Evidence map›Paper›PMID 42539115›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Death in People with Down syndrome: Mortality statistics and novel predictors in US Medicaid and Medicare enrolled adults.

Salina Tewolde, Anthony J Rosellini, Amy Michals, Brian G Skotko, Juan Fortea, Bernard Khor, Samuel Handelman, Eric Rubenstein

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Salina TewoldeBoston University School of Public Health, Department of Epidemiology 715 Albany Street, Boston MA, 02121, USA.ORCID 0009-0005-1512-6903
Anthony J RoselliniBoston University School of Public Health, Department of Epidemiology 715 Albany Street, Boston MA, 02121, USA.ORCID 0000-0002-8385-8621
Amy MichalsBoston University School of Public Health, Center for Health Data Science 715 Albany Street, Boston MA, 02121, USA.ORCID 0000-0003-3287-8099
Brian G SkotkoMassachusetts General Brigham Hospital: Genetics Program; Down Syndrome Clinic 55 Fruit St. Suite 6C; Boston, MA 02114, USA.ORCID 0000-0002-5232-9882
Juan ForteaSant Pau Memory Unit - Neurology Department Sant Antoni Maria Claret, 276; 08025 Barcelona, Spain.ORCID 0000-0002-1340-638X
Bernard KhorBenaroya Research Institute 1201 Ninth Avenue, Seattle, WA 98101-2795; USA.ORCID 0000-0003-4689-5092
Samuel HandelmanAerska Inc., 2093 Philadelphia Pike #2152, Claymont, DE 19703, USA.ORCID 0000-0001-6970-2172
Eric RubensteinBoston University School of Public Health, Department of Epidemiology 715 Albany Street, Boston MA, 02121, USA.ORCID 0000-0002-9146-4497

Funding

Down Syndrome Toward Optimal Trajectories and Health using Medicaid Analytic eXtract (DS-TO-THE-MAX)R01AG073179 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Eric S Rubenstein · 2021 to 2026
$3.5M
NIA NIH HHS R01 AG073179
6 · The paper itself

Abstract

People with Down syndrome have higher age-specific mortality rates compared to the general population as well as peers with other intellectual and developmental disabilities. While a large proportion of mortality is attributable to Alzheimer's disease, many die prior to Alzheimer's diagnosis and some live to old ages, dying without Alzheimer's. Our objectives were to use 11 years of Medicaid and Medicare data to describe characteristics and factors related to death in adults with Down syndrome and use machine learning to identify which conditions most strongly predict death in the full population and stratified by age. We identified death using Center for Medicare and Medicaid Systems reported date of death health conditions using ICD 9 and 10 codes. We used a case-control design with risk set sampling to have that controls to mimic the distribution of times of incident Alzheimer's disease. We trained gradient boosted trees to identify strongest predictors. Our cohort included 137,293 adults with Down syndrome. Among those, 30,894 (22.5%) died during the study period. Mean age at death among those who died was 55 years (SD=10). Mean age of death in those with Alzheimer's disease was 59 (SD=7) and those without was 52 (SD=12). The most influential predictors of mortality were any claim for dementia, any claim for pneumonia, re-occurring claim for cardiovascular disease three years before index death, and any claim for heart failure and epilepsy. Our results align with previous clinical work and highlight intervenable areas to reduce mortality in the Down syndrome population.

Indexed as

Alzheimer’s diseaseDisabilityMedicaidMedicaremortalitypneumonia

Identifiers

PMID42539115
PMCPMC13419647

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.