Africa faces significant health disparities, with less than half of its population accessing essential health services, and most countries allocating less than 10% of gross domestic product (GDP) to healthcare
[1]
. The rising prevalence of CKD and NCDs—such as diabetes, hypertension, obesity, and cardiovascular diseases—poses a substantial public health challenge, leading to increased morbidity, mortality, and economic burden
[2]
.
Effective CKD management requires early detection, primary care engagement, and timely specialist intervention. However, barriers such as geographic inaccessibility, workforce shortages (
Figure 1
), low disease awareness, and inadequate screening hinder optimal care delivery. The Global Kidney Health Atlas (GKHA) 2019 highlights the need for expanded registries and early detection programs, particularly in low-income settings
[3]
.
Emerging telehealth solutions offer promise for overcoming these barriers. The “NCDs Made Easy” platform was developed over a decade to leverage telemedicine for improving CKD/NCDs management
[4]
-
[7]
. It supports primary care providers with evidence-based guidelines in a user-friendly digital interface. This cloud-based program was tested in the Egypt Information Prevention and Treatment of CKD (EGIPT-CKD) project, funded by the International Society of Nephrology
[8]
-
[10]
.
In this manuscript, we validate the platform through a cross-sectional study conducted in a remote, underserved village in Damanhur city, Egypt, demonstrating the role of telemedicine in bridging gaps in CKD/NCDs care in a low-resource country.
Figure 1. The global number of people for every single medical doctor according to the world health statistics 2020.
2. Study Aim and Objectives2.1. Aim
To evaluate the effectiveness of the “NCDs Made Easy” web-based telehealth platform:
http://www.telencds.com/
in promoting early detection, prevention, management, and research of CKD and related NCDs in underserved populations in Africa.
2.2. Specific Objectives
Early Detection: To leverage telemedicine for identifying CKD/NCDs, and associated cardiorenal risk factors, especially in remote and underserved areas. Hypothesis: There is a higher prevalence of CKD/NCDs and their risk factors among participants, many of whom are unaware due to limited primary healthcare access.
Prevention: To provide a simple, effective tool for early identification and management, enabling timely intervention that can improve outcomes. Hypothesis: Early detection facilitates interventions that favorably impact disease progression.
Optimise pre-end stage renal disease (ESRD) care: The platform aims to improve pre-ESRD care by enabling early detection of CKD complications. Hypothesis: Many patients start renal replacement therapy with advanced issues and unplanned vascular access. The system optimizes low-clearance clinics by timely identifying complications and applying evidence-based recommendations.
Workforce Support: To address healthcare worker shortages by enabling non-specialist personnel to implement guideline-based management through automated, participant-centered reports and visit action plans.
Health Literacy: To raise awareness among participants about CKD/NCDs and their long-term consequences via automated reports, educational outreach, and guidelines implemented by trained non-professional personnel. Hypothesis: Aware- ness levels will improve through these interventions.
Data Collection and Public Health: To build a comprehensive database of CKD/ NCDs in primary healthcare and deprived areas, facilitating data-driven public health strategies and research. Hypothesis: Automated data extraction and coding will support analysis and inform health policies.
3. Methodology3.1. Study Design
This study will employ a prospective observational cross-sectional design, including 600 adult participants in a remote village.
3.2. Primary and Secondary Outcome
Primary outcomes: Incidence of CKD, diabetes mellitus, hypertension, obesity and cardiovascular disease.
Secondary outcomes: all CKD/NCDs risk factors and participants NCDs literacy.
3.3. Inclusion and Exclusion Criteria
Inclusion criteria: Any adult person ≥18 years old and residing in the target village regardless of gender, religion, or ethnicity and signed informed consent for participation.
Exclusion criteria: Any adult person <18 years old, any person not residing in the target village, or known ESRD patient, or refuse to sign the informed consent.
3.4. Recruitment and Sampling Methods
A community-based meeting was held with the village leaders, where the principal investigator discussed the benefits of the project and its positive impact on the village inhabitants. The discussion included details on the participation process, associated risks and benefits, the signing of informed consent, and the right to withdraw at any time.
