TY - JOUR
AU - Agvall, Björn
AB - Abstract Aims Patients with heart failure (HF) have high costs, morbidity, and mortality, but it is not known if appropriate pharmacotherapy (AP), defined as compliance with international evidence-based guidelines, is associated with improved costs and outcomes. The purpose of this study was to evaluate HF patients’ health care utilization, cost and outcomes in Region Halland (RH), Sweden, and if AP was associated with lower costs. Methods and results A total of 5987 residents of RH in 2016 carried HF diagnoses. Costs were assigned to all health care utilization (inpatient, outpatient, emergency department, primary health care, and medications) using a Patient Encounter Costing methodology. Care of HF patients cost €58.6 M, (€9790/patient) representing 8.7% of RH’s total visit expenses and 14.9% of inpatient care (IPC) expenses. Inpatient care represented 57.2% of this expenditure, totalling €33.5 M (€5601/patient). Receiving AP was associated with significantly lower costs, by €1130 per patient (P < 0.001, 95% confidence interval 574–1687). Comorbidities such as renal failure, diabetes, chronic obstructive pulmonary disease, and cancer were significantly associated with higher costs. Conclusion Heart failure patients are heavy users of health care, particularly IPC. Receiving AP is associated with lower costs even adjusting for comorbidities, although causality cannot be proven from an observational study. There may be an opportunity to decrease overall costs and improve outcomes by improving prescribing patterns and associated high-quality care. Heart failure, Resource utilization, Health care costs, Guidelines, Prescribing Introduction At present, approximately 26 million people worldwide are living with heart failure (HF).1 The prevalence of HF was estimated to be 2.2% in Sweden in 2010, consistent with similar countries.2,3 Heart failure is a common disease with a high mortality, morbidity, and impact on quality of life.4–6 According to a Swedish HF registry study, annual all-cause mortality approaches 21%, consistent with a European study that found all-cause mortality rates of 7.2% in stable HF patients and 31.9% in unstable HF patients.7,8 Once diagnosed, HF is a lifelong condition, and HF patients have extensive and expensive health care needs.9–11 Heart failure is the most frequent discharge diagnosis within internal medicine in Sweden, with 44% of HF patients hospitalized at least once per year.9,12 In 2000, the total annual cost for patients with HF in Sweden was reported to be in the range of SEK 5.0–6.7 billion (€469–628 M), which is dominated by hospital care (HC), especially inpatient care (IPC).10,12–15 Although improvements in preventative care have decreased the incidence of HF, these patients are living longer with their disease, leading to a persistent prevalence of HF over time.16 The financial burden of HF is understood to be high and the hospitalization is the dominant main driver of the cost.17,18 However, the costs of outpatient care (OPC), primary care, diagnostic investigations, and medications vary considerably in previous studies. These discrepancies in health care costs of HF probably due to varying methodology and there is no standardized methodology collecting cost data today. While the clinical and financial burden of HF is understood to be high, there is an incomplete understanding of the nature of the costs associated with the care of HF patients in Sweden, as well as the drivers of those costs across the health care system and complete patient pathway. Appropriate pharmacotherapy (AP) for HF according to international guidelines has been shown in large studies to decrease rates of death and hospitalization, but it’s effect on total cost of care in a real-world setting is unknown.19 Heart failure patients have multiple comorbidities, and while much of their health care utilization is associated with HF, they also have significant health care utilization due to other cardiopulmonary, metabolic, and mental health issues.14,15,20 Using a novel data analytic and costing engine developed in Region Halland (RH), Sweden, we carried out a comprehensive analysis of HF patients’ health care utilization and costs to see if patients who received pharmacotherapy in line with international guidelines had different patterns of cost and utilization in a real-world setting. Methods Data source This is a retrospective analysis of a cohort encompassing all health care encounters in 2016 within RH in Sweden. Analysis was performed on data within the Regional Healthcare Information Platform (RHIP).21 The RHIP system links pseudo anonymized data regarding all health care encounters and health care utilization in the region, using data collected routinely