The research team, led by investigators at Anhui Medical University, turned to the Ma’anshan birth cohort, a longitudinal study that has followed children from birth with repeated measurements of body length or height and weight at every follow-up visit. Rather than treating growth as a series of isolated snapshots, the scientists modeled each child’s body mass index trajectory as a continuous curve spanning the first years of life. This approach allowed them to extract eleven distinct growth markers, including the timing and magnitude of the adiposity peak in infancy, the timing and level of the adiposity rebound in early childhood, the steepness of growth slopes during infancy, toddlerhood, and the preschool and school-age periods, and the cumulative area under the BMI curve across each developmental window.

Two of these markers deserve particular attention because they encode milestones that pediatricians have watched for decades. The adiposity peak is the point in the first year or so of life when a baby’s BMI reaches its maximum before naturally declining. The adiposity rebound, first described in the 1980s by French researchers as a simple predictor of later obesity, marks the moment when the BMI curve bottoms out and begins climbing again. An early rebound has long been associated with elevated obesity risk, but the new study goes further by embedding these milestones within a comprehensive, quantitative portrait of each child’s growth across the entire early life course.

To characterize growth patterns rather than individual markers alone, the team applied k-means clustering, an unsupervised machine learning technique that groups children according to the overall similarity of their BMI trajectories. This data-driven classification revealed distinct growth archetypes within the cohort, some of which corresponded to persistently high or rapidly rising BMI across multiple developmental stages. When the researchers examined cardiometabolic outcomes at school age, including waist circumference, blood pressure, blood glucose, and blood lipids, the differences between these archetypes proved striking. Children in high-risk growth patterns were significantly more likely to display clustered cardiometabolic risk factors, a composite indication of emerging metabolic syndrome.

The numbers are arresting. Among boys, membership in a high-risk growth pattern was associated with a relative risk of 6.75 for clustered cardiometabolic risk factors, with a 95 percent confidence interval of 3.81 to 12.92. Among girls, the relative risk climbed to 8.34, with a confidence interval of 4.15 to 18.69. In practical terms, a child whose early growth curve resembles a high-risk archetype faces several times the odds of exhibiting the combined metabolic warning signs, such as central adiposity alongside elevated blood pressure, glucose, or adverse lipid profiles, compared with a child on a low-risk trajectory. Crucially, when the researchers stratified their analyses by obesity status, the associations remained significant, suggesting that the information encoded in growth patterns is not simply a proxy for whether a child is currently obese.

Ten of the eleven growth markers showed significant associations with clustered cardiometabolic risk in both boys and girls. The lone exception was the age at adiposity peak, whose association proved to be sex-specific, a nuance the authors highlight as evidence that growth-related risk may unfold differently along biological sex lines. The remaining markers, spanning the BMI values at peak and rebound, the timing of the rebound, the slopes of BMI gain in infancy, toddlerhood, and later childhood, and the cumulative BMI exposure measured as area under the curve in each period, all carried statistical weight. This breadth implies that no single developmental window holds a monopoly on risk; instead, cardiometabolic vulnerability appears to accumulate across the life course, from the first months of infancy through the transition into school age.

The statistical machinery behind these conclusions reflects the growing sophistication of life-course epidemiology. The team fitted childhood BMI growth curves using linear mixed modeling, a framework well suited to the irregular, repeated measurements that characterize real-world cohort data. The analysis was carried out with the EGGLA R package, an open-source tool for growth curve modeling that the authors make available on GitHub, lowering the barrier for other research groups to adopt the same methodology. By combining flexible curve fitting with clustering and conventional risk estimation, the study demonstrates how modern computational tools can convert routine pediatric measurements, the kind recorded at every well-child visit, into clinically meaningful risk stratification.

What makes the findings compelling is their grounding in a prospective birth cohort rather than retrospective recall. The Ma’anshan birth cohort has collected anthropometric data from birth onward, meaning the growth curves were constructed from measurements taken as children developed, not reconstructed years later. At school age, the same children underwent direct assessment of cardiometabolic risk factors, creating a temporal chain from early growth to measurable health outcomes. The study was approved by the Committee of Bio-Medical Ethics of Anhui Medical University, and informed consent was obtained from all participants. The cohort itself has been previously described in the International Journal of Epidemiology, and earlier analyses from the same group have examined how birth outcomes and early growth relate to the age at adiposity rebound.

