The research, conducted by Samia Islam Sara, Mohammad Lutfur Rahman and Jannatul Ferdous Momo of the Department of Urban and Regional Planning at Jahangirnagar University in Savar, Dhaka, set out to evaluate the housing quality of middle-income households in an urban area of Bangladesh. The team adopted a quantitative research approach, collecting data through a structured questionnaire administered to households in the study area. After screening, a total of 300 valid responses were included in the analysis, a sample size the authors justify with reference to established recommendations for survey research in the social sciences.

What makes the study methodologically interesting is the way it treats housing quality. Rather than measuring quality solely through objective indicators such as floor area or building age, the researchers captured residents’ own perceptions of their dwellings on an ordered scale. Because the outcome variable was ordinal, meaning households rated their housing quality in ranked categories rather than on a continuous scale, the team turned to ordinal logistic regression, a statistical technique designed for exactly this kind of ordered categorical outcome. The method, grounded in the framework developed by statistician Alan Agresti, models the probability that a household falls into a higher rather than a lower quality category as a function of multiple explanatory variables simultaneously.

Those explanatory variables were deliberately broad. The researchers drew on decades of housing literature to assemble nine domains of potential influence: dwelling unit characteristics, user comfort, neighborhood features, building services, structural quality, economic factors, maintenance, sustainability, and social support. This multidimensional framing reflects a shift in housing scholarship since the 1990s, when researchers such as Roderick Lawrence argued that housing quality cannot be reduced to physical structure alone but must encompass the environment, services, and social context in which a dwelling is embedded. Earlier work on residential environment quality and neighborhood attachment, as well as studies linking housing conditions to health outcomes, reinforced the case for treating housing as a system rather than a single object.

The headline finding is one of cautious adequacy. Most middle-income households in the study perceived their housing as fair to good, a result that suggests the group is neither thriving in high-quality formal housing nor trapped in the worst conditions of informal settlements. That middle position, however, is precisely why the authors argue the group deserves policy attention. International bodies, including the OECD in its analysis of the squeezed middle class, have documented how middle-income households in rapidly urbanizing economies face mounting pressure on housing affordability, often spending large shares of income on accommodation that may still fall short of comfort and service standards.

Within the regression results, a consistent set of predictors emerged as statistically significant. Crowding, meaning the number of occupants relative to the space available, was a key determinant, echoing a long line of environmental psychology research showing that household and community crowding degrade satisfaction and well-being. Ventilation also mattered, a finding with particular resonance in Dhaka’s hot, humid climate, where airflow shapes both thermal comfort and health. Access to essential services, the use of quality construction materials, affordability, and the presence of a supportive neighborhood each added explanatory power. In other words, the physical fabric of the dwelling, the economics of occupying it, and the social fabric around it all contributed independently to how residents judged their housing.

The significance of neighborhood effects deserves emphasis. The finding that supportive neighborhoods predict perceived housing quality aligns with a substantial body of evidence, including studies of socially contrasting neighborhoods in the United Kingdom and research on low-cost housing in Malaysia, showing that satisfaction with place depends heavily on social and environmental conditions beyond the front door. For urban planners in developing countries, this suggests that investments in street-level infrastructure, safety, and community facilities may yield returns in perceived housing quality that are comparable to improvements in the dwellings themselves.

The study’s policy implications are framed explicitly for decision-makers. The authors state that the research provides evidence for policymakers and urban planners seeking to enhance housing standards, especially for middle-income groups in rapidly growing cities, particularly in developing countries. Because the significant predictors are actionable, reducing crowding through floor-area and occupancy standards, enforcing ventilation requirements in building codes, expanding access to essential services such as water and sanitation, promoting quality materials, and addressing affordability, the findings translate relatively directly into regulatory and programmatic levers. The work also situates itself within the United Nations Sustainable Development Goals framework, which calls for adequate, safe and affordable housing for all, and within UN-Habitat’s recent emphasis on placing housing at the centre of urban futures.

The research team is careful to acknowledge the limits of a single-city, single-income-group survey. Perceptions of housing quality are shaped by cultural expectations, as theorized in the work of Amos Rapoport on culture, architecture and design, and by the specific housing market conditions of Dhaka, one of the world’s most densely populated cities. The authors therefore propose that future research could conduct a comparative analysis across different income groups in rapidly urbanizing cities, which would reveal whether the drivers of housing quality identified here hold for lower-income households facing more acute deprivation, or for higher-income households with greater choice, and whether they generalize across other developing-country contexts.

For a field often dominated by case studies of slums on one side and gated developments on the other, the study fills a genuine gap by quantifying the experience of the urban middle. Its message is ultimately an optimistic one: the factors that most strongly shape how middle-income residents judge their homes are measurable, comparable and, in principle, fixable. As cities across South Asia, Africa and Latin America absorb hundreds of millions of new residents in the coming decades, evidence of this kind, grounded in the stated perceptions of the households themselves, will be essential for steering urban growth toward housing that is not merely sheltered but genuinely adequate.

