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China is a vast country, and its populations have various lifestyles and face different environmental exposures. Building a representative, population based cohort covering diverse populations can therefore be challenging.8 9 Difficulties include detailed phenotyping at baseline survey, minimising attrition during follow-up, and ensuring sustained funding.

ations have various lifestyles and face different environmental exposures. Building a representative, population based cohort covering diverse populations can therefore be challenging.8 9 Difficulties include detailed phenotyping at baseline survey, minimising attrition during follow-up, and ensuring sustained funding. Several key points require thorough discussion, and careful planning under the guidance of a national committee is essential before the formal launch of the project. First is deciding which operational model to choose. Typically, a study centre is set up which coordinates daily running with the support of core funding dedicated to the single centre.10 It has the advantage of easily applying the same questionnaire and biosample collection protocol. The UK Biobank study11 and China Kadoorie Biobank (CKB)7 study are successful examples. However, because of insufficient infrastructure in many areas of China, such a model, with central financial support and run by a single centre, often faces formidable obstacles, especially for deep phenotyping cohorts that include rich and granular data on participants such as imaging, multiomics, environmental exposures, and cognitive function. The difficulties include uncertain long term funding and difficulties in maintaining high quality data collection and follow-up. On the other hand, localised cohorts, usually confined to a province or prefecture, can take advantage of local resources, including local government support, on-site close surveillance of project progress, and long term high quality follow-up. However, local researchers may have different ideas about questionnaire contents, types of biosamples to collect, and data sharing policies.

nce or prefecture, can take advantage of local resources, including local government support, on-site close surveillance of project progress, and long term high quality follow-up. However, local researchers may have different ideas about questionnaire contents, types of biosamples to collect, and data sharing policies. The second decision is which health data to collect at baseline survey. This is influenced by available funding and the participants’ readiness to actively engage and cooperate throughout the study. Third is whether to consider repeated measurement. If so, what will be the appropriate time interval—every 5 or 10 years? Finally, the type of follow-up has to be decided. Although not all cohort studies require long term follow-up to answer their original questions, longitudinal cohort studies can provide valuable insights regarding the causes, treatment effects, and prognoses of diseases and health conditions if done cost effectively. Both active and passive follow-up methods can be considered. To build a large, nationwide, deep phenotyping, multiomic cohort, evidence suggests use of the same protocols but to build localised cohorts, with support from both central and local funding. This approach combines successful experiences of CKB,7 which used a centralised model but collected only limited data, with those of the Taizhou Longitudinal Study,12 an example of a localised model which collected a much larger amount of data for each participant but has limited generalisability.

al and local funding. This approach combines successful experiences of CKB,7 which used a centralised model but collected only limited data, with those of the Taizhou Longitudinal Study,12 an example of a localised model which collected a much larger amount of data for each participant but has limited generalisability. However, this hybrid strategy requires detailed planning and vigorous quality control measures. Standardised operation protocols, covering the questionnaire, health check-up, and biosample collection, are important for the success of the project. For questionnaires, because of the varied educational background, especially in rural areas of China, tape recorded face-to-face interviews conducted by professional interviewers13 14 are key to guarantee the quality of the data. For health checks, centralised training, purchase of same brand machines, and cross institute validation of assays are also important. Similar strategies should be considered for follow-up, and the medical chart review process (at least for some of the outcomes) has been shown to improve the quality of outcome data—for example, histopathological classification of tumours. In addition to standardised implementation, clearly defined research objectives and expected outputs are equally important. China’s medical and public health cohort studies should establish clearer research objectives and anticipated deliverables, which would facilitate the translation of findings into clinical practice and be a motivation for sustaining cohorts.

learly defined research objectives and expected outputs are equally important. China’s medical and public health cohort studies should establish clearer research objectives and anticipated deliverables, which would facilitate the translation of findings into clinical practice and be a motivation for sustaining cohorts. To facilitate a high quality, nationally guided design, it may be advisable to start with a demonstration project to gain practical experience and avoid possible pitfalls. Fuqing Cohort study might serve as such a project.15 16 In this cohort, a health check-up centre was set up for the project, and participants were interviewed carefully by professional interviewers. The number of phenotypes and biosamples collected in this cohort enable it to potentially become the deepest phenotyping, multiomic cohort in China. The standardised operation protocols developed in this project can be shared with other researchers who are willing to set up similar cohorts and jointly share data with the research community. A national centre can also be set up to coordinate data sharing, including building a cloud sharing system, centralised data curation and cleaning, a centralised biobanking system, and a centralised testing centre to generate omics data.

