An analysis of elder abuse rates in Milwaukee County.

INTRODUCTION: The elder abuse and neglect burden in Milwaukee County, Wisconsin, is substantial, with 3384 reports made from 2006 to 2009. Current prevalence estimates are determined from reported cases only and are likely underestimated. Provider awareness of victim and perpetrator characteristics is necessary to increase recognition and response. METHODS: A cross-sectional analysis of elder abuse and neglect cases reported to the Milwaukee County Department on Aging (MCDA) from 2006 to 2009 was performed to provide a profile of the county's elder abuse burden by victim, perpetrator, and reporter characteristics. Annual reporting trends were identified using Poisson regression analysis. RESULTS: Fifty-eight percent of MCDA reports of abuse were substantiated after investigation. Victims in Milwaukee County tended to be older than 75 (64%), female (64%), and white (62%). Reporting rates to the MCDA were significantly lower in 2009 than 2006. Perpetrators were often adult children (48%) or a spouse (14%). Forty percent of life-threatening cases of self-neglect were due to unfulfilled medical needs. Most reports were made by medical professionals (23%), relatives of the victim (21%), and community agencies (18%). Only 13% of elder abuse victims were placed in nursing homes and assisted living centers; many received services to assist independent living. DISCUSSION: Although this study is limited to reported cases only, it provides a valuable profile of pertinent elder abuse characteristics in Milwaukee County. CONCLUSION: Characteristics of vulnerable elders, potential abusers, and investigation outcomes are described to inform clinical practice about this important social issue.

KatharoSeq Enables High-Throughput Microbiome Analysis from Low-Biomass Samples.

Microbiome analyses of low-biomass samples are challenging because of contamination and inefficiencies, leading many investigators to employ low-throughput methods with minimal controls. We developed a new automated protocol, KatharoSeq (from the Greek katharos [clean]), that outperforms single-tube extractions while processing at least five times as fast. KatharoSeq incorporates positive and negative controls to reveal the whole bacterial community from inputs of as few as 50 cells and correctly identifies 90.6% (standard error, 0.013%) of the reads from 500 cells. To demonstrate the broad utility of KatharoSeq, we performed 16S rRNA amplicon and shotgun metagenome analyses of the Jet Propulsion Laboratory spacecraft assembly facility (SAF; n = 192, 96), 52 rooms of a neonatal intensive care unit (NICU; n = 388, 337), and an endangered-abalone-rearing facility (n = 192, 123), obtaining spatially resolved, unique microbiomes reproducible across hundreds of samples. The SAF, our primary focus, contains 32 sOTUs (sub-OTUs, defined as exact sequence matches) and their inferred variants identified by the deblur algorithm, with four (Acinetobacter lwoffii, Paracoccus marcusii, Mycobacterium sp., and Novosphingobium) being present in >75% of the samples. According to microbial spatial topography, the most abundant cleanroom contaminant, A. lwoffii, is related to human foot traffic exposure. In the NICU, we have been able to discriminate environmental exposure related to patient infectious disease, and in the abalone facility, we show that microbial communities reflect the marine environment rather than human input. Consequently, we demonstrate the feasibility and utility of large-scale, low-biomass metagenomic analyses using the KatharoSeq protocol. IMPORTANCE Various indoor, outdoor, and host-associated environments contain small quantities of microbial biomass and represent a niche that is often understudied because of technical constraints. Many studies that attempt to evaluate these low-biomass microbiome samples are riddled with erroneous results that are typically false positive signals obtained during the sampling process. We have investigated various low-biomass kits and methods to determine the limit of detection of these pipelines. Here we present KatharoSeq, a high-throughput protocol combining laboratory and bioinformatic methods that can differentiate a true positive signal in samples with as few as 50 to 500 cells. We demonstrate the application of this method in three unique low-biomass environments, including a SAF, a hospital NICU, and an abalone-rearing facility.