The broader context sharpens the urgency of this work. Cardiovascular disease remains the leading cause of death worldwide, and projections published in the European Journal of Preventive Cardiology anticipate a rising global burden through mid-century. Risk factors that were once considered adult problems, including hypertension, dyslipidemia, and type 2 diabetes, are increasingly documented in children and adolescents, and long-running cohort studies such as Bogalusa have shown that childhood BMI and blood pressure cast long shadows into midlife, influencing adult dyslipidemia, diabetes, and even left ventricular structure. Against this backdrop, identifying modifiable or at least detectable signals in early childhood becomes a public health priority, and growth trajectories are among the most accessible signals available.

The authors conclude that childhood growth patterns and markers across different phases of the life course are closely tied to cardiometabolic health, and they argue that monitoring growth trajectories from infancy onward could enable earlier identification of children at elevated risk. In an era when childhood overweight and obesity are projected to keep climbing globally, the message is that the growth chart pinned to a pediatrician’s wall may be one of the most underused predictive instruments in preventive medicine. A child’s curve, read carefully and early, may whisper warnings about the heart long before any symptom appears, offering families and clinicians a window for intervention measured not in decades but in the crucial first years of life.

Subject of Research: Impact of growth markers and patterns of growth on children’s cardiometabolic health: a life course perspective

Article Title: Impact of growth markers and patterns of growth on children’s cardiometabolic health: a life course perspective

Article References: Luo, L., Tong, J., Wang, X., Huang, Q.-Z., Liu, Y.-K., Lv, P., Wang, J., Geng, C., Gao, H., Gan, H., Geng, M.-L., Zhu, B.-B., Tao, S.-M., Wu, X.-Y., Huang, K., Yan, S.-Q., & Tao, F.-B. (2026). Impact of growth markers and patterns of growth on children’s cardiometabolic health: a life course perspective. World Journal of Pediatrics. https://doi.org/10.1007/s12519-026-01072-z

Image Credits: AI Generated

DOI: 10.1007/s12519-026-01072-z

Keywords: Impact, growth, markers, patterns, children, cardiometabolic, health, life, course, perspective, scientific research

Tailings storage facilities, known in the industry as TSFs, are the embankments and impoundments built to contain the fine-grained residue that remains after gold ore is crushed and processed. On the Witwatersrand, the gold-bearing basin that underlies much of Gauteng Province, these facilities rise as pale, treeless hills that dominate the horizon of townships and informal settlements. Under dry Highveld conditions, particularly during the windy winter months, fine dust can be lifted from tailings surfaces and carried into nearby communities. Residents may inhale respirable dust containing crystalline silica and, in some cases, low concentrations of uranium and other metals associated with gold-bearing ore. Assessing the health risks of such exposure requires not only measurements of dust concentration but also a clear picture of how long, and in what settings, people are actually exposed to whatever the wind carries off the dumps.

That second ingredient is precisely what time-activity data provide, and it is precisely what has been missing for the communities of the West Rand. Exposure assessment in environmental epidemiology depends fundamentally on the product of concentration and duration: a person who spends twelve hours a day outdoors near a dust source faces a very different exposure profile from someone who works in an office across town and sleeps behind closed windows. Yet regulatory models and risk assessments in South Africa, as in many countries, have often relied on generic assumptions imported from international guidelines, typically European or North American time budgets that assume substantial time indoors, in sealed buildings, with mechanical ventilation. Such assumptions map poorly onto communities where many homes are informal structures, where cooking and socializing happen outdoors, where unemployment keeps large numbers of people at home all day, and where children play on unpaved ground close to the tailings themselves.

The research team set out to replace those assumptions with locally observed data. Their study focused on residential communities situated in proximity to tailings storage facilities along the West Rand, the western arm of the Witwatersrand gold field that runs through municipalities such as Merafong and the areas around Randfontein and Westonaria. Participants across a range of age groups were asked to document and recall how they spent their time across a representative day, capturing the categories of activity that exposure scientists use to partition daily life: time indoors at home, time indoors elsewhere such as school or work, time outdoors near the residence, time spent further afield, and time in transit. By collecting this information separately for children of different ages, working-age adults, and older residents, the study was able to construct age-stratified time budgets rather than a single community average.

The age-stratified approach matters because exposure risk is not distributed evenly across a lifetime. Infants and young children breathe more air per unit of body weight than adults, their respiratory tracts are still developing, and their behaviors, crawling on floors, playing in dust, hand-to-mouth contact, can amplify contact with contaminated soil and particulates. School-age children spend large portions of the day in classrooms that may or may not offer protection from outdoor dust, and many walk routes to and from school that pass close to tailings footprints. Adults of working age may be away from the residential exposure zone for much of the day, or, where unemployment is high, may remain in it continuously. Elderly residents, particularly those with pre-existing respiratory or cardiovascular conditions, are among the most vulnerable to particulate pollution yet may spend nearly all of their time within a few hundred meters of home. Without age-specific data, a risk model that averages across these very different patterns will underestimate exposure for some groups and overestimate it for others.

