Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2510.21630

Representing caregiver burden in observational studies: Development of the Caregiver Burden Index (CareBI) using NSOC

Forough Mahpouya·Sabrina Casucci·Suzanne Sullivan·Christopher Barrick
View original source ↗Read source document ↗Export metadata ↓

Abstract

Informal caregiving often carries a significant emotional, physical, and financial toll, yet caregiver burden is often underrepresented in healthcare research and methods. Existing caregiver burden instruments, while valuable in clinical research, often lack compatibility with observational datasets regularly used in health services research and planning. This study introduces the Caregiver Burden Index (CareBI) developed for the National Study of Caregiving (NSOC), that can be used to represent caregiver burden in quantitative models and observational research studies. CareBI was developed and validated using a multistep process that included the identification and preparation of individual NSOC survey items, exploratory and confirmatory factor analysis, score estimation, interpretation, and external validation. The study used data from round 12 of the NSOC. CareBI represents three domains of burden: objective, subjective, and interpersonal, providing a comprehensive view of both the positive and negative aspects of caregiving. It also aligns with the Zarit Burden Interview, a widely used tool for prospectively assessing caregiver burden. Construct validity was assessed by comparing CareBI's relationship with caregiver and care recipient outcomes, as well as sensitivity to known burden-related risk and mitigation factors. Early findings affirm the scale's utility in categorizing low-, moderate-, and high-burden caregivers and guiding resource-oriented strategies. CareBI represents a reproducible tool for embedding caregiver metrics into health operations, predictive modeling, and public policy frameworks, and provides a template for applying operations research and industrial engineering methods to psychosocial measurement challenges in aging and long-term care.

Explore related subjects

stat.AP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Forough Mahpouya, Sabrina Casucci, Suzanne Sullivan, Christopher Barrick. 2025-10-24. Representing caregiver burden in observational studies: Development of the Caregiver Burden Index (CareBI) using NSOC. https://arxiv.org/abs/2510.21630

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Privacy-Preserving Causal Meta-Mediation Analysis with Survival Outcomes

Privacy and data-governance constraints often prevent pooling individual-level data across studies, limiting the use of conventional approaches for causal media- tion analysis in multicenter settings. We propose a federated causal meta-mediation framework for right-censored time-to-event outcomes that enables collaborative es- timation without sharing individual-level data. Our framework targets natural indirect effects in a prespecified population by combining information on mediator and outcome mechanisms across distributed data sources. A site-by-site identifi- cation strategy further allows heterogeneity across data sources to be character- ized, with a variance decomposition separating outcome-related, mediator-related, and interaction components. We develop federated one-step and targeted maxi- mum likelihood estimators that accommodate data-adaptive and machine-learning methods for nuisance-function estimation. The finite-sample performance of the proposed estimators is evaluated through numerical simulations. To illustrate the practical utility of the framework, we apply it on data from the French National Health Data System to evaluate the role of methotrexate coprescription in explain- ing the effect of TNFi versus IL-12/23 inhibitor therapy on treatment persistence among psoriatic patients.

stat.AP↗
arXiv2026

Geospatial Foundation Models Capture Health-Relevant Dimensions of Place Beyond Conventional Social Risk Indices

Area-based social risk indices summarize residents' socioeconomic conditions but incompletely capture physical features of place that may affect health. We evaluated whether numerical representations of physical place produced by four geospatial foundation model families from 2022 satellite data explained residual variance in tract-level associations between the Area Deprivation Index, Social Deprivation Index, and Social Vulnerability Index with health outcomes. We used LightGBM to predict variables from the American Community Survey and 40 chronic disease and health-behavior outcomes from CDC PLACES across 82,646 census tracts in the contiguous United States, evaluating performance across 10 held-out states. Among survey variables, models were moderately predictive of some variables including housing type (R-squared up to 0.54) but weak for disability, unemployment, and income disparity. For health outcomes, models explained up to 54% of variance left unexplained by social risk indices, with the largest gains for annual checkups, arthritis, and high blood pressure. Mean total variance explained by geospatial foundation models across the 40 health-related outcomes increased from 0.31 in the smallest tract-size decile to 0.39 in the largest. Geospatial foundation models capture health-relevant features of place not represented by conventional social risk indices and may usefully augment them in epidemiological analyses.

stat.AP↗
arXiv2026

A spatiotemporal negative binomial model with dynamic dispersion: An application to Tuberculosis infections

Tuberculosis (TB) remains a critical public health concern in Brazil, characterized by pronounced spatial heterogeneity and fluctuating temporal volatility. In this paper, we study monthly TB notifications across 61 microregions of Sao Paulo state from 2001 to 2024. To do this, we introduce a negative binomial spatial integer-valued generalized autoregressive conditional heteroskedastic (INGARCH) model featuring jointly dynamic conditional means and time-varying dispersion. To capture inter-regional spillovers, we incorporate both discrete adjacency structures and a novel continuous distance-based formulation leveraging the Matern correlation function. Parameter estimation via conditional maximum likelihood employs a two-step profile-likelihood iterative scheme, demonstrating solid finite-sample performance in simulation studies. Applied to the Sao Paulo TB surveillance data, the framework substantially outperforms standard Poisson and fixed-dispersion spatiotemporal baselines in empirical fit and uncertainty quantification, maintaining nominal 95% predictive coverage across both dense metropolitan centers and rural microregions. Our results reveal marked spatial heterogeneity in baseline incidence, dynamic overdispersion driven by localized outbreaks, and short-range spatial interaction decay. By accurately modeling spatiotemporal volatility, the proposed methodology provides a robust statistical tool to support public health surveillance, policy-making, and resource allocation.

stat.AP↗