WEBVTT 00:00:00.860 --> 00:00:06.340 position:50% align:middle - [Woman] Jeannie Cimiotti is associate professor in the Nell Hodgson Woodruff School of Nursing at Emory 00:00:06.340 --> 00:00:12.650 position:50% align:middle University and associate program director, Atlanta VA Quality Scholars program. 00:00:12.650 --> 00:00:18.800 position:50% align:middle Her extensive research on the healthcare workforce and patient outcomes has been cited globally. 00:00:18.800 --> 00:00:24.550 position:50% align:middle She has been recognized as outstanding alumni at Columbia University School of Nursing and 00:00:24.550 --> 00:00:31.515 position:50% align:middle for publication excellence by the Association for Professionals in Infection Control and Epidemiology. 00:00:38.070 --> 00:00:41.648 position:50% align:middle - [Dr. Cimiotti] So thank you for the opportunity to present this afternoon some of our 00:00:41.648 --> 00:00:45.804 position:50% align:middle preliminary research findings from our study that was funded through the 00:00:45.804 --> 00:00:47.771 position:50% align:middle National Council of State Boards of Nursing. 00:00:47.771 --> 00:00:53.830 position:50% align:middle The title of our research project was Patient Outcomes, Inpatient Costs, and Hospital Performance During 00:00:53.830 --> 00:00:59.450 position:50% align:middle a Disaster: Implications for the Nurse Licensure Compact. 00:00:59.450 --> 00:01:03.090 position:50% align:middle So just some background, and it's a bit extensive because I think I really need 00:01:03.090 --> 00:01:12.130 position:50% align:middle to refresh your memory on what happened and what has been happening with storms in the United States. 00:01:12.130 --> 00:01:17.538 position:50% align:middle The U.S. has seen a great increase in the number of life-threatening storms. 00:01:17.538 --> 00:01:19.633 position:50% align:middle Many are saying it's due to global warming. 00:01:19.633 --> 00:01:25.437 position:50% align:middle In 2020 alone, we had 25 named storms. 00:01:25.437 --> 00:01:27.297 position:50% align:middle Six of them were major hurricanes. 00:01:27.297 --> 00:01:31.520 position:50% align:middle It's more than double than what we were seeing 10 years earlier. 00:01:31.520 --> 00:01:37.770 position:50% align:middle Severe storms disrupt the activities of daily living, and they affect people across age groups. 00:01:37.770 --> 00:01:43.745 position:50% align:middle But what we really don't know is how these storms impact the delivery of health care services. 00:01:45.890 --> 00:01:48.256 position:50% align:middle So our storm of interest was Hurricane Sandy. 00:01:48.256 --> 00:01:52.860 position:50% align:middle It was one of the major hurricanes. 00:01:52.860 --> 00:01:59.830 position:50% align:middle It was the major hurricane in October 2012, and it began as a tropical storm really on October 22nd 00:01:59.830 --> 00:02:01.650 position:50% align:middle in the Caribbean Sea. 00:02:01.650 --> 00:02:07.160 position:50% align:middle On the 24th, Sandy was upgraded to a Category 1 hurricane when it hit Jamaica 00:02:07.160 --> 00:02:10.140 position:50% align:middle with 80-mile-per-hour winds. 00:02:10.140 --> 00:02:15.596 position:50% align:middle A day later, Sandy was upgraded to a Category 2 hurricane when it hit Cuba 00:02:15.596 --> 00:02:18.160 position:50% align:middle with winds of 105 miles per hour. 00:02:18.160 --> 00:02:21.400 position:50% align:middle It then proceeded to hit Haiti, the Bahamas. 00:02:21.400 --> 00:02:27.920 position:50% align:middle It killed 54 in Haiti, 11 in the Dominican Republic, and 2 in the Bahamas. 00:02:27.920 --> 00:02:33.820 position:50% align:middle The next day, the 26th and the 27th, this hurricane was alternating between Category 1 00:02:33.820 --> 00:02:37.020 position:50% align:middle hurricane and a tropical storm and then back to Category 1. 00:02:37.020 --> 00:02:42.817 position:50% align:middle On the 28th of October, Sandy was a Category 1 hurricane 00:02:42.817 --> 00:02:45.015 position:50% align:middle and it skirted right up the eastern seaboard. 00:02:45.015 --> 00:02:50.400 position:50% align:middle It was moving parallel to Georgia, South Carolina, and North Carolina. 00:02:50.400 --> 00:02:56.134 position:50% align:middle However, on the 29th, it began to move inward towards land on the East Coast 00:02:56.134 --> 00:03:04.040 position:50% align:middle of the U.S. as a Category 2 hurricane but then again it weakened to a post-tropical cyclone. 00:03:04.040 --> 00:03:12.420 position:50% align:middle So I think the important takeaway here with Sandy is, when it made landfall in the eastern United States 00:03:12.420 --> 00:03:15.650 position:50% align:middle on the 29th of October, it was huge. 00:03:15.650 --> 00:03:22.200 position:50% align:middle It was massive, maybe one of the largest hurricanes recorded in U.S. history. 00:03:22.200 --> 00:03:29.220 position:50% align:middle And as it began to move into the northeast, at 2:30 that afternoon on the 29th, 00:03:29.220 --> 00:03:33.910 position:50% align:middle it hit into the Washington, D.C. area and started to move northward. 