From silos to synergy: Transdisciplinary research as a pathway for population and public health
Authors
- Tanvir C TurinDepartment of Family Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Kamran U BasetDepartment of Public Health, School of Pharmacy and Public Health, Independent University Bangladesh, Dhaka, Bangladesh
- Saidur R MashrekyDepartment of Public Health, North South University, Dhaka, Bangladesh
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Published by Bangladesh Medical University (former Bangabandhu Sheikh Mujib Medical University).
Twenty-first-century population and public health challenges are increasingly complex and interconnected and transcend traditional clinical and disciplinary boundaries. Issues such as climate change, rapid urbanization, rising socioeconomic inequality, and the commercial and digital determinants of health create multi-layered burdens that necessitate a fundamental shift beyond conventional research silos. Transdisciplinary research has emerged as a powerful pathway for population health by embracing knowledge plurality and positioning communities as co-creators of evidence. In the context of Bangladesh, the integration of population and public health with environmental, economic, engineering, governance, behavioral, data-informed, and community-based knowledge perspectives is a necessity for addressing interconnected crises, such as escalating dengue outbreaks and the dual burden of disease. This approach fosters pragmatic frameworks grounded in real-world contexts and ethical accountability, though its implementation must navigate significant institutional barriers and power asymmetries in low- and middle-income contexts. The institutionalization of transdisciplinary platforms offers a timely opportunity for systemic reform, shifting national health responses from silos to synergy. By democratizing knowledge production and aligning diverse sectoral actors, transdisciplinary models can strengthen health systems and provide scalable, equity-driven lessons for the Global South. Engaging these structural complexities is fundamental to ensuring that transdisciplinary innovation translates into sustained, socially responsive, and resilient population health impacts.
The complexity of 21st-century health challenges
The health challenges of the 21st century are increasingly complex, interconnected, and shaped by forces that extend far beyond the walls of clinics or laboratories. Issues such as climate change, urbanization, pollution, industrialization. migration, and the growing double burden of disease reveal that no single discipline can generate the solutions needed for sustainable and equitable health outcomes. Bangladesh, similar to many other low- and middle-income countries, exemplifies the epidemiological and structural complexity characteristic of nations undergoing rapid transition [1]. Non-communicable diseases now account for a substantial majority of the national disease burden, while communicable diseases continue to pose significant challenges, resulting in a double burden of diseases [1]. This health burden is further influenced by rapid urban expansion, extreme population density, and climate-related stressors, which converge with shifting cultural and socioeconomic landscapes. These dynamics are increasingly shaped by commercialization, widespread misinformation, and evolving communication paradigms, which, alongside educational disparities and other fundamental determinants, exert sustained pressure on health systems and healthcare access.
Transdisciplinary research: Beyond inter- or multi-disciplinarity
Traditional research, often organized within disciplinary silos such as the biomedical sciences, social sciences, environmental sciences, policy studies, or governance, has generated important advances. Yet, the fragmentation of knowledge has also limited our ability to respond effectively to health challenges that are multi-layered, interdependent, and embedded in broader social and ecological systems.
This is where transdisciplinary research [2] emerges as a powerful pathway for population and public health. Unlike multidisciplinary approaches, which involve parallel contributions from different fields, or interdisciplinary approaches, which integrate methods across disciplines, transdisciplinary research goes further. It creates a shared space where diverse academic and systemic actors, such as researchers, policymakers, and practitioners, engage with communities and social actors as partners in inquiry [3] (Table 1). This approach embraces knowledge plurality, acknowledging that solving 21st-century health challenges requires the respectful integration of pluralistic knowledge systems, including scientific, traditional, and context-specific local expertise, to foster a more democratic and robust knowledge production process [4]. Together, they co-produce knowledge, shaping both the questions that are asked and the solutions that are designed [5]. Transdisciplinarity thus transcends disciplinary and sectoral boundaries to foster pragmatic, real-world grounded frameworks for understanding and acting on complex health problems [3, 5]. At the same time, the implementation of transdisciplinary research is shaped by institutional and sociopolitical realities. Power asymmetries across disciplines and sectors, misaligned academic and funding incentives, and governance arrangements that favor siloed knowledge production can constrain meaningful collaboration and limit the influence of community and civil society voices. Acknowledging these challenges is essential to advancing transdisciplinarity as a context-sensitive and implementable approach rather than a purely aspirational ideal.
