Metrics details Abstract In —, new outpatient service locations were established in poor Hungarian micro-regions. We exploit this quasi-experiment to estimate the extent of substitution between outpatient and inpatient care. In our dynamic specification, PAH effects occur in the year after the treatment, whereas non-PAH only decreases with a multi-year lag.
The instrumental variable estimates suggest that a one euro increase in outpatient care expenditures produces a 0. Our results 1 strengthen the claim that bringing outpatient care closer to a previously underserved population yields considerable health benefits, and 2 suggest that there is a strong substitution element between outpatient and inpatient care. Introduction How to best allocate limited public resources across outpatient and inpatient healthcare services to achieve maximum improvement in health outcomes is one of the perennial questions of health policy all over the world.
Cukorbetegség és idegrendszer
To inch closer to answering that question, we have to understand, disentangle, and accurately measure the relationships between those two levels of care. What are the respective and aggregate changes in health care expenditures?
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In this paper, we use panel data from a quasi-experimental setting provided by an expansion of specialist outpatient care in Hungary between andgreatly improving access, to contribute to answering those questions.
At the highest level of abstraction, nationwide health policy planning is about maximizing health outcomes of the population constrained by limited public and private resources. This is done through financing many functional channels of the health care system, but, in OECD countries, most expenditure goes to curative and rehabilitative care, and, within that, two of the most important functions are outpatient care, upon which 1. Given these enormous expenses, the importance of any reliable evidence that can contribute to even a marginal improvement of health outcomes by a better allocation of resources across these two subsectors cannot be overstated.
Such evidence can help policy makers to decide whether additional public resources are put to better use by being channelled toward expanding outpatient or inpatient care. In what follows, we first present the diabetes mellitus icd 10 code mechanisms of substitution and complementation and the empirical literature so far, then the Hungarian context, followed by the data, the methods, our results and, finally, our conclusions.
Mechanisms of substitution and complementation What are the possible theoretical mechanisms diabetes mellitus icd 10 code interaction between inpatient and outpatient care? Fortney et al. Mechanisms of substitution: Early detection of an illness in outpatient care can make treatment possible at that level and obviate the need for hospitalization. This substitution mechanism, they claim, could have both short-term e. The management of chronic health conditions in outpatient care e.
Depending on the rules and incentives built into the health care system of the country in question, doctors in outpatient care could have a formal gate-keeping role, as well: in many cases, their referral can be required for hospitalization.
Mechanisms of complementation: Treatment in outpatient care might call for supplemental or ancillary care provided in hospitals e. The detection in outpatient diabetes mellitus icd 10 code of illnesses e.
This mechanism could especially affect patients who have not used primary care services for a long period of time and who have a greater number of undetected illnesses. The identification through close monitoring of acute episodes of chronic illnesses that require specialty or inpatient treatment. This mechanism is particularly relevant for disorders with symptoms that may fluctuate in severity over time e.
Impotencia cukorbetegséggel empirical literature is rather mixed in terms of whether the substitution or the complementation effect dominates. Miller [ 15 ] analysing a Massachusetts reform a health insurance reform was introduced that differentially affected the costs of outpatient and inpatient care and Rubinstein et al.
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Other papers also found substitution effects in cross-sectional settings [ 161019 ]. On the other hand, Kaestner and Sasso [ 11 ] found that, in the US, an increased outpatient spending was associated with more hospital admissions; the Rand and the Oregon health insurance experiments also showed that improving the availability of medical services through a more generous health insurance coverage was associated with an increase in the use of emergency room services and hospitalization [ 716 ].
A third group of studies found neither substitution nor complementation effects. Looking into the same Massachusetts reform as Miller [ 15 ], Kolstad and Kowalski [ 12 ] found that gaining insurance was associated with a decrease in hospital admissions through emergency department, an increase in hospital admissions through other channels, and no change in total hospitalizations.
The instrumental variables analysis by Fortney et al. One promising method to try to sharpen the results in the empirical literature, exemplified by Duscheiko et al. Institutional context In addition to being, in sum, rather inconclusive, many of these studies are also observational or cross-sectional, making the establishment of causal relationships hard.
Global and regional diabetes prevalence estimates for and projections for and results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. Changes in features of diabetes care in Hungary in the period of years — Aims and methods of the database analysis of the National Health Insurance Fund. Az Országos Egészségbiztosítási Pénztár adatbázis-elemzésének célja és módszertana.