00:03:33.910 --> 00:03:41.314 position:50% align:middle At 8 p.m. on the 29th, that storm made landfall in Atlantic City, New Jersey with hurricane-force 00:03:41.314 --> 00:03:43.490 position:50% align:middle winds of 90 miles per hour. 00:03:43.490 --> 00:03:48.340 position:50% align:middle Now, what some folks might fail to have realized, I know our meteorologists knew, 00:03:48.340 --> 00:03:52.400 position:50% align:middle but maybe laypersons didn't that… Well, it was a full moon. 00:03:52.400 --> 00:03:56.500 position:50% align:middle And when we have a full moon, the tides are significantly higher. 00:03:56.500 --> 00:04:02.050 position:50% align:middle So now we have a hurricane coming into New York Harbor at 90 miles per hour, 00:04:02.050 --> 00:04:06.120 position:50% align:middle and New York Harbor isn't that big if you're familiar with that area of the northeast. 00:04:06.120 --> 00:04:12.293 position:50% align:middle With a full moon with high tide, there was a 14-foot wave surge that entered 00:04:12.293 --> 00:04:16.126 position:50% align:middle New York Harbor and then would proceed up the Hudson River. 00:04:16.490 --> 00:04:21.800 position:50% align:middle Now, the Hudson River is just that—it's not a large river but it's well-known—separates New York 00:04:21.800 --> 00:04:22.560 position:50% align:middle and New Jersey. 00:04:22.560 --> 00:04:28.990 position:50% align:middle So you had massive flooding on each side of that river, so it meant lower Manhattan and most of New Jersey, 00:04:28.990 --> 00:04:36.940 position:50% align:middle especially mid to northern New Jersey took a really severe hit when this hurricane made landfall. 00:04:36.940 --> 00:04:42.290 position:50% align:middle Now, this image that's here, hopefully you can see it, this is the 1 train in New York City, 00:04:42.290 --> 00:04:44.290 position:50% align:middle the uptown to the Bronx. 00:04:44.290 --> 00:04:50.154 position:50% align:middle And if you're familiar with the underground rail, they have steep elevators that are really… 00:04:50.154 --> 00:04:55.344 position:50% align:middle I mean, you're going to the underground so they're really lengthy and at a significant incline or decline, 00:04:55.344 --> 00:05:00.260 position:50% align:middle depending on if you're going up or down. 00:05:00.260 --> 00:05:03.190 position:50% align:middle But you could see here this whole train station was flooded. 00:05:03.190 --> 00:05:08.440 position:50% align:middle The water has already come up to the very top of the escalators and to the area where people would enter 00:05:08.440 --> 00:05:11.520 position:50% align:middle into the station. 00:05:11.520 --> 00:05:14.260 position:50% align:middle And like I had mentioned, it impacts populations. 00:05:14.260 --> 00:05:18.180 position:50% align:middle If you look at this image on the left, this man carrying his wife on his shoulders... 00:05:18.180 --> 00:05:21.460 position:50% align:middle When I first saw this image, I thought he was near the ocean. 00:05:21.460 --> 00:05:25.710 position:50% align:middle He's walking down a street in Hoboken, New Jersey trying to take his wife to safety. 00:05:25.710 --> 00:05:31.630 position:50% align:middle And in the photo on the upper right, people being evacuated in boats, again, 00:05:31.630 --> 00:05:32.810 position:50% align:middle in Hoboken, New Jersey. 00:05:32.810 --> 00:05:36.520 position:50% align:middle The streets were that flooded that they needed to be evacuated via boat. 00:05:36.520 --> 00:05:38.830 position:50% align:middle And in the background of that photo, you can see there's an ambulance. 00:05:38.830 --> 00:05:40.400 position:50% align:middle That ambulance isn't going anywhere. 00:05:40.400 --> 00:05:41.100 position:50% align:middle It's stranded. 00:05:41.100 --> 00:05:43.800 position:50% align:middle So EMS was at a standstill. 00:05:43.800 --> 00:05:49.060 position:50% align:middle And when we have disasters of any sort, we've seen this recently, there's a food famine. 00:05:49.060 --> 00:05:54.750 position:50% align:middle So people know when storms are coming, they typically go to the stores, they buy all kinds of, 00:05:54.750 --> 00:05:59.030 position:50% align:middle you know, perishable and non-perishable items thinking that they might not be able to get to a store 00:05:59.030 --> 00:06:00.200 position:50% align:middle for several days. 00:06:00.200 --> 00:06:05.118 position:50% align:middle So there was a huge food famine during Hurricane Sandy. 00:06:05.118 --> 00:06:12.040 position:50% align:middle But gratefully—you know, New Jersey residents were grateful as we all are in our 00:06:12.040 --> 00:06:14.170 position:50% align:middle time of need—the National Guard were available. 00:06:14.170 --> 00:06:20.072 position:50% align:middle And Gov. Chris Christie brought in the National Guard at the time saying this was the most devastation 00:06:20.072 --> 00:06:22.212 position:50% align:middle that had ever occurred in New Jersey. 