Subject of Research: Statistical assessment of perceived housing quality among middle-income urban households in Bangladesh

Article Title: Housing quality assessment in an urban area: perspectives from the context of a developing country

Article References: Sara, S. I., Rahman, M. L., & Momo, J. F. (2026). Housing quality assessment in an urban area: perspectives from the context of a developing country. SN Social Sciences, 6(11), Article 555. https://doi.org/10.1007/s43545-026-01870-2

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01870-2

Keywords: housing quality, middle-income households, Bangladesh, Dhaka, ordinal logistic regression, urban planning, housing affordability, crowding, ventilation, neighborhood effects, developing countries, sustainable development

The research, led by Serah Kabui Kahuri and colleagues, focused on the Laisamis and Saku sub-counties, an ecologically diverse region of roughly 26,104 square kilometers within Kenya’s arid and semi-arid lands. These rangelands are critical to the national economy: they cover more than 70 percent of Kenya, host most of the country’s national parks and reserves, and support over 75 percent of its cattle herds. Kenya’s pastoral sector is valued at approximately US$1.13 billion, with livestock accounting for 92 percent of that figure. Yet degradation continues through overgrazing, the spread of invasive species such as Acacia reficiens and Opuntia stricta, subdivision and fencing of land, and the erosion of traditional ecological governance.

Protection Motivation Theory, originally developed to explain protective health behavior, proposes that people act when they appraise both a threat and their capacity to cope with it. Threat appraisal combines perceived vulnerability and perceived severity, while coping appraisal includes self-efficacy, response efficacy, and response costs. The research team extended this framework by adding two constructs rarely tested in restoration studies: perceived political willingness, meaning pastoralists’ belief that the government genuinely prioritizes restoration, and perceived incentives, meaning the expected economic benefits from restoring degraded land. Reliability analysis confirmed that these constructs adequately captured the psychological, institutional, and economic dimensions of restoration behavior.

Collecting data from mobile pastoralist communities posed a methodological challenge. Because settlements shift spatially and temporally, the 2019 census could not serve as a reliable sampling frame. Instead, the researchers generated 10-metre-resolution land-use and land-cover change maps from fused Landsat and Sentinel-2 imagery to identify settled areas, then validated potential homesteads through community consultations with local chiefs and key informants. A fixed allocation of 25 household representatives was drawn from each of seven administrative units, targeting 175 respondents. After 15 refusals and the exclusion of 38 questionnaires with substantial missing data or uniform response patterns, 122 valid observations remained for analysis.

The dependent variable, pro-environmental behavior, was measured on a five-point Likert scale covering actions such as rotational grazing, planting native grasses and trees, reducing livestock density, and investing in fencing, water management, or reseeding. Because extreme categories were sparse, responses were collapsed into three ordinal levels: weak, modest, and strong. Ordinal logistic regression models, estimated by maximum likelihood and validated against an ordered probit specification, assessed how the extended theory constructs influenced behavior. The Brant test confirmed that the proportional odds assumption held in all models.

The results were unambiguous. Response efficacy emerged as the strongest predictor of restoration participation, with a coefficient of 1.629, meaning pastoralists who believe restoration measures actually work are far more likely to engage. Perceived vulnerability followed at 1.203, then self-efficacy at 1.150 and perceived incentives at 0.811. Perceived political willingness also had a significant positive effect at 0.571. In contrast, response costs, the perceived expense, labor burden, and long time lag before benefits materialize, had a significant negative effect, with a coefficient of −0.911 in the baseline model. Notably, perceived severity and the rewards of continuing current practices showed no statistically significant relationship with restoration behavior.

The marginal effects sharpen the picture. A unit increase in response efficacy reduces the probability of weak pro-environmental behavior by roughly 0.109 percentage points while raising the probability of strong behavior by about 0.252 percentage points. A unit increase in response cost does the reverse, shifting pastoralists toward weaker engagement. Perceived incentives and political willingness similarly shift respondents up the behavioral scale. Overall, the study demonstrates that coping appraisal, beliefs about one’s ability and the effectiveness of the response, exerts a greater influence on restoration behavior than threat appraisal, suggesting that awareness campaigns alone are insufficient to trigger sustained action.

Demographic patterns added further nuance. Men constituted 66.4 percent of respondents, reflecting the male-dominated nature of pastoral activities in northern Kenya, though the substantial share of women participants signals a shift toward gender equity in pastoral labor, likely driven by male outmigration and livelihood diversification. Education was strikingly low, with 69.7 percent of respondents lacking formal schooling, and 72.1 percent earning below 5,000 Kenyan shillings monthly. These constraints matter: prior research has identified income limitations as the strongest predictor of non-adoption of restoration practices in arid Kenya, and low literacy complicates engagement with formal instruments such as Land Degradation Neutrality frameworks or payments for ecosystem services.

The significance of perceived political willingness points to the visible role of ongoing initiatives. Projects supported by UNDP and the Global Environment Facility have delivered in situ restoration, training, and in-kind inputs under the Nature and People as One RAMAT model, while The Restoration Initiative, running from 2018 to 2025, provided community capacity building, water-management works, and seeds. Since 2009, county governments and NGOs have also supported Environmental Management Committees responsible for sustainable woody-vegetation harvesting. Pastoralists who observe such tangible government commitment appear more willing to invest their own labor and resources in restoration, reinforcing a virtuous cycle of trust and participation.

