The team drew on an unusually rich data infrastructure. Using the Centers for Medicare and Medicaid Services Chronic Conditions Data Warehouse, they identified beneficiaries aged 65 and older who were alive and Alzheimer’s-free in 2011 and then tracked whether claims-coded Alzheimer’s disease appeared between 2012 and 2016. After excluding movers, people who died or could not be linked across the follow-up window, and those with prevalent disease at baseline, the analytic cohort comprised 181,614 residents of Miami-Dade County, of whom 7,606, or about 4.2 percent, developed claims-identified Alzheimer’s disease during follow-up. Each beneficiary’s residential Census block was linked to vegetation measurements derived from Landsat satellites, the workhorse of long-term environmental monitoring, and models adjusted for age, sex, race and ethnicity, neighborhood median household income, and neighborhood walkability measured by Walk Score.

The core exposure metric was the Normalized Difference Vegetation Index, or NDVI, a spectral index computed from the contrast between how vegetation absorbs red light for photosynthesis and scatters near-infrared radiation from its internal leaf structure. Higher NDVI values indicate denser, more photosynthetically active vegetation, and the index has become the default greenness measure in health studies because it is reproducible, spatially continuous, and available in archives stretching back decades. Yet NDVI has well-known limits: it measures photosynthetic activity, not tree canopy, park quality, accessibility, or vegetation type, and it says nothing about whether residents actually use the green spaces near them. The new study adds another layer of caution by showing that even the numerical value of NDVI assigned to a neighborhood depends heavily on choices researchers make before the analysis begins.

Those choices fall into two categories that geographers have long formalized as the modifiable areal unit problem and the modifiable temporal unit problem. The first, known as MAUP, recognizes that statistical relationships can change when data are aggregated at different spatial scales or zoning systems. The second, MTUP, is its temporal analogue: results can shift depending on how measurements are segmented and averaged across time. Both problems are textbook material in spatial epidemiology, but they had rarely been tested systematically for a chronic neurodegenerative outcome. The Miami-Dade team did exactly that, generating 32 distinct NDVI products by combining four raster resolutions (30, 120, 250, and 500 meters), two temporal acquisition strategies (single-date imagery versus seasonal composites averaging all cloud-free September-to-November scenes), two temporal specifications (2011 baseline versus a 2011-plus-2016 sum), and two metrics (mean NDVI and NDVI range within each block).

The headline result was reassuring in direction but revealing in detail. Across all four spatial resolutions, higher seasonal mean NDVI in 2011 was consistently associated with lower odds of incident Alzheimer’s disease. Each 0.1-unit increase in NDVI was linked to roughly 18 to 23 percent lower odds, with the strongest association at the 250-meter resolution, where the odds ratio was 0.77. Notably, the native 30-meter Landsat resolution, the finest available, did not produce the best-fitting models; moderate aggregation at 120 and 250 meters captured neighborhood-scale greenness more consistently, apparently by smoothing pixel-level noise while retaining meaningful local variation. The researchers used the Quasi-likelihood under the Independence Model Criterion, a generalization of the Akaike Information Criterion for their clustered regression framework, to compare model fit, and the 250-meter seasonal model scored best within its set, though neighboring resolutions performed similarly.

Temporal aggregation mattered just as much. When the team used a single cloud-free image from fall 2011 instead of the seasonal composite, the inverse association weakened considerably, with odds ratios closer to 0.92 to 0.96 per 0.1-unit NDVI increase. Seasonal compositing, the authors argue, reduces the influence of anomalous acquisition dates, residual cloud and atmospheric contamination, and short-term vegetation fluctuations, all of which are especially relevant in subtropical Miami where cloud-free imagery is scarce. In a striking contrast, the single-date models fit best at the finest 30-meter resolution, while the seasonal models favored moderate aggregation, suggesting that the apparent spatial scale of a greenness effect depends on how the exposure is captured in time. A two-time-point metric combining 2011 and 2016 NDVI confirmed that the spatial pattern of associations was robust, though it was interpreted strictly as a sensitivity analysis because the 2016 measurement postdates some incident cases.

The study also probed an exploratory metric with a very different personality: NDVI range, the difference between the maximum and minimum pixel values within each Census block. Whereas mean NDVI was consistently protective, higher NDVI range was generally associated with higher Alzheimer’s incidence, and the association was fragile, weakening or disappearing at coarser resolutions. The authors are careful to note that range is not a measure of greenspace fragmentation, connectivity, or landscape morphology; it is simply a crude indicator of within-block spectral variability that is highly sensitive to extreme pixels and to the number of pixels available. Still, its positive association hints that heterogeneous residential environments, where lush vegetation coexists with sparse cover, may tell a different story than uniformly green blocks, a question that will require proper landscape-configuration metrics to resolve.

