The research team, led by Pannipa Boontein and colleagues from the Faculty of Nursing at Mahidol University, together with Roongnapa Khampang of the Siriraj Health Policy Unit, conducted the review in accordance with the PRISMA 2020 reporting guidelines and its literature-search extension, PRISMA-S. They searched five major databases, including PubMed, Scopus, ProQuest, ScienceDirect and ClinicalKey for Nursing, for studies published between January 2015 and 31 January 2026. From 1,396 records screened, fourteen studies ultimately met the eligibility criteria: nine randomised controlled trials and five quasi-experimental studies, all conducted in adults with COPD and all comparing nurse-led self-management interventions against usual care, standard practice or comparators not led by nurses.

The interventions under scrutiny varied in design but shared a common philosophy. Rather than treating patients as passive recipients of care, nurse-led self-management programmes aim to equip people with the skills to recognise early warning signs of exacerbation, use inhalers correctly, manage medications, stay physically active, and act decisively when symptoms deteriorate. Some programmes drew on the Chronic Care Model, a widely cited framework for organising long-term condition management, and several incorporated advance care planning discussions alongside practical education. The comparators, by contrast, typically involved conventional nurse-led education or routine outpatient follow-up, which makes the meta-analytic comparisons conceptually subtle: in some trials the control group was not truly inert.

Methodological quality was appraised using the Joanna Briggs Institute critical appraisal tools, and the authors retained all fourteen studies regardless of their appraisal scores. Six studies were judged to be of high quality and one of low quality, with the remainder falling in between. Effects were pooled as standardised mean differences or mean differences using random-effects models, the standard approach when studies are expected to differ in populations, settings and intervention intensity. One outcome, hospital length of stay, was analysed primarily with a fixed-effect model, with a random-effects model used as a sensitivity analysis. The review was not prospectively registered, and the authors transparently note that the outcome criterion for eligibility was broadened during peer review, although they state this did not affect any meta-analysis.

The headline finding concerns hospital length of stay. Across two randomised controlled trials, patients who received nurse-led self-management interventions spent approximately one day less in hospital than those receiving usual care, with a mean difference of minus 1.02 days and a 95 percent confidence interval of minus 1.73 to minus 0.32. In an era when every avoided hospital day translates into substantial cost savings and reduced exposure to hospital-acquired complications, a one-day reduction per admission is not trivial. Yet the authors are careful to flag a critical statistical caveat: a single trial contributed 94.5 percent of the weight in that pooled estimate. In practical terms, the finding rests almost entirely on one study, and the certainty of the evidence, assessed using the GRADE framework, was rated as very low for this outcome and for every other outcome in the review.

The remaining pooled results were less decisive. Emergency department visits showed a point estimate favouring the intervention, with a mean difference of minus 0.62 and a confidence interval of minus 1.27 to 0.03, which narrowly crossed the line of statistical significance. COPD Assessment Test scores, a validated patient-reported measure of symptom burden, yielded a standardised mean difference of minus 0.42 with a confidence interval of minus 0.93 to 0.10, again suggesting a possible benefit that did not reach conventional thresholds. Hospital admissions and six-minute walk test distances, a standard measure of functional exercise capacity, showed no statistically significant differences between groups. For self-management capabilities and psychosocial well-being, the authors reported findings narratively rather than pooling them, reflecting the heterogeneity of the measurement instruments used across the included studies.

Some of the most intriguing results emerged from exploratory post hoc leave-one-out analyses, in which the researchers systematically removed individual studies to test the robustness of the pooled estimates. When they omitted one trial whose comparator was conventional nurse-led education, the picture changed noticeably: the remaining two studies showed a statistically significant reduction in emergency department visits, and COPD Assessment Test scores showed a borderline reduction, with a standardised mean difference of minus 0.55 and a confidence interval of minus 1.09 to minus 0.00. This pattern carries an important methodological lesson. When the control group in a trial already receives a structured educational intervention delivered by nurses, the apparent advantage of adding a formal self-management component is diluted. Comparators, in other words, are not neutral, and heterogeneity in control conditions can mask real effects.