during the standard course of care. Region Halland is a county with approximately 320 000 residents over 5500 km2 in southwestern Sweden. The regional government is the payer for the vast majority of the health care provided to the population, and delivers all of the IPC, most of the specialized OPC, and about half of the primary health care (PHC). There are one elective and two acute care hospitals in the area owned and run by RH, and 48 PHC clinics, about half of which are privately run but where care is paid for by the region. Two PHC centres covering approximately 20 000 residents declined to participate in the study. Consequently, the population was selected from approximately 300 000 individuals. Inclusion criteria Heart failure patients ≥45 years of age who have received an ICD-10 diagnosis of HF during the years 2012 through 2016 were included. The ICD codes used for diagnosis of HF are summarized in Table 1. These patients encountered some form of health care contact in RH during this period and were alive and living in RH at some point in 2016, as noted in the Swedish National Population Registry. Table 1 Displays the ICD-10 codes used searching for patients with HF diagnosis in Region Halland ICD 10 code . Description . I110 Hypertensive heart disease with heart failure I420 Dilated cardiomyopathy I423 Endomyocardial (eosinophilic) disease I424 Endocardial fibroelastosis I425 Other restrictive cardiomyopathy I426 Alcoholic cardiomyopathy I427 Cardiomyopathy due to drug and external agent I428 Other cardiomyopathies I429 Cardiomyopathy, unspecified I430 Cardiomyopathy in infectious and parasitic diseases classified elsewhere I431 Cardiomyopathy in metabolic diseases I432 Cardiomyopathy in nutritional diseases I438 Cardiomyopathy in other diseases classified elsewhere I500 Congestive heart failure I501 Left ventricular failure I509 Heart failure, unspecified ICD 10 code . Description . I110 Hypertensive heart disease with heart failure I420 Dilated cardiomyopathy I423 Endomyocardial (eosinophilic) disease I424 Endocardial fibroelastosis I425 Other restrictive cardiomyopathy I426 Alcoholic cardiomyopathy I427 Cardiomyopathy due to drug and external agent I428 Other cardiomyopathies I429 Cardiomyopathy, unspecified I430 Cardiomyopathy in infectious and parasitic diseases classified elsewhere I431 Cardiomyopathy in metabolic diseases I432 Cardiomyopathy in nutritional diseases I438 Cardiomyopathy in other diseases classified elsewhere I500 Congestive heart failure I501 Left ventricular failure I509 Heart failure, unspecified Open in new tab Table 1 Displays the ICD-10 codes used searching for patients with HF diagnosis in Region Halland ICD 10 code . Description . I110 Hypertensive heart disease with heart failure I420 Dilated cardiomyopathy I423 Endomyocardial (eosinophilic) disease I424 Endocardial fibroelastosis I425 Other restrictive cardiomyopathy I426 Alcoholic cardiomyopathy I427 Cardiomyopathy due to drug and external agent I428 Other cardiomyopathies I429 Cardiomyopathy, unspecified I430 Cardiomyopathy in infectious and parasitic diseases classified elsewhere I431 Cardiomyopathy in metabolic diseases I432 Cardiomyopathy in nutritional diseases I438 Cardiomyopathy in other diseases classified elsewhere I500 Congestive heart failure I501 Left ventricular failure I509 Heart failure, unspecified ICD 10 code . Description . I110 Hypertensive heart disease with heart failure I420 Dilated cardiomyopathy I423 Endomyocardial (eosinophilic) disease I424 Endocardial fibroelastosis I425 Other restrictive cardiomyopathy I426 Alcoholic cardiomyopathy I427 Cardiomyopathy due to drug and external agent I428 Other cardiomyopathies I429 Cardiomyopathy, unspecified I430 Cardiomyopathy in infectious and parasitic diseases classified elsewhere I431 Cardiomyopathy in metabolic diseases I432 Cardiomyopathy in nutritional diseases I438 Cardiomyopathy in other diseases classified elsewhere I500 Congestive heart failure I501 Left ventricular failure I509 Heart failure, unspecified Open in new tab Exclusion criteria Patients <45 years of age were excluded from the analyses, as these were thought to likely represent either coding errors or congenital heart disease. Patients with no health care encounters or cost in 2016 were also excluded from the analyses. Patients living in other regions or visitors from abroad with temporary need for medical care in RH, but who did not receive continuous care in RH were excluded as well. Study population A total of 6089 individuals with health care encounters coded with HF diagnoses during the period 2012–2016 were residents of RH for all or part of 2016. Seventeen patients were excluded who had no contact with the health service or use of medications in 2016. Among the