The village has a total population of approximately 1,000 individuals. The study included the adult population (≥18 years old), resulting in a total sample size of 600 participants.
3.5. Data Collection and Laboratory Methods
The village consists of 22 main families. We assigned 10 motivated volunteers to cover the total number of participants, with 2 volunteers designated as team leaders. Each volunteer was responsible for 50-70 participants. The team included 2 nursing staff from the village for blood and urine sampling.
Blood and urine samples were collected daily identified by barcode and transported in an incubator to the headquarters laboratory of the Damanhur Medical National Institute (DMNI) for chemical analysis on daily bases. Tests included fasting blood glucose, serum creatinine, urinalysis, and urine albumin/creatinine ratio.
Laboratory data were coded and results were entered into the online program by the headquarters team at DMNI into the participants’ electronic medical record through the unique barcode system.
Statistically significant results were considered if p < 0.05 and were marked by “*” in tables.
3.7. Ethical Considerations
This study was designed in adherence to ethical standards and underwent review and approval by the institutional Ethics Review Board at General organization of teaching hospitals and institutes (GOTHI), Egypt under registration number. All participants provided informed consent after receiving a thorough explanation of the study procedures, potential risks, and benefits. Data privacy and confidentiality were strictly maintained; all data was stored in a secure, encrypted database, and participants were identified by study IDs rather than personal information. Participants were informed that they could withdraw from the study at any time without affecting their standard care.
3.8. NCDs Made Easy Program:
The “NCDs Made Easy” program is a web-based telehealth tool designed to facilitate the early identification, prevention, and management of CKD and related NCDs. Aligned with the international society of nephrology (ISN) screening and intervention toolkit and and KDIGO CKD guidelines/2024, it provides an electronic portal for entering demographic, clinical, and laboratory data to assess individual renal and cardiovascular risk (
Figure 2
).
Abbreviations: NCDs, Chronic non-communicable diseases.Figure 2. Telemedicine diagramatic representation of “NCDs Made Easy Program”.
The platform integrates definitions, risk assessments, diagnosis, follow-up care, referrals, and management protocols through coded messages linked to specific variable values within the database. This ensures standardized, guideline-based management.
Pilot studies funded by the ISN Clinical Research Program have demonstrated the platform’s effectiveness in community screening programs. The program aims to bridge the gap between primary care and specialists, particularly in resource-limited settings, by enabling non-specialist healthcare workers to implement standardized, evidence-based protocols
[10]
[11]
.
Data entry is conducted via a secure, web-based interface where healthcare workers input patient demographic, clinical, laboratory, and lifestyle data. Embedded algorithms assess individual risk levels for CKD and other NCDs based on the integrated guidelines, informing personalized management plans that include lifestyle modifications, pharmacotherapy, and follow-up scheduling.
Designed for deployment in primary care settings, community screening events, and remote clinics, the system supports multilingual input to serve diverse populations. Data privacy and security are maintained in compliance with international standards like The General Data Protection Regulation (GDPR).
The program adheres to the ISN algorithm for CKD and NCDs screening and management, incorporating the latest KDIGO guidelines, including updates on CKD-related anemia and mineral and bone disorders
[8]
. This structured, evidence-based approach facilitates early detection, risk stratification, and comprehensive management tailored for resource-limited settings (
Figure 3
).
Abbreviations: CKD, Chronic kidney diseases; NCDs, Chronic non-communicable diseases; KDIGO, Kidney diseases improving global outcome guidelines; IDF, International diabetes federation; ADA, American diabetes association; JNC, Joint national committee for hyper-tension; AHA, American heart association; WHO, World health organization.Figure 3. The CKD/NCDs guidelines used in the in “NCDs Made Easy Program” database.
For CKD diagnosis, the platform automatically identifies CKD based on input data, evaluating estimated GFR, proteinuria status, and radiological assessments according to KDIGO criteria. It then generates appropriate management plans aimed at delaying progression of kidney and cardiovascular health.