The authors are admirably candid about the limitations of their synthesis. Beyond the very low certainty of evidence across the board, the included studies pooled randomised and quasi-experimental designs together, although only one quasi-experimental study contributed to any meta-analysis. The interventions themselves differed in dose, duration, delivery mode and content, making it difficult to isolate which components drive any observed benefit. Publication bias cannot be excluded, and the review’s broad eligibility criteria, while inclusive, mean that the pooled estimates aggregate studies that a stricter protocol might have separated. The authors conclude that adequately powered randomised trials with standardised comparators are needed before nurse-led self-management can be recommended with confidence as a means of reducing healthcare utilisation in COPD.

Why does this matter beyond the pulmonary clinic? COPD is the third leading cause of chronic morbidity worldwide and a major driver of avoidable hospital admissions, particularly in ageing populations. Health systems experimenting with chronic disease management models are actively searching for interventions that shift care away from expensive acute episodes and toward prevention and self-efficacy. Nurses, who often have more sustained patient contact than physicians, are natural candidates to deliver such programmes, and the present review suggests that when they do, hospital stays may shorten and symptom burden may fall. But the evidence base remains thin, dominated by small trials with heterogeneous designs and inconsistent comparators. The Mahidol team’s work is best read as a rigorous map of what is known and, more importantly, of what is not.

The practical takeaway for clinicians and policymakers is one of tempered optimism. Nurse-led self-management interventions appear safe, plausible and potentially beneficial, with the strongest signal so far attached to reduced hospital length of stay, and secondary hints of fewer emergency visits and milder symptoms emerging only in sensitivity analyses. Until larger, well-designed trials with clearly defined control conditions are completed, the findings should inform rather than dictate practice. What the review does establish is that the research question is worth the investment: if a structured programme delivered by nurses can reliably shave even a single day off each COPD admission, the cumulative benefit for patients, families and health budgets could be considerable. The next generation of trials, the authors suggest, will determine whether that promise holds.

Subject of Research: Nurse-led self-management interventions for healthcare utilisation and clinical outcomes in adults with chronic obstructive pulmonary disease

Article Title: Effectiveness of nurse-led self-management interventions on healthcare utilisation and clinical outcomes in adults with chronic obstructive pulmonary disease: a systematic review and meta-analysis

Article References: Boontein, P., Junphongsri, P., Sanehjaroen, P., Yaowares, P., Poungkaew, A., Jamkrajang, T., & Khampang, R. (2026). Effectiveness of nurse-led self-management interventions on healthcare utilisation and clinical outcomes in adults with chronic obstructive pulmonary disease: a systematic review and meta-analysis. BMC Nursing. https://doi.org/10.1186/s12912-026-05502-1

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05502-1

Keywords: COPD, nurse-led intervention, self-management, systematic review, meta-analysis, hospital length of stay, emergency department visits, healthcare utilisation, COPD Assessment Test, randomised controlled trials, nursing research, chronic disease management

The study, led by Edith Visser of the Department of Epidemiology at Frisius MC in Leeuwarden, together with colleagues from University Medical Centre Groningen, Radboud University Medical Centre and other Dutch institutions, enrolled adults aged 18 and over who had been referred to post-COVID outpatient clinics at three hospitals in the Netherlands. All participants experienced persistent symptoms following a SARS-CoV-2 infection. In total, 1,155 patients formed the analytic cohort, a population that was 67 percent female with a median age of 50 years. At their initial clinic visit, the median time since infection stood at 24.1 weeks, placing most patients well into the chronic phase of their illness when they were first characterised.