While the detailed numerical results reside in the full publication, the study’s contribution can be understood at two levels. At the level of raw evidence, it provides measured, locally grounded estimates of the hours per day that West Rand residents of different ages spend in locations and microenvironments relevant to tailings-derived dust exposure. At the level of method, it demonstrates a practical protocol for gathering such data in resource-constrained, high-exposure settings, where survey-based recall and structured questionnaires are often the only feasible instruments. Studies of this kind commonly reveal that total time outdoors near the home is substantially higher than default values assumed in international exposure models, particularly for children and for adults who are not formally employed, and that time in enclosed, mechanically ventilated environments is correspondingly lower. The West Rand findings are significant precisely because they anchor risk calculations in the actual rhythms of life in mining-adjacent communities rather than in borrowed assumptions.

The broader context makes the work urgent. The Witwatersrand has produced gold since 1886, and the region’s legacy tailings footprint extends across hundreds of square kilometers, much of it now surrounded or penetrated by residential development that expanded during and after apartheid-era planning, when Black communities were frequently located on marginal land close to mine dumps. Re-mining operations, in which companies reprocess old tailings to extract residual gold, add a contemporary layer of activity: trucks, conveyors, and processing plants that can re-suspend dust from deposits that had partially stabilized. Community organizations and environmental justice groups on the West Rand have for years raised concerns about respiratory illness, silicosis-like symptoms, and the long shadow of mining waste, while regulatory attention to residential proximity has grown through frameworks such as South Africa’s National Environmental Management Act and guidelines addressing buffer distances around tailings facilities. Sound policy in this contested space depends on defensible exposure estimates, and defensible exposure estimates depend on exactly the kind of time-activity data this study supplies.

The findings also speak to an international audience of exposure scientists, because time-activity patterns are among the most context-dependent variables in all of environmental health research. A time budget measured in a North American suburb, with its sealed houses, air conditioning, and car commutes, is simply a different object from one measured in a settlement of informal dwellings where daily life unfolds outdoors. Researchers in the exposure sciences have increasingly recognized that microenvironment models, which calculate total exposure as the sum of concentrations multiplied by time spent in each distinct setting, are only as good as the time-location inputs they receive. Studies from low- and middle-income countries remain underrepresented in this literature relative to the size of the populations potentially affected, which makes locally generated datasets from places like the West Rand valuable well beyond their immediate geography. They offer reference points for other mining-affected regions of southern Africa, and for the wider Global South, where tailings facilities and residential communities frequently coexist.

For residents themselves, the practical significance lies in what follows from measurement. Age-specific time budgets can inform where and when dust-control interventions will deliver the greatest benefit: rehabilitating tailings surfaces with vegetation or covers, adjusting re-mining activities during high-wind seasons, considering buffer zones in land-use planning, and targeting school and household-level measures, such as improved ventilation practices and dust-suppression around play areas, toward the groups whose daily routines place them most directly in the exposure pathway. Public health authorities can use the same data to refine health risk assessments and to prioritize environmental monitoring locations. Community advocates gain a documented, quantified account of how mining infrastructure shapes the daily lives of those living in its shadow, evidence that can carry weight in consultations, licensing processes, and debates over land use.

The study also underscores a deceptively simple point that recurs throughout exposure science: people are not passive receptors of pollution, and the dose they receive reflects the texture of their days. Understanding that texture, hour by hour and age group by age group, is an unglamorous but essential foundation for protecting public health in landscapes shaped by extraction. For the communities of the West Rand, where gold mine waste has been a neighbor for generations, having their actual patterns of movement and activity captured in the peer-reviewed literature represents a step toward risk assessments that see them as they are. It is a reminder that the path from a mine dump to a person’s lungs runs not only through the air, but through the ordinary details of where people live, work, learn, and play, and that measuring those details is where credible environmental health protection begins.

As gold mining continues its long retreat across the Witwatersrand, the waste it leaves behind will outlast the industry itself, and the populations living alongside that waste will continue to grow. Research that documents, with precision, how those populations inhabit their environment converts a legacy of neglect into an evidence base for action. The age-specific time-spent patterns reported for the West Rand provide a template for how such evidence can be gathered and a foundation on which dust management, urban planning, and public health interventions can now be built, in South Africa and in every other region where communities and mine tailings share the same ground.

