Health Care Worker Deficits in Indiana

I recently became ill and tried to get an urgent appointment to my insurance assigned clinic. The scheduler told me they could squeeze me in, in three weeks. When I explained I was hoping for the next few days since I was in distress, I was told I could try to get a waiver to go to the emergency room. Not wanting to clog the already over-burdened Wishard ER, I offered to see anybody who was available, since I didn't have a preferred primary care physician. In fact, the last several times I have been to the clinic in the last 5 years, I have randomly seen whomever was available, including a nurse practitioner. Apparently now I was only able to see the exact physician I saw 2 years ago, whose first available appointment was in three weeks. After spending several hours on the phone complaining to every administrator I could find, I was finally given an appointment in 4 days (2 if you don't count the weekend).

This inspired me to investigate the geography of the physician shortage in the U.S. As a baseline, I looked at the international data about physician and nurse distribution. According to OECD data, the U.S. ranks 18th of top 20 OECD countries for number of physicians per 1,000 population (see Table 1). I obtained the physician and nursing data by averaging 2005-2010, and ranked countries by physician density.

TABLE 1: HEALTH CARE WORKERS BY COUNTRY (OECD)

Country	Rank	Physicians /1,000	Nurses /1,000
Austria	1	4.6	7.4
Norway	2	3.9	13.9
Switzerland	3	3.8	14.7
Spain	4	3.7	4.5
Sweden	5	3.6	10.9
Germany	6	3.5	10.5
Italy	7	3.4	6.2
Denmark	8	3.4	14.5
France	9	3.3	8.0
Israel	10	3.3	5.0
Ireland	11	2.9	12.7
Belgium	12	2.9	14.8
Australia	13	2.9	10.0
Netherlands	14	2.8	8.3
Luxembourg	15	2.7	11.0
Finland	16	2.7	9.4
UK	17	2.6	9.7
US	18	2.4	10.6
New Zealand	19	2.4	9.4
Canada	20	2.3	9.0

Then I examined the U.S. by state, since research indicates that physicians tend to congregate geographically unevenly, typically based on compensation rates, so there is likely to be greater physician density in some states, and greater physician deficit in other states. Similarly nursing and other non-MD health care professionals are not evenly distributed. I used American Community Survey data (2011, 3-year estimate) to obtain number of health care workers by state for three specific occupations: physicians, registered nurses, and "other health care diagnosing and treating practitioners." I divided these by the 2010 Census total population for each state. Additionally, I obtained the number of residents per state over the age of 60 years, presuming this population will tend to utilize health care services far more frequently than younger populations. I list the relevant data for each of these groups per state (Table 2).

Additionally, I ranked each state by an index formula based on these four populations. Using a Z-score for each state for each group, I added the health care workers, and subtracted the older population, creating a composite score indicating the accessibility of health care workers per population in each state. In the composite formula, it seemed that physician totals were the most important for general health care access, so doubled that number in the equation. Each state's ranking is listed in Table 2, from least available health care workers per state, to most, per population.

Indiana ranks 40th (Graph 1). A 2012 study by the Association of American Medical Colleges states that, for Indiana, "These already severe shortages are going to become even more prevalent when considering that the number of Indiana residents over age 65 will double between 2000 and 2030, the segment of the population that uses health care services the most." The study specifically identifies, as of 2007, a 5,000 physician shortage for Indiana alone.

As a check on my health care worker index, I used CDC death rates to generate a comparison for validity. I used two measures, CDC infant mortality rates from 2007-2008 by state (CDC Wonder), and death rates (age adjusted) for all causes from 1998-2010 by state (CDC Wonder). My health care worker index had a correlation of -0.46 with infant mortality, and -0.55 for all-age death rates (see Graph 2), indicating a reasonably strong relationship--as the density of health care workers increases, both infant mortality and death rates go down, and vice versa. The correlation with the composite index is greater than any of the individual measures.

GRAPH 1: PHYSICIAN DENSITY BY STATE

GRAPH 2: RATES OF DEATH VS HEALTH CARE WORKER INDEX (CDC, 1998-2010, ALL CAUSES, AGE ADJUSTED)