00:06:22.212 --> 00:06:23.380 position:50% align:middle He brought in the Guard. 00:06:23.380 --> 00:06:32.570 position:50% align:middle They had vehicles that they could travel and navigate the flooded streets and help where help was needed. 00:06:32.570 --> 00:06:37.340 position:50% align:middle In this case in this image, you see them transporting an elderly gentleman probably 00:06:37.340 --> 00:06:42.480 position:50% align:middle to a shelter or what could be a hospital because the ambulances were nonfunctional. 00:06:42.480 --> 00:06:45.760 position:50% align:middle They were taking people to the hospital also. 00:06:45.760 --> 00:06:49.740 position:50% align:middle Now, when it came to the hospitals, there was also another dilemma. 00:06:49.740 --> 00:06:51.060 position:50% align:middle Hospitals lost power. 00:06:51.060 --> 00:06:53.300 position:50% align:middle The whole state lost power. 00:06:53.300 --> 00:06:57.600 position:50% align:middle And hospitals have generators and that's fine. 00:06:57.600 --> 00:07:01.120 position:50% align:middle But when the hospital started to flood, there was an issue. 00:07:01.120 --> 00:07:02.650 position:50% align:middle The generators failed. 00:07:02.650 --> 00:07:05.720 position:50% align:middle They couldn't have staff walking through several feet of water. 00:07:05.720 --> 00:07:07.980 position:50% align:middle They had to get patients out. 00:07:07.980 --> 00:07:13.720 position:50% align:middle Three major hospitals closed in New Jersey during this time—Jersey City Medical Center, 00:07:13.720 --> 00:07:16.260 position:50% align:middle Palisades Medical Center, Hoboken Medical Center. 00:07:16.260 --> 00:07:17.710 position:50% align:middle These are large hospitals. 00:07:17.710 --> 00:07:19.610 position:50% align:middle Patients had to be put somewhere. 00:07:19.610 --> 00:07:21.890 position:50% align:middle There was no way for us to maneuver staff. 00:07:21.890 --> 00:07:23.610 position:50% align:middle They couldn't get staff home. 00:07:23.610 --> 00:07:27.530 position:50% align:middle They couldn't bring relief staff in if they were available. 00:07:27.530 --> 00:07:31.230 position:50% align:middle People didn't want to leave their homes, whether they were nurses or physicians. 00:07:31.230 --> 00:07:35.010 position:50% align:middle They might have had small children at home or other family or elderly to care for. 00:07:35.010 --> 00:07:40.100 position:50% align:middle They weren't going to leave in this time of desperation to go to the hospitals even though the National Guard 00:07:40.100 --> 00:07:44.451 position:50% align:middle could have brought them in if need be, but they definitely were… 00:07:44.451 --> 00:07:48.552 position:50% align:middle These hospitals were challenged probably like no other time in the state. 00:07:50.360 --> 00:07:55.180 position:50% align:middle This is just an image that shows you a map that was provided by FEMA. 00:07:55.180 --> 00:07:56.420 position:50% align:middle You see New Jersey. 00:07:56.420 --> 00:08:03.110 position:50% align:middle Right here, New Jersey is very small, a small state, but it's densely populated. 00:08:03.110 --> 00:08:09.840 position:50% align:middle And the green areas are the low areas that had very little impact from the storm and none in New Jersey 00:08:09.840 --> 00:08:11.130 position:50% align:middle fell in the green area. 00:08:11.130 --> 00:08:13.660 position:50% align:middle The purple areas were the highest impact. 00:08:13.660 --> 00:08:15.770 position:50% align:middle You can see that's along the coastline. 00:08:15.770 --> 00:08:21.850 position:50% align:middle You can see that there's a line on this map that shows you where the storm was traveling up the coast. 00:08:21.850 --> 00:08:23.770 position:50% align:middle It went right into that New York Harbor. 00:08:23.770 --> 00:08:26.720 position:50% align:middle You could see the purple area. 00:08:26.720 --> 00:08:30.720 position:50% align:middle New York City and Long Island is completely in the purple area. 00:08:30.720 --> 00:08:37.010 position:50% align:middle And then most of Central New Jersey was in the high impact area which is the red, 00:08:37.010 --> 00:08:44.150 position:50% align:middle and then the moderate impact area were some of those western counties. 00:08:44.150 --> 00:08:52.160 position:50% align:middle Now, FEMA actually has definitions for storm surge, and we use these definitions in our analyses. 00:08:52.160 --> 00:09:02.520 position:50% align:middle You should also know that New Jersey being small only has 21 counties, and these counties were designated or 00:09:02.520 --> 00:09:06.810 position:50% align:middle categorized by their definition of storm severity. 00:09:06.810 --> 00:09:12.886 position:50% align:middle So the very high areas of storm severity were those where greater than 10,000 people were exposed 00:09:12.886 --> 00:09:17.820 position:50% align:middle to the surge, and there were 9 counties in New Jersey that met those criteria, 00:09:17.820 --> 00:09:20.410 position:50% align:middle most of them right along the coastline. 