Table 1 Transdisciplinary research: The core values, guiding principles, defining characteristics, and operationalising practices
Dimension | Illustrative attributes |
Core values | • Equity and inclusivity – valuing all forms of knowledge and participation |
Guiding principles | • Co-production of knowledge – integrating disciplinary, professional, and experiential expertise |
Defining characteristics | • Integration – weaving diverse epistemologies and perspectives |
Operationalizing practices | • Co-design and co-planning – joint problem definition, goal setting, and methodology development |
Why population and public health needs transdisciplinarity
The complexities of population and public health underscore the need for transdisciplinary approaches [6]. Health outcomes are determined by a constellation of various determinants, such as social, economic, cultural, and environmental determinants. For example, tackling non-communicable diseases requires not only biomedical innovation but also insights from behavioral science, urban planning and built environment, and the commercial determinants of health-including the market dynamics of processed, high-sodium, and ultra-processed food industries. Addressing maternal and child health involves gender studies, education, anthropology, and sociology as much as obstetrics and pediatrics. Responding to pandemics demands the convergence of virology, political science, ethics, data science, and community development. In each of these examples, siloed expertise can only go so far; real impact requires transdisciplinary collaboration.
Community as co-creator: The ethical and social accountability imperative
Community engagement and involvement are cornerstones of transdisciplinary research work [7]. Research that discounts communities from framing problems and shaping interventions risks irrelevance or even harm. By positioning communities as rights-holders in research and involving them as co-creators rather than passive recipients (Figure 1), transdisciplinary research not only enhances rigor and relevance but also contributes to decolonizing and democratizing knowledge production. This “research with, not on” ethos ensures that community voices are centered for ethical and effective knowledge creation, although its operationalization must navigate social stratification, risks of tokenism or cultural appropriation, gender norms, and political mediation through genuinely inclusive facilitation practices [8].

Figure 1 Community centered cross -disciplinary and-sectoral transdisciplinary research actors
The case for transdisciplinarity in Bangladesh
Against this conceptual backdrop, Bangladesh represents a particularly salient context in which to consider both the promise and the limits of transdisciplinary approaches. Bangladesh’s ongoing epidemiological transition and structural vulnerabilities do not exist in isolation; rather, they manifest as intersecting systems spanning biomedical, socio-ecological, and political–economic domains. For example, the escalating frequency of dengue outbreaks, driven by the interaction of unplanned waste disposal systems, the rapid proliferation of construction-related breeding sites, and shifting monsoon patterns, reveals the presence of deeply embedded upstream determinants that defy narrowly defined disciplinary categories.
While institutional silos have historically constrained collaboration in Bangladesh, there is an increasing need to transcend these traditional boundaries to achieve sustainable development. Evolving beyond fragmented mandates and hierarchical structures requires academic and policy environments to place greater value on integrating community-centered knowledge with scientific expertise. Embracing a transdisciplinary approach, one that intentionally harmonizes diverse sectors and democratizes the research process, is increasingly a necessity for Bangladesh to address their own interconnected population and public health challenges and to develop socially responsive, resilient, and durable solutions.