During participation in the “NCDs Made Easy” program, individuals undergo a comprehensive assessment through simplified questionnaires designed for early detection and prevention of NCDs. Data collection spans multiple domains:
These forms include validation checks to ensure data accuracy and completeness and support multilingual input to accommodate diverse user populations. All collected data are automatically linked to a coded database aligned with KDIGO and NCDs evidence-based management guidelines.
The “NCDs Made Easy” program produces comprehensive, coded output reports for participants, physicians, and dietitians after each visit as shown in
Figure 4
. Each participant receives a unique international code, and all data—including physical measurements, point-of-care tests, and lab results—are automatically coded and stored in a centralized database. The number of messages in each report varies according to patient-specific variables such as risk factors, kidney function level, and visit details, without altering the overall structure or narrative of the report.
Abbreviations: CKD, Chronic kidney diseases; NCDs, Chronic non-communicable diseases.Figure 4. Display of “NCDs Made Easy Program” outcome reports.
Includes drug prescription tools (available in English and Arabic), laboratory follow-up reports, and modules for NCDs special events such as:
All reports are generated automatically, ensuring standardized, guideline-based management and efficient data utilization for research and health planning.
The “NCDs Made Easy” program enhances research efforts by streamlining data collection, study management, and analysis. All participant visit’s data, investigations, and calculations are automatically coded and exported into Excel files, reducing manual errors, saving time, and lowering costs. The platform allows principal investigators to add multiple study topics and enroll participants into various predefined subgroups—such as CKD, proteinuria, low GFR, or mineral and bone disorder (MBD)—with the flexibility to include participants in multiple categories. Study data are accessible exclusively to the investigator, who can extract datasets at any time for interim analyses. This facilitates ongoing comparison between groups at baseline and follow-up, supporting robust statistical evaluation and advancing research on CKD and NCDs.
The platform adopts a cloud-based architecture, utilizing scalable servers to handle large datasets across multiple sites. Key IT features include:
To ensure the security and privacy of participant data, several concrete measures have been implemented. Data is encrypted both in transit and at rest using Advanced Encryption Standard and stored on a secured dedicated server with robust access controls. Access is restricted to authorized personnel through role-based access controls. The data was audited by the research department and ethical committe, Damanhur medical national institute. Participant data is anonymized to protect privacy, with identifiable information separated and securely stored. Compliance with the General Data Protection Regulation includes obtaining explicit consent, ensuring the right to access and rectify data, and implementing data minimization principles. These measures uphold the highest standards of data security and privacy, safeguarding participants’ sensitive information.
Plans include developing mobile app versions for smartphones and tablets to increase accessibility, and deploying AI-driven analytics for predictive CKD/NCDs risk modeling.
4. Results
The study included 600 adult participants residing in a remote village near Damanhur city, Egypt, with 397 female participants (65.5%). The mean age of all participants was 42.22 ± 12.05 years (
Table 1
). The personal history of NCDs among participants included diabetes mellitus (11.3%), hypertension (19.67%), obesity (24.7%), and chronic kidney disease (2.83%) as shown in
Table 1
and
Table 2
. Additionally, risk factors for the development and progression of CKD were identified: 30.33% of participants reported using over-the-counter medicines, 6.17% used herbal medications, 7.1% had a history of renal stones, and 4.1% had history of treated hepatitis C virus infection.
<xref ref-type="bibr" rid="scirp.144465-"></xref>Table 1. Descriptive data of physical measures and laboratory investigations.