The authors argue that their findings provide empirical support for the extended Protection Motivation Theory and carry direct policy implications for achieving Land Degradation Neutrality under Sustainable Development Goal indicator 15.3.1. Restoration policies, they contend, should strengthen local capacity through training and extension services, demonstrate the effectiveness of interventions via pilot sites and demonstration projects, provide appropriate financial and non-financial incentives such as input subsidies and payments for ecosystem services, and reinforce governance support alongside conventional awareness-raising. Because the analysis relies on cross-sectional data from a single pastoral context, the researchers caution that the findings identify associations rather than definitive causal relationships, and they recommend longitudinal designs, Bayesian approaches, and governance-extended structural equation modeling in future work, along with closer study of social networks, risk preferences, and customary institutions that shape collective restoration action.

Subject of Research: Psychological and institutional determinants of pastoralists' participation in rangeland restoration in Marsabit County, Kenya

Article Title: Determinants of pastoralists’ participation in rangeland restoration in Laisamis and Saku sub-counties, Marsabit County, Kenya

Article References: Kahuri, S. K., Kwakwa, P. A., Kumi, S., & Wireko-Gyebi, R. S. (2026). Determinants of pastoralists’ participation in rangeland restoration in Laisamis and Saku sub-counties, Marsabit County, Kenya. Frontiers in Environmental Economics, 5, Article 1861585. https://doi.org/10.3389/frevc.2026.1861585

Image Credits: AI Generated

DOI: 10.3389/frevc.2026.1861585

Keywords: rangeland restoration, pastoralism, Kenya, Protection Motivation Theory, land degradation neutrality, drylands, pro-environmental behavior, ordinal logistic regression, perceived incentives, political willingness, Marsabit County, SDG 15

The research team, led by Carl Berghult, Joseph Mellberg, Lachlan Anderson, Erica Olfson, and David Shau of the Anne Marion Burnett School of Medicine at Texas Christian University and the Texas Hip and Knee Foundation in Fort Worth, undertook an ambitious cross-sectional analysis of the entire population of orthopaedic surgery residents in ACGME-accredited programs. Rather than sampling, they assembled a census: 3,844 residents across 215 orthopaedic residency programs. The investigators built their dataset from publicly accessible sources, including program rosters, institutional directories, social media profiles, and medical school websites, a method that allowed them to map the educational origins of virtually every orthopaedic resident in the United States without touching any private or identifiable information.

The analytical design is what gives the study its distinctive angle. Because the researchers could not determine the exact date each resident sat for Step 1, they used postgraduate year, or PGY level, as a temporal proxy for the scoring era. Residents in their first through third years of training, PGY-1 through PGY-3, would have entered the Match during or after the transition period surrounding the pass/fail switch, while PGY-4 and PGY-5 residents represented cohorts that matched under the old numeric scoring regime. This stratification allowed the team to compare whether the relationship between medical school characteristics and residency program rank differed between Match eras, effectively using the resident population itself as a natural archive of the transition.

The central finding is striking. Among PGY-1 through PGY-3 residents, the more recent Match-era cohorts, medical school rank correlated significantly with the rank of the residency program the graduate ultimately joined. Among PGY-4 and PGY-5 residents, who matched under numeric Step 1 scoring, no such significant association was observed. In other words, before the scoring change, a graduate’s medical school pedigree showed no measurable statistical relationship with the standing of their orthopaedic program; after the transition, that relationship emerged. The authors are careful to frame this as a temporal association observed in an observational, cross-sectional study, not proof of causation, but the pattern is consistent with the hypothesis that when a familiar screening metric disappears, selection processes shift toward other signals, including institutional prestige.

The magnitude of some institutional effects deserves attention. Graduates of Tier 1 medical schools, as ranked by U.S. News & World Report, had the highest median residency program rank in the dataset, at 47. Even more dramatic was the divide between allopathic and osteopathic graduates: MD students had approximately ten-fold higher odds of training in a higher-ranked orthopaedic residency program compared with DO students. The study also examined what the authors call orphan status, meaning medical schools with no affiliated orthopaedic residency program of their own. Graduates of such schools faced roughly 40 percent lower odds of matching into a higher-ranked program. These are not trivial differences; they suggest that structural features of a medical school, some of them dating back to decisions made long before an applicant ever matriculated, are woven into match outcomes.

Methodologically, the team treated residency program rank as an ordinal outcome and deployed a battery of statistical tools appropriate to that structure. Chi-square tests assessed categorical associations, while univariate and multivariate ordinal logistic regression quantified the odds of landing in a higher-ranked program across school characteristics, controlling for other variables. Spearman correlation coefficients measured the rank-order relationship between medical school rank and residency program rank. Ordinal logistic regression is particularly well suited to this kind of question because residency program quality is inherently a ranked, non-continuous quantity, and the approach allows researchers to model the probability of falling into a higher rather than a lower category without assuming equal spacing between ranks.