A call to action: Building synergy for the future
As Bangladesh advances in the global health research landscape, establishing a transdisciplinary research platform in population and public health presents a timely opportunity. Such a platform would unite scholars across disciplines, bridging the population and public health with the environmental, economic, social, and engineering sciences, alongside law and policy experts, while ensuring communities are active partners in shaping research agendas. By moving “from silos to synergy”, Bangladesh can strengthen its health systems and offer scalable, equity-driven lessons to the Global South on how transdisciplinary approaches advance resilience and sustainability. At the same time, the institutional and structural challenges outlined above point to critical areas for deeper inquiry, methodological innovation, and systemic reform. Engaging these complexities more fully will be required to translate the promise of transdisciplinarity into sustained population and public health impact. To realize this, we call on academic institutions, funders, and policymakers to prioritize transdisciplinary research through dedicated funding, institutional support, and enabling policies. Researchers must step beyond disciplinary boundaries to engage meaningfully with practitioners, decision-makers, and communities to co-produce the knowledge systems required for a more equitable and resilient future.



Variables | Frequency (%) |
Indication of colposcopy |
|
Visual inspection of the cervix with acetic acid positive | 200 (66.7) |
Abnormal pap test | 13 (4.3) |
Human papilloma virus DNA positive | 4 (1.3) |
Suspicious looking cervix | 14 (4.7) |
Others (per vaginal discharge, post-coital bleeding) | 69 (23.0) |
Histopathological diagnosis | |
Cervical Intraepithelial Neoplasia 1 | 193 (64.3) |
Cervical Intraepithelial Neoplasia 2 | 26 (8.7) |
Cervical Intraepithelial Neoplasia 3 | 32 (10.7) |
Invasive cervical cancer | 27 (9.0) |
Chronic cervicitis | 17 (5.6) |
Squamous metaplasia | 5 (1.7) |
Groups based on pre-test marks | Pretest | Posttest Marks (%) | Difference in pre and post-test marks (mean improvement) | P |
Didactic lecture classes | ||||
<50% | 36.6 (4.8) | 63.2 (9.4) | 26.6 | <0.001 |
≥50% | 52.8 (4.5) | 72.4 (14.9) | 19.6 | <0.001 |
Flipped classes | ||||
<50% | 36.9 (4.7) | 82.2 (10.8) | 45.4 | <0.001 |
≥50% | 52.8 (4.6) | 84.2 (10.3) | 31.4 | <0.001 |
Data presented as mean (standard deviation) | ||||
Background characteristics | Number (%) |
Age at presentation (weeks)a | 14.3 (9.2) |
Gestational age at birth (weeks)a | 37.5 (2.8) |
Birth weight (grams)a | 2,975.0 (825.0) |
Sex |
|
Male | 82 (41) |
Female | 118 (59) |
Affected side |
|
Right | 140 (70) |
Left | 54 (27) |
Bilateral | 6 (3) |
Delivery type |
|
Normal vaginal delivery | 152 (76) |
Instrumental delivery | 40 (20) |
Cesarean section | 8 (4) |
Place of delivery |
|