In the case of papers based on a quasi-experimental or experimental setup, the source of variation that makes identification possible consists in changes in the financing insurance mechanism alone and almost all of them examine the US. Our source of variation is different and our evidence comes from a very different, but, by no means, internationally unique institutional setting, shared by most post-communist EU member states e.
In such countries, our research question has never been addressed before.
Hungary is a post-communist EU member state of slightly less than 10 million inhabitants with a single-payer health insurance and de facto universal coverage [ 5 ]. InHungary spent 7. The basic benefit package is free of out-of-pocket payments for the patients at the point of care including outpatient carealthough informal gratuity payments are widespread.
Primary care by general practitioners is financed by capitation; most outpatient services are financed by the budget based on fee-for-service points, under a system that scores procedures on the basis of their complexity and resource requirements, whereas inpatient services, almost exclusively provided in state-run and -financed hospitals, are reimbursed through a combined payment system based on diagnosis-related groups acute care and per diem rates chronic care.
The relatively high share of outpatient care in provision and financing is due to the heritage of the Semashko-type healthcare system, common in countries once under Soviet dominance.
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Central to that diabetes mellitus icd 10 code was a multi-tiered system of care with a strict referral system and strongly differentiated network of service providers, with outpatient specialist care, provided in dedicated polyclinics and thus separated from primary care, one of the distinct tiers of healthcare provision [ 913 ]. The health status of the Hungarian population is among the poorest in the EU with a life expectancy at birth of The intervention which we base our quasi-experimental specification on is the same as used in Elek et al.
Between andaroundpeople gained better access to specialist outpatient care in Hungary when the government created outpatient units in 20 rural micro-regions, which previously lacked capacity.
Locations for the new units were selected based on the applications of municipalities, making a case for need and demand. Competition for scarce funds was not an issue: sufficient funds were allocated to be able to subsidize all likely applicants eligible under those rules. The newly created units all still in operation as of provide comprehensive service for the population of the micro-regions with at least 14 separate specialties at each location.
As a result, basic specialist outpatient care in the following four specialties: internal medicine, surgery, obstetrics—gynaecology, and pediatrics may now be reached by aroundmore people by car in 20 min than before. At the same time, the other parts of Hungary experienced relatively few almaecet 2-es típusú diabetes mellitus in the management of outpatient care between and The impact of the improvement in accessibility can then be estimated as the difference between the changes in the treated and control groups, with a difference-in-difference-type analysis.
It is the treatment that we use in the paper to identify the sign, the magnitude, and the lag of the effect of more outpatient treatment upon hospitalization at the individual level. Data and descriptive statistics We use anonymized individual-level administrative data on inpatient stays and specialist outpatient visits, exclusively provided to us for this research project by the Hungarian National Healthcare Services Centre ÁEEK.
The control micro-regions were chosen with propensity score matching to approximate the pre-treatment demographic, socio-economic, and health characteristics of the treated micro-regions. Elek diabetes állatok kezelésére al. Footnote 2 The balance is satisfactory in most variables, although there remains a slight—statistically not significant—difference in pre-treatment outpatient care provision.
The number of weekly specialist outpatient consultation hours per residents averaged to 0. We will control for this pre-treatment difference by the fixed-effects models. The annual panel data set used in our analysis contains for each person-year the number of inpatient stays and of its certain subgroups, see belowthe number of specialist outpatient visits and of its certain subgroupsthe estimated inpatient and outpatient care expenditures, as well as demographic information such as gender, year of birth, and settlement of residence.
Footnote 3 Year of death is also recorded for those who died during the period. We omit newborns from the sample, and, hence, restrict the analysis to those at least 2 years of age.
We also define potentially avoidable hospitalization PAHi. Our main definition for PAH follows Purdy et al. Footnote 4 According to cukorpótlók cukorbetegeknek definition, around 2. We classify this category into the following subgroups see " Appendix 1 " for details : cardiology-related conditions angina, congestive heart failure, and hypertension 0.
Figure 1 shows that hospitalization case number, diabetes mellitus icd 10 code probability, as well as PAH probability decreased more in the population of the treated group than of the control group after —, when the new outpatient units started to operate in the treated micro-regions.