00:09:20.410 --> 00:09:22.900 position:50% align:middle In fact, they were all along the coastline of some sort. 00:09:22.900 --> 00:09:31.960 position:50% align:middle On the high area of surge impact or storm severity were 5,000 to 10,000 people exposed to the storm surge 00:09:31.960 --> 00:09:36.110 position:50% align:middle with greater than $100 million in wind damage and over 8 inches of rain. 00:09:36.110 --> 00:09:42.710 position:50% align:middle And, again, another nine counties in New Jersey were classified in the high-impact area. 00:09:42.710 --> 00:09:48.805 position:50% align:middle And then there's the moderate storm severity impact area where you have 100 to 500 people exposed 00:09:48.805 --> 00:09:54.180 position:50% align:middle to the surge, $10 to $100 million in damage, 4 to 8 inches of rain, 00:09:54.180 --> 00:09:59.160 position:50% align:middle and there were 3 counties in New Jersey that fell into that category. 00:09:59.160 --> 00:10:06.520 position:50% align:middle So the overall study aims for this presentation and for our project were to determine if the supply of nurses 00:10:06.520 --> 00:10:12.480 position:50% align:middle in New Jersey hospitals were adequate to meet the demands for patients during Hurricane Sandy 00:10:12.480 --> 00:10:18.180 position:50% align:middle and to determine if patient outcomes would have been improved if additional resources, 00:10:18.180 --> 00:10:25.310 position:50% align:middle if they could have brought nurses in from other states to practice in the storm surge areas. 00:10:25.310 --> 00:10:30.460 position:50% align:middle So, our methods, it was a cross-sectional analysis of secondary data. 00:10:30.460 --> 00:10:38.289 position:50% align:middle We used primarily data from the fourth quarter of 2011 as a comparison, and the fourth quarter of 2012 00:10:38.289 --> 00:10:40.653 position:50% align:middle which was when Hurricane Sandy struck. 00:10:40.653 --> 00:10:45.990 position:50% align:middle Our patient claims data were from the state in-patient database. 00:10:45.990 --> 00:10:49.330 position:50% align:middle Those were available through the Agency for Healthcare Research and Quality, 00:10:49.330 --> 00:10:52.620 position:50% align:middle their Healthcare Cost and Utilization Project. 00:10:52.620 --> 00:10:58.790 position:50% align:middle Hospital characteristics were obtained from the American Hospital Association Annual Survey, 00:10:58.790 --> 00:11:03.500 position:50% align:middle and New Jersey staffing was available from the New Jersey Department of Health. 00:11:03.500 --> 00:11:06.120 position:50% align:middle New Jersey only has 72 acute care hospitals. 00:11:06.120 --> 00:11:07.940 position:50% align:middle Again, it's a small hospital. 00:11:07.940 --> 00:11:13.120 position:50% align:middle Sixty-six hospitals were included in our analyses because those were the hospitals that had sufficient 00:11:13.120 --> 00:11:16.450 position:50% align:middle data available on their staffing. 00:11:16.450 --> 00:11:22.190 position:50% align:middle So our variables and measures of interest were the hospital characteristics, bed size. 00:11:22.190 --> 00:11:30.500 position:50% align:middle We categorized that into three categories—teaching status or whether hospitals had medical residents, 00:11:30.500 --> 00:11:35.610 position:50% align:middle whether they were members of the Council of Teaching Hospitals, high technology. 00:11:35.610 --> 00:11:37.940 position:50% align:middle Don't forget this was 2012. 00:11:37.940 --> 00:11:43.740 position:50% align:middle So we looked to see if hospitals had implemented the electronic medical record or not. 00:11:43.740 --> 00:11:45.610 position:50% align:middle Magnet accreditation. 00:11:45.610 --> 00:11:53.060 position:50% align:middle If hospitals had been accreditated as a Magnet hospital, it's interesting to note that New Jersey has 00:11:53.060 --> 00:11:58.160 position:50% align:middle always had the largest percent of Magnet hospitals, and it had the first Magnet hospital, 00:11:58.160 --> 00:12:01.190 position:50% align:middle Hackensack Medical Center. 00:12:01.190 --> 00:12:09.240 position:50% align:middle And we also categorized hospitals based on the fact if they were designated as a safety-net hospital. 00:12:09.240 --> 00:12:09.924 position:50% align:middle Nurse staffing. 00:12:09.924 --> 00:12:15.836 position:50% align:middle They used two measures—the full-time equivalent of RNs and the patient to nurse ratio. 00:12:15.836 --> 00:12:17.542 position:50% align:middle We had unit types. 00:12:17.542 --> 00:12:18.913 position:50% align:middle We had several unit types. 00:12:18.913 --> 00:12:24.970 position:50% align:middle Most of our analysis, we collapsed them into ICU or non-ICU. 00:12:24.970 --> 00:12:30.770 position:50% align:middle The patient data, we actually had data available on 87,701 patients. 