Achieving long term sustainability in cohort studies depends on several critical elements: maintaining high participant retention, ensuring a stable and skilled project team, integrating diverse data sources, and securing sustainable funding.

Achieving long term sustainability in cohort studies depends on several critical elements: maintaining high participant retention, ensuring a stable and skilled project team, integrating diverse data sources, and securing sustainable funding. Although successful initial recruitment is essential to launching a cohort study, long term sustainability depends more critically on participant retention (ie, minimising loss to follow-up). While a “good” retention rate depends on factors such as the study population, follow-up intensity, and outcome types, maintaining high retention rates requires proactive engagement strategies, including regular communication, personalised follow-ups, and community involvement. Building trust through ongoing engagement is essential for sustaining participation over time. The Framingham Heart Study (FHS), with a 99% retention rate of participants returning for scheduled examinations, shows how sustained participant involvement supports data quality.17 In China, CKB7 and CHARLS5 have achieved retention rates of approximately 80% and 86%, respectively, through community focused recruitment and consistent follow-up. CHARLS also dedicates considerable resources to tracking participants who migrate within China or are temporary dropouts, ensuring minimal loss to follow-up. Despite these successes, challenges in participant retention remain. In an analysis of the Medical Graduates Cohort Study in China,18 which tracks the career development and health of medical graduates, the authors identified significant attrition due to the demanding schedules and geographic mobility of the participants. Younger adults and those in stressful careers are particularly prone to dropping out, posing a risk of selection bias due to differential loss to follow-up (ie, when dropout is associated with either exposure or outcome). This could undermine the data quality and study validity. Notably, dropout during follow-up could present an even greater problem than initial non-participation, because continued participation in follow-ups may be influenced by ongoing experience with the outcome of interest.19 This selection effect can exacerbate bias, particularly in longitudinal studies where long term trends and causal relations are being examined.

nt an even greater problem than initial non-participation, because continued participation in follow-ups may be influenced by ongoing experience with the outcome of interest.19 This selection effect can exacerbate bias, particularly in longitudinal studies where long term trends and causal relations are being examined. This underscores the need for innovative retention strategies (eg, flexible participation schedules or digital communication options),20 as well as statistical approaches (eg, inverse probability weighting, sensitivity analyses), to evaluate and mitigate the potential impact of loss to follow-up.

nt an even greater problem than initial non-participation, because continued participation in follow-ups may be influenced by ongoing experience with the outcome of interest.19 This selection effect can exacerbate bias, particularly in longitudinal studies where long term trends and causal relations are being examined. This underscores the need for innovative retention strategies (eg, flexible participation schedules or digital communication options),20 as well as statistical approaches (eg, inverse probability weighting, sensitivity analyses), to evaluate and mitigate the potential impact of loss to follow-up. Maintaining a stable and skilled project team is a critical yet challenging aspect of cohort studies, as continuity of the team directly influences data quality and consistency. Experienced staff provide vital expertise in data collection, participant management, and follow-up protocols. Staff turnover is a recognised challenge in large scale cohorts internationally.4 However, achieving team stability can be particularly difficult in China’s rapidly evolving research landscape, where several contextual factors intensify the problem. In China, the high turnover of team members—typically research staff or students, who often leave within two to three years—is often driven by limited career progression opportunities, temporary contracts, or demanding work conditions. While well designed protocols are essential, their successful implementation relies on well trained and motivated staff—particularly for tasks like participant engagement, biological sample handling, and quality assurance during follow-up. The Nurses’ Health Study deals with these challenges through continuous training, career development, and opportunities for team members to engage in research.4 These efforts include involving team members in data analysis and allowing them to contribute to publications, which fosters commitment and professional growth. Involving early career researchers in operations can create future leaders familiar with the study. Providing mentorship and incremental responsibilities prepares them for leadership roles, ensuring a smooth transition as senior members step away. For cohort studies in China, adopting such strategies—mentorship, structured training opportunities, and professional development programmes—could help cultivate a committed workforce, enhancing the quality of cohort studies.