The methodological heart of the study lies in its use of K-means clustering, a workhorse algorithm of unsupervised machine learning. Rather than asking clinicians to sort patients into categories by intuition, the researchers fed the algorithm the pattern of reported post-COVID symptoms recorded at each patient’s first appointment. K-means works by iteratively assigning each data point to the nearest of a predefined number of cluster centres, then recalculating those centres as the mean of the points assigned to them, until the grouping stabilises. The result is a partition of the patient population into groups whose members resemble one another more closely than they resemble anyone else, without any prior assumption about what those groups should look like. The researchers complemented this with multiple correspondence analysis, a related dimensionality-reduction technique suited to categorical data, to visualise and validate the symptom structure underlying the clusters.

Four clusters emerged from the analysis, and their relative sizes were strikingly uneven. The largest group by far, accounting for 47.7 percent of the cohort, was labelled the Respiratory-dominant and Dyspnoea cluster, characterised chiefly by breathing difficulties. The second-largest, at 27.6 percent, was the Fatigue-dominant and Neurocognitive cluster, in which profound tiredness coexisted with cognitive complaints. A third group, the Fatigue-dominant and Multisystem cluster, made up 12.7 percent of patients and carried the broadest symptom burden, spanning multiple organ systems. The smallest, at 11.9 percent, was the Respiratory-dominant and Airway-Systemic cluster. Notably, the authors highlight, only around 12 percent of patients belonged to the group with predominantly systemic symptoms, a finding that challenges assumptions that post-COVID is primarily a multisystem disorder in clinic-referred populations.

Clustering patients is only scientifically useful if the groups mean something clinically, and this is where the study’s second phase becomes important. The researchers examined how patients in each cluster moved through the healthcare system during their initial care. Patients in the two fatigue-dominated clusters were referred significantly more often to occupational therapy, speech and language therapy, mental health services, or a rehabilitation programme than those in the respiratory-dominant clusters, with all comparisons reaching statistical significance at p below 0.01. Patients in the respiratory-dominant groups, by contrast, more frequently received pharmacological treatment, reflecting the different therapeutic logic of managing airway and breathing symptoms compared with the functional and cognitive impairments that dominate fatigue-led illness.

The long-term outlook was assessed after a mean follow-up of 2.5 years, when all surviving participants were invited to complete a digital questionnaire covering their current health status and quality of life. The analysis leaned heavily on patient-reported outcome measures, or PROMs, instruments that capture how patients themselves rate their fatigue, functional capacity, quality of life and psychological state. Among the tools referenced in the study are the Checklist Individual Strength for fatigue, the Hospital Anxiety and Depression Scale, known as HADS, the EuroQol 5-Dimensions questionnaire for health-related quality of life, and the Clinical COPD Questionnaire for respiratory-specific health status. These instruments translate subjective experience into quantifiable scores that can be compared across groups and over time.

The follow-up data delivered the study’s most consequential finding: the fatigue-dominated clusters continued to show a higher disease burden at two and a half years, reflected in worse PROM outcomes compared with the respiratory-dominated clusters. In other words, the symptom pattern identified at the very first clinic visit predicted who would still be struggling years later. Patients whose initial illness was dominated by breathlessness and respiratory complaints fared comparatively better on these self-reported measures, while those in the fatigue and neurocognitive or multisystem groups remained more impaired. This prognostic separation, visible from day one, suggests that a simple symptom-based classification performed at intake could help clinics identify early which patients will need the most intensive and sustained support.

Perhaps the most troubling trend in the data concerns mental health. Anxiety and depression scores, measured with the HADS instrument, increased over time in all four clusters, not merely in the groups with the heaviest physical symptom burden. This universal deterioration cuts against any hopeful narrative of spontaneous recovery across the board and underscores the psychological toll of living with a chronic, poorly understood condition. It also carries practical implications for care planning: mental health support cannot be reserved for the fatigue clusters alone, because rising psychological distress appears to be a shared feature of the post-COVID experience regardless of the dominant physical symptoms.

