted in Malaysia among future healthcare providers, which showed that females have better knowledge than males [23]. This implies that gender remains an independent predictor of high knowledge even when controlling for other factors. This finding is similar to another study conducted by Rabbani in 2023 [19]. Our study indicated that younger individuals (<30 years) are about three times more likely to achieve a high knowledge score than older individuals (50 years and above). This indicated that younger individuals may be more engaged with recent information or training, leading to higher knowledge scores. Younger healthcare professionals are often more recently trained and may be more familiar with updated guidelines and best practices, especially in specialized areas like DS nutrition. Recent graduates tend to have more up-to-date knowledge due to newer curricula that emphasize evidence-based practice and emerging trends.

Our study reported that individuals with more experience (2-5 years) are about 2.5 times more likely to score high in knowledge compared to the staff with only one year of experience. This experience range is a critical period for accumulating relevant knowledge, possibly due to active learning and direct application in clinical settings. This suggests that individuals in this experience range are likely at the peak of learning and skill acquisition, actively applying recent education in clinical settings. Healthcare workers with more years of experience tend to benefit from on-the-job training, mentorship, and direct patient care, which reinforces theoretical knowledge. Our finding indicated that nutritionists are likely to have a high knowledge score compared to other professionals caring for children with DS. This result consisted in a chi-square analysis that revealed that profession is associated with high knowledge in DS with a P-value of 0.010. This result is consistent with the fact that nutritionists specialize in dietary needs, making them naturally more knowledgeable in the specific area of DS nutrition. Their training equips them to address complex nutritional needs, including those associated with genetic disorders like DS, which often involve unique metabolic and dietary requirements. This suggests that assessments focused on nutrition might disproportionately favour nutritionists over other health professionals.

This research revealed that those working in neonatology are twice as likely to achieve a high knowledge score compared to those in other departments. This could be due to the specific demands and complexity of neonatal care, requiring higher levels of knowledge and expertise in dealing with children with Down syndrome. Neonatal care is highly specialized and requires detailed knowledge of developmental disorders, including DS. These findings suggest the need for tailored training and education interventions, focusing on specific groups (e.g., males, older staff, nurses, midwives, staff working less than one year, and staff working in other departments that take care of children with DS apart from neonatology) who may benefit from additional knowledge support to improve their competencies in clinical practice. If this issue of poor knowledge remains untackled, the issue may negatively affect the care the children are receiving and the information their parents are getting. The study's strength lies in its inclusion of healthcare workers from multiple health facilities, which enhances the generalizability of the findings across different clinical settings. The limitation of this study is the use of a cross-sectional design, which limits the ability to establish cause-effect relationships between the predictors and knowledge scores. Longitudinal studies would be necessary to confirm whether these demographic and professional factors directly lead to higher knowledge acquisition over time.

Conclusion     

The results highlighted that our participants have poor to moderate knowledge of Down syndrome. This study identified several significant predictors of high knowledge scores of children of Down syndrome. This study highlighted these predictors include gender (female), age (under 30), years of experience (2-5 years), professional role (nutritionist), and department of work (neonatology). We recommend establishing clinical education and training tailored to improve care for children with DS.

Competing interests     

The authors declare no competing interests.

Authors' contributions     

Conception and study design: Joselyne Rugema, Eric Matsiko, Donatilla Mukamana, Leon Mutesa, Eric Baganizi, Jean Paul Rwabihama and Per Ashorn. Data collection: Joselyne Rugema and Vedaste Bagweneza. Data analysis and interpretation: Joselyne Rugema, Eric Matsiko, Donatilla Mukamana, Leon Mutesa and Alice Muhayimana. Manuscript drafting: Joselyne Rugema, Eric Matsiko, Donatilla Mukamana and Leon Mutesa and Alice Muhayimana. Manuscript revision: Joselyne Rugema, Alice Muhayimana, Michael Mugisha, Eric Matsiko, Donatilla Mukamana, Eric Baganizi, Jean Paul Rwabihama, Per Ashorn and Leon Mutesa. All the authors have read and agreed to the final manuscript.

Acknowledgments     

A vote of thanks goes to the participants who participated in this study; we acknowledge the funding received from ELMA project at the University of Rwanda for sponsoring this study-related activity.

Tables and figure     

Table 1: general characteristics of the study population (n=337)

Table 2: association of socio-demographic characteristics with level of knowledge using chi-square (n=337)

Table 3: multivariate logistic regression analysis of association between factors and outcome (n=337)

Figure 1: percentage of questions regarding Down Syndrome correctly answered (n=337)

References