Home delivery by traditional birth attendant | 30 (15) |
Hospital delivery by midwife | 120 (60) |
Hospital delivery by doctor | 50 (25) |
Prolonged labor | 136 (68) |
Presentation |
|
Cephalic | 144 (72) |
Breech | 40 (20) |
Transverse | 16 (8) |
Shoulder dystocia | 136 (68) |
Maternal diabetes | 40 (20) |
Maternal age (years)a | 27.5 (6.8) |
Parity of mother |
|
Primipara | 156 (78) |
Multipara | 156 (78) |
aMean (standard deviation), all others are n (%) | |
Background characteristics | Number (%) |
Age at presentation (weeks)a | 14.3 (9.2) |
Gestational age at birth (weeks)a | 37.5 (2.8) |
Birth weight (grams)a | 2,975.0 (825.0) |
Sex |
|
Male | 82 (41) |
Female | 118 (59) |
Affected side |
|
Right | 140 (70) |
Left | 54 (27) |
Bilateral | 6 (3) |
Delivery type |
|
Normal vaginal delivery | 152 (76) |
Instrumental delivery | 40 (20) |
Cesarean section | 8 (4) |
Place of delivery |
|
Home delivery by traditional birth attendant | 30 (15) |
Hospital delivery by midwife | 120 (60) |
Hospital delivery by doctor | 50 (25) |
Prolonged labor | 136 (68) |
Presentation |
|
Cephalic | 144 (72) |
Breech | 40 (20) |
Transverse | 16 (8) |
Shoulder dystocia | 136 (68) |
Maternal diabetes | 40 (20) |
Maternal age (years)a | 27.5 (6.8) |
Parity of mother |
|
Primipara | 156 (78) |
Multipara | 156 (78) |
aMean (standard deviation), all others are n (%) | |
Mean escape latency of acquisition day | Groups | ||||
NC | SC | ColC | Pre-SwE Exp | Post-SwE Exp | |
Days |
|
|
|
|
|
1st | 26.2 (2.3) | 30.6 (2.4) | 60.0 (0.0)b | 43.2 (1.8)b | 43.8 (1.6)b |
2nd | 22.6 (1.0) | 25.4 (0.6) | 58.9 (0.5)b | 38.6 (2.0)b | 40.5 (1.2)b |
3rd | 14.5 (1.8) | 18.9 (0.4) | 56.5 (1.2)b | 34.2 (1.9)b | 33.8 (1.0)b |
4th | 13.1 (1.7) | 17.5 (0.8) | 53.9 (0.7)b | 35.0 (1.6)b | 34.9 (1.6)b |
5th | 13.0 (1.2) | 15.9 (0.7) | 51.7 (2.0)b | 25.9 (0.7)b | 27.7 (0.9)b |
6th | 12.2 (1.0) | 13.3 (0.4) | 49.5 (2.0)b | 16.8 (1.1)b | 16.8 (0.8)b |
Average of acquisition days | |||||
5th and 6th | 12.6 (0.2) | 14.6 (0.8) | 50.6 (0.7)b | 20.4 (2.1)a | 22.4 (3.2)a |
NC indicates normal control; SC, Sham control; ColC, colchicine control; SwE, swimming exercise exposure. aP <0.05; bP <0.01. | |||||
Categories | Number (%) |
Sex |
|
Male | 36 (60.0) |
Female | 24 (40.0) |
Age in yearsa | 8.8 (4.2) |
Education |
|
Pre-school | 20 (33.3) |
Elementary school | 24 (40.0) |
Junior high school | 16 (26.7) |
Cancer diagnoses |
|
Acute lymphoblastic leukemia | 33 (55) |
Retinoblastoma | 5 (8.3) |
Acute myeloid leukemia | 4 (6.7) |
Non-Hodgkins lymphoma | 4 (6.7) |
Osteosarcoma | 3 (5) |
Hepatoblastoma | 2 (3.3) |
Lymphoma | 2 (3.3) |
Neuroblastoma | 2 (3.3) |
Medulloblastoma | 1 (1.7) |
Neurofibroma | 1 (1.7) |
Ovarian tumour | 1 (1.7) |
Pancreatic cancer | 1 (1.7) |
Rhabdomyosarcoma | 1 (1.7) |
aMean (standard deviation) | |



Test results | Disease | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) | ||
Yes | No | ||||||
Reid’s score ≥ 5 | Positive | 10 | 15 | 37.0 | 94.5 | 40.1 | 93.8 |
