The Nursing Home Quality Care Act was designed to help California’s nursing homes increase worker wages and staffing levels. The 2004 legislation has resulted in the state’s roughly 1,100 homes reaping an additional $880 million in funding.
I approached the story with a simple question: Did taxpayers get their money’s worth?
To find out, I pulled nursing home financial data off of the Web site of the Office of Statewide Health Planning and Development. The data has information about staffing levels, wages and each facility’s revenue, expenses and profit margin. The department has two different types of data and we ultimately decided to use the data that the OSHPD officials slice and dice a bit to assure that all homes are comparable.
My colleague Agustin Armendariz took the reports for 2004 through 2008 and placed them into one Excel spreadsheet that I could use for my analysis. Even though the new law didn’t kick in until 2005, the 2004 reports helped provide a baseline to measure funding levels and performance.
With the mega-file on my zippy MacBook Pro, I called up a data expert at OSHPD and reached out to Charlene Harrington, a UC San Francisco professor and nationally recognized expert on nursing homes who has written studies using the same data. I asked numerous questions, hoping to learn the limitations of the data and how to approach standard measures of nursing home staffing and finances.
To get started, I focused the analysis on homes that gained the most from the new law. Those were the homes that counted at least half of their “patient days” as “Medi-Cal days,” indicating that the money was subject to the funding increases. (Nursing homes also get revenue from Medicare and health insurers.)
We left out homes with fewer than 59 beds because those homes are allowed to calculate their overall staffing rate in a way that’s tough to track with the data available from OSHPD. For instance, they are allowed to count some managers as direct-care workers.
We also removed some homes from the analysis if their data was incomplete or included numbers that were at least 20 times higher or lower than their peers. It’s standard to remove outliers from an analysis such as this.
Once we built a fence around the analysis, we ended up with 645 homes that rely the most on Medi-Cal reimbursements.
Then I took a close look at two areas: staffing and wages. I analyzed staffing two ways, looking at staffing rate changes and also the staffing levels compared to a state minimum mandated by law.
For staffing rates, I focused on the changes from 2004 to 2008, the most recent data available. I focused on the industry-standard measure, “nursing hours per patient day,” which is the number of hours of direct attention each patient is supposed to receive from caregivers each day.
To measure the staffing rate, we added up the hours worked by permanent and temporary nurse practitioners, nurses, licensed vocational nurses, certified nursing assistants, psychiatric technicians and orderlies and divided it by the number of patient days for each home.
To reach a number of homes that cut staff, I counted facilities where the average annual staffing rate change over those years was a negative number.
Next I looked for nursing homes where the staffing level in 2008 fell below 3.2 nursing hours per patient day – the minimum written into state law in 2000.
Looking at those two measures seemed to be a fair way to determine which homes were not, in fact, boosting staffing with the new funds.
Most of the homes in California that benefited from the law boosted their staffing rates, overall staffing number or wages. But 259 homes, according to our analysis, either cut staffing, reduced wages or saw staffing levels fall below the state mandated minimum.
If a home met any of those three criteria, they were counted as one of the 259 homes. Twenty-seven homes, however, actually saw a funding decrease during the years we analyzed.
For wages, we also tried to keep things simple. The data allows users to calculate each home’s hourly wage for nurses, licensed vocational nurses and nursing assistants. I took a look at the homes where the hourly wage for any of the three categories was less in 2008 than it had been in 2004. We could have adjusted this data for inflation, which would have shown far steeper decreases in wages at nursing homes. But instead we looked at straight dollar changes.
I also looked at the net income – a measure of profit that is included in the OSHPD data. Nursing homes rely on revenue from Medi-Cal as well as other sources such as Medicare and private insurers. All of these sources can be part of the overall net income. Some of the homes – across the board – reported a decline in net income, a reality that’s reflected in all of the conclusions we reached in the story.
To calculate the total change in revenue to homes, we added up the Medi-Cal funding changes (almost all increases) that homes saw on a year-to-year basis from 2004 through 2008.
To trigger the additional revenue under the law, nursing homes had to first put up a fee that represents, on average, less than 5 percent of each home’s spending.
We chose not to back out that fee, instead relying on the gross amount of revenue. The nursing home industry objected to this approach. But we looked at this way: We wouldn’t report the annual salary of a CEO based on how much they kept after taxes.
That fee is returned to homes in proportion to their costs as well as to the number of Medi-Cal patients they serve.
We also found that over two dozen homes that fell short in either staffing or wages actually saw a decline in Medi-Cal revenue.
Finally, I used data at a Web site maintained by the California Healthcare Foundation to look at the average number of deficiencies cited during nursing home surveys funded by Medicare.
I entered the number of shortcomings for the homes into a spreadsheet, seeing that homes that made the steepest staff cut – more than an average of 2 percent per year from 2004 through 2008 – also had about 30 percent more deficiencies than the average home.
Once I crunched enough numbers to make my eyes cross, I called up Steve Doig, a professor at the Cronkite School of Journalism at Arizona State University who specializes in computer-assisted reporting. I explained my basic methodology to him and he thought it was fair.
We presented findings to the California Association of Health Facilities, sharing a spreadsheet and our methodology. That group represents nursing homes in the Capitol, and we wanted to take the opportunity well before publication to learn if our logic was flawed. They did take exception to some of our work, which you can read about in more detail here. Based on their feedback we made some modifications, but we chose not to deviate from our analysis, which followed standard, accepted practices for analyzing nursing home data.
From there, my colleague Armendariz made many of the same calculations that I made in Excel in the computer language SQL, to double-check the results. At that point we also decided to access the freshest batch of state data. Additionally, we downloaded new OSHPD data in late February.
At the end of the process, we arrived at our major finding: 232 of the 645 homes we looked at either cut staffing, slashed wages or fell below the state staffing minimum in 2008 – even though they saw funding increases since the law was passed.
Some homes got more money and cut staffing or wages, or saw their staffing minimums drop – but were left out of the analysis because of their small size.
Among them were Applewood Care Center in Sacramento and Homewood Care Center in San Jose.
Both homes had fewer than 59 beds. But they are referenced in the story because they illustrate other issues or trends. Homewood was penalized by the state but was able to use funding increases from the state to help pay legal costs. Applewood, on the other hand, was cited as an example of a home that got more money and made cuts and had seen repeated serious violations for lapses in care.