TABLE 2: HEALTH CARE WORKERS BY STATE

State	Rank	Physicians & surgeons /1,000	Registered nurses /1,000	Other health practitioners /1,000	60+ Population	Z-Index
Florida	50	2.5	8.6	2.7	23%	-1.96
Nevada	49	2.1	6.8	2.4	17%	-1.55
West Virginia	48	2.2	10	2.9	23%	-1.5
Arkansas	47	1.9	9.4	2.7	20%	-1.48
Oklahoma	46	2	7.9	2.6	19%	-1.43
Arizona	45	2.2	7.6	2.6	19%	-1.38
South Carolina	44	2.4	8.5	2.6	20%	-1.25
Alabama	43	2.1	9.7	2.7	19%	-1.12
Mississippi	41	2	9.5	2.6	18%	-1
New Mexico	41	2.3	7.4	2.8	19%	-1
Indiana	40	2.2	10	2.6	18%	-0.95
Idaho	38	1.9	7.1	3.1	17%	-0.67
Hawaii	38	2.7	7.1	3.1	20%	-0.67
Georgia	36	2.1	7.4	2.6	16%	-0.63
Louisiana	36	2.6	8.5	2.6	18%	-0.63
Texas	35	2.3	7.3	2.5	15%	-0.58
Delaware	34	2.9	11	2.6	20%	-0.47
Kentucky	33	2.6	10.2	2.8	19%	-0.45
Tennessee	32	2.7	9.1	2.9	19%	-0.38
Missouri	31	2.8	9.8	2.9	20%	-0.28
Oregon	30	3	7.5	3.1	20%	-0.25
Michigan	29	2.9	8.9	3	20%	-0.18
Illinois	27	2.8	9.1	2.7	18%	-0.16
Ohio	27	2.9	10.5	2.9	20%	-0.16
Maine	26	3.4	10.5	3	22%	-0.14
California	25	2.6	7.1	2.9	16%	-0.13
Virginia	24	2.7	8.2	3	18%	-0.1
Wyoming	23	1.3	7.4	3.6	16%	-0.09
Montana	22	2.4	9.8	3.4	21%	-0.02
Wisconsin	21	2.4	10.2	3.2	19%	0.01
North Carolina	20	2.7	9.1	3.1	19%	0.07
Iowa	18	2.1	11.2	3.6	20%	0.14
Kansas	18	2.7	10.4	2.9	18%	0.14
Utah	17	2.2	6.6	2.8	13%	0.36
South Dakota	16	2.2	11.6	3.4	19%	0.45
Pennsylvania	15	3.5	10.7	3.4	21%	0.67
New Jersey	14	3.4	8.9	3.2	19%	0.69
New Hampshire	13	2.9	12.1	3.3	19%	0.75
North Dakota	12	2	11.3	3.7	19%	0.76
Rhode Island	11	4	10.3	2.9	20%	0.79
Washington	10	2.9	8.5	3.5	18%	0.92
Connecticut	9	3.9	10.2	3.3	20%	1.2
Minnesota	8	3.1	10.7	3.5	18%	1.28
New York	7	4	9	3.4	19%	1.31
Colorado	6	2.8	8.3	3.7	16%	1.39
Nebraska	5	2.7	10.7	3.8	18%	1.41
Maryland	4	4	9.9	3.2	18%	1.64
Massachusetts	3	4.3	11	3.3	19%	1.77
Vermont	2	3.3	11	4	20%	1.85
Alaska	1	2.5	8.7	3.2	10%	1.98

Gun Ownership and Firearm Homicide Rates

While tracking down a Facebook meme from MoveOn.org about the relationship between gun ownership and firearm murder rates, I fitfully realized that we don't have gun ownership data, thanks to legislation that forbids tracking of firearms. Research that claims to look at the relationship between gun ownership and crime base their analysis on 2001 data from the CDC's Behavioral Risk Factor Surveillance System survey from 2001, with a sample size of 201,881. Presuming people told the truth when asked if they have guns, and the CDC was able to contact a representative sample of people, they provide a margin of error rate averaging 1.8 (range 1-2.9)--however, given the nature of this population, I am skeptical of both of these presumptions. The CDC asked three questions--gun ownership, whether their guns were loaded, and whether their guns were kept in an unlocked place + loaded. Summary results can be found in the journal Pediatrics. Not to mention that this study is over 10 years old, and the world has become a very different place since 9/11, economic disaster, and a Black Democrat was elected to the presidency.

I found only one other possible source of data for gun ownership, a proxy measure from internet news, The Daily Beast, which, from 2011-2012, tracked the number of NICS background checks for firearms by state. The primary problem with this data is that each state can create its own policies for tracking and reporting background checks, so it is difficult to compare between state rates. For example, they mention that Kentucky mandates monthly checks for concealed weapons, creating a presumably inflated number, and indeed, the largest of background checks relative to the other states. Further, this is only a measure of background checks, which neither considers illegal weapons, nor whether the individual actually purchased the weapon. One might propose that both of these are random sources of error, equally distributed through states. However, given the vast differences between states, I doubt this can be considered equally distributed variance. There is a 0.38 correlation between the 2001 BRFSS gun ownershp study, and the 2012 Daily Beast background checks data. This is a moderate correlation, indicating that the data may have some reasonable level of consistency in predicting actual gun ownership, especially if combined into an index score.