Variable Name
Number
Minimum
Maximum
Mean ± SD
Age (Year)
600
18
90
42.22 ± 12.05
SBP (mmHg)
584
61
195
115.76 ± 17.92
DBP (mmHg)
584
50
121
77.15 ± 35.27
MAP
584
60
131
92.58 ± 26.36
FBG (mg/dl)
208
51
420
90.40 ± 38.17
RBG (mg/dl)
263
55
266
92.56 ± 26.31
Weight (Kg)
582
43
186
80.81 ± 16.17
Height (cm)
582
100
188
161.69 ± 09.75
BMI (Kg/m2)
582
17.8
57.4
31.69 ± 07.84
Waist circumference (cm)
571
60
159
109.42 ± 14.84
Hip circumference (cm)
571
61
155
109.44 ± 14.83
WHR
571
0.61
02.07
0.88 ± 0.14
uACR (mg/g)
397
1.1
1315
20.92 ± 92.77
Serum Creatinine (mg/dl)
441
0.04
4.13
0.84 ± 0.27
estimated GFR (ml/min/1.73 m2)
441
17.2
233
127.57 ± 20.78
Abbreviations: SBP: Systolic blood pressure, DBP: Diastolic blood pressure, MAP: Mean arterial pressure, FBG: Fasting blood glucose, BMI: Body mass index, WHR: Waist/hip ratio, ACR: urine Albumin/creatinine ratio.
<xref ref-type="bibr" rid="scirp.144465-"></xref>Table 2. Descriptive data of lifestyle issues and medical history.
Variable Name
Categories
Number
%
Gender
Male: 207/600
34.50%
Female: 397/600
65.50%
History of kidney disease
17/600
2.83%
History of diabetes
68/600
11.33%
New Diabetic participants
6/419
1.4%
Prediabetics participants
20/419
4.8%
Prediabetic participants
20/287
6.5%
Total number of diabetic participants
61/310
16.44%
History of hypertension
118/600
19.67%
New hypertensive participants
27/482
5.6%
Total hypertensive participants
145/600
25.4%
History of cardiovascular disease (CVD)
39/600
6.50%
Over-the-counter medicines
182/600
30.33%
Herbal medications
37/600
6.17%
History of renal stones
43/600
7.1%
Hepatitis-C virus treatment
25/600
4.17%
Family history of kidney disease
82/600
13.67%
Family history of diabetes mellitus
264/600
44.00%
Family history of hypertension
311/600
51.83%
Family history of CVD
43/600
7.16%
Hypertension was prevalent in 118 participants (19.67%), with mean systolic blood pressure of 115.76 ± 17.92 mmHg, diastolic blood pressure of 77.15 ± 35.27 mmHg, and mean arterial pressure of 92.58 ± 26.36 mmHg. The total number of hypertensive participants after screening increased to 145, with 27 newly discovered cases, constituting 18.6% of the total hypertensive participants. Among those with a history of hypertension, 50% had uncontrolled blood pressure according to the 10th Joint National Committee on Hypertension classification. Specifically, 33 participants (29%) were in stage 1 hypertension, and 24 participants (21%) were in stage 2 hypertension (
Table 2
and
Table 3
).
<xref ref-type="bibr" rid="scirp.144465-"></xref>Table 3. Percentage distribution of participants according to: body mass index (BMI), estimated glomerular filtration rate (eGFR), arterial blood pressure control according to 10th JNC, and American Diabetes Association (ADA) based on blood glucose measurement.
Category
Number
%
BMI classification of participants (n = 582)
Underweight (<18 kg/m2)
2
0.35%
Normal (18 - <25 kg/m2)
101
17.44%
Overweight (25 - <30 kg/m2)
161
29.71%
Obese (30 - <35 kg/m2)
98
16.93%
X-obese (≥35 kg/m2)
45
7.77%
eGFR categories (n = 441):
(ml/min/1.73 m2)
Stage 1
349
79.14%
Stage 2
84
19.05%
Stage 3
6
1.36%
Stage 4
1
0.23%
Stage 5
1
0.23%
Stage of Arterial blood pressure (n = 114) hypertensive participants
Controlled ABP
57
50%
Stage 1 hypertension
33
29.0%
Stage 2 hypertension
24
21.0%
ADA classification of participants (n = 418) by blood glucose
Normal
392
93.8%
Prediabetes
20
4.8%
Diabetes
6
1.4%
ADA classification: Fasting blood glucose: Normal < 100 mg/dl, Prediabetes 125 - 100 mg/dl, and Diabetes ≥ 126 mg/dl. 2 hours post prandial blood glucose: NORMAL < 140 mg/dl, Prediabetes: 140-199 mg/dl, and Diabetes ≥ 200 mg/dl.