00:12:30.770 --> 00:12:36.730 position:50% align:middle We had their demographic information, age, gender, race, ethnicity, and categories 00:12:36.730 --> 00:12:41.014 position:50% align:middle of economic status, comorbidities. 00:12:41.014 --> 00:12:44.110 position:50% align:middle These were potentially 29. 00:12:44.110 --> 00:12:46.868 position:50% align:middle They were based on the Elixhauser Comorbidity Index. 00:12:46.868 --> 00:12:50.350 position:50% align:middle We had unit type, and we had admission and discharge month. 00:12:50.350 --> 00:12:58.340 position:50% align:middle Our patient outcomes of interest for this presentation were transfer out or transfer status. 00:12:58.340 --> 00:13:01.860 position:50% align:middle Did a hospital have to transfer their patients out? 00:13:01.860 --> 00:13:05.310 position:50% align:middle And that's to another facility, not the home, but did they transfer them out? 00:13:05.310 --> 00:13:10.980 position:50% align:middle Patient mortality, patient readmission, and patient length of stay. 00:13:10.980 --> 00:13:15.180 position:50% align:middle Our statistical analyses were descriptive and inferential. 00:13:15.180 --> 00:13:21.640 position:50% align:middle We examined continuous variables and presented their means and standard deviations. 00:13:21.640 --> 00:13:25.550 position:50% align:middle Categorical variables as numbers and percents. 00:13:25.550 --> 00:13:28.210 position:50% align:middle We used linear and logistic regression models. 00:13:28.210 --> 00:13:31.900 position:50% align:middle We conducted univariate, multivariate analysis. 00:13:31.900 --> 00:13:36.610 position:50% align:middle For this presentation, we presented the robust multivariate analysis. 00:13:36.610 --> 00:13:43.190 position:50% align:middle And we also projected the need for nurse staffing as the observed minus the expected FTEs, 00:13:43.190 --> 00:13:46.202 position:50% align:middle and we based this on the work of [inaudible] and colleagues. 00:13:48.200 --> 00:13:54.500 position:50% align:middle So when we looked at hospital characteristics by storm impact area, and again if you recall, 00:13:54.500 --> 00:13:57.530 position:50% align:middle we're limited to 66 hospitals. 00:13:57.530 --> 00:14:02.110 position:50% align:middle We found that 39 of those hospitals were in the high-impact area. 00:14:02.110 --> 00:14:07.320 position:50% align:middle The majority of them 67% were large hospitals. 00:14:07.320 --> 00:14:08.590 position:50% align:middle Roughly a third were moderate. 00:14:08.590 --> 00:14:10.850 position:50% align:middle There were no small hospitals in the high-impact area. 00:14:10.850 --> 00:14:18.610 position:50% align:middle 62% were teaching hospitals, 85% did have an electronic medical record in place, 00:14:18.610 --> 00:14:24.390 position:50% align:middle 28% were our safety-net hospitals, and 44% were Magnet hospitals. 00:14:24.390 --> 00:14:29.360 position:50% align:middle In the high-impact area, the distribution was quite similar with hospitals 00:14:29.360 --> 00:14:33.680 position:50% align:middle impacted on bed size, but we did see 9% of the smaller hospitals. 00:14:33.680 --> 00:14:37.370 position:50% align:middle Again, 61% were teaching hospitals. 00:14:37.370 --> 00:14:39.910 position:50% align:middle Again, high technology at 78%. 00:14:39.910 --> 00:14:48.210 position:50% align:middle The safety-net hospitals again impacted 30% of them, and 35% were Magnet hospitals. 00:14:48.210 --> 00:14:54.790 position:50% align:middle Very few hospitals again wherein, if you remember, it was only three counties in the moderate area 00:14:54.790 --> 00:15:00.000 position:50% align:middle of storm impact and we saw 100% of those hospitals, though the number was small, 00:15:00.000 --> 00:15:01.230 position:50% align:middle they were moderately sized. 00:15:01.230 --> 00:15:10.010 position:50% align:middle 50% of them were teaching hospitals, 75% high technology, and 25% were Magnet hospitals. 00:15:10.010 --> 00:15:18.650 position:50% align:middle When we looked at the overall characteristics of the patients, the 87,701, on average, 00:15:18.650 --> 00:15:19.964 position:50% align:middle they were 50 years of age. 00:15:19.964 --> 00:15:24.860 position:50% align:middle Slightly more female at 57%. 00:15:24.860 --> 00:15:30.970 position:50% align:middle 62% of them were white, 17% African-American, 21% other. 00:15:30.970 --> 00:15:33.294 position:50% align:middle 13% were Hispanic. 00:15:33.294 --> 00:15:39.400 position:50% align:middle And when we looked at the income brackets, we see that the majority of them were in a very 00:15:39.400 --> 00:15:45.710 position:50% align:middle high-income quartile, and the next [inaudible 00:15:43] the 51% to 75%. 00:15:45.710 --> 00:15:50.560 position:50% align:middle I think you should probably know that New Jersey is a very wealthy state. 00:15:50.560 --> 00:15:53.310 position:50% align:middle The people that live in New Jersey, due to the cost of living there, 00:15:53.310 --> 00:15:55.940 position:50% align:middle often have a substantial income. 