remaining 6072 patients, 85 patients <45 years of age were also excluded. The remaining study cohort of HF in RH consisted of 5987 patients. Study procedure The cohort was divided into those who received AP and those who did not (non-AP). Appropriate pharmacotherapy was defined as patients receiving medication in accordance with evidence-based guidelines from the European Society of Cardiology,19 specifically that these patients should receive both beta-blockade and renin–angiotensin–aldosterone blockade. Appropriate pharmacotherapy patients were defined as those who were consistently taking both a beta-blocker and a renin–angiotensin–aldosterone system (RAAS) inhibitor (either an angiotensin-converting enzyme inhibitor or angiotensin receptor blocker). The medications data came from two sources: (i) Swedish Prescribed Drugs Register and (ii) Apotekets Dosdispensering (Apodos) (database of patients, usually elderly who receive medications in sachets or small amounts as opposed to the usual provision from pharmacy). Patients who were living with a diagnosis of HF for more than 180 days during 2016 were considered to be receiving AP if they had three or more medication pickups of in each category from the pharmacy, or if they received more than 250 days of these medications through Apodos. Patients who were alive and living with HF for 0–90 days and 91–180 days during 2016 were considered to be receiving AP if they had one or more and two or more pharmacy pickups for each medication, respectively. Health care utilization From RHIP, data were collected on the number of hospital admissions, hospital days, visits to emergency department (ED), and the number of OPC contacts that patients with HF had during the year 2016. Outpatient care was attributed to HC and PHC and to visits to doctors, nurses, and other care providers (physiotherapist, occupational therapist, nurse, psychologist, and laboratory personnel). Health care costs Resource utilization costs were assigned to each patient encounter using a novel Patient Encounter Costing (PEC) methodology.22 This is a modified form of time-driven activity-based costing, previously described by Kaplan.23 In this method, all encounter costs are based on two components: (i) separately billable procedures and attributable costs (including drug, radiology studies, and laboratory tests) and (ii) unit costs for resources and quantities of those resources used. Separately attributable costs are assigned to each patient encounter based on established tables of internal RH-derived costs, which are calculated and used internally by RH and updated regularly based on actual expenses incurred. Resource unit costs for services that are not internally priced (e.g. inpatient ward utilization, physician services) were determined based on total expenditures for a particular resource (e.g. an inpatient ward, outpatient clinic, ED and PHC) divided by that ward or clinic’s actual production. The unit of analysis for IPC was hospital bed-days, and for outpatient and PHC it was clinic visits. Hospital bed-days for each specific inpatient ward, and outpatient clinic visits for each specific outpatient clinic, were assumed to have the same cost basis. An individual encounter’s cost is then the product of the amount of resource used (e.g. number of bed-days) with the unit resource cost, plus all separately costed procedures or other attributable costs. Total costs of care were then calculated as the sum of all encounter costs for each patient over the course of the year. Statistical analysis Descriptive statistics were calculated to characterize the patient cohort. The prevalence of comorbidities was calculated based on the principal and secondary diagnoses of visits across all care venues from 2012 to 2016. The χ2 test was performed to compare the baseline characteristics between AP and non-AP groups. A P-value <0.05 was considered statistically significant. We calculated health care utilization for each of four care venues of HC (IPC, OPC, and ED) and PHC. An independent two-sample t-test was conducted to compare the means of the total cost among the AP and the non-AP group. A multiple linear regression model was used to assess the association between patients’ overall health care cost and drug compliance during 2016. Total health care cost was regressed against AP along with age and multiple comorbidities as independent variables. All the recorded data was stored in Microsoft SQL Server 2016 which was used for calculations. Further analyses were conducted using MATLAB 2017a (MathWorks, Inc., Natick, MA, USA) and SAS v 9.4 (SAS Foundation, Cary, NC, USA). The study was advertised in official written communication from RH, and patients were given the opportunity to opt-out of the study. The