prepares them for leadership roles, ensuring a smooth transition as senior members step away. For cohort studies in China, adopting such strategies—mentorship, structured training opportunities, and professional development programmes—could help cultivate a committed workforce, enhancing the quality of cohort studies. Cohort studies often struggle with the financial and logistical demands of long term follow-up. Linking with administrative data (eg, hospital records and medical claims data) offers an efficient solution and can continue even after participants have dropped out.21 For example, CKB participants are all followed for any hospital admission and mortality through linkages with health insurance databases.7 While many multidisciplinary cohorts like CHARLS5 already include socioeconomic measures, disease focused cohorts often lack such context. Integrating additional administrative data (eg, health or social insurance records) could provide a more holistic view of participants’ socio-economic status and its interaction with health outcomes. By capturing data at a scale and frequency beyond those of typical cohort follow-ups, administrative records significantly extend the study’s reach and depth. Effective use of administrative data requires clear and transparent consent processes, as participants need to understand they are agreeing to long term involvement, with the option to withdraw at any time. It also requires careful navigation of ethical and legal considerations, including privacy protection, secure data handling, and government support to establish clear regulations and facilitate responsible data access.

ed to understand they are agreeing to long term involvement, with the option to withdraw at any time. It also requires careful navigation of ethical and legal considerations, including privacy protection, secure data handling, and government support to establish clear regulations and facilitate responsible data access. Enhancing research visibility and promoting reuse of cohort data are another perspective to improve sustainability, as studies that are widely accessed and cited by the research community are more likely to attract continued funding, institutional support, and collaboration opportunities. One effective approach to support this is through research consortiums to promote collaboration and resource sharing. The China Cohort Consortium, launched in 2017, exemplifies this approach by providing a platform for sharing and harmonising cohort data.22 The consortium facilitates data sharing among multiple studies, offering an overview of study designs, findings, and achievements.23 Additionally, the consortium promotes the development of standardised methods for data collection, storage, exchange, and harmonisation. Over the past few years, the consortium has expanded from its initial 20 cohorts to 153 cohorts, encompassing over 50 universities and hospitals. However, despite its progress, the consortium still faces challenges in fully harmonising data across different cohorts, limiting its ability to effectively integrate datasets for joint research activities. Continued efforts to improve data standardisation, metadata documentation, and compatibility are essential to maximise the consortium’s impact on medical research.

Although successful initial recruitment is essential to launching a cohort study, long term sustainability depends more critically on participant retention (ie, minimising loss to follow-up). While a “good” retention rate depends on factors such as the study population, follow-up intensity, and outcome types, maintaining high retention rates requires proactive engagement strategies, including regular communication, personalised follow-ups, and community involvement. Building trust through ongoing engagement is essential for sustaining participation over time. The Framingham Heart Study (FHS), with a 99% retention rate of participants returning for scheduled examinations, shows how sustained participant involvement supports data quality.17 In China, CKB7 and CHARLS5 have achieved retention rates of approximately 80% and 86%, respectively, through community focused recruitment and consistent follow-up. CHARLS also dedicates considerable resources to tracking participants who migrate within China or are temporary dropouts, ensuring minimal loss to follow-up. Despite these successes, challenges in participant retention remain. In an analysis of the Medical Graduates Cohort Study in China,18 which tracks the career development and health of medical graduates, the authors identified significant attrition due to the demanding schedules and geographic mobility of the participants. Younger adults and those in stressful careers are particularly prone to dropping out, posing a risk of selection bias due to differential loss to follow-up (ie, when dropout is associated with either exposure or outcome). This could undermine the data quality and study validity. Notably, dropout during follow-up could present an even greater problem than initial non-participation, because continued participation in follow-ups may be influenced by ongoing experience with the outcome of interest.19 This selection effect can exacerbate bias, particularly in longitudinal studies where long term trends and causal relations are being examined.