00:15:55.940 --> 00:16:03.048 position:50% align:middle So that's most likely the reason why we see that the higher-income populations were impacted. 00:16:07.460 --> 00:16:11.710 position:50% align:middle When we looked at nurse staffing, we compared it to the quarter before. 00:16:11.710 --> 00:16:19.800 position:50% align:middle So we took the third quarter of 2012 and compared it to the fourth quarter of 2012 to just see if there was any 00:16:19.800 --> 00:16:26.170 position:50% align:middle shift in the number of nurses that might have been brought in to work during Hurricane Sandy, 00:16:26.170 --> 00:16:28.500 position:50% align:middle and we saw very little difference in staffing. 00:16:28.500 --> 00:16:30.840 position:50% align:middle None of it was significantly different. 00:16:30.840 --> 00:16:39.095 position:50% align:middle Tiny little uptick in ICU staffing and intermediate units which included not only intermediate adult 00:16:39.095 --> 00:16:41.201 position:50% align:middle but pediatric and some of the specialty units. 00:16:41.201 --> 00:16:50.320 position:50% align:middle Med-surg, a slight decrease in the staffing but overall it was such a minimal increase in the staffing 00:16:50.320 --> 00:16:56.060 position:50% align:middle from that quarter prior to the storm and during the storm. 00:16:56.060 --> 00:17:02.610 position:50% align:middle When we looked at patient outcomes in the fourth quarter 2012, we found on average the patient length 00:17:02.610 --> 00:17:04.530 position:50% align:middle of stay was roughly 5 days. 00:17:04.530 --> 00:17:08.707 position:50% align:middle 6% of the patients were readmitted. 00:17:08.707 --> 00:17:10.353 position:50% align:middle 2% of them died. 00:17:10.353 --> 00:17:15.481 position:50% align:middle The table on the lower part of this slide shows the results from our multivariate analysis. 00:17:15.481 --> 00:17:20.784 position:50% align:middle These were linear or logistic regression models but the complete set of controls of patient 00:17:20.784 --> 00:17:22.267 position:50% align:middle and hospital characteristics. 00:17:23.176 --> 00:17:34.405 position:50% align:middle We found that for each additional patient added to a nurse's workload resulted in a higher likelihood and a 00:17:34.405 --> 00:17:41.690 position:50% align:middle significant likelihood that patients would be transferred out of the ICU and the intermediate units. 00:17:41.690 --> 00:17:47.175 position:50% align:middle We also saw that significantly less likely to be transferred out of a med-surg. 00:17:47.175 --> 00:17:52.620 position:50% align:middle There were no significant differences in mortality so that's kind of a moot point, 00:17:52.620 --> 00:17:54.300 position:50% align:middle but we've included it in the table. 00:17:54.300 --> 00:18:02.840 position:50% align:middle When we looked at readmission, we see that there was a statistically significant 00:18:02.840 --> 00:18:10.650 position:50% align:middle relationship there between staffing so for, you know, each additional patient added to the nurse's workload 00:18:10.650 --> 00:18:13.430 position:50% align:middle resulted in the decrease in readmissions. 00:18:13.430 --> 00:18:19.130 position:50% align:middle A higher likelihood of that happening in the ICU but across all units. 00:18:19.130 --> 00:18:24.530 position:50% align:middle So the patients weren't being readmitted, and the length of stay, 00:18:24.530 --> 00:18:31.234 position:50% align:middle the only significant finding was that it was slightly increased in length of stay in the intermediate units. 00:18:32.300 --> 00:18:38.130 position:50% align:middle Then we estimated the shortage of RN FTEs by county for the fourth quarter of 2012. 00:18:38.130 --> 00:18:42.010 position:50% align:middle The map on the left represents the intensive care units. 00:18:42.010 --> 00:18:46.250 position:50% align:middle The map on the right, the non-intensive care units. 00:18:46.250 --> 00:18:52.310 position:50% align:middle What's of concern here or all the areas of concern because they all represent shortages, 00:18:52.310 --> 00:18:55.740 position:50% align:middle but the darker blue areas are where the severe shortage was. 00:18:55.740 --> 00:19:04.130 position:50% align:middle And you can see in the intensive care units, you had four counties that really had a 75% to 100% 00:19:04.130 --> 00:19:09.820 position:50% align:middle shortage of RNs in their ICUs which is substantial. 00:19:09.820 --> 00:19:15.150 position:50% align:middle But when you look at the right, the non-intensive care units, 00:19:15.150 --> 00:19:24.110 position:50% align:middle you see a much larger number of counties in those dark blue areas, meaning 75% to 100% shortage of RNs. 00:19:24.110 --> 00:19:30.500 position:50% align:middle And even the next lighter shade is 50% to 75%. 00:19:30.500 --> 00:19:36.340 position:50% align:middle So almost the entire state of New Jersey, they all had a shortage of some sort 00:19:36.340 --> 00:19:38.320 position:50% align:middle during Hurricane Sandy. 