study was approved by the Institutional Review Boards (IRB) of the Lund University (Sweden) 2016/517 and Partners Healthcare (Boston, USA). Results As of 1 January 2016, 4718 patients had a previous HF diagnosis, and an additional 1269 patients were diagnosed with HF during 2016, giving an incidence of four new cases of HF/1000 population/year. The distribution of the number of patients, age distribution and mortality for the entire cohort as well as in the AP and non-AP cohorts are illustrated in Table 2. For the total population of women, average age was 82.0 years and the corresponding calculation for men was 77.3 (P < 0.0001). Patients being >70 years of age represented 82.2% of the patients, with 69.6% being >75 years. The most common relevant comorbidities are displayed in Table 2. Table 2 Baseline characteristics of AP and non-AP groups Characteristics . Total population (%) . AP (%) . Non-AP (%) . P-value . Basic of characteristics  Age (years) (mean) 79.4 76.8 81.1 <0.0001  Total patients (n) 5987 2423 (40.5) 3564 (59.5) <0.0001  Gender 0.006   Women (n) 2706 (45) 1043 (17) 1663 (28)   Men (n) 3281 (55) 1380 (23) 1901 (32) Mortality  Deceased during 2016 (n) 841 (16) 140 (6) 701 (20) <0.0001 Comorbidities  Hypertension (n) 4433 (74) 1841 (76) 2592 (73) 0.005  IHD (n) 2936 (49) 1316 (54) 1620 (45) <0.0001  Arrhythmia (other than AF) 1279 (21) 520 (21) 759 (21) 0.88  AF (n) 3226 (54) 1331 (55) 1895 (53) 0.18  Cardiomyopathy (n) 619 (10) 366 (15) 253 (7) <0.0001  VHD (n) 1155 (19) 474 (20) 681 (19) 0.66  CVI (n) 983 (16) 351 (14) 632 (18) 0.0009  Diabetes (n) 1522 (25) 696 (29) 826 (23) <0.0001  Renal failure (n) 1488 (25) 463 (19) 1025 (29) <0.0001  COPD (n) 940 (16) 322 (13) 618 (17) <0.0001  Dementia (n) 520 (09) 135 (6) 385 (11) <0.0001  Depression (n) 991 (17) 352 (15) 639 (18) 0.0005  Tumors (n) 1309 (22) 442 (18) 867 (24) <0.0001 Characteristics . Total population (%) . AP (%) . Non-AP (%) . P-value . Basic of characteristics  Age (years) (mean) 79.4 76.8 81.1 <0.0001  Total patients (n) 5987 2423 (40.5) 3564 (59.5) <0.0001  Gender 0.006   Women (n) 2706 (45) 1043 (17) 1663 (28)   Men (n) 3281 (55) 1380 (23) 1901 (32) Mortality  Deceased during 2016 (n) 841 (16) 140 (6) 701 (20) <0.0001 Comorbidities  Hypertension (n) 4433 (74) 1841 (76) 2592 (73) 0.005  IHD (n) 2936 (49) 1316 (54) 1620 (45) <0.0001  Arrhythmia (other than AF) 1279 (21) 520 (21) 759 (21) 0.88  AF (n) 3226 (54) 1331 (55) 1895 (53) 0.18  Cardiomyopathy (n) 619 (10) 366 (15) 253 (7) <0.0001  VHD (n) 1155 (19) 474 (20) 681 (19) 0.66  CVI (n) 983 (16) 351 (14) 632 (18) 0.0009  Diabetes (n) 1522 (25) 696 (29) 826 (23) <0.0001  Renal failure (n) 1488 (25) 463 (19) 1025 (29) <0.0001  COPD (n) 940 (16) 322 (13) 618 (17) <0.0001  Dementia (n) 520 (09) 135 (6) 385 (11) <0.0001  Depression (n) 991 (17) 352 (15) 639 (18) 0.0005  Tumors (n) 1309 (22) 442 (18) 867 (24) <0.0001 AF, atrial fibrillation; AP, appropriate pharmacology; COPD, chronic obstructive pulmonary disease; CVI, cerebrovascular insult; IHD, ischaemic heart disease; n, numbers; Non-AP, non-appropriate pharmacology; VHD, valvular heart disease. Open in new tab Table 2 Baseline characteristics of AP and non-AP groups Characteristics . Total population (%) . AP (%) . Non-AP (%) . P-value . Basic of characteristics  Age (years) (mean) 79.4 76.8 81.1 <0.0001  Total patients (n) 5987 2423 (40.5) 3564 (59.5) <0.0001  Gender 0.006   Women (n) 2706 (45) 1043 (17) 1663 (28)   Men (n) 3281 (55) 1380 (23) 1901 (32) Mortality  Deceased during 2016 (n) 841 (16) 140 (6) 701 (20) <0.0001 Comorbidities  Hypertension (n) 4433 (74) 1841 (76) 2592 (73) 0.005  IHD (n) 2936 (49) 1316 (54) 1620 (45) <0.0001  Arrhythmia (other than AF) 1279 (21) 520 (21) 759 (21) 0.88  AF (n) 3226 (54) 1331 (55) 1895 (53) 0.18  Cardiomyopathy (n) 619 (10) 366 (15) 253 (7) <0.0001  VHD (n) 1155 (19) 474 (20) 681 (19) 0.66  CVI (n) 983 (16) 351 (14) 632 (18) 0.0009  Diabetes (n) 1522 (25) 696 (29) 826 (23) <0.0001  Renal failure (n) 1488 (25) 463 (19) 1025 (29) <0.0001  COPD (n) 940 (16) 322 (13) 618 (17) <0.0001  Dementia (n) 520 (09) 135 (6) 385 (11) <0.0001  Depression (n) 991 (17) 352 (15) 639 (18) 0.0005  Tumors (n) 1309 (22) 442 (18) 867 (24) <0.0001 Characteristics . Total population (%) . AP (%) . Non-AP (%) . P-value . Basic of characteristics  Age (years) (mean) 79.4 76.8 81.1 <0.0001  Total patients (n) 5987 2423 (40.5) 3564 (59.5) <0.0001  Gender 0.006   Women (n) 2706 (45) 1043 (17) 1663 (28)   Men (n) 3281 (55) 1380 (23) 1901 (32) Mortality  Deceased during 2016 (n) 841 (16) 140 (6) 701 (20) <0.0001 Comorbidities  Hypertension (n) 4433 (74) 1841 (76) 2592 (73) 0.005  IHD (n) 2936 (49) 1316 (54) 1620 (45) <0.0001  Arrhythmia (other than AF) 1279 (21) 520 (21) 759 (21) 0.88  AF (n) 3226 (54) 1331 (55) 1895 (53) 0.18  Cardiomyopathy (n) 619 (10) 366 (15) 253 (7) <0.0001  VHD (n) 1155 (19) 474 (20) 681 (19) 0.66  CVI (n) 983 (16) 351 (14) 632 (18) 0.0009  Diabetes (n) 1522 (25) 696 (29) 826 (23) <0.0001  Renal failure (n) 1488 (25) 463 (19) 1025 (29) <0.0001  COPD (n) 940 (16) 322 (13) 618 (17) <0.0001  Dementia (n) 520 (09) 135 (6) 385 (11) <0.0001  Depression (n) 991 (17) 352 (15) 639 (18) 0.0005  Tumors (n) 1309 (22) 442 (18) 867 (24) <0.0001 AF, atrial fibrillation; AP, appropriate pharmacology; COPD, chronic obstructive pulmonary disease; CVI, cerebrovascular insult; IHD, ischaemic heart disease; n, numbers; Non-AP, non-appropriate pharmacology; VHD, valvular heart disease. Open in new tab General patterns of health care utilization Health care of HF patients cost €58.6 M, which represented 8.7% of the total health care expenses of the region, disproportionate to the 2.2% of the population represented. The largest category of expenditure was on IPC which was €33.5 M, or 14.9% of the region’s total inpatient expenditures, and just over half (57.2%) of the total costs of care for HF patients. Emergency department care costs accounted for €3.1 M or 11.3% of total ED expenses for the region. Hospital care represented 77.7% of the total expenses of our study cohort, which included IPC, ED, and specialty OPC. Primary health care accounted for 8.3% of the total cost of present cohort. The distribution of health care costs is displayed in Table 3. The AP and non-AP patients had significantly different costs even within age-matched cohort’s illustrated in Figure 1. Figure 1 Open in new tabDownload slide The distribution of mean cost per patient for appropriate pharmacotherapy and non-appropriate pharmacotherapy groups divided into age levels is shown. Figure 1 Open in new tabDownload slide The distribution of mean cost per patient for appropriate pharmacotherapy and non-appropriate pharmacotherapy groups divided into age levels is shown. Table 3 Describes the distribution of per-patient mean health care costs divided into inpatient care, out-patient care, emergency department, primary care, and medication Care providers . Total population . AP . Non-AP . P-value . Mean . SD . Mean . SD . Mean . SD . Hospital care  In-patient care 5601 11 759 4574 10 545 6297 12 471 <0.0001  Emergency department 522 935 451 914 570 945 <0.0001  Out-patient care 1490 6837 1201 2484 1686 8617 0.007 Primary health care 809 1113 806 1091 811 1128 0.86 Medication 1369 2845 1264 2161 1440 3227 0.19 Total health care costs 9790 8297 10 806 <0.0001 Care providers . Total population . AP . Non-AP . P-value . Mean . SD . Mean . SD . Mean . SD . Hospital care  In-patient care 5601 11 759 4574 10 545 6297 12 471 <0.0001  Emergency department 522 935 451 914 570 945 <0.0001  Out-patient care 1490 6837 1201 2484 1686 8617 0.007 Primary health care 809 1113 806 1091 811 1128 0.86 Medication 1369 2845 1264 2161 1440 3227 0.19 Total health care costs 9790 8297 10 806 <0.0001 1 SEK = 0.107 EUROS based on the average conversion rate for CY 2016. AP, appropriate pharmacology; Non-AP, non-appropriate pharmacology. Open in new tab Table 3 Describes the distribution of per-patient mean health care costs divided into inpatient care, out-patient care, emergency department, primary care, and medication Care providers . Total population . AP . Non-AP . P-value . Mean . SD . Mean . SD . Mean . SD . Hospital care  In-patient care 5601 11 759 4574 10 545 6297 12 471 <0.0001  Emergency department 522 935 451 914 570 945 <0.0001  Out-patient care 1490 6837 1201 2484 1686 8617 0.007 Primary health care 809 1113 806 1091 811 1128 0.86 Medication 1369 2845 1264 2161 1440 3227 0.19 Total health care costs 9790 8297 10 806 <0.0001 Care providers . Total population . AP . Non-AP . P-value . Mean . SD . Mean . SD . Mean . SD . Hospital care  In-patient care 5601 11 759 4574 10 545 6297 12 471 <0.0001  Emergency department 522 935 451 914 570 945 <0.0001  Out-patient care 1490 6837 1201 2484 1686 8617 0.007 Primary health care 809 1113 806 1091 811 1128 0.86 Medication 1369 2845 1264 2161 1440 3227 0.19 Total health care costs 9790 8297 10 806 <0.0001 1 SEK = 0.107 EUROS based on the average conversion rate for CY 2016. AP, appropriate pharmacology; Non-AP, non-appropriate pharmacology. Open in new tab The most common primary admission diagnosis in 2016 was HF itself (12.4%), followed by atrial fibrillation (6.3%), pneumonia (6.1%), acute myocardial infarction (5.8%), and chronic obstructive pulmonary disease (COPD) (4.8%). There were a total of 5764 inpatient admissions to hospital, with HF patients admitted an average of 1.0 times per year, with 48% of the patients having at least one admission to hospital in 2016. In the AP group, 43% were admitted to