Subject of Research: Age-specific time-spent patterns among residents living near gold mine tailings storage facilities in the West Rand, South Africa

Article Title: Age-specific time-spent patterns among residents living near gold mine tailings storage facilities in the West Rand, South Africa

Article References: Kalumbi, L. R., Masekameni, M. D., Utembe, W., & Brouwer, D. (2026). Age-specific time-spent patterns among residents living near gold mine tailings storage facilities in the West Rand, South Africa. Journal of Exposure Science & Environmental Epidemiology. https://doi.org/10.1038/s41370-026-00972-6

Image Credits: AI Generated

DOI: 10.1038/s41370-026-00972-6

Keywords: Age-specific, time-spent, patterns, residents, living, near, gold, mine, tailings, storage, facilities, West

Measles spreads efficiently when susceptible people are concentrated in the same population, allowing outbreaks to expand rapidly once the virus is introduced. Vaccination interrupts that chain of transmission by reducing the number of people who can become infected and pass the virus onward. The first measles-containing vaccine dose is therefore a central marker of childhood protection, although a single dose does not provide complete protection for every child. The study’s estimated coverage of 58.3%, with a 95% confidence interval of 55.5% to 61.1%, indicates that many children remained outside the reach of routine or supplementary immunization services during the period represented by the survey. The researchers describe this coverage as below the level needed for herd protection. In practical terms, low overall coverage can conceal even more serious local vulnerabilities: a national average may appear moderate while particular districts or communities have far fewer vaccinated children and therefore greater potential for sustained transmission.

The investigators analyzed records for 3,436 children aged between 12 and 59 months. The survey data were collected during fieldwork in 2018 and 2019 and formed the basis of the 2020 Somalia Demographic and Health Survey. To estimate vaccination prevalence, the researchers applied the survey’s sampling weights, including the variable V005, so that the results reflected the design of the national survey rather than simply treating every sampled record as equally representative. The outcome was coded as vaccinated or unvaccinated according to whether the child had received MCV1. Because children were sampled within households and communities, the study used multilevel logistic regression rather than a single-level model. This approach is designed to separate associations linked to individual or household characteristics from variation associated with the broader community context. It cannot prove that a particular factor causes vaccination, but it can show how strongly measured characteristics are associated with the odds of receiving a dose while accounting for clustered data.

The geographic analysis revealed a strong spatial pattern. Global Moran’s I, a statistic used to test whether similar values are arranged near one another rather than distributed randomly, was 0.653, with a probability value below 0.001. That result indicates significant positive spatial autocorrelation: areas with relatively high vaccination levels tended to be near other areas with relatively high levels, while areas with lower coverage tended to cluster with other low-coverage areas. The researchers then used Getis-Ord Gi* hotspot analysis to identify concentrations of unusually high or low values and applied SaTScan Bernoulli models to search for statistically defined geographic clusters. Together, these techniques produced a map of concentrated vaccination advantage and disadvantage. Hotspots were identified in the northern regions of Togdheer, Sool, Sanaag and Bari. Coldspots appeared in the southern regions of Gedo, Lower Juba and Bakool, where low uptake was geographically concentrated rather than isolated.

The multilevel findings add detail to that map. Children aged 48 to 59 months had substantially higher adjusted odds of having received MCV1 than the comparison age group, with an adjusted odds ratio of 16.32. An odds ratio compares the odds of an outcome between groups after accounting for other variables in the model; it is not the same as a percentage increase in vaccination coverage. The strong age association may reflect the cumulative opportunity for a child to encounter vaccination services as time passes, although the survey design cannot establish the exact reason. Maternal education was also associated with uptake. Children whose mothers had secondary education had 1.83 times the adjusted odds of vaccination compared with the reference category. The authors interpret this pattern as consistent with the potential importance of health knowledge, communication with services and the ability to navigate vaccination schedules, while recognizing that education may also be connected with other social and economic conditions.

Household economic position and mobility were likewise associated with MCV1. Children from middle-income households had 1.88 times the adjusted odds of vaccination compared with the reference wealth group. The study also found higher adjusted odds among children in nomadic residence, with an adjusted odds ratio of 1.60. That result does not mean that all nomadic families experience easier access to immunization. Instead, it shows an association within this dataset and may reflect the reach of particular outreach activities, differences in service delivery or other characteristics measured or unmeasured in the survey. Regional differences were especially large: children in Togdheer had 6.3 times higher odds of vaccination than children in the reference region. Such contrasts emphasize that national vaccination strategies cannot rely solely on uniform service delivery. The same intervention may perform differently depending on transport routes, settlement patterns, local health infrastructure and the ability of families to return for scheduled care.