Negative | 17 | 258 |
|
|
|
| |
Swede score ≥ 5 | Positive | 20 | 150 | 74.1 | 45.0 | 11.8 | 94.6 |
Negative | 7 | 123 |
|
|
|
| |
Swede score ≥ 8 | Positive | 3 | 21 | 11.1 | 92.3 | 12.5 | 91.3 |
Negative | 24 | 252 |
|
|
|
| |
a High-grade indicates a score of ≥5 in both tests; PPV indicates positive predictive value; NPV, negative predictive value | |||||||
Test | Sensitivity (%) | Specificity (%) | Positive predictive value (%) | Negative predictive value (%) |
Reid’s score ≥ 5 | 37.0 | 94.5 | 40.0 | 93.8 |
Swede score ≥ 5 | 74.1 | 45 | 11.8 | 94.6 |
Swede score ≥ 8 | 11.1 | 92.3 | 12.5 | 91.3 |
Test | Sensitivity (%) | Specificity (%) | Positive predictive value (%) | Negative predictive value (%) |
Reid’s score ≥ 5 | 37.0 | 94.5 | 40.0 | 93.8 |
Swede score ≥ 5 | 74.1 | 45 | 11.8 | 94.6 |
Swede score ≥ 8 | 11.1 | 92.3 | 12.5 | 91.3 |
Narakas classification | Total 200 (100%) | Grade 1 72 (36%) | Grade 2 64 (32%) | Grade 3 50 (25%) | Grade 4 14 (7%) |
Complete recoverya | 107 (54) | 60 (83) | 40 (63) | 7 (14) | - |
Near complete functional recovery but partial deformitya | 22 (11) | 5 (7) | 10 (16) | 6 (12) | 1 (7) |
Partial recovery with gross functional defect and deformity | 31 (16) | 7 (10) | 13 (20) | 10 (20) | 1 (7) |
No significant improvement | 40 (20) | - | 1 (1.5) | 27 (54) | 12 (86) |
aSatisfactory recovery bGrade 1, C5, 6, 7 improvement; Grade 2, C5, 6, 7 improvement; Grade 3, panpalsy C5, 6, 7, 8, 9, Grade 4, panpalsy with Hornon’s syndrome. | |||||
Narakas classification | Total 200 (100%) | Grade-1 72 (36%) | Grade-2 64 (32%) | Grade-3 50 (25%) | Grade-4 14 (7%) |
Complete recoverya | 107 (54) | 60 (83) | 40 (63) | 7 (14) | - |
Near complete functional recovery but partial deformitya | 22 (11) | 5 (7) | 10 (16) | 6 (12) | 1 (7) |
Partial recovery with gross functional defect and deformity | 31 (16) | 7 (10) | 13 (20) | 10 (20) | 1 (7) |
No significant improvement | 40 (20) | - | 1 (1.5) | 27 (54) | 12 (86) |
aSatisfactory recovery bGrade 1, C5, 6, 7 improvement; Grade 2, C5, 6, 7 improvement; Grade 3, panpalsy C5, 6, 7,8,9, Grade 4, panpalsy with Hornon’s syndrome. | |||||
Variables in probe trial day | Groups | ||||
NC | SC | ColC | Pre-SwE Exp | Post-SwE Exp | |
Target crossings | 8.0 (0.3) | 7.3 (0.3) | 1.7 (0.2)a | 6.0 (0.3)a | 5.8 (0.4)a |
Time spent in target | 18.0 (0.4) | 16.2 (0.7) | 5.8 (0.8)a | 15.3 (0.7)a | 15.2 (0.9)a |
NC indicates normal control; SC, Sham control; ColC, colchicine control; SwE, swimming exercise exposure. aP <0.01. | |||||
Pain level | Number (%) | P | ||
Pre | Post 1 | Post 2 | ||
Mean (SD)a pain score | 4.7 (1.9) | 2.7 (1.6) | 0.8 (1.1) | <0.001 |
Pain categories | ||||
No pain (0) | - | 1 (1.7) | 31 (51.7) | <0.001 |
Mild pain (1-3) | 15 (25.0) | 43 (70.0) | 27 (45.0) | |
Moderete pain (4-6) | 37 (61.7) | 15 (25.0) | 2 (3.3) | |
Severe pain (7-10) | 8 (13.3) | 2 (3.3) | - | |