hospital at least once compared to 51% of the non-AP group. The number of health care contacts is illustrated in Table 4. The average number of days of care for each hospital admission was 6.9 for the total study cohort, 6.4 days in the AP group, and 7.1 in the non-AP group. Table 4 Displays the number of healthcare contacts distributed in hospital care and primary health care Care provider . Total population . AP . Non-AP . P-value . Hospital care  In-patient care   Admissions (n) 1.0 0.8 1.1 0.0001   Hospital days (n) 6.6 5.1 7.6 0.0001   ED visits (n) 1.2 1 1.3 0.0001  Out-patient care   Physician (n) 2.2 2.3 2.2   Nurse (n) 2.0 1.8 2.2   Others (n) 1.6 1.9 1.3   Total contacts (n) 5.8 6.0 5.7 0.6 Primary health care  Physician (n) 5.3 5.3 5.5  District nurse (n) 3.3 3.3 3.1  Nurse (n) 1.6 1.6 1.4  Others (n) 6.4 6.4 6.0  Total contacts (n) 16.6 17.4 16.0 0.002 Care provider . Total population . AP . Non-AP . P-value . Hospital care  In-patient care   Admissions (n) 1.0 0.8 1.1 0.0001   Hospital days (n) 6.6 5.1 7.6 0.0001   ED visits (n) 1.2 1 1.3 0.0001  Out-patient care   Physician (n) 2.2 2.3 2.2   Nurse (n) 2.0 1.8 2.2   Others (n) 1.6 1.9 1.3   Total contacts (n) 5.8 6.0 5.7 0.6 Primary health care  Physician (n) 5.3 5.3 5.5  District nurse (n) 3.3 3.3 3.1  Nurse (n) 1.6 1.6 1.4  Others (n) 6.4 6.4 6.0  Total contacts (n) 16.6 17.4 16.0 0.002 AP, appropriate pharmacology; ED, emergency department; Non-AP, non-appropriate pharmacology; Others, other care providers (physiotherapist, occupational therapist, nurse, psychologist, and laboratory personnel). Open in new tab Table 4 Displays the number of healthcare contacts distributed in hospital care and primary health care Care provider . Total population . AP . Non-AP . P-value . Hospital care  In-patient care   Admissions (n) 1.0 0.8 1.1 0.0001   Hospital days (n) 6.6 5.1 7.6 0.0001   ED visits (n) 1.2 1 1.3 0.0001  Out-patient care   Physician (n) 2.2 2.3 2.2   Nurse (n) 2.0 1.8 2.2   Others (n) 1.6 1.9 1.3   Total contacts (n) 5.8 6.0 5.7 0.6 Primary health care  Physician (n) 5.3 5.3 5.5  District nurse (n) 3.3 3.3 3.1  Nurse (n) 1.6 1.6 1.4  Others (n) 6.4 6.4 6.0  Total contacts (n) 16.6 17.4 16.0 0.002 Care provider . Total population . AP . Non-AP . P-value . Hospital care  In-patient care   Admissions (n) 1.0 0.8 1.1 0.0001   Hospital days (n) 6.6 5.1 7.6 0.0001   ED visits (n) 1.2 1 1.3 0.0001  Out-patient care   Physician (n) 2.2 2.3 2.2   Nurse (n) 2.0 1.8 2.2   Others (n) 1.6 1.9 1.3   Total contacts (n) 5.8 6.0 5.7 0.6 Primary health care  Physician (n) 5.3 5.3 5.5  District nurse (n) 3.3 3.3 3.1  Nurse (n) 1.6 1.6 1.4  Others (n) 6.4 6.4 6.0  Total contacts (n) 16.6 17.4 16.0 0.002 AP, appropriate pharmacology; ED, emergency department; Non-AP, non-appropriate pharmacology; Others, other care providers (physiotherapist, occupational therapist, nurse, psychologist, and laboratory personnel). Open in new tab This population consumed €8.2 M of outpatient prescription pharmaceuticals in 2016, representing 14.0% of total health expenses in this population and 5.8% of the total pharmaceutical expenses of the region in 2016. Antithrombotic agents (including novel oral anticoagulants) represented the largest category of drug costs, at €1.2 M or 18% of the total, followed by diabetes and respiratory medications. The multivariate linear regression showed that receiving AP was associated with significantly lower costs, by €1130 per patient (P < 0.0001, 95% confidence interval 574–1687). Comorbidities such as cerebrovascular insult (CVI), renal failure, diabetes, COPD, and cancer were significantly associated with higher costs. Age was significantly associated with a lower cost, but by a small absolute value (€55). See Table 5 for details. Table 5 Displays the results of multivariate linear regression comparing AP and non-AP cohort Variables . Estimate . Std. Error . Statistic . P-value . 2.5% . 97.5% . Intercept 10 644.21 1075.02 1.06 <0.001 8536.79 12 751.85 Appropriate pharmacology −1130.40 283.69 −0.43 <0.001 −1686.54 −574.27 Diabetes 1845.37 316.71 0.62 <0.001 1224.50 2466.25 RF 5288.79 321.24 1.76 <0.001 4659.04 5918.54 COPD 3101.32 373.64 0.89 <0.001 2368.85 3833.80 Tumours 3488.10 330.65 1.13 <0.001 2839.91 4136.29 Age −54.72 12.99 −0.45 <0.001 −80.18 −29.27 Variables . Estimate . Std. Error . Statistic . P-value . 2.5% . 97.5% . Intercept 10 644.21 1075.02 1.06 <0.001 8536.79 12 751.85 Appropriate pharmacology −1130.40 283.69 −0.43 <0.001 −1686.54 −574.27 Diabetes 1845.37 316.71 0.62 <0.001 1224.50 2466.25 RF 5288.79 321.24 1.76 <0.001 4659.04 5918.54 COPD 3101.32 373.64 0.89 <0.001 2368.85 3833.80 Tumours 3488.10 330.65 1.13 <0.001 2839.91 4136.29 Age −54.72 12.99 −0.45 <0.001 −80.18 −29.27 Open in new tab Table 5 Displays the results of multivariate linear regression comparing AP and non-AP cohort Variables . Estimate . Std. Error . Statistic . P-value . 