The categorization scheme the researchers used reveals how many dimensions of medical school identity they considered. Beyond the U.S. News & World Report rankings and the Tier 1 designation, schools were classified as allopathic versus osteopathic, public versus private, and orphan versus non-orphan status. Each of these categories captures a different facet of institutional infrastructure. Allopathic versus osteopathic status reflects two parallel systems of medical education in the United States with different historical pathways to licensure. Public versus private status often correlates with funding structures and state-based admissions pipelines. Orphan status speaks to the presence or absence of an in-house orthopaedic residency, which can shape access to mentorship, research opportunities, letters of recommendation from orthopaedic faculty, and early exposure to the specialty, all factors long suspected to influence match success.

Why would the pass/fail transition amplify the role of institutional characteristics? The logic articulated in the study is straightforward. Before 2022, Step 1 scores served as a standardized, nationwide screening tool that programs could apply uniformly to every applicant, regardless of origin. With the numeric signal gone, programs lost their most convenient common denominator and may have turned to alternative applicant characteristics to fill the gap. Where a candidate went to school, whether that school hosts its own orthopaedic department, and the degree-granting tradition behind the diploma are all visible on an application in ways that raw ability is not. If programs lean more heavily on such proxies, the statistical imprint of institutional background on match outcomes would be expected to deepen, exactly the pattern the PGY-stratified analysis detected.

The authors are explicit about the limitations that bound their conclusions. The use of PGY level as a proxy for Step 1 scoring era is an approximation, not a direct measurement, since individual examination dates were unavailable and some residents delay graduation or take the examination at different points in their training. The cross-sectional design captures a single snapshot of the resident population rather than following individuals over time, and observational data of this kind cannot establish that the scoring change caused the observed associations. Residency program rank itself, derived from reputation-based rankings, is an imperfect measure of training quality. The study also relied on publicly available information, which, while ethically clean and free of privacy concerns, may contain occasional inaccuracies in self-reported credentials or affiliations.

Nevertheless, the implications ripple outward across medical education. For applicants, the findings suggest that in the pass/fail era, the road to a top orthopaedic residency may run more visibly through institutional channels, making mentorship, research access, and home-program support more decisive than ever. For osteopathic medical students and graduates of schools without affiliated orthopaedic residencies, the ten-fold and 40 percent odds differences quantify a structural headwind that predates any individual performance. For residency programs and policy makers, the results raise a pointed question about equity: if removing a standardized numeric metric shifts selection toward characteristics that track institutional privilege, the profession may need to develop new, fairer ways to evaluate the growing pool of pass/fail-era applicants. With no external funding and no declared competing interests, the study offers an open-access, data-driven starting point for that conversation as the first fully pass/fail-era Match cohorts continue to move through training.

Subject of Research: The association between medical school characteristics and orthopaedic residency match outcomes before and after the USMLE Step 1 pass/fail transition

Article Title: Medical school ranking and orthopaedic match outcomes in the USMLE Step 1 pass/fail era: a PGY-stratified analysis

Article References: Berghult, C., Mellberg, J., Anderson, L., Olfson, E., & Shau, D. (2026). Medical school ranking and orthopaedic match outcomes in the USMLE Step 1 pass/fail era: a PGY-stratified analysis. BMC Medical Education. https://doi.org/10.1186/s12909-026-10594-z

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10594-z

Keywords: orthopaedic surgery residency, USMLE Step 1, pass/fail scoring, medical school ranking, residency match, medical education, applicant selection, MD versus DO, ordinal logistic regression, ACGME, PGY-stratified analysis, health equity

The analytical foundation of the study is a cross-sectional secondary analysis of 1,600 adults who gave valid responses to the hesitancy item in the 2024 Turkish General Social Survey. Responses were recorded on a five-point agreement scale, from strong disagreement to strong agreement. The researcher treated the item in two complementary ways: descriptively, as a binary indicator of hesitancy, and analytically, as an ordinal outcome that preserves the graded intensity of doubt. Covariates were organized into three conceptual domains — sociodemographic and socioeconomic characteristics, health-related characteristics, and information, belief, and attitudinal factors. All analyses were unweighted, and the core statistical engine was ordinal logistic regression, a technique suited to outcomes that are ordered but not continuous.

Ordinal logistic regression estimates the odds of falling into a higher rather than a lower category of an ordered outcome, and its results are expressed as odds ratios with 95 percent confidence intervals. The modeling strategy proceeded in two stages. Univariable models first screened each covariate individually for an association with hesitancy. A multivariable model then adjusted simultaneously for all covariates, allowing the researcher to identify which factors retained independent associations after accounting for the others. The researcher also reported the Akaike information criterion, a measure that penalizes model complexity and helps compare the fit of competing models, and used variance inflation factors to check for multicollinearity — the statistical problem that arises when predictor variables overlap so heavily that their individual effects become impossible to separate.