Maintaining a stable and skilled project team is a critical yet challenging aspect of cohort studies, as continuity of the team directly influences data quality and consistency. Experienced staff provide vital expertise in data collection, participant management, and follow-up protocols. Staff turnover is a recognised challenge in large scale cohorts internationally.4 However, achieving team stability can be particularly difficult in China’s rapidly evolving research landscape, where several contextual factors intensify the problem. In China, the high turnover of team members—typically research staff or students, who often leave within two to three years—is often driven by limited career progression opportunities, temporary contracts, or demanding work conditions. While well designed protocols are essential, their successful implementation relies on well trained and motivated staff—particularly for tasks like participant engagement, biological sample handling, and quality assurance during follow-up. The Nurses’ Health Study deals with these challenges through continuous training, career development, and opportunities for team members to engage in research.4 These efforts include involving team members in data analysis and allowing them to contribute to publications, which fosters commitment and professional growth. Involving early career researchers in operations can create future leaders familiar with the study. Providing mentorship and incremental responsibilities prepares them for leadership roles, ensuring a smooth transition as senior members step away. For cohort studies in China, adopting such strategies—mentorship, structured training opportunities, and professional development programmes—could help cultivate a committed workforce, enhancing the quality of cohort studies.

Cohort studies often struggle with the financial and logistical demands of long term follow-up. Linking with administrative data (eg, hospital records and medical claims data) offers an efficient solution and can continue even after participants have dropped out.21 For example, CKB participants are all followed for any hospital admission and mortality through linkages with health insurance databases.7 While many multidisciplinary cohorts like CHARLS5 already include socioeconomic measures, disease focused cohorts often lack such context. Integrating additional administrative data (eg, health or social insurance records) could provide a more holistic view of participants’ socio-economic status and its interaction with health outcomes. By capturing data at a scale and frequency beyond those of typical cohort follow-ups, administrative records significantly extend the study’s reach and depth. Effective use of administrative data requires clear and transparent consent processes, as participants need to understand they are agreeing to long term involvement, with the option to withdraw at any time. It also requires careful navigation of ethical and legal considerations, including privacy protection, secure data handling, and government support to establish clear regulations and facilitate responsible data access.

Enhancing research visibility and promoting reuse of cohort data are another perspective to improve sustainability, as studies that are widely accessed and cited by the research community are more likely to attract continued funding, institutional support, and collaboration opportunities. One effective approach to support this is through research consortiums to promote collaboration and resource sharing. The China Cohort Consortium, launched in 2017, exemplifies this approach by providing a platform for sharing and harmonising cohort data.22 The consortium facilitates data sharing among multiple studies, offering an overview of study designs, findings, and achievements.23 Additionally, the consortium promotes the development of standardised methods for data collection, storage, exchange, and harmonisation. Over the past few years, the consortium has expanded from its initial 20 cohorts to 153 cohorts, encompassing over 50 universities and hospitals. However, despite its progress, the consortium still faces challenges in fully harmonising data across different cohorts, limiting its ability to effectively integrate datasets for joint research activities. Continued efforts to improve data standardisation, metadata documentation, and compatibility are essential to maximise the consortium’s impact on medical research.

Securing long term funding remains one of the most critical challenges in maintaining large scale cohort studies in China. Traditionally, national grants have served as the primary source of support, but these often cover only the initial stages of cohort establishment5 7 24 whereas long term follow-up requires continuous investment. To tackle this gap, a variety of funding models have emerged, each with its own advantages and limitations. These models differ in their structure and scope: national level funding (eg, the ChinaHEART cohort9) ensures substantial financial backing but relies heavily on centralised planning and coordination; local funding (eg, the Taizhou Longitudinal Study12) adds necessary resources but may face challenges in achieving broader applicability; and international collaborations25 (eg, CKB and CHARLS) provide considerable financial support but require strong partnerships and long term commitments. China’s national funding agencies are central to cohort studies.6 9 26 However, their project based and time limited nature creates uncertainties for long term support. Common challenges include reliance on single source government funding, limited data standardisation and integration across cohorts, and insufficient deep phenotyping and lacking multiomics data—factors that collectively constrain sustained research value.