aPain scores according to the visual analogue scale ranging from 0 to 10; SD indicates standard deviation | ||||
Surgeries | Number (%) | Satisfactory outcomes n (%) |
Primary surgery (n=24) |
|
|
Upper plexus | 6 (25) | 5 (83) |
Pan-palsy | 18 (75) | 6 (33) |
All | 24 (100) | 11 (46) |
Secondary Surgery (n=26) |
|
|
Shoulder deformity | 15 (58) | 13 (87) |
Wrist and forearm deformity | 11 (42) | 6 (54) |
All | 26 (100) | 19 (73) |
Primary and secondary surgery | 50 (100) | 30 (60) |
Mallet score 14 to 25 or Raimondi score 2-3 or Medical Research grading >3 to 5. | ||
Narakas classification | Total 200 (100%) | Grade-1 72 (36%) | Grade-2 64 (32%) | Grade-3 50 (25%) | Grade-4 14 (7%) |
Complete recoverya | 107 (54) | 60 (83) | 40 (63) | 7 (14) | - |
Near complete functional recovery but partial deformitya | 22 (11) | 5 (7) | 10 (16) | 6 (12) | 1 (7) |
Partial recovery with gross functional defect and deformity | 31 (16) | 7 (10) | 13 (20) | 10 (20) | 1 (7) |
No significant improvement | 40 (20) | - | 1 (1.5) | 27 (54) | 12 (86) |
aSatisfactory recovery bGrade 1, C5, 6, 7 improvement; Grade 2, C5, 6, 7 improvement; Grade 3, panpalsy C5, 6, 7,8,9, Grade 4, panpalsy with Hornon’s syndrome. | |||||
Trials | Groups | ||||
NC | SC | ColC | Pre-SwE Exp | Post-SwE Exp | |
1 | 20.8 (0.6) | 22.1 (1.8) | 41.1 (1.3)b | 31.9 (1.9)b | 32.9 (1.8)a, b |
2 | 10.9 (0.6) | 14.9 (1.7) | 37.4 (1.1)b | 24.9 (2.0)b | 26.8 (2.5)b |
3 | 8.4 (0.5) | 9.9 (2.0) | 32.8 (1.2)b | 22.0 (1.4)b | 21.0 (1.4)b |
4 | 7.8 (0.5) | 10.4 (1.3) | 27.6(1.1)b | 12.8 (1.2)b | 13.0 (1.4)b |
Savings (%)c | 47.7 (3.0) | 33.0 (3.0) | 10.0 (0.9)b | 23.6 (2.7)b | 18.9 (5.3)b |
NC indicates normal control; SC, Sham control; ColC, colchicine control; SwE, swimming exercise exposure. aP <0.05; bP <0.01. cThe difference in latency scores between trials 1 and 2, expressed as the percentage of savings increased from trial 1 to trial 2 | |||||


Lesion-size | Histopathology report | Total | |||||
CIN1 | CIN2 | CIN3 | ICC | CC | SM | ||
0–5 mm | 73 | 0 | 0 | 0 | 5 | 5 | 83 |
6–15 mm | 119 | 18 | 1 | 4 | 0 | 0 | 142 |
>15 mm | 1 | 8 | 31 | 23 | 12 | 0 | 75 |
Total | 193 | 26 | 32 | 27 | 17 | 5 | 300 |
CIN indicates cervical intraepithelial neoplasia; ICC, invasive cervical cancer; CC, chronic cervicitis; SM, squamous metaplasia | |||||||
| Histopathology report | Total | ||||||
CIN1 | CIN2 | CIN3 | ICC | CC | SM | |||
Lesion -Size | 0-5 mm | 73 | 0 | 0 | 0 | 5 | 5 | 83 |
6-15 mm | 119 | 18 | 1 | 4 | 0 | 0 | 142 | |
>15 mm | 1 | 8 | 31 | 23 | 12 | 0 | 75 | |
Total | 193 | 26 | 32 | 27 | 17 | 5 | 300 | |
CIN indicates Cervical intraepithelial neoplasia; ICC, Invasive cervical cancer; CC, Chronic cervicitis; SM, Squamous metaplasia | ||||||||
Group | Didactic posttest marks (%) | Flipped posttest marks (%) | Difference in marks (mean improvement) | P |
<50% | 63.2 (9.4) | 82.2 (10.8) | 19.0 | <0.001 |
≥50% | 72.4 (14.9) | 84.2 ( 10.3) | 11.8 | <0.001 |
Data presented as mean (standard deviation) | ||||