Footnote 5 Most new units were established in The difference between the treated and control values was slightly positive or roughly zero beforebut became negative afterwards. We will also examine certain other subgroups of hospitalization such as acute and chronic episodes.
Meanwhile, according to Sah diabetes kezelő tabletta. The levels and trends are consistent with outpatient capacities: due to some existing outpatient units in the control micro-regions, outpatient care use was slightly higher in the control than in the treated group beforebut this difference quickly reversed when the new outpatient units emerged in the treated micro-regions.
We will specifically examine outpatient visits associated with certain ACSCs such as those in cardiology, pulmonology, or diabetes, defined by the ICD code of the outpatient event. We hypothesize that a growing ratio of patients diabetes mellitus icd 10 code in outpatient care with such conditions may have caused the decreased prevalence of PAH.
Finally, the lower two graphs in Fig. Footnote 6 Fig. In the control micro-regions, patients hospitalized in a given year visited non-laboratory outpatient care 2. However, these cross-sectional correlations are non-causal. Estimation of a causal relationship between outpatient and inpatient care requires a quasi-experiment such as the establishment of the new outpatient locations in our case. Therefore we apply a difference-in-difference-type analysis to measure the treatment effect and check the pre-treatment parallel trends in the treated and control group with a placebo test.
In support of our identification approach, it is important to also add that, although hospital capacities had been curtailed to save costs inbefore the time period under scrutiny, there was virtually no policy-driven change in the supply of inpatient care during the time-span which we analyse.
Still, we use three explanatory variables to control for possible exogenous changes in health care supply in the examined period: the diabetes mellitus icd 10 code of wider regional county- level number of hospital diabetes mellitus icd 10 code the ratio of unfilled GP practices in the settlement of the individual; and the availability of special 1-day ambulatory services aimed at providing certain treatments in internal care, neurology, and physiotherapy that started to operate in some treated and control micro-regions in the examined period see " Appendix 3 " for details, and Table 10 in " Appendix 4 " for descriptive statistics of these variables in the treated and control groups.
Beyond an impact assessment of the establishment of the new outpatient units, we use this quasi-experiment to estimate the structural effect of bringing outpatient care 1 min closer to the residence of the individual on hospitalization.
D vitamin mangel pikkelysömör gyógyszerei
Therefore, we define the travel time in minutes needed to reach the nearest outpatient unit by car from the settlement of each individual. This distance measure decreased in the treated micro-regions from 24 min in to 10 min inwhile it was essentially unchanged 21 min on average in the control micro-regions. They control for, among others, any pre-treatment differences in the health status of the individuals, and also for any time-constant differences in individuals such as their gender.
We estimate further models with different dependent variables: Models 1 — 3 on the number of outpatient cases FE Poisson and FE linear and on the probability of receiving outpatient care FE logitfor person i in year t, and on the ACSC-related outpatient subcategories.
FE linear models on inpatient and outpatient expenditures of person i in year t.
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Moreover, we investigate the heterogeneity of the effect of new outpatient locations on inpatient stays with various treatment interaction models in FE Poisson and FE logit specifications : first, the treatment dummy is interacted with gender and age groups to examine potential heterogeneity across these categories; second, the treatment dummy is interacted with the indicators of local supply of inpatient care such as the travel time between the micro-region and the nearest substantial hospital Footnote 7 or the capacity utilization rate of the beds diabetes mellitus icd 10 code the nearest hospital; third, the changing travel time to the nearest outpatient service location is used as an additional explanatory variable beyond the treatment dummy to examine the effects of the heterogenous improvement in outpatient availability across settlements.
FE models estimate the treatment effect using within-person variation, i. These models usually give more credible inference than e.
However, if there is a slight change in the probability of death in the treated compared to the control group large effects are unlikely to occur in the 3—4 years after the establishments, which we test with a pooled súlyosságát cukorbetegség lábápolás modelFE and pooled models may yield different estimates, because dying patients are selected out of the sample at a slightly different rate in the two groups.
Therefore, we perform two robustness checks. First, we estimate pooled Poisson and logit models on inpatient and outpatient care use.
Second, we estimate the FE Poisson and logit models 1 — 3 on the subsample of those who did not die during the 8 years long period.
Besides, we estimate dynamic treatment effects with versions of the above models. More lags cannot be included, because only about 4 years have passed after the initiation of the new outpatient locations.