Obesity was assessed in 571 participants using body mass index (BMI) and waist/hip ratio (WHR). The mean BMI was 31.69 ± 7.84 kg/m2. Participants were categorized based on BMI as follows: underweight 2 (0.35%), normal weight 101 (17.44%), overweight 161 (29.71%), obese 98 (16.93%), and extremely obese (X-obese) 45 (7.77%) as shown in
Table 3
. The mean WHR was 0.88 ± 0.14, indicating visceral obesity in 217 participants (38%). Obesity is highly prevalent among diabetic and hypertensive participants (
Table 3
).
A history of diabetes mellitus was present in 68 participants (11.33%). Among the 419 participants with no history of diabetes, fasting or 2-hour postprandial blood glucose tests identified 6 new cases of diabetes and 20 participants (6.5%) in the prediabetic stage (
Table 3
).
Regarding CKD evidence, we noted a history of renal stones in 43 participants (7.1%), which is a strong indicator of CKD. However, due to the lack of documentation at the time of screening, these cases could not be definitively classified as CKD, and participants were referred for further reassessment. CKD evidence was assessed through urine ACR and eGFR calculated using the CKD-EPI equation. Serum creatinine analysis was adjusted to the Isotope Dilution Mass Spectrometry technique (
Table 3
).
Proteinuria was assessed in 398 participants by measuring second morning mid-stream urine ACR with standard precautions. The mean ACR was 0.88 ± 0.14 mg/g, with proteinuria being positive in 34 participants (8.54%) (
Table 1
). These positive cases were referred for further confirmation with their healthcare providers.
Serum creatinine was measured in 441 participants, with a mean value of 0.84 ± 0.27 mg/dL. The eGFR was assessed and classified according to KDIGO guidelines: Stage 1 (79.14%), Stage 2 (19.05%), Stage 3 (1.36%), Stage 4 (0.23%), and Stage 5 (0.23%) as shown in
Table 3
. Proteinuria is corrolated with age, history of diabetes, history of hypertension, and obesity.
Table 4
shows division of the cohort in two groups. The proteinuria group included 34 patients while patients without proteinuria (n = 364) were grouped in the non-proteinuria group. No statistically significant difference was found between proteinuria group and non-proteinuria group regarding the gender, prevalence of obesity, abdominal obesity and cardiovascular diseases (p = 0.696, 0.458, 0.116, and 0.227 respectively). There was a statistically significant difference between the two groups regarding the age. The mean age in proteinuria group was 49.9 years compared to 42.4 years in non-proteinuria group (p = 0.001). The mean Glomerular filtration rate was 89.7 and 113.6 mL/min in both groups respectively (p = 0.001). Hypertension and diabetes mellitus were more common in proteinuria group than the non-proteinuria group (p = 0.030 and 0.007 respectively).
Furthermore, we divided screened people in two groups as shown in
Table 5
: (Group 1) Reduced eGFR (<90 ml/min/1.73m2) and (Group 2) normal eGFR group (≥90 ml/min/1.73m2). Males were more common in group 1 (68.5%) compared to group 2 (30.1%). Patients in group 1 showed statistically significant more age than patients in group 2. The mean age was 48.95 years in group 1 compared to 40.49 years in group 2 (p = 0.001). Hypertension, diabetes mellitus, and abdominal obesity were significantly more common in group 1 compared to group 2 (p = 0.005, 0.016, and 0.009 respectively).
Finally, we screened this remote village with segregation of participants with NCDs from all the village without the need of professional personnel to screen all the village. They visit them to see only about 1/5th of the studied population with positive data.
<xref ref-type="bibr" rid="scirp.144465-"></xref>Table 4. Comparison between proteinuria group (n = 34) and non-proteinuria group (n = 364) regarding other risk factors.