2.5% . 97.5% . Intercept 10 644.21 1075.02 1.06 <0.001 8536.79 12 751.85 Appropriate pharmacology −1130.40 283.69 −0.43 <0.001 −1686.54 −574.27 Diabetes 1845.37 316.71 0.62 <0.001 1224.50 2466.25 RF 5288.79 321.24 1.76 <0.001 4659.04 5918.54 COPD 3101.32 373.64 0.89 <0.001 2368.85 3833.80 Tumours 3488.10 330.65 1.13 <0.001 2839.91 4136.29 Age −54.72 12.99 −0.45 <0.001 −80.18 −29.27 Variables . Estimate . Std. Error . Statistic . P-value . 2.5% . 97.5% . Intercept 10 644.21 1075.02 1.06 <0.001 8536.79 12 751.85 Appropriate pharmacology −1130.40 283.69 −0.43 <0.001 −1686.54 −574.27 Diabetes 1845.37 316.71 0.62 <0.001 1224.50 2466.25 RF 5288.79 321.24 1.76 <0.001 4659.04 5918.54 COPD 3101.32 373.64 0.89 <0.001 2368.85 3833.80 Tumours 3488.10 330.65 1.13 <0.001 2839.91 4136.29 Age −54.72 12.99 −0.45 <0.001 −80.18 −29.27 Open in new tab Discussion In this study, the complete health system costs related to HF within a Swedish region were analysed, including IPC, OPC specialty, PHC and pharmaceuticals. The average annual costs for patients with HF was €9790, with hospitalization dominating the expense. Patients receiving AP, consisting of RAAS inhibition and beta-blockade, were only 40% of the study cohort and had significantly lower costs compared with patients who did not receive AP, adjusting for common significant medical comorbidities. This difference was driven by fewer hospitalizations, fewer visits to the ED and shorter stays when hospitalized. In addition, AP was associated with a lower mortality, only 6% compared to 20% in the non-AP group. Our study cohort of 5987 patients represented an HF prevalence of 2.0% and an incidence of 0.4%, consistent with other published reports.2,3,24 The mortality rate was 15.5% which is also consistent with other studies.7,8 Some of the reduced mortality and utilization may be related to underlying differences between the AP and non-AP patients in this non-randomized observational study. The non-AP patients were older than AP patients, which could partially explain the higher mortality in the non-AP group. Higher rates of hypertension, ischaemic heart disease, and cardiomyopathy in the AP group were counterbalanced by higher rates of COPD, dementia, renal failure, tumour diseases, depression, and CVI in the non-AP group, the size of the effect of these differing comorbidities is difficult to quantify. We found that AP and non-AP patients had significantly different costs even within age-matched cohorts (Figure 1), making it less likely that the difference in utilization between the groups is primarily driven by age. In fact, the differences in utilization were primarily in patients <75 years old, with more similar costs between AP and non-AP patients over 75 years of age. In this study, the number of hospital days per year was on average 6.6, which is consistent with other Swedish inpatient studies, but above what has been reported in PHC-based studies of HC.14,15,20 Furthermore, the average length of stay of 6.9 days in this study would be consistent with earlier studies averaging 7–10 days per admission. Another Swedish study found that around two-thirds of HF patients could be expected to be admitted during 1 year, which is more than the 49% of the patients in this cohort who were admitted.14 This suggests that the present study includes comprehensive data and captures lower-acuity HF patients that were not in previous studies, as well as an anecdotal secular trend towards decreased use of IPC. The HF patient population, with per-capita expenses of over €9790, is an attractive target for improving costs by better co-ordinating care. This figure is slightly higher than prior Swedish studies that have found costs closer to €7672 in 2005 (or €8142 adjusted for inflation).14 This previous study was conducted with data from 2005 when PHC data was not easy to obtain, which could explain part of the cost differences between studies. This higher cost could partly be as a result of this analysis looking at all HF patients as well as a more comprehensive approach to costs than has previously been possible. The cost is dominated in particular by hospitalization, suggesting that interventions that reduce the need for hospitalization have the greatest potential to affect expenditures. It is previously known that the recommended treatment with RAAS inhibition and beta-blockers reduce morbidity.19 Still, nearly 60% of HF patients in our study were not treated according to the recommendations, which cannot be explained solely by differences in comorbidities or higher incidence of contraindications. It is likely that receiving AP is a marker for a more