ime limited nature creates uncertainties for long term support. Common challenges include reliance on single source government funding, limited data standardisation and integration across cohorts, and insufficient deep phenotyping and lacking multiomics data—factors that collectively constrain sustained research value. To ensure stability, we advocate joint support from national and local governments. A representative model is the Jilin-Meihe City Longitudinal Cohort Study (JMCS) built around the concept of “data-driven research across the life course and intergenerational population,” which received ¥26m (£2.7m; €3.1m; $3.6m) from the Jilin provincial government over five years. Similar models, such as the West China Health and Ageing Trend study,26 demonstrate the success of “ex-post” mechanisms, where local governments provide upfront funding and central agencies reimburse based on data quality. This funding approach encourages local governments to invest in cohort studies by sharing financial responsibility with national agencies. By requiring predefined data quality criteria for reimbursement, it also incentivises high quality data collection and management. Such joint support not only bridges funding gaps between central and local levels but also enhances sustainability by fostering local ownership and accountability. Ultimately, combining national oversight with local engagement creates a more resilient funding environment that supports long term cohort maintenance, transforming cohort development from short term project based tasks into sustainable and infrastructure level scientific platforms.

local ownership and accountability. Ultimately, combining national oversight with local engagement creates a more resilient funding environment that supports long term cohort maintenance, transforming cohort development from short term project based tasks into sustainable and infrastructure level scientific platforms. For cohorts that meet data quality criteria, standardised access fees for shared data could help generate revenue and reinvest in study operations, fostering a sustainable funding cycle (fig 1). The UK Biobank exemplifies this model: by enabling global researchers to access high quality data for a fee, it has achieved financial sustainability while advancing scientific discovery. This approach not only ensures continuous data collection but also supports the production of high quality research outputs. Conceptual framework of data sharing and sustainable funding for cohort studies Although still limited, private sector involvement in China has the potential to provide financial resources, equipment, and technical support.11 With appropriate policy guidance, such involvement could become an important supplement to public funding sources. Mobilising community resources through charitable organisations and corporate sponsors can also diversify funding.7 11 27 Donations and tax exempt contributions could directly support cohort studies.

rt.11 With appropriate policy guidance, such involvement could become an important supplement to public funding sources. Mobilising community resources through charitable organisations and corporate sponsors can also diversify funding.7 11 27 Donations and tax exempt contributions could directly support cohort studies. Linking cohort data with administrative healthcare records is a cost effective way to improve data quality and reliability. Lessons from comprehensive data integration efforts in Europe28 could inform a similar framework in China. International collaborations, as seen in projects like CKB,7 provide crucial funding and expertise, enabling Chinese research with global standards and enhancing access to international funding opportunities. However, such collaborations must also tackle regulatory and data governance challenges.29 Through diverse funding sources, building strategic partnerships, and developing integrated data frameworks, China’s cohort studies can achieve long term resilience and exert great influence on public health research and policy. Cohort studies in China face major challenges: maintaining participant engagement, ensuring a stable follow-up team, integrating data effectively, and securing consistent funding Sustainability of cohort studies in China relies on a hybrid operational model: building cohorts locally and adhering to consistent, high quality data collection protocols Models need to be supported through a funding strategy in which researchers secure local funding to initiate cohorts with national support for longer term follow-up

Sustainability of cohort studies in China relies on a hybrid operational model: building cohorts locally and adhering to consistent, high quality data collection protocols Models need to be supported through a funding strategy in which researchers secure local funding to initiate cohorts with national support for longer term follow-up Consortium formation and fee based access could facilitate data sharing between academic centres to create a scalable and sustainable cohort model Where appropriate, China’s cohort construction could benefit from a shift from isolated, short term projects toward more infrastructure oriented, enduring resources that support long term operation, continuous data updating, and stronger integration across cohorts at both national and global levels

China’s national funding agencies are central to cohort studies.6 9 26 However, their project based and time limited nature creates uncertainties for long term support. Common challenges include reliance on single source government funding, limited data standardisation and integration across cohorts, and insufficient deep phenotyping and lacking multiomics data—factors that collectively constrain sustained research value.

For cohorts that meet data quality criteria, standardised access fees for shared data could help generate revenue and reinvest in study operations, fostering a sustainable funding cycle (fig 1). The UK Biobank exemplifies this model: by enabling global researchers to access high quality data for a fee, it has achieved financial sustainability while advancing scientific discovery. This approach not only ensures continuous data collection but also supports the production of high quality research outputs. Conceptual framework of data sharing and sustainable funding for cohort studies Although still limited, private sector involvement in China has the potential to provide financial resources, equipment, and technical support.11 With appropriate policy guidance, such involvement could become an important supplement to public funding sources. Mobilising community resources through charitable organisations and corporate sponsors can also diversify funding.7 11 27 Donations and tax exempt contributions could directly support cohort studies.