The results paint a striking picture of hesitancy as a social and attitudinal phenomenon rather than a medical one. After adjustment, higher vaccine hesitancy was independently associated with being aged 35 to 44 years, with an adjusted odds ratio of 1.67 and a confidence interval of 1.14 to 2.44. Having only a primary school education raised the odds by a factor of 1.44, with a confidence interval spanning 1.07 to 1.93, and living in the Aegean region carried an adjusted odds ratio of 1.48, ranging from 1.05 to 2.09. Higher self-rated religiosity also predicted greater hesitancy. In other words, midlife adults with limited formal education, stronger religious self-identification, and residence in a specific geographic context formed the demographic core of vaccine skepticism in the sample.

The protective side of the ledger is equally informative. University education was associated with lower hesitancy, with an adjusted odds ratio of 0.68 and a confidence interval of 0.51 to 0.90, and a master’s degree was associated with a far steeper reduction, at an adjusted odds ratio of 0.40 with a confidence interval of 0.22 to 0.73. The single most powerful attitudinal buffer was confidence in scientists: respondents with high trust in the scientific community had an adjusted odds ratio of 0.52, with a confidence interval of 0.32 to 0.85, meaning their odds of scoring higher on the hesitancy scale were roughly half those of people lacking that confidence. The gradient across educational levels suggests that each additional tier of schooling compounds the effect, a dose-response pattern that strengthens the causal plausibility of the education-hesitancy link.

Perhaps the most surprising finding concerns health itself. Most health-related characteristics — the conditions one might expect to shape personal experience with medicine — were not independently associated with hesitancy once sociodemographic and attitudinal factors were taken into account. The main exception was arthritis or rheumatism, which retained its own association with higher hesitancy. The study also found that general vaccine hesitancy was not significantly associated with COVID-19 vaccine uptake, a decoupling that suggests people may accept a specific emergency vaccine while still harboring generalized doubts about immunization, or vice versa. For public health planners, this means that the population most likely to refuse vaccines cannot be reliably identified from medical records or chronic disease registries alone.

The methodological choices deserve scrutiny because they shape what the study can claim. By analyzing hesitancy as an ordinal outcome, the study respects the psychological reality that doubt comes in degrees — someone who slightly agrees that vaccines do more harm than good is not equivalent to someone who strongly agrees. The unweighted analysis is a limitation worth noting: because the survey data were not weighted to match the national population structure, the reported associations describe the analytic sample rather than precise national estimates. The cross-sectional design also means that all findings are correlational; the study can identify which factors travel together with hesitancy, but it cannot establish whether education, for example, causes lower hesitancy or whether both are downstream of some deeper variable such as cognitive style or social trust.

The regional signal from the Aegean adds a geographic layer that is easy to overlook in attitudinal research. Türkiye’s regions differ in economic structure, health service density, and cultural orientation, and the NUTS classification used in the study allows these contextual differences to enter the statistical model directly. Why the Aegean stands out is not answered by the data, but the finding echoes a broader lesson from vaccination research: hesitancy clusters geographically, and regional context can carry information that individual-level measures miss. Clustering matters practically because localized pockets of low acceptance can sustain disease outbreaks even in populations with acceptable overall coverage, as measles resurgences in several countries have demonstrated.

The prominence of religiosity and trust in scientists places this study squarely within the international literature on the behavioral and social drivers of vaccination, the framework that the World Health Organization’s Strategic Advisory Group of Experts on Immunization has promoted through instruments such as the BeSD framework. Across many countries, generalized trust — trust in institutions, in expertise, in the people who make and regulate vaccines — has proven to be a more consistent predictor of acceptance than any particular medical fact. The Turkish findings align with that pattern: confidence in scientists cut the odds of hesitancy nearly in half, while religiosity raised them, and both effects survived adjustment for education, age, region, and health status. Attitudes, not biology, appear to carry the explanatory weight.

What emerges is a coherent set of priorities for intervention. Communication campaigns that simply restate vaccine safety may be preaching to the converted if they reach audiences whose skepticism is rooted in low institutional trust or in religious framing that the messages never address. The strong educational gradient points to the long game — investment in education as structural immunization policy — while the centrality of trust in scientists suggests that trusted local messengers, including physicians who hold community credibility, may be more effective than centralized messaging. The study’s limitations, including its cross-sectional design, unweighted analyses, and reliance on a single general hesitancy item, mean the findings should be read as a map of associations rather than a causal blueprint. But as a nationally grounded snapshot of vaccine doubt in a country of over 85 million people, the analysis delivers a clear message: to understand hesitancy, look first at who people are, where they live, and whom they trust — not merely at what illnesses they carry.

