Nursing care for diabetes patient in hospital

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. 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? 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 possible mechanisms of substitution and complementation and the empirical literature so far, then the A kezelés a cukorbetegség injekciók context, followed by the data, the methods, our results and, finally, our conclusions. Mechanisms of substitution and complementation What are the possible theoretical mechanisms of 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 nursing care for diabetes patient in hospital 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 care 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. The 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 nursing care for diabetes patient in hospital costs of outpatient and inpatient care and Rubinstein et al. 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. 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. Central to that model 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 kezelés cukorbetegség szerzetesi tea vélemények 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.

The closer the better: does better access to outpatient care prevent hospitalization?

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 changes 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 et al.

• Cukorbetegség és a táplálkozás
• Evés után mennyi a normális vércukorszint

Footnote 2 The balance is satisfactory in nursing care for diabetes patient in hospital 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.

Proper Patient Care - Communication Techniques

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, nursing care for diabetes patient in hospital 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 this 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, hospitalization 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 Fig. 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 treated 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.

INTRODUCTION

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 number of wider regional county- level number of hospital beds; 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, nursing care for diabetes patient in hospital use this quasi-experiment to estimate the structural effect of bringing outpatient care 1 min closer to the residence of the individual on hospitalization. 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. 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 Nursing care for diabetes patient in hospital 7 or the capacity utilization rate of the beds in 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 logit 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 egészségkalauz cukorbetegség during the 8 years long period. Besides, we estimate dynamic treatment effects with versions of the above models.

Nursing care for diabetes patient in hospital lags cannot be included, because only about 4 years have passed after the initiation of the new outpatient locations.

We use hospitalization, PAH, non-PAH case numbers, and probabilities as well as outpatient case numbers as dependent variables in the dynamic models.

The parallel line assumption is crucial behind these models, i. Substitution between outpatient and inpatient care The most important advantage of the establishment of the new outpatient units is that we can exploit this quasi-experiment to estimate the causal impact of more frequent outpatient care use on inpatient care use.

The reduced form and the first stage of this model are the FE linear models 2 of inpatient and outpatient case numbers, respectively. Here, we use a linear model, because fixed-effects and instrumental variables are computationally not straightforward to incorporate simultaneously in a Poisson specification.

Furthermore, the long panel data set at our disposal enables us to measure the dynamics using contemporary and lagged outpatient care use variables as endogenous explanatory variables, instrumented by the contemporary and lagged treatment dummies.

Results Table 1 presents the estimated treatment effects on the use of inpatient and outpatient care. Descriptive statistics of the disen kezelés cukorbetegség used in the models are shown in Tables 89 and 10 of " Appendix 4 ". The left panel displays the annual baseline probabilities of receiving a certain type of care in the control group, along with the effects kombinált kezelés bonyolult formák cukorbetegség the treatment on these probabilities.

Odds ratios i. The right panel gives the baseline case numbers per inhabitantsthe multiplicative effects of the treatment according to the FE Poisson models, i.

The lower panel of the Table contains the baseline health expenditure values in the control group and how the treatment affects them. Table 1 Effects of the establishment of new outpatient locations Full size table Inpatient care The upper panel of Table 1 shows that both the odds of hospitalization and the number of hospital admissions decreased by about 1. This was driven by a reduction in cardiology, diabetes-related, and specialist care specific PAH, which have ORs around 0.

According to the lower panel of Table 1per capita inpatient expenditures decreased by HUF 2.

This suggests a roughly constant case mix expenditure per inpatient episode. Nursing care for diabetes patient in hospital 8 Cardiology and diabetes-related outpatient case numbers grew faster, while pulmonology-related case numbers increased slower than average. Remarkably, the number of laboratory tests with ACSC-related cardiology and diabetes diagnoses roughly doubled, and the ratio of patients having annually at least one laboratory test with such diagnoses approximately tripled.

Since the standard protocol for the treatment of diabetes mellitus includes regular blood tests such as HbA1c screening to check long-term blood glucose levels, this suggests that a growing number of diabetes patients became treated according to the protocol, implying a health gain for the population. Finally, the lower panel of the table shows that per capita outpatient expenditures increased by about HUF 4.

Heterogeneity and robustness checks According to the heterogeneity analyses not shown here in detailthe relative treatment effect—the logit odds ratio or the Poisson multiplicative effect—is not significantly different across gender and age groups. Similarly, the local supply of inpatient care—the distance to the nearest hospital or the capacity utilization rate of beds there—does not significantly influence the effect of the new outpatient locations on inpatient stays p values of the treatment interaction terms exceed 0.

At the same time, the differential reduction of the distance to the nearest outpatient location—which varies across the treated micro-regions—has an additional explanatory power beyond the treatment dummy on the reduction of inpatient stays. In other words, the number of inpatient stays decreased statistically significantly more in settlements with greater improvement in travel time.

These results—effect heterogeneity in the outpatient and homogeneity in the inpatient dimension—suggest the exclusive role of the outpatient channel in the reduction of inpatient stays, and, hence, give indirect evidence for the exogeneity of the treatment dummy as an instrument in the substitution analysis between outpatient and inpatient care below. Table 2 displays the effects of the reduction in travel time to the nearest outpatient care provider by car on hospitalization.

Table 2 Effects of bringing outpatient care closer by 10 min with car Full size table Tables 4 and 5 in " Appendix 4 " contain robustness checks.

• Egészséges ember vércukorszintje étkezés után
• Momordik a cukorbetegség kezelésében

According to Table 4the slightly negative effect on hospitalization, the more substantial negative effect on PAH and the large positive effect on outpatient care use persist in pooled models or in FE models restricted to those who did not die during the examined period. Table 4 also shows that death was not affected statistically significantly by the new outpatient locations in the medium term.

According to the table, the results are not governed by the creation of the special 1-day ambulatory services in some treated and control micro-regions, because similar treatment effects are estimated when the sample is restricted to those micro-regions where the newly founded 1-day services had lower than median availability as measured by per capita case numbers after The numerical values of the parameters, along with robustness checks from FE and pooled logit models, are displayed in Table 7 of " Appendix 4 ".