The study was funded by the Netherlands Organisation for Health Research and Development, ZonMw, under grant 10430302250002, with the funders having no role in study design, data collection, analysis, interpretation, or the decision to publish. Ethical oversight followed Dutch legislation, with the retrospective use of pre-existing clinical data deemed exempt from full review and informed consent waived for that component, while written informed consent was obtained from all respondents to the follow-up questionnaire. The authors declared various competing interests, including trial-related roles for two investigators with pharmaceutical companies outside the submitted work, while the remaining authors reported none.

For clinicians and health system planners, the message of this research is that the post-COVID population, despite its notorious heterogeneity, is not an undifferentiated mass. Distinct symptom-based groups can be identified with a standard clustering algorithm at the point of first assessment, and those groups differ meaningfully in the care they consume and the outcomes they experience. The authors argue that these findings can support more tailored management strategies based on patients’ symptom profiles, enabling effective care planning and earlier access to appropriate interventions. A patient assigned to a fatigue-dominant cluster at intake might be fast-tracked toward rehabilitation and multidisciplinary support, while a respiratory-dominant patient might be directed toward pharmacological management of airway symptoms. As health systems worldwide continue to absorb the long tail of the pandemic, evidence that a data-driven snapshot taken in the first months of illness can forecast the years that follow offers a practical tool for allocating scarce specialist resources where they are needed most.

Subject of Research: Symptom-based clustering of post-COVID patients and its association with healthcare utilisation and long-term health outcomes

Article Title: Symptom-based clustering of post-COVID patients and its association with healthcare utilisation and long-term health outcomes: a multicentre cohort study

Article References: Visser, E., Fokkens, A. S., Koning, K. J., van Geffen, W. H., Visser, A., & Kuijvenhoven, J. C. (2026). Symptom-based clustering of post-COVID patients and its association with healthcare utilisation and long-term health outcomes: a multicentre cohort study. BMC Medicine. https://doi.org/10.1186/s12916-026-05307-8

Image Credits: AI Generated

DOI: 10.1186/s12916-026-05307-8

Keywords: post-COVID, long COVID, K-means clustering, symptom clusters, fatigue, dyspnoea, healthcare utilisation, patient-reported outcome measures, anxiety, depression, cohort study, BMC Medicine

The study, published in PLOS Mental Health by Gallen Mlenge, Odass Bilame, and Lutengano Mwinuka, draws on the fifth wave of the Tanzania National Panel Survey, conducted in 2020 and 2021. The panel survey is a household-based instrument designed to be representative of the national population, which makes it a rare and valuable window into health behaviour in a low-income setting where routine administrative data on care-seeking is often incomplete. From the survey, the researchers analysed a sample of 11,891 individuals aged 15 years and above who had non-missing outcome data, making it one of the largest attempts to characterise healthcare utilisation patterns in the Tanzanian population.

One of the central methodological challenges the authors faced is familiar to anyone working on mental health in low- and middle-income countries: population surveys rarely include clinical diagnostic instruments. Instead, the team used a proxy measure. Mental health need was identified through the Washington Group Short Set item on difficulty remembering or concentrating, applied at the severe threshold. This item is a recognised proxy associated with common mental disorders, and while it cannot substitute for a clinical interview, it provides a consistent, internationally comparable indicator that can be embedded in large multipurpose surveys. Using this definition, 54 individuals in the sample were classified as having proxy-identified mental health need, corresponding to a weighted prevalence of 0.45 percent of the adult population.

The outcome measure was deliberately broad. The researchers counted any visit to a formal provider in the four weeks preceding the interview, regardless of the reason for the visit. This captures the overall propensity to engage with the formal health system rather than care-seeking for a specific condition. Overall, weighted formal healthcare utilisation stood at just 19.2 percent, meaning that fewer than one in five Tanzanians aged 15 and above had contacted a formal provider in the previous month. In a country where the burden of disease includes both communicable and increasingly non-communicable conditions, that figure underscores how much of the population remains outside the reach of routine formal care.