00:19:38.320 --> 00:19:43.530 position:50% align:middle We'd looked at a couple different models, 10%, 20% increases, and we found out that, 00:19:43.530 --> 00:19:53.070 position:50% align:middle if you increased the number of RN FTEs by 20%, New Jersey would have been able to eliminate the 00:19:53.070 --> 00:19:57.060 position:50% align:middle shortage in all counties state-wide. 00:19:57.060 --> 00:20:00.330 position:50% align:middle So our study did have limitations. 00:20:00.330 --> 00:20:04.970 position:50% align:middle Nurse staffing data are collected on a monthly basis, and they are reported quarterly. 00:20:04.970 --> 00:20:10.850 position:50% align:middle There were no other metrics of nurse staffing available to us despite our pleas with the New Jersey State 00:20:10.850 --> 00:20:12.110 position:50% align:middle Department of Health. 00:20:12.110 --> 00:20:16.860 position:50% align:middle So we do acknowledge that if we would have had nurse staffing at least a monthly, a weekly, 00:20:16.860 --> 00:20:21.930 position:50% align:middle or a daily level, we would have had maybe more robust findings. 00:20:21.930 --> 00:20:29.830 position:50% align:middle Nurse data were reported as a number of patients per RN and lack details on the employment status of RNs. 00:20:29.830 --> 00:20:34.880 position:50% align:middle We do not know if the RNs were full-time, part-time, per diem, or supplied by a 00:20:34.880 --> 00:20:36.740 position:50% align:middle supplemental staffing agency. 00:20:36.740 --> 00:20:42.300 position:50% align:middle And our findings were limited to the hospitals in New Jersey. 00:20:42.300 --> 00:20:50.210 position:50% align:middle So these findings might not have been generalizable to hospitals in other states or in other settings. 00:20:50.210 --> 00:20:57.210 position:50% align:middle So in conclusion, our estimates show that the supply of RNs in New Jersey was not sufficient during Hurricane 00:20:57.210 --> 00:21:00.670 position:50% align:middle Sandy to meet that demand of the patient demand. 00:21:00.670 --> 00:21:07.440 position:50% align:middle There was a significant association between RN staffing and patient outcomes. 00:21:07.440 --> 00:21:11.920 position:50% align:middle States need to be more proactive in their efforts to ensure that they have an adequate and 00:21:11.920 --> 00:21:14.120 position:50% align:middle flexible nurse workforce. 00:21:14.120 --> 00:21:20.330 position:50% align:middle If we don't know that now during the current COVID-19 pandemic, I don't think we'll ever learn. 00:21:20.330 --> 00:21:27.900 position:50% align:middle And the nurse licensure compact model should be adopted by states nationwide to allow for easy transition 00:21:27.900 --> 00:21:31.211 position:50% align:middle of state of nurses across geographic areas. 00:21:31.211 --> 00:21:36.429 position:50% align:middle Hospitals were struggling, still are, during COVID-19 where they were trying to issue 00:21:36.429 --> 00:21:43.140 position:50% align:middle emergent licenses, you know, agencies trying to help with the effort. 00:21:43.140 --> 00:21:47.600 position:50% align:middle But if we hadn't had the compact in place, it would have been so much more easier for nurses 00:21:47.600 --> 00:21:53.200 position:50% align:middle to travel from one state to another to help during not only storms that are occurring every year 00:21:53.200 --> 00:21:55.730 position:50% align:middle but during our global pandemic. 00:21:55.730 --> 00:22:01.490 position:50% align:middle At this time, I'd like to acknowledge my collaborators, Dr. Yin Li who's an assistant professor 00:22:01.490 --> 00:22:02.940 position:50% align:middle at Emory University. 00:22:02.940 --> 00:22:08.860 position:50% align:middle She was actually my co-PI and was responsible for a lot of analyses. 00:22:08.860 --> 00:22:13.480 position:50% align:middle Dr. Jason Hockenberry was a co-investigator. 00:22:13.480 --> 00:22:15.780 position:50% align:middle He's a professor at Yale University. 00:22:15.780 --> 00:22:25.420 position:50% align:middle And a special thanks to NCSBN for their ongoing support of nursing research and all of their efforts 00:22:25.420 --> 00:22:32.680 position:50% align:middle in practice, regulation, and helping to ensure that we have a healthy and 00:22:32.680 --> 00:22:36.880 position:50% align:middle substantial workforce in the United States. 00:22:36.880 --> 00:22:40.510 position:50% align:middle Thank you very much for your time, and all questions are appreciated. 00:22:40.510 --> 00:22:41.433 position:50% align:middle Thank you. 00:23:05.000 --> 00:23:06.471 position:50% align:middle - [Dr. Li] Hello, everyone. 00:23:08.004 --> 00:23:13.041 position:50% align:middle Thank you very much for watching our presentation, and I'm the co-PI on this project. 00:23:13.041 --> 00:23:17.638 position:50% align:middle And Jeannie can't make it today so I'm here to answer your questions. 