Characteristics
All (n= 398)
Proteinuria Group (n = 34)
Non-proteinuria Group (n = 364)
p
Frequency (%)
Frequency (%)
Frequency (%)
Gender:
Male
140 (35.2%)
13 (38.2%)
127 (34.9%)
0.696
Female
258 (64.8%)
21 (61.8%)
237 (65.1%)
Age (Years): mean (SD)
43.06 (11.92)
49.91 (14.30)
42.41 (11.49)
0.001*
GFR (mL/min): Median (IQR)
112.4 (93.1 - 139.6)
89.7 (68.75 - 118.4)
113.6 (94.5 - 140.3)
0.001*
Comorbidities:
Hypertension
102 (25.63%)
14 (41.18%)
88 (24.18%)
0.030*
Diabetes Mellitus
56 (14.07%)
10 (29.41%)
46 (12.64%)
0.007*
Obesity (n = 385)
319 (82.86%)
25 (78.13%)
294 (83.29%)
0.458
Abdominal obesity (n = 377)
157 (41.64%)
18 (54.55%)
139 (40.41%)
0.116
CVD
27 (6.78%)
4 (11.76%)
23 (6.32%)
0.227
<xref ref-type="bibr" rid="scirp.144465-"></xref>Table 5. Comparison between Low estimated glomerular filtration rate (eGFR) group (n = 92) and Normal eGFR group (n = 394) regarding CKD/CVD risk factors.
Characteristics
All (n= 441)
Low eGFR Group (n = 92)
Normal eGFR Group (n = 394)
p
Frequency (%)
Frequency (%)
Frequency (%)
Gender:
Male
168 (38.1%)
63 (68.5%)
105 (30.1%)
0.001*
Female
273 (61.9%)
29 (31.5%)
244 (69.9%)
Age (Years): mean (SD)
42.25 (12.34)
48.95 (13.45)
40.49 (11.42)
0.001*
ACR (mg/gm): Median (IQR)
5.42 (3.1 - 9.8)
5.9 (3.07 - 11.0)
5.3 (3.1 - 9.4)
0.308
Comorbidities:
Hypertension
109 (24.72%)
33 (35.87%)
76 (21.78%)
0.005*
Diabetes Mellitus
54 (12.24%)
18 (19.57%)
36 (10.32%)
0.016*
Obesity (n = 433)
349 (80.60%)
72 (80.00%)
277 (80.76%)
0.871
Abdominal obesity (n = 425)
157 (36.94%)
43 (48.86%)
114 (33.83%)
0.009*
CVD
30 (6.80%)
7 (7.61%)
23 (6.59%)
0.730
Normal eGFR: ≥90 ml/min/1.73m2, Low eGFR: <90 ml/min/1.73m2.
5. Discussion
The “NCDs Made Easy” program demonstrates significant potential in addressing the healthcare gaps associated with CKD and related NCDs in resource-limited settings. This study, conducted in a remote village near Damanhur city, Egypt, validates the effectiveness of the platform in early detection, prevention, and management of CKD and NCDs. The results highlight several key findings and implications for future healthcare strategies.
Hypertension was prevalent in 19.67% of the participants (118/600), with the mean SBP at 115.76 ± 17.92 mmHg, DBP at 77.15 ± 35.27 mmHg, and MAP at 92.58 ± 26.36 mmHg. After screening, 27 participants were newly diagnosed with hypertension, constituting 18.6% of the total hypertensive participants. Among those with a history of hypertension, 50% had uncontrolled blood pressure, classified as stage 1 (29%) and stage 2 (21%) hypertension according to the 10th Joint National Committee on Hypertension. These findings underscore the importance of regular screening and monitoring to identify and manage undiagnosed or uncontrolled hypertension, reducing the risk of cardiovascular complications
[7]
[12]
.
Obesity was assessed in 571 participants using BMI and WHR. The mean BMI was 31.69 ± 7.84 kg/m2, with participants categorized as underweight (0.35%), normal weight (17.44%), overweight (29.71%), obese (16.93%), and extremely obese (7.77%). The mean WHR was 0.88 ± 0.14, indicating visceral obesity in 38% of participants. These results highlight the high prevalence of obesity and its associated risks, emphasizing the need for targeted interventions to promote healthy lifestyles and reduce obesity-related health issues
[7]
[13]
.