Subject of Research: Sociodemographic, health-related, and attitudinal correlates of general vaccine hesitancy among adults in Türkiye

Article Title: Vaccine hesitancy in Türkiye: sociodemographic, health-related, and attitudinal correlates in the 2024 Turkish general social survey

Article References: Sönmez, Ö. F. (2026). Vaccine hesitancy in Türkiye: sociodemographic, health-related, and attitudinal correlates in the 2024 Turkish general social survey. BMC Public Health. https://doi.org/10.1186/s12889-026-29795-8

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29795-8

Keywords: vaccine hesitancy, Türkiye, Turkish General Social Survey, public health, trust in scientists, religiosity, education, ordinal logistic regression, BMC Public Health, immunization, health behavior, social determinants

The research team, led by Xinke Liu and Mingqing Wei of Dongzhimen Hospital at Beijing University of Chinese Medicine, together with colleagues including Jing Shi and Jinzhou Tian, conducted a cross-sectional online survey of physicians in January 2025. The final analytic sample comprised 393 physicians drawn from 27 provincial-level regions of China, a geographic spread that gives the results unusually broad coverage for a study of this kind. Rather than simply asking doctors for general impressions, the investigators built a dedicated measurement instrument called the Chinese Medicine Service Quality scale, or CMSQ, which integrates general service-quality principles familiar from mainstream health services research, the philosophy of patient-centered care, and service characteristics that are specific to traditional Chinese medicine, such as herbal decoction services and the evaluation of TCM clinical practice.

The technical rigor of the scale’s validation is a central feature of the study. The six-domain CMSQ demonstrated high internal consistency, with a total Cronbach’s alpha of 0.945 and domain-level alphas ranging from 0.784 to 0.896, values that indicate the items within each domain reliably measure a coherent underlying construct. Confirmatory factor analysis supported the proposed six-factor structure, with a comparative fit index of 0.946, a Tucker-Lewis index of 0.938, a root mean square error of approximation of 0.056, and a standardized root mean square residual of 0.053, all of which fall within conventional thresholds for acceptable model fit. Importantly, the authors also tested measurement invariance and found it supported across hospital levels and hospital types, meaning the instrument measures the same constructs in tertiary referral centers and smaller community institutions alike, a prerequisite for fair comparisons between different tiers of the Chinese health system.

When the scores themselves were examined, a clear pattern emerged. Physicians rated pre-visit responsiveness most favorably, with a domain mean of 4.31 on the scale, followed closely by appointment service accessibility at 4.30. These findings suggest that the front end of the TCM care pathway, the scheduling systems and the way institutions respond to patient inquiries before an appointment, has improved considerably and is now seen by providers as a relative strength. At the other end of the ranking, medication supply services received the lowest domain rating at 3.96, and within that domain, the waiting time for herbal decoctions stood out as the single lowest-rated item in the entire survey, with a mean of 3.61 and a standard deviation of 1.22. For a system in which individually prepared herbal formulas are a cornerstone of treatment, a bottleneck at the decoction stage represents a tangible friction point that patients experience directly.

The subgroup analyses added a geographic and institutional dimension to the picture. Among the physician and institutional characteristics examined, city tier showed the most consistent differences in service-quality ratings, indicating that the level of urban development of a physician’s location is more strongly associated with perceived service quality than factors such as hospital level or hospital type. This pattern echoes well-known disparities in Chinese healthcare more broadly, where megacities concentrate resources, specialist expertise, and modern infrastructure, while smaller cities often struggle to match the same operational standards. The fact that such disparities appear even in physicians’ assessments of their own institutions underscores how deeply structural these differences are.

Perhaps the most consequential analysis concerned the link between service quality and physicians’ willingness to recommend their institutions, a proxy for institutional loyalty and confidence that has parallels in patient recommendation metrics used worldwide. Using ordinal logistic regression with adjustment for covariates, the researchers found that higher recommendation intention was associated with three domains in particular: pre-visit responsiveness, with an adjusted odds ratio of 1.278 and a 95 percent confidence interval of 1.112 to 1.469 and a p value below 0.001; medication supply services, with an adjusted odds ratio of 1.132 and a confidence interval of 1.036 to 1.237 and a p value of 0.006; and TCM practice evaluation, with an adjusted odds ratio of 1.144 and a confidence interval of 1.019 to 1.284 and a p value of 0.022. In plain terms, physicians who felt their institutions were responsive before visits, reliable in supplying medications, and supportive of quality TCM clinical practice were significantly more likely to speak well of those institutions.

To capture the physicians’ own priorities in their own words, the team also collected open-ended suggestions and analyzed 283 substantive responses using latent Dirichlet allocation, a topic-modeling technique that identifies recurring themes in large text corpora without imposing predefined categories. The modeling surfaced three broad clusters of priorities. The first concerned institutional capacity and clinical standardization, reflecting a desire for stronger organizational foundations and more consistent clinical protocols. The second centered on clinical quality, affordability, and the care experience, linking the technical excellence of TCM practice to the financial accessibility of services and the overall patient journey. The third cluster addressed operational management, workforce development, and service accessibility, pointing to the everyday machinery of scheduling, staffing, training, and logistics that determines whether a well-designed service model actually functions in practice.

Taken together, the results sketch a system in which the visible, patient-facing elements of TCM care have advanced faster than the back-office operations that support them. Physicians clearly appreciate improvements in responsiveness and appointment access, but the weak ratings for medication supply and decoction waiting times suggest that the supply chain for herbal medicines, from procurement through preparation to dispensing, has become the limiting factor in perceived service quality. The regression findings reinforce this interpretation, because medication supply services emerged as one of only three domains independently associated with recommendation intention. An institution that cannot reliably deliver the medicines its physicians prescribe undermines both clinical trust and staff morale, regardless of how skilled its practitioners may be.

