The next specification includes controls for TIME_AT_SCENE and TTME_TO_HOSP. The TIMEJTO_HOSP variable can be thought of as a control for the distance from the patient to the hospital, although we show later that the hospital allocation (and thus expected travel time) arc conditioned on the patient’s severity. The TIME_AT_SCENE is more difficult to interpret.
It is also worth noting the large differences between patients in No 911 counties and other counties in the level of technology possessed by the hospital which receives the patients. None of the No 911 patients receive treatment in a certified trauma center, and only a quarter go to hospitals with cardiac catheterization laboratories. Likewise, the emergency room volume and size of hospitals is much lower in No 911 counties. There are also significant differences between Basic and Enhanced 911 counties in the provision of hospital care, but these differences are not as dramatic once the four largest counties are excluded.
Of course, both TIME_TO_SCENE and TME_TO_HOSP will depend on the location of a given patient relative to the hospitals, and variation across counties in the average proximity of patients to hospitals is a potential source of unobserved heterogeneity which must be considered in interpreting our results. We partially alleviate this problem in several of our specifications by including controls for TIME_T0_H0SP in the regressions concerning TIME_T0_SCENE, and vice versa. For example, in the analysis of the determinants of TIME_TO_SCENE, the variable TIME_TO_HOSP acts as a control for the remoteness of the patient’s location.
The Impact of 911 Systems and Hospital Choice on Ambulance Response Times and
Mortality: The Case of Cardiac Arrest
We now turn to an analysis of individual cardiac incidents. We evaluate the effects of the 911 infrastructure on patient outcomes, as well as on several “intermediate inputs” to patient outcomes, in particular, several components of response time. We focus on intermediate inputs for several reasons. First, since 911 provides service benefits through an investment in information technology, we are inherently interested in disentangling the extent to which 911 provides services which are more timely and better respond to patient characteristics. Second, mortality is a very noisy measure of the productivity of the emergency response system, and even in our large dataset, we see only a few thousand deaths from cardiac incidents, and only a few hundred in the counties without Е9П systems. Third, even in these cases, we expect that the policy variables will have a significant impact on outcomes in only a small subset of the cases. Many of the patients who die, would die regardless of the response time; and many patients who survive did not rely heavily on the emergency response system. However, if we establish that 911 reduces response time, we can rely on a number of clinical studies which provide direct evidence about the benefits of faster response times for mortality. Electronic Payday Loans Online
As described in Section П, we expect that the level of 911 technology will respond to political demand as well as demographic factors related to the efficiency of the service in a particular locality. While much of our productivity analysis will focus on a subset of cardiac patients in Pennsylvania, a within-state analysis can provide only limited insight as to the factors which determine the allocation of 911 services (and their productivity benefits) to different subsets of the population. Thus, in Table 4, we consider the determinants of adoption of the level of 911 service in a national cross-section of counties. As expected, POPULATION is significantly correlated with adoption; politically, counties with a relatively high proportion of Perot voters tend to adopt lower levels of 911, consistent with the emphasis of the Peroi movement on limited government expenditure. As well, counties in states with regulations about training had higher levels of 911 adoption. This legislation either requires or recommends standardized training programs in association with 911 programs, and may farther proxy for the institutional support for 911 provided by the state boards which oversee 911 centers. We intapret this result to indicate that states which provide legislative support and guidance for 911 systems have a higher propensity to adopt 911 services. Thus, we conclude that 911 adoption responds to efficiency motivations as well as political and regulatory factors which may be unrelated to efficiency.
We begin by describing the characteristics of three groups of counties in Pennsylvania: those with no 911, Basic 911, and Enhanced 911- Because four counties are significantly larger, more dense, and have more hospitals than the others, we also report the counties with E911 excluding the four largest counties (we will also report specifications which exclude these four counties in our subsequent regression analysis). There are some systematic differences between the demographic characteristics of the counties which have made different adoption decisions about 911. The largest and most densely populated counties, as well as those with the highest income and largest police and health budgets, tend to have adopted Enhanced 911.
In particular, each patient is assigned a Glasgow Score which is a number between 0-15 which indicates the severity of the heart attack (lower numbers imply higher severity with 3 being the worst and 0 indicating “unknown” or “missing”). While the bulk of observations are coded with the weakest severity (GLASGOW = 15), there exists a substantial minority for which there is variation in the data.
Our patient-level variables are drawn from a database of every ambulance ride in Pennsylvania which could be linked to a hospital discharge during 1995 (approximately 170,000 observations). This dataset is gathered by the Pennsylvania Department of Health and has only recently been made available to a limited number of researchers; we are not aware of prior work on this database (or a similar ambulance-level database) by health care economists. how to get out of payday loans
Our information about hospitals is obtained from the American Hospital Association (AHA) annual hospital inventory survey. We use this information to provide information at three different levels of analysis. First, when we study the incentives of hospitals to adopt technology, we consider the availability of hospital technology at any hospital within a county. For example, CERTIFIED TRAUM CNTR represents the presence of a certified trauma center in a given county, while HOSP PER SQ. MILE represents the density of hospitals. We also consider the number of recorded cardiac incidents which required ambulance service in 1995 (COUNTY CARDIAC PATENTS). Second, in our patient-level productivity analysis, we link hospital characteristics to our patient-level database in order to control for hospital quality as well as analyze the allocation process which assigns patients to hospitals. Third, we consider the hospital as the unit of analysis when we consider how technology investments interact with the share of cardiac patients who are treated in a given hospital. cash payday loans
In addition to the county level variables, we include in our analysis two “911” variables which are drawn from NENA state-level surveys which indicate whether there is implemented legislation guiding the administration of 911 systems (in particular, governing training policies for workers using the systems) (911_TRAIN_LAW) or whether legislation has been passed but not yet implemented (911_TRAIN_PLAN). These variables are intended to be proxies for the level of administrative information and assistance provided by the state.