Older adults

The critical need for population-level interventions to support the health needs of the growing population of older adults is widely recognized1. In addition, there is a need for novel indicators to monitor wellness as a resource for living and a means for prediction and prevention of changes in community health status2. Smart homes, defined as residential infrastructure equipped with technology features that enable passive monitoring of residents to proactively support wellness, have the potential to support older adults for independence at the residence of their choice. However, a characterization of the current state of smart homes research as a population health intervention is lacking. In addition, there is a knowledge translation gap between the smart homes research and public health practice communities. The EBPH movement identifies three types of evidence along a continuum to inform population health interventions: Type 1 (something should be done), Type 2 (this should be done) and Type 3 (how it should be done)3. Type 2 evidence consists of a classification scheme for interventions (emerging, promising, effective and evidence-based)3. To illustrate typology use with an example: the need for population health interventions for aging populations is well known (Type 1 evidence), many studies show that smart home technologies can support aging in place (Type 2 evidence) but there are few, if any, examples of smart homes as population health interventions to support aging in place (Type 3 evidence). Our research questions for this systematic review are: 1) What categories of Type 2 evidence from the scientific literature uphold smart homes as an EBPH intervention? 2) What are the novel health indicators identified from smart home studies to inform design of a community health registry that supports prediction and prevention of negative changes in health status? 3) What stakeholders are reported in studies that contribute Type 2 evidence for smart homes as an EBPH intervention? 4) What gaps exist between Type 2 and Type 3 evidence for smart homes as an EBPH intervention?

Objective

This study aims to 1) characterize the state of smart homes research as a population health intervention to support aging in place through systematic review and classification of scientific literature using an evidence-based public health (EBPH) typology and 2) identify novel indicators of health captured by monitoring technologies to inform design of a community health registry.

Influenza is a significant public health problems in the US leading to over one million hospitalizations in the elderly population (age 65 and over) annually. While influenza preparedness is an important public health issue, previous research has not provided comprehensive analysis of season-by-season timing and geographic shift of influenza in the elderly population. These findings fail to document the intricacies of each unique influenza season, which would benefit influenza preparedness and intervention. The annual harmonic regression model fits each season of disease incidence characterized by its own unique curve. Using this model, characteristics of the seasonal curve for each state and each season can be compared. We hypothesize that travelling waves of influenza in the 48 contiguous states differ dramatically in each influenza season.

Objective

In surveillance it is imperative that we know when and where a disease first begins. The objective of this study was to examine trends in traveling waves of influenza in the US elderly population. Preparedness for influenza is an important yet difficult public health goal due to variability in annual strains, timing, and shift of the influenza virus. In order to better prepare for influenza epidemics, it is important to assess seasonal variation across individual influenza seasons on a state-by-state basis. This approach will lead to effective interventions especially for susceptible populations such as the elderly.

Accurate and precise estimation of disease rates for a given population during a specified time frame is a major concern for public health practitioners and researchers in biosurveillance. Many diseases follow distinct patterns; incidence and prevalence of many diseases increase approximately exponentially with age, including many cancers, respiratory infections, and gastroenteritis. With increasing demographic information available in biosurveillance systems leading to more complex and comprehensive disease databases, seeking concise and informative summary measures of disease burden over space and time is becoming more critical for public health surveillance. In this paper we present two summary measures of disease burden in the elderly that simultaneously reflect disease dynamics and population characteristics.

Objective

To better estimate disease burden in the elderly population we illustrate an approach—the Slope Intercept Modeling for Population Linear Estimation (SIMPLE) method—that summarizes age-specific disease rates in the 65+ population using the observed exponential increase in disease rates with age in this dynamic and rapidly growing population subgroup.