00:23:17.638 --> 00:23:24.990 position:50% align:middle So let's just give a couple of minutes, and let's see what kind of questions we're looking at. 00:23:27.740 --> 00:23:36.910 position:50% align:middle Just so you'll know before any questions, yeah, so I just want to let you know that what we're 00:23:36.910 --> 00:23:41.799 position:50% align:middle presenting here today are some preliminary analyses from these projects. 00:23:41.799 --> 00:23:52.120 position:50% align:middle And this is [inaudible] project, and we will have more analyses, results published soon. 00:23:56.489 --> 00:23:57.094 position:50% align:middle Let's see. 00:24:04.510 --> 00:24:06.319 position:50% align:middle Okay. Here's your question. 00:24:08.212 --> 00:24:11.350 position:50% align:middle "Very interesting use of the data and a creative approach." 00:24:11.350 --> 00:24:12.480 position:50% align:middle Thank you. 00:24:12.480 --> 00:24:15.949 position:50% align:middle "Do you have intention to apply this approach?" 00:24:15.949 --> 00:24:26.071 position:50% align:middle Well, the first approach that we used in this analysis are to calculate the RN FTEs, 00:24:26.071 --> 00:24:35.445 position:50% align:middle which is a very traditional or a very commonly used approach to examine the supply of nurses 00:24:35.445 --> 00:24:37.514 position:50% align:middle and also the demand of nurses. 00:24:37.514 --> 00:24:44.666 position:50% align:middle And this approach actually is commonly used in projecting nurse supply and the demand. 00:24:44.666 --> 00:24:52.636 position:50% align:middle In the second approach that we used in this study is actually to examine the association between their 00:24:52.636 --> 00:24:55.461 position:50% align:middle staffing and patient outcomes. 00:24:55.461 --> 00:25:04.482 position:50% align:middle And also something that we have not presented here but is still under the preparation for manuscript is that 00:25:04.482 --> 00:25:12.358 position:50% align:middle we're going to predict how the patient outcomes will be changed or will be improved if we have additional nurse 00:25:12.358 --> 00:25:15.623 position:50% align:middle staffing available during Hurricane Sandy. 00:25:15.623 --> 00:25:24.824 position:50% align:middle And I believe that because this is a very interesting project because it's for examining nurse staffing 00:25:24.824 --> 00:25:32.674 position:50% align:middle during a disaster, and so we believe that this will have some comments or some overlaps for any other types 00:25:32.674 --> 00:25:36.412 position:50% align:middle of disaster where nurses are in high demand. 00:25:36.412 --> 00:25:43.653 position:50% align:middle And also... I see another comment on, "Having weathered the hurricane and now COVID, 00:25:43.653 --> 00:25:45.011 position:50% align:middle do you think public health..." 00:25:45.011 --> 00:25:53.777 position:50% align:middle Yeah, so that's something that we can imply or inform the future studies and COVID in terms of how nurse 00:25:53.777 --> 00:25:58.287 position:50% align:middle staffing can play a critical role during this COVID pandemic. 00:25:58.287 --> 00:26:04.527 position:50% align:middle And another question asked, "Do you think public health nursing will be a higher 00:26:04.527 --> 00:26:06.100 position:50% align:middle priority in the future?" 00:26:06.100 --> 00:26:07.981 position:50% align:middle We believe so. 00:26:07.981 --> 00:26:16.732 position:50% align:middle We do believe so, but public health nursing is actually now the focus for our study because we were 00:26:16.732 --> 00:26:19.642 position:50% align:middle specifically focused on acute care hospitals. 00:26:19.642 --> 00:26:23.390 position:50% align:middle But public health nursing, I believe that is very important, 00:26:23.390 --> 00:26:27.381 position:50% align:middle play a very critical role during a pandemic like COVID. 00:26:27.381 --> 00:26:31.926 position:50% align:middle Yeah, that is really great question. 00:26:31.926 --> 00:26:39.144 position:50% align:middle And then we believe that public health nurses, that they also served very important services 00:26:39.144 --> 00:26:47.416 position:50% align:middle during this pandemic like preventive care and testing services, and they are very important in controlling 00:26:47.416 --> 00:26:50.643 position:50% align:middle and ending this pandemic. 00:26:56.520 --> 00:26:57.722 position:50% align:middle Another question. 00:27:00.827 --> 00:27:03.201 position:50% align:middle [inaudible] storm evaluation. 00:27:03.201 --> 00:27:09.233 position:50% align:middle I wonder Dr. Tchenkov [SP] can elaborate this question a little bit more. 00:27:12.120 --> 00:27:17.844 position:50% align:middle So if there's no questions, then we would really like to thank the support 00:27:17.844 --> 00:27:26.022 position:50% align:middle from NCSBN and thank you for their funding support and also all of their support on this project. 00:27:26.022 --> 00:27:29.299 position:50% align:middle And more paper will come in from this project. 00:27:29.299 --> 00:27:30.627 position:50% align:middle Thank you very much.