A history of diabetes mellitus was present in 11.33% of participants (68/600). Among the 419 participants without a history of diabetes, fasting or 2-hour postprandial blood glucose tests identified 6 new cases of diabetes and 20 participants (6.5%) in the prediabetic stage. This underscores the utility of the program in identifying undiagnosed diabetes and prediabetes, facilitating timely intervention and management to prevent disease progression
[2]
[14]
.
The study identified a history of renal stones in 7.1% of participants (43/600), which is a strong indicator of CKD. However, due to the lack of documentation at the time of screening, these cases were referred for further reassessment. CKD evidence was assessed through urine ACR and eGFR calculated using the CKD-EPI equation. Proteinuria was detected in 8.54% of participants (34/398), with a mean ACR of 0.88 ± 0.14 mg/g. Age, eGFR level, history of diabetes and hypertension was a predictor for development og proteinuria.
Serum creatinine was measured in 441 participants, with a mean value of 0.84 ± 0.27 mg/dL. The eGFR was classified according to KDIGO guidelines: Stage 1 (79.14%), Stage 2 (19.05%), Stage 3 (1.36%), Stage 4 (0.23%), and Stage 5 (0.23%). These findings emphasize the effectiveness of the program in early CKD detection and classification, allowing for timely intervention and management to slow disease progression
[8]
[12]
.
The program’s integration of evidence-based guidelines into a digital platform allows for the early identification of CKD complications, optimizing pre-ESRD care. Training non-specialist personnel to use the platform effectively addressed workforce shortages and improved disease management. The educational components and automated reports raised awareness among participants about CKD/ NCDs and their long-term consequences
[8]
[13]
.
The automatic data extraction and coding into Excel files facilitated comprehensive data analysis, supporting public health strategies and research. The integration of detailed data collection into routine practice can inform health policies and improve care delivery in underserved areas
[3]
[4]
.
6. Limitations and Future Research
While the study demonstrates the feasibility and effectiveness of the “NCDs Made Easy” program, limitations include the reliance on a well trainned volunteer and paramedical staff for data collection, which may introduce variability although considering all confounders and covariates in this issue. This point is considered one of stengths in our model of NCDs screening. Future research should focus on larger-scale studies to evaluate long-term impacts on disease prevalence, progression, healthcare costs, and patient quality of life. Additionally, implementing more robust training and quality control measures for data collection can further enhance the reliability of the findings.
7. Conclusions
The “NCDs Made Easy” program considered one step toward the solutions for improving CKD and NCD management in low-resource settings and preESRD care. Also, it has the potential to significantly impact primary healthcare and pre-ESRD care. By integrating evidence-based guidelines into a user-friendly digital platform, it enables early detection, prevention, and ongoing care, this may contribute to better health outcomes and health system resilience.
Further large-scale studies are needed to confirm these findings and explore the program’s broader applicability and impact.
Acknowledgements
We extend our gratitude to the General Organization of Teaching Hospitals and Institutes, Clinical Research Department for funding the research and providing the IRB approval.
Great thanks to the staff members of the Nephrology Department and Clinical Laboratory Department of Damanhur Medical National Institute for their unwavering support of this project. Special thanks to the Nursing Education Team for their invaluable efforts in training and educating participants and volunteers.
We also express our deep appreciation to the International Society of Nephrology (ISN) Clinical Research Committee and Sister Renal Center Committee, for their support in establishing the “NCDs Made Easy Program” during the ISN Damanhur-Sheffield SRC program and the EGIPT-CKD program.
Funding
The study was funded by GOTHI, Clinical Research Department.
Statements and Declarations
The “NCDs Made Easy Program” is a proprietary product developed by the primary author. The platform is registered with the Egyptian Information Technology Industry Development Authority under registration number 3752/2021. The dedicated program website is
http://www.telencds.com/
.