A third source of estimation about guns is gun laws themselves, while, as above, not a measure of gun ownership as such, but one might presume that a breadth of gun safety laws would be associated with fewer guns. A 2010 report from Mayors Against Guns, Trace the Guns, tracked 10 different specific laws across all states. In the data provided below, I compiled this list into an index score from 0-10, with 10 being the maximum number of gun-safety laws, and 0 being none. There is a -0.36 correlation between gun laws and number of background checks, and -0.73 correlation between gun laws and gun ownership The first correlation is moderate, indicating that the more gun safety laws that a state has enacted, the fewer background checks that were reported. This could indicate either greater subversion of gun laws where they are more tightly regulated, or less interest in purchasing guns in states with more gun safety laws. The latter correlation is quite strong, indicating that in states with more gun safety laws, far fewer people admitted to owning guns in the 2001 CDC survey, than in states where there were fewer gun safety laws. Combined, these three measures are reasonably consistent with each other, although clearly problems exist in attempting to use these to determine actual gun prevalence rates in states.

Firearm death data is more reliable, although problems still exist. The FBI tracks national crime data as it is reported by states, and hosts that data publicly at the Bureau of Justice Statistics Web site. Critics are concerned that this data includes both suicide and self-defense data. I do not know if this assertion is true. However, regarding self-defense, Gary Kleck, a well-known pro-gun activist and researcher, indicates that multiple studies provide a range of 6-12% of firearm homicides that were likely "justifiable" homicides. A second source of firearm death data, from the CDC Web site, filters out suicide data, thus includes only "true homicide" rates, although it likely includes self-defense. However, given the relatively small representation of self-defense homicides, the impact on the data is likely marginal. In the data provided below, I used the total firearm homicides from 2006-2010, to control for specific, outlier years of high or low homicide rates. There is a 0.96 correlation between the 2011 FBI homicide rates, and the 2006-10 CDC firearm homicide rates. This indicates an incredibly high level of consistency between these two separate data sources, thus a substantial likelihood of reliability that these measures accurately represent non-suicide, firearm homicide rates.

Measuring criminally-inflicted death is one approach to the gun safety discussion. However, what seems more relevant are the total number of gun deaths, not just homicide. In terms of a direct, linear correlation, there is no clear relationship between gun ownership, background checks, or gun laws, and firearm homicide (correlations less than abs(0.15)). However, the picture changes dramatically when you combine all firearm deaths: homicide, suicide, and accidental deaths. In this case, correlations for each of the three gun-prevalence measures range from 0.25 for the number of background checks, 0.69 for gun ownership rates, and -0.68 for gun laws (Graph 1). So while there is only a small relationship between the number of background checks and firearm death per state, there is a very strong positive relationship between measured gun ownership rates and firearm death (i.e., as gun ownership goes up, so do firearm deaths), and a very strong negative relationship between gun safety laws and firearm deaths (i.e., as states enact more gun safety laws, firearm deaths go down).

Graph 1: All Firearm Deaths, 2006-2010 vs. Gun Ownership, 2001

Despite the poor bivariate linear relationship between gun-prevalence variables and firearm homicide, controlling for other variables generates a far stronger relationship. Specifically, both poverty and inequality contribute to firearm homicide rates, and controlling for these at the state level allows a strong relationship to appear (I also tested for 2010 urbanization but there seems to be little relationship at the state-level). Explaining the 4-variable relationship is complicated (see below). However, the 3-variable relationship between poverty, gun ownership and firearm homicides is easy to show visually with a scatterplot (Graph 2). Below you can see two trendlines, one for state poverty levels below 15% (blue), and the other for poverty levels above 15% (green). The negative slope of the line representing wealthier states indicates that higher gun ownership rates are related to lower levels of firearm homicide rates (35 states). The positive slope of the line for poorer states indicates that higher rates of gun ownership are related to higher rates of firearm homicide (15 states).

So while there isn't a simple bivariate linear relationship between gun ownership and firearm homicide rates, there is clearly a relationship once you control for poverty, and even moreso once you also control for levels of inequality. Looking just at firearm deaths in total, you don't even have to control for poverty and inequality to demonstrate a very strong relationship between gun ownership and firearm death. (Poverty data is from the 2011 American Community Survey, 5-year estimate, as is the inequality data. Inequality here is measured by the ratio of the number of households in the state living on less than $15,000 to the number of households living on more than $100,000.)

Graph 2: Firearm Homicides, 2006-2010 vs. Gun Ownership, 2001, controlling for poverty

Linear equation for the 4-variable interaction:
Firearm Homicides = -0.073 - 0.678(Inequality) + 1.068(Poverty) - 0.258(Gun Ownership X Poverty) + 0.042(Gun Ownership)
This equation explains 47% of the variance, p<0.000, and the Gun Ownership X Poverty interaction variable is significant at p<0.042.