Why should anyone outside the remote-sensing community care? Because the greenness–health literature is increasingly cited in urban planning debates, from tree-planting initiatives to heat mitigation programs, and because prior studies in this very field have reached conclusions using wildly different exposure definitions. Some used 300-meter buffers, others Census blocks or road-network buffers; some used a single satellite image, others annual averages or multi-year moving windows. Reviews have found that buffer sizes in published studies range from about 30 to 5,000 meters. The new analysis shows that holding the population, outcome, covariates, and statistical model constant while varying only the exposure definition changes both the magnitude of the effect and which model fits best. Scale selection, the authors conclude, is not a technical preprocessing detail but a substantive component of epidemiologic inference that should be tested and reported explicitly.

The authors are equally candid about the study’s limits. Alzheimer’s status came from insurance claims rather than clinical adjudication, so delayed diagnosis, underdiagnosis, and coding variation are possible, though such misclassification would likely attenuate rather than inflate the observed associations. Residential Census blocks capture where people live, not where they walk, socialize, or spend their days, a limitation known as the Uncertain Geographic Context Problem. The cohort excluded movers and people with incomplete address linkage, potentially producing a more residentially stable and healthier sample than the county at large. Walk Score data from 2013 were assigned to 2011 residences, and the analysis did not adjust for air pollution, heat, or noise, which correlate with greenness and may independently affect cognitive health. The observed associations therefore reflect residential greenness within a broader urban environmental context rather than isolated effects of vegetation alone.

Even so, the practical message is clear and, in its way, empowering. NDVI remains a valuable, scalable screening tool for identifying neighborhoods where low greenness coincides with older-adult vulnerability, and the protective signal itself survived every scale test the researchers threw at it. But translating a satellite index into a greening prescription requires more: information on tree canopy versus grass, vegetation type, park access, safety, land ownership, and community priorities. Future work, the authors suggest, should pair higher-frequency satellite products such as MODIS or harmonized Landsat-Sentinel data with mobility-aware exposure measures and policy-actionable greenspace indicators. In the meantime, the study stands as a reminder that in environmental epidemiology, the map you choose to draw helps determine the world you find.

Subject of Research: Spatial and temporal scale sensitivity of satellite-derived NDVI greenness exposure assessment in relation to Alzheimer's disease incidence among older Medicare beneficiaries

Article Title: Spatial and Temporal Scale Sensitivity in NDVI-Based Greenness Exposure Assessment for Alzheimer’s Disease Incidence: A Miami-Dade County Medicare Cohort Study

Article References: Southworth, J., Chaudhary, A., Dewald, J. R., Ma, R., Safaei, M., Szapocznik, J., & Brown, S. C. (2026). Spatial and Temporal Scale Sensitivity in NDVI-Based Greenness Exposure Assessment for Alzheimer’s Disease Incidence: A Miami-Dade County Medicare Cohort Study. Environmental Advances, Article 100765. https://doi.org/10.1016/j.envadv.2026.100765

Image Credits: AI Generated

DOI: 10.1016/j.envadv.2026.100765

Keywords: Alzheimer's disease, NDVI, greenness exposure, remote sensing, Medicare cohort, modifiable areal unit problem, modifiable temporal unit problem, Landsat, spatial epidemiology, urban green space, Miami-Dade County, dementia risk

The methodological core of the study is what statisticians call multilevel latent class analysis, or MLCA. Traditional variable-centered approaches ask how single factors, such as partner age or substance use, relate to outcomes like condomless sex. Person-centered mixture models instead search for hidden subgroups within a population, defined by co-occurring patterns across many indicator variables simultaneously. The multilevel extension is crucial for sexual network data, because each participant, or ego, nominated multiple partners, or alters, creating a nested structure in which partnerships are clustered within individuals. Ignoring this nesting can distort estimates and obscure the fact that a single man may move fluidly between very different kinds of sexual encounters. The researchers modeled both levels at once: lower-level classes describing types of individual partnerships, and upper-level groups describing the overall composition of each man’s sexual network.

Data collection relied on an egocentric social network approach implemented through the Network Canvas platform, with participants recruited through a modified respondent-driven sampling strategy led by a community-based sexual health clinic. Each participant named the people with whom he had anal or vaginal sex in the past six months and reported detailed characteristics of each relationship: whether the partner was a main or casual partner, whether condoms were always used, which sexual positions involved condomless contact, whether the partner disclosed an HIV status, and how emotionally close the respondent felt to that person. Covariates included alcohol and other substance use when the dyad met to have sex, the respondent’s current PrEP use, country of nativity, and total sexual network size. Participants averaged 34.5 years of age, 88 percent were born outside the United States, and 57 percent reported currently using PrEP.

The analysis converged on a three-class, two-group solution. At the partnership level, three distinct sexual partner typologies emerged. The first, accounting for 37 percent of partnership contexts, was an elevated-risk context with a main sexual partner: seroconcordant dyads in which both partners disclosed being HIV-negative, marked by high emotional closeness, main partnership status, and a high probability of versatile condomless sex. The second class, 35 percent of contexts, was labeled selective and close casual partnerships, in which men reported condomless sex in only one position, such as insertive or receptive anal sex exclusively, alongside moderate emotional closeness. The third class, 27 percent, comprised low-exposure casual partnerships with little condomless sex, variable closeness, and a high probability that the partner’s HIV status was simply unknown. At the network level, two groups emerged: one dominated by casual partnerships, accounting for 54 percent of sexual partnerships, and one centered on a main sexual partner, accounting for 46 percent.

