Evidence map›Paper›PMID 34120512›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2022

A Simple Method for Simulating Dementia Onset and Death within an Existing Demographic Model.

Carolyn M Rutter, Ifeanyi Edochie, Esther M Friedman, Mary E Slaughter, Margaret M Weden

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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

5 authors.

Carolyn M RutterRAND Corporation, Santa Monica, CA, USA.ORCID 0000-0002-4396-8594
Ifeanyi EdochieRAND Corporation, Santa Monica, CA, USA.
Esther M FriedmanRAND Corporation, Santa Monica, CA, USA.
Mary E SlaughterRAND Corporation, Santa Monica, CA, USA.
Margaret M WedenRAND Corporation, Santa Monica, CA, USA.

Funding

Estimating the Future Availability of Family Care for Alzheimers Disease in the U.S.R21AG055815 · NIA · RAND CORPORATION · PI FRIEDMAN, ESTHER M. · 2017 to 2018
$540k
NIA NIH HHS R21 AG055815
6 · The paper itself

Abstract

backgroundDementia is a common disease that has an impact on both the affected individual and family members who provide caregiving. Simulation models can assist in setting policy that anticipates public health needs by predicting the demand for and availability of care.

objectiveWe developed a relatively simple method for simulating the onset of dementia that can be used in combination with an existing microsimulation model.

methodsWe started with Socsim, a demographic microsimulation model that simulates a population with family kinship networks. We simulated dementia in the Socsim population by simulating the number of individuals diagnosed with dementia in their lifetime and the ages of onset and death from dementia for each of these dementia cases. We then matched dementia cases to the simulated population based on age at death, so for each individual, we simulate whether they develop dementia and, if so, their age at onset. This approach simulates dementia onset but does not alter the demographic model's simulated age of death.

resultsWe selected model dementia parameters so that the combined Socsim-Dementia model reproduces published dementia prevalence rates and survival times after diagnosis.

conclusionsAdding simulation of dementia to a kinship network model enables prediction of the availability of family caregivers for people with dementia under a range of different assumptions about future fertility, mortality, and dementia risk. We demonstrated how to add simulation of dementia onset and death to an existing microsimulation model to obtain a method for predicting dementia prevalence in the context of another more detailed model. The approach we developed can be generalized to simulate other progressive health conditions that affect mortality.

Indexed as

CaregiversDementiaFamilyHealth ServicesHumansPrevalencedemographic microsimulationkinship models

Identifiers

PMID34120512
PMCPMC8633039

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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.