To explain who used care and who did not, the authors turned to the Andersen Behavioural Model, a long-standing framework in health services research that organises determinants of utilisation into predisposing characteristics, enabling resources, and need factors. They estimated survey-weighted logistic regressions producing adjusted odds ratios and predicted probabilities. The results revealed a system that sorts people by demography and geography rather than by need. Female sex, older age, specifically 55 years and above, and residence in the Southern Highlands zone were each independently associated with higher utilisation, suggesting that both biological and structural factors shape contact with the health system.

The most striking finding, however, concerned household wealth. Utilisation rose in a strong dose-response gradient across wealth quintiles: the richer the household, the more likely its members were to have visited a formal provider. Individuals in the richest quintile had 2.71 times higher adjusted odds of utilisation compared with those in the poorest quintile. In other words, the ability to pay, and the enabling resources that come with it, such as transport, proximity to facilities, and the capacity to absorb the indirect costs of seeking care, appear to be among the dominant gatekeepers to formal healthcare in Tanzania. This gradient is a direct challenge to the principle of equitable access that underpins universal health coverage ambitions.

Against this backdrop, the finding on mental health need is quietly devastating. Proxy-identified mental health need was not significantly associated with formal healthcare utilisation. The adjusted odds ratio was 1.27, with a 95 percent confidence interval spanning 0.53 to 3.04 and a p-value of 0.588. While the point estimate leans in the direction of slightly higher utilisation among those with identified need, the interval is wide and crosses unity, meaning the data cannot distinguish the effect from no effect at all. People whose survey responses signalled severe difficulty remembering or concentrating were, statistically, no more likely to have seen a formal provider in the previous month than anyone else.

The authors were careful about the limits of that subgroup. Only 54 individuals carried the proxy-identified need flag, and missing outcome data affected 505 individuals in a pattern classified as Missing Not At Random, meaning that whether someone’s outcome was missing was related to the outcome itself. To probe how fragile the subgroup estimates might be, the team conducted an extreme bounds sensitivity analysis. The result was a stark illustration of statistical uncertainty: true utilisation in the subgroup could plausibly range from 2.0 percent to 92.7 percent. The authors therefore describe their findings for the mental health need subgroup as exploratory, a candid caveat that is rare and welcome in this literature.

That honesty matters, because the temptation in global mental health research is often to overread thin data. Here, the study does two things at once. It establishes with reasonable confidence the broad structural determinants of care-seeking in Tanzania, since the full sample of nearly 12,000 people provides substantial statistical power for the wealth, sex, age, and zone effects. At the same time, it demonstrates precisely why mental health need remains nearly invisible in household survey data: the affected group is small, the proxy is coarse, and the missingness is informative. The study is as much a methodological lesson as a substantive one, showing what national panels can and cannot reveal about the health-seeking behaviour of people with mental health conditions.

The policy implications, nonetheless, point in a clear direction. If wealth is the strongest predictor of who reaches a doctor, then removing financial barriers, through mechanisms such as fee exemptions, insurance expansion, or support for indirect costs, should sit at the centre of efforts to make healthcare utilisation more equitable in Tanzania. And if residents of some geographic zones systematically lag behind, then strengthening service availability in lower-utilisation regions becomes an equally urgent priority. For the mental health community specifically, the study suggests that integrating mental health need into general health system planning will require better measurement, larger samples, and data systems that do not lose the very people whose outcomes are hardest to capture. Until then, the quiet finding that mental health need does not move the needle on care-seeking in Tanzania stands as both a data point and a warning about how much the health system, and the surveys that measure it, still miss.