The study also carries methodological significance for the broader field of complementary and integrative medicine research. Service-quality assessment in TCM has long been hampered by the absence of validated, provider-centered instruments that respect the modality’s distinctive features, from individualized herbal formulations to the integration of traditional diagnostic methods with modern clinical workflows. By demonstrating strong reliability, acceptable confirmatory factor fit, and measurement invariance across institutional strata, the CMSQ offers researchers and administrators a reusable framework that can be deployed in future surveys, quality-improvement programs, and policy evaluations. The authors position it as a preliminary but psychometrically supported, physician-centered instrument, and its open-access publication means that other teams can scrutinize, replicate, and extend the validation work.

The authors, whose work was supported by the Strategic Research and Consulting Project of the Chinese Academy of Engineering and several provincial and national research programs, emphasize that strengthening operational reliability must go hand in hand with enhancing TCM-specific clinical quality and organizational support. The study was approved by the Institutional Review Board of Dongzhimen Hospital and conducted in accordance with the Declaration of Helsinki. As traditional Chinese medicine continues to expand within China’s integrated health system and attracts growing international interest, this survey provides a rare quantitative baseline from the people best placed to judge where the system works and where it strains: the physicians on the front line. Their message is consistent and actionable, celebrating genuine gains in accessibility and responsiveness while demanding focused investment in the medication supply chain, workforce development, and the standardization of clinical practice that will determine whether the next generation of TCM services can match the expectations of both providers and patients.

Subject of Research: Physician-assessed service quality of traditional Chinese medicine healthcare in China

Article Title: Assessing traditional Chinese medicine service quality from physicians’ perspectives: a nationwide survey in China

Article References: Liu, X., Wei, M., Ni, J., Li, T., Yao, L., Li, F., Shi, J., & Tian, J. (2026). Assessing traditional Chinese medicine service quality from physicians’ perspectives: a nationwide survey in China. BMC Complementary Medicine and Therapies. https://doi.org/10.1186/s12906-026-05549-2

Image Credits: AI Generated

DOI: 10.1186/s12906-026-05549-2

Keywords: traditional Chinese medicine, service quality, physician survey, health services research, CMSQ scale, psychometric validation, medication supply, herbal decoction, patient-centered care, China, ordinal logistic regression, topic modeling

The research team, led by Muhammad Fauzan Azima A and Robert Kurniawan of the Polytechnic of Statistics STIS in Jakarta, together with colleagues from Universitas Sumatera Utara and several Jakarta and Surabaya universities, chose Java for a reason that may seem paradoxical at first glance. Despite recording relatively low readings on the Air Quality Index compared with some of the world’s most notoriously polluted regions, Java carries an exceptionally high burden of pneumonia among children under five. That mismatch suggested to the authors that pollution concentrations alone cannot explain the island’s pediatric respiratory crisis, and that the vulnerability of the population, driven by dense settlement and industrial activity, must be built into any credible assessment of risk.

To capture that vulnerability, the researchers assembled a multilayered dataset covering five major air pollutants: fine particulate matter known as PM2.5, nitrogen dioxide, ground-level ozone, sulfur dioxide, and methane. Alongside these they incorporated climatic variables that influence how pollutants disperse, accumulate, or linger over the landscape, and two structural sensitivity factors, industrial density and population density, that describe how many people and how much polluting economic activity are concentrated in a given area. The conceptual framework draws on established vulnerability science, in which risk emerges not only from the hazard itself but from the exposure of populations and their sensitivity to harm, a perspective rooted in widely cited frameworks for vulnerability analysis in sustainability science.

The methodological heart of the study is a family of geographic clustering techniques designed to group districts and cities according to their overall air pollution vulnerability. Conventional clustering methods treat every observation as independent, which can scatter related regions into different categories and produce maps that make little spatial sense. The authors therefore tested a spatially constrained approach known as the Spatial Generalized Fuzzy C-Means method, or SGFCM. Fuzzy clustering, unlike hard classification, allows each region to hold partial membership in several clusters at once, reflecting the reality that vulnerability grades smoothly across administrative boundaries. Adding the spatial component penalizes classifications that place neighboring regions into wildly different categories, yielding contiguous, interpretable zones of risk.

After comparing clustering configurations, the team found that the SGFCM method with three clusters produced the most favorable evaluation results, striking the best balance between statistical fit and spatial coherence. The resulting map divided Java into low, moderate, and high pollution vulnerability zones. The geographic pattern was striking: clusters with elevated and moderate air pollution vulnerability were concentrated predominantly in the western and eastern portions of the island. Western Java encompasses the sprawling Jakarta metropolitan area, one of the largest urban agglomerations on Earth, while eastern Java hosts major industrial corridors and densely populated urban centers around Surabaya. The central corridor of the island, by contrast, contains more of the lower-vulnerability territory, much of it rural and less industrialized.

