Institutions Using Survey Data from the National ALS Registry

• 1. Creating a Predictive ALS Model and Targeted Outreach

The goal of this initiative is to establish a dataset of known ALS diagnoses along with current demographic, clinic, and healthcare resources to create a predictive model that will direct targeted outreach into those geographic areas. Data to be analyzed include: demographics, clinical data, social, family, and occupational history. Statistical analyses will focus on simple characterization of the data and predictive modeling. Basic descriptive statistical measures will be calculated to summarize study information. Specifically, frequencies, percentages, means, and medians will be generated. Group differences will be tested using chi-square, t-tests, ANOVA, and nonparametric measures as appropriate. Predictive modeling will be performed with logistic and/or linear regression.

• 2. ALS Prevalence and Risk Factors

The aim is to investigate the national prevalence rate of ALS and compare demographic information. We will investigate potential environmental and occupational risk factors for ALS that exist within certain regions to explain any discrepancy between national prevalence and prevalence within certain regions. We will explore potential risk factors, such as residential proximity to farmland, well water consumption and water quality, and exposure to pesticides, herbicides, and heavy metals, may explain a difference in the prevalence rate of ALS in these areas.

• 3. Geospatial analysis of heavy metal water contamination as a risk factor for ALS

The research study from the Columbia University Health Sciences will assess the Geo-spatial distribution of heavy metals such as arsenic, lead, mercury in water and their correlation to the prevalence of ALS cases in the US. The primary goal is to assess the spatial correlation between ALS prevalence and exposure to heavy metals as a risk factor for disease progression. GeoDa will be used for the application of Poisson Kriging to map ALS mortality risk and geographically weighted regression would be used to account for the varying regression coefficient over space. Moran’s statistic, calculated using aggregated ALS prevalence data would be used to identify the disease hot-spots. A vector layer of the heavy metal distribution in water systems will be used to assess the spatial correlation between heavy metal exposure as a risk factor for Amyotrophic Lateral Sclerosis.

• 4. Geographic association of ALS and fungal poisoning

This study aims to test the hypothesis that there is a geographical association in the continental USA between the origin of reports of certain types of acute mushroom poisoning (described below) and the origin of subjects with amyotrophic lateral sclerosis (ALS). This is the first study to seek associations between ALS and prior exposure to specific environmental chemicals predicted to have the capacity to induce human motorsystem disease.

• 5. Social Factors and the Development, Progression, and Healthcare Provision for People with ALS

This study aims to understand the relationship of social, behavioral, and environmental factors to the experience of the disease is important. The project has several goals: (1) contribute to the understanding of ALS (2) use social science to contribute to the improvement in the management of the disease, (4) assess if sociological theories that were developed for other diseases are useful in understanding ALS, and (5) create an opportunity to engage researchers in a conversation across disciplines in the fight against ALS. At this time, the request is for data that has been previously collected, so requires no more time than the participant has already committed to the registry and does not require additional effort. Anyone who has been diagnosed with ALS may participate.

• 6. Study of ALS Reversals: Demographics, Disease Characteristics, Treatments, and Co-morbidities.

Amyotrophic Lateral Sclerosis (ALS or Lou Gehrig’s Disease) is an almost universally fatal disease for which there is no cure. There are, however, a handful of cases of people diagnosed with ALS who get better and never relapse. We call these cases “ALS reversals”. We aim to collect all known cases of ALS reversals and compile them into a database for comparison to patients with more typically progressive ALS and future study. Specifically, we are comparing information on the demographics, disease characteristics, treatments, and medical histories of these patients. Our hope is that, by doing this, we will be able to find differences in patients who have had reversals that we will be able to use to help other people with ALS. We ask that potential participants send us medical records that would allow us to independently verify their diagnoses and improvements or to sign a release form so that we could obtain these records from the hospitals or clinics where they were treated directly. If, after reviewing these records, we are able to include the potential participant in our study, his or her information would be de-identified and included with the other cases we are able to confirm.