Subject of Research: Determinants of formal healthcare utilisation among adults with and without proxy-identified mental health need in Tanzania

Article Title: Patterns and determinants of formal healthcare utilisation among individuals with and without proxy-identified mental health need in Tanzania: A cross-sectional analysis of the National Panel Survey

Article References: Mlenge, G., Bilame, O., & Mwinuka, L. (2026). Patterns and determinants of formal healthcare utilisation among individuals with and without proxy-identified mental health need in Tanzania: A cross-sectional analysis of the National Panel Survey. PLOS Mental Health, 3(9), e0000644. https://doi.org/10.1371/journal.pmen.0000644

Image Credits: AI Generated

DOI: 10.1371/journal.pmen.0000644

Keywords: Tanzania, healthcare utilisation, mental health, health equity, household wealth, National Panel Survey, Washington Group Short Set, Andersen Behavioural Model, low- and middle-income countries, cross-sectional analysis, health policy, survey methods

The most striking demographic divide was generational. Among respondents aged 45 and older, 54.99 percent reported using traditional Chinese medicine services in the past year, compared with 47.21 percent of those under 45. That gap of nearly eight percentage points suggests that the therapeutic traditions of herbal medicine, acupuncture, tuina massage and related practices continue to resonate more strongly with older cohorts, even as younger Chinese consumers increasingly encounter wellness products and traditional remedies through modern commercial channels. The researchers also found a geographic gradient: urban registered residents reported a utilisation rate of 49.98 percent, while their rural counterparts lagged at 46.61 percent. Although the urban-rural difference was smaller than the generational one, it points to persistent structural inequities in who can actually reach traditional Chinese medicine providers, from hospital departments staffed by licensed practitioners to township clinics offering herbal prescriptions.

Methodologically, the study is notable for the care taken in sampling. The team used stratified multistage sampling across six cities in Hubei, a central Chinese province of roughly 58 million people that includes both the major metropolis of Wuhan and extensive rural hinterlands. By layering the sampling frame, the investigators ensured that their final sample of 3,639 respondents reflected the province’s demographic composition rather than over-representing easily surveyed urban populations. The researchers then applied multivariable logistic regression, a statistical technique that estimates the independent effect of each factor on the odds of service use while holding all other measured variables constant. Crucially, they did not stop at a single pooled model. Instead, they repeated the analysis separately within two strata, splitting the sample by generation, with 45 years as the cutoff, and by registered residence status, the hukou designation that in China still shapes access to schools, hospitals and social services.

The overall regression model identified several factors associated with higher utilisation. Employment in traditional Chinese medicine itself, or in integrated Chinese and Western medicine settings, was linked to greater use of these services, an intuitive finding that likely reflects both occupational exposure and cultural affinity among practitioners and their families. More telling was the role of supply: greater regional availability of traditional Chinese medicine resources was associated with higher utilisation, indicating that access to practitioners, pharmacies and clinics is a genuine bottleneck rather than a constraint that demand simply routes around. In health services research, this pattern is often described as supply-induced utilisation, and it carries a practical implication for policymakers, because expanding the physical footprint of traditional Chinese medicine services can measurably change who receives care.

Health status emerged as another powerful predictor, but in the opposite direction. Respondents who rated their own health as good were less likely to have used traditional Chinese medicine services in the previous year. This inverse association is consistent with a need-driven model of healthcare seeking: people who feel well have less reason to consult any provider, whether they prescribe herbs or pharmaceuticals. The finding also aligns with the traditional framing of Chinese medicine as a resource people draw on when managing chronic conditions, pain, or the accumulating ailments of middle and older age, rather than as a routine preventive service for the healthy. For health planners, the implication is that demand for traditional Chinese medicine will tend to rise with the burden of chronic disease, which is itself climbing as China’s population ages.

One of the study’s more intriguing results concerned household income. The middle income category showed a positive association with utilisation, but the upper category did not. This non-linear pattern complicates any simple assumption that wealthier households simply buy more traditional medicine. One plausible reading is that middle-income families have both the financial means and the motivation to seek these services, while the wealthiest households may preferentially use high-end Western-style private care or have less need for any subsidised service. The authors note that the observed patterns of association varied across the generation and registered-residence strata, meaning that a factor that predicted use among older or urban respondents might behave differently among younger or rural ones. This heterogeneity is precisely why the team argued for population-specific planning rather than one-size-fits-all policy.