References
World Health Organization (2025) Global Health Expenditure Database. >https://apps.who.int/nha/database
GBD 2019 Diseases and Injuries Collaborators (2020) Global Burden of 369 Diseases and Injuries in 204 Countries and Territories, 1990-2019: A Systematic Analysis for the Global Burden of Disease Study 2019. Lancet, 396, 1204-1222.
Karam, S., Amouzegar, A., Alshamsi, I.R., et al. (2024) Capacity for the Management of Kidney Failure in the International Society of Nephrology Middle East Region: Report from the 2023 ISN Global Kidney Health Atlas (ISN-GKHA). Kidney International Supplements, 13, 57-70.
Tan, J., Mehrotra, A., Nadkarni, G.N., He, J.C., Langhoff, E., Post, J., et al. (2018) Telenephrology: Providing Healthcare to Remotely Located Patients with Chronic Kidney Disease. American Journal of Nephrology, 47, 200-207. >https://doi.org/10.1159/000488004
Osman, M.A., Okel, J., Okpechi, I.G., Jindal, K. and Bello, A.K. (2017) Potential Applications of Telenephrology to Enhance Global Kidney Care. BMJ Global Health, 2, e000292. >https://doi.org/10.1136/bmjgh-2017-000292
Sugawara, Y., Hirakawa, Y. and Nangaku, M. (2024) Telemedicine in Nephrology: Future Perspective and Solutions. Clinical Kidney Journal, 17, ii1-ii8. >https://doi.org/10.1093/ckj/sfae267
Cheung, A.K., Chang, T.I., Cushman, W.C., Furth, S.L., Hou, F.F., Ix, J.H., et al. (2021) KDIGO 2021 Clinical Practice Guideline for the Management of Blood Pressure in Chronic Kidney Disease. Kidney International, 99, S1-S87. >https://doi.org/10.1016/j.kint.2020.11.003
Stevens, P.E., Ahmed, S.B., Carrero, J.J., Foster, B., Francis, A., Hall, R.K., et al. (2024) KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney International, 105, S117-S314. >https://doi.org/10.1016/j.kint.2023.10.018
Gouda, Z., Mashaal, G., Bello, A.K., El Attar, A., El Kemmry, T., El Reweny, A. and El Nahas, M. (2009) Egypt Information, Prevention, and Treatment of Chronic Kidney Disease (EGIPT-CKD) Programme: Community-Based Screening for CKD and Other Non-Communicable Chronic Diseases in Egypt. Saudi Journal of Kidney Diseases and Transplantation, 22, 1055-1063.
Ene-Iordache, B., Perico, N., Bikbov, B., Carminati, S., Remuzzi, A., Perna, A., et al. (2016) Chronic Kidney Disease and Cardiovascular Risk in Six Regions of the World (ISN-KDDC): A Cross-Sectional Study. The Lancet Global Health, 4, e307-e319. >https://doi.org/10.1016/s2214-109x(16)00071-1
Obrador, G.T., Mahdavi-Mazdeh, M. and Collins, A.J. (2011) Establishing the Global Kidney Disease Prevention Network (KDPN): A Position Statement from the National Kidney Foundation. American Journal of Kidney Diseases, 57, 361-370. >https://doi.org/10.1053/j.ajkd.2010.12.006
Adeloye, D. and Basquill, C. (2014) Estimating the Prevalence and Awareness Rates of Hypertension in Africa: A Systematic Analysis. PLOS ONE, 9, e104300. >https://doi.org/10.1371/journal.pone.0104300
Mars, M. (2013) Telemedicine and Advances in Urban and Rural Healthcare Delivery in Africa. Progress in Cardiovascular Diseases, 56, 326-335. >https://doi.org/10.1016/j.pcad.2013.10.006
ElSayed, N.A., McCoy, R.G., Aleppo, G., Bajaj, M., Balapattabi, K., Beverly, E.A., et al. (2024) 9. Pharmacologic Approaches to Glycemic Treatment: Standards of Care in Diabetes-2025. Diabetes Care, 48, S181-S206. >https://doi.org/10.2337/dc25-s009