Final EEOC rule sets limits for financial incentives on wellness programs

PBS NewsHour

Employer wellness programs can gather medical information from employees and spouses -- so long as financial incentives or penalties don't exceed 30 percent of the annual cost for an individual in the company's group health plan, according to final rules issued by the Equal Employment Opportunity Commission Monday. Although such penalties or incentives could run into the hundreds or even thousands of dollars, the programs are considered voluntary -- and therefore legal, the commission said. The rules seek to ensure "wellness programs actually promote good health and are not just used to collect or sell sensitive medical information about employees and family members or to impermissibly shift health insurance costs to them," the EEOC said. But the final rules drew immediate concern from some groups. Jennifer Mathis, director of programs for the Bazelon Center for Mental Health Law, says the new rule rolls back protections in existing law.

Federal Lawsuit Challenges Notre Dame's Birth Control Rules

U.S. News

The South Bend Tribune reports the lawsuit was filed Tuesday in U.S. District Court for Northern Indiana. In addition to Notre Dame's abortion policies, it challenges the Trump administration's interim rules allowing universities to disregard a requirement of the Affordable Care Act that health plans cover birth control for women without out-of-pocket costs.

Judge Rejects Massachusetts Challenge to Trump Birth Control Rules

U.S. News

BOSTON (Reuters) - A federal judge on Monday rejected a lawsuit by Massachusetts' attorney general challenging new rules by President Donald Trump's administration that make it easier for employers to avoid providing insurance that covers women's birth control.

Judge Tosses Massachusetts Lawsuit Over Birth Control Rules

U.S. News

A federal judge has tossed the Massachusetts attorney general's lawsuit against President Donald Trump's administration over rules allowing more employers to opt out of providing no-cost birth control to women.

A Generalized Fellegi-Sunter Framework for Multiple Record Linkage With Application to Homicide Record Systems Machine Learning

We present a probabilistic method for linking multiple datafiles. This task is not trivial in the absence of unique identifiers for the individuals recorded. This is a common scenario when linking census data to coverage measurement surveys for census coverage evaluation, and in general when multiple record-systems need to be integrated for posterior analysis. Our method generalizes the Fellegi-Sunter theory for linking records from two datafiles and its modern implementations. The multiple record linkage goal is to classify the record K-tuples coming from K datafiles according to the different matching patterns. Our method incorporates the transitivity of agreement in the computation of the data used to model matching probabilities. We use a mixture model to fit matching probabilities via maximum likelihood using the EM algorithm. We present a method to decide the record K-tuples membership to the subsets of matching patterns and we prove its optimality. We apply our method to the integration of three Colombian homicide record systems and we perform a simulation study in order to explore the performance of the method under measurement error and different scenarios. The proposed method works well and opens some directions for future research.