The researchers quantified the contribution of community-level conditions using the intra-cluster correlation coefficient, or ICC. The ICC indicated that 13.1% of the variance in vaccination status was attributable to differences between communities. In a hierarchical dataset, this measure helps show whether children living in the same community resemble one another in their likelihood of vaccination beyond what can be explained by their individual characteristics. A community contribution of this magnitude suggests that place-based factors matter: the presence and reliability of health facilities, outreach schedules, local information networks, security conditions, transport and the organization of mobile services may all influence whether families can obtain immunization. The analysis did not measure every one of these mechanisms directly, so the ICC should not be read as identifying a single cause. Rather, it supports the conclusion that interventions focused only on household behavior would leave part of the vaccination gap untouched.

The study’s conclusions direct attention to the southern coldspots and to groups that may be missed by facility-based programs. The authors recommend prioritizing geographically identified low-coverage areas, strengthening maternal health literacy and expanding mobile vaccination outreach for underserved rural and nomadic populations. A mobile approach can bring services closer to communities whose distance, movement or limited transport options makes routine clinic attendance difficult. Geospatial results can help planners decide where such activities are most urgently needed, while multilevel findings can help tailor communication and delivery strategies to local circumstances. The analysis is based on secondary, anonymized, publicly available survey data and therefore describes associations at the population level rather than tracking children over time. Even with that limitation, its combination of weighted survey estimates, spatial statistics and hierarchical regression provides a more targeted view than a single national coverage figure. For Somalia, the message is clear: closing the measles immunity gap will require not only more doses, but also a precise understanding of where access and protection remain weakest.

The study’s design is particularly useful for distinguishing national coverage from the distribution of coverage. A weighted prevalence describes the estimated share of eligible children who had received MCV1, whereas the spatial statistics examine whether vaccination levels are arranged in recognizable geographic patterns. These answer different questions: the first indicates the overall scale of the immunity gap, while the second helps determine whether low uptake is concentrated enough to justify place-specific planning. The significant Moran’s I result and the hotspot and coldspot analyses therefore complement, rather than duplicate, the regression findings.

The multilevel framework also changes how the reported associations should be interpreted. In a conventional regression, observations from the same community may be treated as independent even though they can share health facilities, outreach teams, information sources and environmental constraints. Accounting for clustering reduces the risk that such shared circumstances will be mistaken for purely individual effects. The community-level variance identified by the investigators consequently supports a delivery-oriented interpretation: improving vaccination may require changes in how services are organized and reached, not simply stronger demand among caregivers.

Age deserves careful consideration when translating the results into program decisions. Older children have had more time to encounter routine services or catch-up activities, so the age association may signal delayed rather than absent access. It also suggests that programs should actively identify children who have passed the usual vaccination contact point without receiving MCV1. This is different from assuming that younger children are inherently less likely to benefit; they may simply have had fewer opportunities by the time of observation. The cross-sectional survey cannot establish the sequence of events behind the association.

Similarly, the findings on education, wealth and residence should guide investigation rather than serve as fixed classifications of families. These characteristics can be connected to several pathways at once, including awareness, affordability of travel, trust, service availability and the timing of outreach. The study’s adjusted odds ratios show relationships after inclusion of measured variables, but they do not identify which pathway is responsible or demonstrate that changing one characteristic would produce a specific increase in uptake. Local follow-up could therefore help determine whether the most effective response is communication, transport support, improved session reliability, or a combination of approaches.

Because the analysis used existing anonymized survey data, it provides a broad population-level assessment without exposing participants to additional data collection. Its findings can establish priorities for vaccination planning, but implementation should still be accompanied by monitoring. Repeated coverage assessments and evaluation of outreach in the designated low-coverage areas would help determine whether geographic inequalities narrow over time and whether improvements reach children who remain unvaccinated, rather than merely shifting the national average.

Subject of Research: Geographic and social determinants of first-dose measles vaccination among children in Somalia

Article Title: Spatial patterns and determinants of childhood measles vaccination in Somalia a multilevel and geospatial analysis of the 2020 demographic and health survey

Article References: Abdillahi, A. M., Ali, I. H., Omer, H. A., Said, N. I., Bile, H. S., & Muse, A. H. (2026). Spatial patterns and determinants of childhood measles vaccination in Somalia a multilevel and geospatial analysis of the 2020 demographic and health survey. Discover Social Science and Health. https://doi.org/10.1007/s44155-026-00416-4

Image Credits: AI Generated

DOI: 10.1007/s44155-026-00416-4

Keywords: Measles vaccination, Somalia, MCV1, Child health, Geospatial analysis, Multilevel modeling, Vaccine coverage, Health inequities, Spatial, patterns, determinants, childhood