general pattern of high quality, evidence-based care, thus efforts focused solely on improving pharmacotherapy are unlikely to have an impact addressing the full 23% cost differential. However, a package intervention incorporating AP has a high potential to improve costs and outcomes in an HF population, most likely through reducing inpatient utilization, which accounts for more than half of the costs in this population. Emergency department care, which is often singled out as an opportunity for ‘waste reduction’ accounts for around 5% of expenses, and primary care, specialty OPC, and pharmaceuticals are each <15% of the total. Rather than being seen as a place where costs should be controlled, we would argue that the ED and PHC should be targets of investment as cost-effective alternatives to inpatient admission. In PHC, the main task is to optimize the treatment and follow-up of patients with HF and it has been shown in previous Swedish studies to reduce HC and, consequently, costs.20 Pharmaceuticals represents 14% of total costs in this study, below the 18% reported by Agvall et al.14,20 in 2005, but higher than the 7–8% reported in 2014. Given relatively low pharmaceutical costs, and that prior HF quality interventions have not shown significant impact on pharmaceutical expenditures, pharmaceuticals are not a likely effective target for cost reduction.20 Limitations The analysis was performed in a single region of Sweden, which may not be representative of both patients and care patterns in other parts of the country. However, RH’s relatively small size and stable population mitigate the problem of patients crossing system boundaries during the study timeline. The data were collected through the provision of care through a standard electronic health records system, and not through a research protocol, which means that there may be issues with the data quality and coding. Given standard clinical practices, data regarding hospital patient encounters, diagnostic and therapeutic procedures performed, and drugs prescribed and filled are expected to be robust. However, as diagnostic codes are not used for financial compensation in PHC these are not as consistently filled out, so patients who receive the majority of their care in PHC may not have all their diagnoses captured in our analysis. The lower total costs, related to decreased ED and inpatient utilization could be driven by a number of factors, including unmeasured confounders between the populations, socioeconomic differences between patients who do and do not receive AP, other element of high-quality care such as better counselling and closer monitoring which may be associated with being under the care of providers who provide AP, as well as better intrinsic disease control from being on AP. Conclusion Among HF patients in RH, only 40% receive AP according to national and international guidelines, and receiving AP was associated with significantly lower health care costs than those who do not receive AP. Total costs, as well as the differences in costs between AP and non-AP patients, are driven primarily by IPC. There may be an opportunity to decrease costs and improve patient experience by investing in PHC, ED and outpatient specialty care to improve not only AP but the related aspects of high-quality care that together likely lead to these outcomes, a hypothesis which should be tested through randomized controlled trials. Acknowledgements The authors would like to thank Dr Anne Ekberg-Jansson and Dr Stefan Lonn of Region Halland for their support with this manuscript. Conflict of interest: none declared. References 1 Ponikowski P , Anker SD, AlHabib KF, Cowie MR, Force TL, Hu S et al. Heart failure: preventing disease and death worldwide . ESC Heart Fail 2014 ; 1 : 4 – 25 . Google Scholar Crossref Search ADS PubMed WorldCat 2 Mosterd A , Hoes AW. Clinical epidemiology of heart failure . Heart 2007 ; 93 : 1137 – 1146 . Google Scholar Crossref Search ADS PubMed WorldCat 3 Zarrinkoub R , Wettermark B, Wandell P, Mejhert M, Szulkin R, Ljunggren G et al. 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Google Scholar Crossref Search ADS PubMed WorldCat Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2020. For permissions, please email: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
TI - Receiving care according to national heart failure guidelines is associated with lower total costs: an observational study in Region Halland, Sweden
JO - European Heart Journal - Quality of Care and Clinical Outcomes
DO - 10.1093/ehjqcco/qcaa020
DA - 2020-03-14
UR - https://www.deepdyve.com/lp/oxford-university-press/receiving-care-according-to-national-heart-failure-guidelines-is-do6fBBotnJ
SP - 1
EP - 1
VL - Advance Article
IS - 
DP - DeepDyve
ER -