State	Firearm Murder Rate	CDC Wonder, 2006-2010, Firearm Homicide (no suicide)	CDC, All Firearm Deaths, 2006-10	Background Check Rate/Population	Gun Ownership, 2001	TraceTheGuns, 2010, All Gun Laws Combined	Poverty, 2011 ACS, 5-year	Income Inequality: <15k:>100k, 2011 ACS, 5 year
Alabama		7	17.1	8388	51.7	4	17.6	1.10452
Alaska	0.022535211	3.3	18	11068	57.8	0	9.5	0.243236
Arizona	0.034730914	5	14.8	4944	31.1	0	16.2	0.633314
Arkansas	0.037722908	5.2	15.2	7968	55.3	1	18.4	1.362871
California	0.032748161	4.3	8.6	2842	21.3	8	14.4	0.363118
Colorado	0.014515808	2.1	10.9	7835	34.7	4	12.5	0.440104
Connecticut	0.026301063	2.4	5.1	6335	16.7	9	9.5	0.285685
Delaware	0.031180401	4.5	9.5	3127	25.5	5	11.2	0.385371
Florida		4.5	12.2	4081	24.5	2	14.7	0.722721
Georgia	0.038191577	5.2	12.8	4447	40.3	2	16.5	0.721028
Hawaii	0.000735294	0.7	3	1208	8.7	9	10.2	0.311204
Idaho	0.010841837	1.2	12.3	8156	55.3	0	14.3	0.850145
Illinois	0.029381966	4.8	8.3	7937	20.2	8	13.1	0.483338
Indiana	0.028223319	3.8	11.2	6867	39.1	2	14.1	0.789097
Iowa	0.006237689	0.9	6.5	4681	42.8	7	11.9	0.709595
Kansas	0.025587101	2.7	10.5	6864	42.1	0	12.6	0.632461
Kentucky	0.023046785	3.2	13.2	58196	47.7	0	18.1	1.234068
Louisiana	0.088682991	10.1	19	6711	44.1	0	18.4	1.000716
Maine	0.009036145	1.1	8.6	6634	40.5	2	12.8	0.842797
Maryland	0.047107724	6.5	11	2231	21.3	8	9	0.241683
Massachusetts	0.018631643	1.7	3.5	3033	12.6	9	10.7	0.373835
Michigan	0.045528126	5	11.1	4125	38.4	7	15.7	0.755491
Minnesota	0.008107089	1.3	6.7	7844	41.7	5	11	0.443991
Mississippi	0.046511628	7.4	17.3	7082	55.3	1	21.6	1.558142
Missouri	0.046084488	5.3	13.6	8002	41.7	1	14.3	0.830348
Montana	0.007077856	1.6	15.3	12786	57.7	1	14.6	0.957503
Nebraska	0.023001095	2.2	8	4347	38.6	5	12	0.684228
Nevada	0.027767494	4.2	15.4	5047	33.8	0	12.9	0.468341
New Hampshire	0.004559271	0.6	7	8971	30	2	8	0.301071
New Jersey	0.030595996	3	5.1	938	12.3	10	9.4	0.262094
New Mexico	0.029140359	4	14.6	6491	34.8	0	19	0.940948
New York	0.022964186	2.7	5.1	1652	18	10	14.5	0.499277
North Carolina	0.035133718	4.6	12.2	4811	41.3	5	16.1	0.864843
North Dakota	0.008915305	0.6	8.7	11901	50.7	2	12.3	0.795394
Ohio	0.02981711	3.5	9.5	5102	32.4	1	14.8	0.812913
Oklahoma	0.03492402	4	13.9	9094	42.9	0	16.3	1.003063
Oregon	0.010441138	1.4	10.8	6398	39.8	5	14.8	0.701096
Pennsylvania	0.037002047	4.2	10.6	7218	34.7	5	12.6	0.629322
Rhode Island	0.004748338	1.6	4.4	2106	12.8	7	12.8	0.560825
South Carolina	0.048216216	5.5	13.8	6322	42.3	2	17	1.00953
South Dakota	0.006142506	0.8	9.1	9977	56.6	0	13.8	0.890987
Tennessee	0.038449417	5.3	15.2	7638	43.9	2	16.9	1.044275
Texas	0.027797662	4.1	10.7	5261	35.9	0	17	0.61675
Utah	0.009406657	1.1	9.7	7827	43.9	3	11.4	0.425006
Vermont	0.006389776	0.9	9.3	5227	42	1	11.3	0.594292
Virginia	0.02599675	3.5	10.6	5119	35.1	4	10.7	0.341807
Washington	0.011747212	1.8	8.9	7157	33.1	2	12.5	0.419157
West Virginia	0.023205613	2.7	13.9	11670	55.4	0	17.5	1.528704
Wisconsin	0.014067171	1.9	8.2	8436	44.4	2	12	0.615174
Wyoming	0.019503546	1.2	16	10517	59.7	2	10.1	0.456652