With the vulnerability zones established, the researchers turned to the central question of the study: does membership in a high or moderate vulnerability cluster actually correspond to higher rates of toddler pneumonia? They employed association analysis to test the relationship between cluster membership and pneumonia incidence categories, and then applied ordinal logistic regression, a statistical technique suited to outcome variables that fall into ordered categories, to quantify the strength of the relationship while accounting for the ordered nature of the pneumonia prevalence data. Ordinal regression has proven valuable in earlier health research for modeling progressive outcomes, and it allowed the team to estimate how the odds of falling into a higher pneumonia prevalence category changed with the vulnerability classification of a region.

The answer was unambiguous. Regions falling within the moderate or high air pollution vulnerability clusters were 2.67 times more susceptible to elevated toddler pneumonia prevalence than regions in the low pollution clusters. The study’s authors emphasize that this association does not single out one pollutant as the culprit; rather, it demonstrates that the composite condition of high exposure combined with high sensitivity, the double burden referenced in the study’s title, is what tracks most closely with the disease burden carried by the island’s youngest residents. Fine particulates such as PM2.5 are small enough to penetrate deep into developing lungs and have been linked in prior research to pneumonia hospitalizations in children across China, Southeast Asia, and beyond. Nitrogen dioxide, largely a byproduct of traffic and combustion, has previously been associated with acute respiratory tract infections in Indonesian children in earlier studies, giving biological plausibility to the spatial patterns the team uncovered.

What distinguishes this study from much of the existing literature is its treatment of place rather than individual exposure. By clustering whole districts and cities, the approach speaks directly to the geography of risk, identifying which territories deserve priority intervention. That matters for policy in a country where air quality management is coordinated nationally but experienced locally. Indonesia’s Ministry of Environment and Forestry has maintained an Air Pollutant Standard Index regime and a strategic plan targeting air pollution control, and the Ministry of Health publishes national health profiles tracking childhood pneumonia. Yet the new findings suggest that policies calibrated to AQI readings alone may systematically underestimate risk in places where dense populations and heavy industry amplify the consequences of a given pollution load. A moderate pollutant concentration in a district packed with families and factories may exact a far greater toll in pediatric pneumonia than a nominally higher concentration in a sparsely settled area.

The authors are careful about the limitations inherent in an ecological design. The analysis links regional-level environmental and structural indicators with regional-level disease statistics, so it cannot determine which individual children fell ill or disentangle every pathway connecting pollution to infection. Pneumonia in toddlers is multifactorial, influenced by nutrition, vaccination coverage, housing conditions, healthcare access, and indoor air quality from cooking fuels, factors that operate alongside ambient pollution. Data availability is also constrained; the team notes that the underlying data will be made available on reasonable request, reflecting the reliance on official statistics and remote sensing-derived environmental measurements. Still, the consistency of the association, with nearly a threefold elevation in the odds of high pneumonia prevalence in vulnerable clusters, is difficult to dismiss as statistical noise.

The implications stretch well beyond Java. Rapidly urbanizing regions across South and Southeast Asia face the same configuration of dense population, expanding industry, and rising pollutant loads, and the Health Effects Institute’s State of Global Air reporting has repeatedly flagged children’s health as a central casualty of the region’s air quality crisis. The Java study offers those regions a transferable template: spatially constrained fuzzy clustering of exposure and sensitivity data can generate actionable vulnerability maps even where dense networks of ground-level pollution monitors are lacking, because satellite-derived pollutant estimates can stand in for sparse instrumentation. For Indonesian policymakers, the immediate message is concrete. The western and eastern vulnerability belts identified by the study, home to tens of millions of people and countless children under five, are the territories where targeted measures, from industrial emission controls and traffic management to strengthened pediatric respiratory services, are most likely to reduce the island’s stubborn burden of toddler pneumonia. The study’s authors frame their work as a direct aid to stakeholders crafting air pollution management policy, and with a nearly threefold difference in disease risk separating the most and least vulnerable zones, the map they have drawn is one that public health officials in Jakarta, Surabaya, and beyond can hardly afford to ignore.

Subject of Research: Spatial association between air pollution exposure-sensitivity clusters and toddler pneumonia incidence on Java Island, Indonesia

Article Title: The double burden of air pollution exposure and sensitivity: association with toddler pneumonia in high-density areas of Java

Article References: Azima. A, M. F., Kurniawan, R., Gio, P. U., Hikmah, H., Wongsonadi, S. K., Lestari, G. D., & Yuniarto, B. (2026). The double burden of air pollution exposure and sensitivity: association with toddler pneumonia in high-density areas of Java. Air Quality, Atmosphere & Health, 19(9), Article 203. https://doi.org/10.1007/s11869-026-02088-0

Image Credits: AI Generated

DOI: 10.1007/s11869-026-02088-0

Keywords: air pollution, toddler pneumonia, spatial clustering, fuzzy c-means, PM2.5, nitrogen dioxide, Java Island, vulnerability, ordinal logistic regression, Indonesia, public health, urban air quality