The stratified analyses carry weight for China’s ongoing health equity debates. The hukou system, which ties social benefits to a person’s registered place of origin, has long produced a two-tier healthcare landscape in which rural migrants and rural residents face longer travel distances, thinner insurance coverage and fewer specialist options. The finding that rural respondents used traditional Chinese medicine services less often, despite these therapies being historically rooted in rural communities and folk practice, suggests that the modern institutional form of traditional Chinese medicine, delivered through hospitals and licensed clinics, has not fully penetrated the countryside. Meanwhile, the generational gap raises questions about whether younger Chinese, who grew up in an era of rapid biomedical expansion and digital health platforms, will sustain demand for traditional services as the current older cohort ages, or whether utilisation patterns will converge over time.

From a technical standpoint, the study’s design has both strengths and limits that readers should keep in mind. As a cross-sectional survey, it captures a snapshot in time and cannot establish causal direction: it remains possible, for example, that people who work in traditional medicine develop stronger cultural commitments, or that unmeasured health conditions drive both income patterns and service use. The analysis relied on self-reported utilisation over the previous twelve months, which is vulnerable to recall error, and the published materials indicate a complete-case analysis, meaning respondents with missing data on key variables were excluded. The team also encoded a local-customs indicator, reflecting regional cultural practices, which speaks to their effort to capture the cultural context that the abstract identifies as a determinant of use alongside health needs, socioeconomic resources and accessibility. Ethics approval was obtained from the Medical Ethics Committee of Wuhan University, and all participants provided written informed consent.

The research was supported by the National Natural Science Foundation of China, the Fundamental Research Funds for the Central Universities, and the Knowledge Innovation Project of Wuhan, with the funders playing no role in study design, analysis or the manuscript. The authors, including corresponding author Chao Wang, declared no competing interests. The article was published open access on 30 September 2026, making the full questionnaire, variable definitions and coding rules available in supplementary materials for other researchers to scrutinise or replicate.

What makes this study resonate beyond Hubei is its methodological lesson: aggregate statistics about traditional medicine use can mask the very disparities that policy most needs to address. A provincial average of nearly 49 percent tells planners little about where to build clinics, how to design insurance benefits, or which outreach programs might close the gap between urban and rural residents or between generations. By disaggregating the data and showing that the drivers of utilisation differ across strata, the Wuhan University team has provided a template for evidence-based planning of traditional medicine services, not just in China but in any health system where traditional and complementary therapies coexist with biomedical care. As China’s national strategy continues to promote the integration of Chinese and Western medicine, studies of this kind will be essential for ensuring that the promised benefits reach older and younger, urban and rural populations alike, rather than accruing only to those who already live closest to the services they need.

Subject of Research: Determinants of traditional Chinese medicine service utilisation across generations and urban-rural populations in Hubei Province, China

Article Title: Factors associated with traditional Chinese medicine service utilisation across generations and urban-rural populations in Hubei Province, China: a cross-sectional study

Article References: Huang, W.-J., Huang, Z.-P., Cai, Y.-M., Li, X., Zhu, R., Cheng, X., Lu, Z.-X., & Wang, C. (2026). Factors associated with traditional Chinese medicine service utilisation across generations and urban-rural populations in Hubei Province, China: a cross-sectional study. BMC Complementary Medicine and Therapies. https://doi.org/10.1186/s12906-026-05606-w

Image Credits: AI Generated

DOI: 10.1186/s12906-026-05606-w

Keywords: traditional Chinese medicine, healthcare utilisation, Hubei Province, urban-rural disparity, cross-sectional study, health equity, generational differences, logistic regression, complementary medicine, healthcare access, China, aging population