Perhaps the most striking finding concerns what these typologies reveal about trust. Consistent with the dyadic framework for HIV prevention developed by Karney and colleagues, the study found that risk behaviors clustered within emotionally close relationships rather than anonymous encounters. Condomless sex was most prevalent in main partnerships, where intimacy and trust may paradoxically substitute for barrier protection, and lowest in casual partnerships where serostatus was undisclosed. Partners in the low-exposure casual class frequently had unknown HIV seroconcordance, which may itself have prompted greater caution. Meanwhile, classes with higher perceived closeness involved partners who disclosed being HIV-negative, more versatile condomless practices, and more substance use when the dyad met for sex. This inversion of the intuitive risk hierarchy, in which the most trusted relationships carry the most condomless exposure, has been documented in prior research among Latino men and now receives a rigorous person-centered confirmation.

The multinomial logistic regression results sharpened the picture of who occupies which network context. Respondents currently using PrEP had 1.60 times greater odds of belonging to the group whose networks were composed primarily of main partnerships with elevated risk contexts, relative to the casual-network group. Latino men born outside the United States had 43 percent lower odds of having elevated-risk main-partner contexts compared to those born in the country. Network size mattered as well: every additional partner reduced the odds of belonging to the main-partnership group by 29 percent. Substance use when the dyad met to have sex was independently associated with roughly threefold higher odds of condomless sex in a random intercept model, and within the multilevel model, substance use sharply reduced the odds of membership in low-exposure casual classes relative to elevated-risk main-partner contexts.

These associations carry important caveats that the authors themselves emphasize. The data are cross-sectional, so the temporal direction of the PrEP finding remains unresolved: men may adopt PrEP because they are in higher-exposure partnerships, or their partnerships may take a particular shape because PrEP has reduced their perceived need for condoms. PrEP, when taken as directed, is extremely effective against HIV acquisition but does not prevent other sexually transmitted infections, and systematic reviews have documented elevated STI diagnosis rates among PrEP users, potentially reflecting both reduced condom use and increased testing frequency. The study also captured only the daily-pill form of PrEP, predating wider availability of long-acting injectable options, and the reporting window overlapped with the COVID-19 pandemic, when social distancing measures may have altered sexual network patterns in ways that differ from pre- and post-pandemic behavior.

Cultural and structural context adds further layers of complexity. Miami-Dade is a distinctive setting in which 69 percent of residents identify as Latino, more than half were born outside the United States, and 68 percent of adults speak Spanish. Approximately one-third of foreign-born participants had lived in the country for a year or less, a period that may be insufficient to establish close, serious partnerships and during which navigating new social and sexual environments can shape partner selection, communication, and trust. Prior research suggests that cultural values such as collectivism and familismo can support condom use, while acculturative processes, language barriers between partners, and hypermasculine norms may complicate safer-sex negotiation. The authors argue that future work should move beyond a simple US-born versus foreign-born dichotomy and apply syndemic theory or intersectional perspectives to capture how immigration experiences, stigma, substance use, and cultural values interact to shape vulnerability.

The practical implications point toward interventions designed at the level of the dyad and the network rather than the isolated individual. Couples-based programs can facilitate conversations about relationship agreements, the increasing openness of sexual partnerships, and the role of substances in sexual decision-making, while network interventions that engage opinion leaders can diffuse prevention messaging through the social structures the study has now characterized in detail. The researchers also propose expanding clinic screening tools to include brief egocentric sexual network modules, allowing providers to tailor recommendations, whether PrEP, doxycycline post-exposure prophylaxis, routine testing, or negotiated safety agreements, to the specific partnership typologies a patient actually experiences. By replacing blunt risk categories with empirically derived partnership profiles, the study offers a template for precision HIV and STI prevention that respects the fluid, context-dependent reality of Latino men’s sexual lives in one of America’s hardest-hit communities.

Subject of Research: Sexual partnership typologies and HIV/STI risk contexts among Latino men in Miami analyzed with multilevel latent class analysis of egocentric sexual network data

Article Title: Sexual Partner Typologies Among Latino Men Living in Miami, Florida: A Multilevel Latent Class Analysis

Article References: Despres, L., Rodriguez, E., Johnson, A. L., Bravo, A., Vilchez, L., Doblecki-Lewis, S., Fallon, S., & Kanamori, M. (2026). Sexual Partner Typologies Among Latino Men Living in Miami, Florida: A Multilevel Latent Class Analysis. Archives of Sexual Behavior. https://doi.org/10.1007/s10508-026-03441-1

Image Credits: AI Generated

DOI: 10.1007/s10508-026-03441-1

Keywords: HIV, sexually transmitted infections, Latino men, Miami-Dade County, multilevel latent class analysis, sexual networks, PrEP, condomless sex, substance use, dyadic prevention, immigration and acculturation, syndemic theory