Gender differences in Heart Failure; Data on Outcomes and Costs

Authors

H.P. Cremers (PhD)^1*, L.J.H.J. Theunissen (MD)², P.P.M. Essers (MSc)³, A.R.T. van de Ven (MD, MBA)⁴, R. Spee (PhD, MD)², R. Verbunt (PhD, MD)², L. Otterspoor (PhD, MD)⁵, J.C. Post (PhD, MD)⁵, R. Tio (PhD, MD)⁵, F.G.M.H. van Asperdt (MD)⁶, N. Jegerings³, C. Verstappen (MSc)⁵, M. Peeters⁶, D. Stevelink⁴, E. Huijbers (MD)⁷, G. Smits (MD)⁸, A. Lucas (PhD, MD)⁹, J. Hurlebaus (PhD)¹⁰, P. van Haelst (PhD, MD)¹⁰, H.P.A van Veghel (PhD)¹ & L.R.C. Dekker (PhD, MD)^{5, 11}

1. Netherlands Heart Network, Veldhoven, the Netherlands
2. Máxima Medical Center, Veldhoven, the Netherlands
3. Health insurance CZ, Tilburg, the Netherlands
4. St. Anna hospital, Geldrop, the Netherlands
5. Catharina hospital, Eindhoven, the Netherlands
6. Elkerliek hospital, Helmond, the Netherlands
7. GP organization DOH, Eindhoven, the Netherlands
8. GP organization POZOB, Veldhoven, the Netherlands
9. Diagnostics for You, Eindhoven, the Netherlands
10. Roche Diagnostics, Rotkreuz, Switzerland
11. Department of Electrical Engineering, department of Biomedical Technology, Eindhoven University of Technology, the Netherlands

ABSTRACT

Background: Heart failure (HF) is a global epidemic. Although gender differences in HF care have been described previously, less is known regarding patient relevant outcomes and associated healthcare costs. In line with the Value Based Healthcare (VBHC) methodology, this study aims to assess differences in both outcomes and healthcare costs among males and females diagnosed with HF, to create insights enabling improvement of patient value.

Methods: In this retrospective cohort study outcome data (i.e. NYHA class, long-term survival, left ventricular ejection fraction (LVEF)) are gathered in 4 hospitals in the Netherlands between January 2013 and July 2015. Data regarding healthcare costs were indicated by a Dutch health insurance company. T-tests and chi-square tests were used to assess potential differences in outcome and economic data among males and females diagnosed with HF, using SPSS 25.0 and SAS Enterprise Guide 6.1.

Results: A total of 398 HF-patients were included for the analyses. Males (n=199) had a more complex medical profile (i.e. anemia (53,8%; p<0,01), reduced ejection fraction (HFrEF) (33,7%; p<0,01), and lower LVEF (39,5%; p<0,01)) compared to females. No differences in outcomes were observed between both genders. Furthermore, male patients generated significantly more costs in the first year after diagnosis (€7.070,44; p<0,01), as well as overall healthcare costs (€9.402,47; p=0,03) than females.

Conclusion: Although males diagnosed with HF showed a more complex medical profile and higher healthcare costs compared to females, no significant differences in patient relevant outcomes were shown. Future HF management strategies should consider gender differences when trying to optimize patient value.

KEYWORDS

Gender; heart failure; healthcare costs; health outcomes

INTRODUCTION

Heart failure (HF) remains a substantial health problem [1-3] with, only in the Netherlands, a prevalence of 227.000 patients and a mortality of 7.600 HF-patients in 2017 [4]. Due to the aging population, prevalence and mortality rates of heart failure are expected to increase rapidly [5]. HF is characterized as a syndrome caused by functional and structural heart defects [6], leading to a high burden of disease for the individual patient and increased healthcare costs for society [7]. In the Dutch population prevalence and mortality rates for HF differ between males and females [8,4], i.e. higher prevalence rates among females and higher mortality rates among males. Hence, the expectation is that gender differences in patient relevant outcomes and healthcare costs can be observed as well. For that reason, in the present study differences in patient relevant outcomes and associated healthcare costs of males and females diagnosed with HF will be investigated. In the future, differences between both groups may result in different treatment approaches.

In addition to differences in mortality and prevalence, clinical and laboratory characteristics also contrast between males and females diagnosed with HF [9,10]. Although differences between both genders are highlighted, numerous recent studies draw additional attention to the women’s heart [11,12], mainly since women are underrepresented in previous research [12]. Due to the frequently demonstrated differences between males and females diagnosed with HF [13-15] one may wonder whether males and females need individualized diagnostic and treatment trajectories in order to improve outcomes for both sexes more specifically. However, before such decisions can be made, insight in the most relevant outcomes for males and females should be provided.

In order to improve patient relevant outcomes and reduce healthcare costs, the last decade more attention is given to Value Based Healthcare (VBHC) as a strategy to reform healthcare [16,17]. As defined by Porter [16-18] in VBHC patient value needs to be an overarching goal, in which patient value is defined as patient relevant outcomes divided by the healthcare costs spent. Although aplenty research indicated potential differences in characteristics between males and females with HF [13-15], in HF care new insights can be gained when differences between patient relevant outcomes of both sexes are assessed. Moreover, HF care has a large impact on national healthcare costs (€940 million annually in the Netherlands in 2011) [19], but differences between males and females concerning their associated healthcare costs have not been assessed yet.

Aim of the present study is to assess the differences in patient relevant outcomes and healthcare costs among males and females diagnosed with HF. Information on outcomes and costs may provide important insights regarding the value of patients diagnosed with HF, and potential insight in order to improve patients’ value.

METHODS

Population and design

Data in the present study are retrospectively derived from a cohort of patients diagnosed with HF in one of the four hospitals (i.e. one heart center and three referring hospitals) involved in the Netherlands Heart Network (NHN). The NHN is a network-collaboration of healthcare professionals in primary, secondary, and tertiary care (i.e. cardiologists, GPs, nurses, home care organizations, ambulance services, pharmacists, thrombosis services, and diagnostic centers) and aims to continuously improve the patient value for cardiac patients in one region in the Netherlands, called South East Brabant, with an area of adherence of approximately 800,000 inhabitants. For this purpose, working groups on medical conditions are initiated consisting of healthcare providers from primary, secondary, and tertiary care in which, based on Value Based Healthcare principles, transmural standards of care are developed and implemented in the full care cycle in order to continuously improve patient relevant outcomes and reduce healthcare costs. A more detailed description of the design and methodology of the NHN can be found elsewhere [20]. For the present study the focus is on outcomes most relevant to HF-patients and the associated healthcare costs. In order to assess the healthcare costs, a collaboration with one of the largest Dutch healthcare insurance companies (i.e. CZ) was initiated.

Patients were included in the present study if they were newly diagnosed with HF between January 1^st 2013 and July 1^st 2015. In the Dutch medical system newly diagnosed HF-patients are registered with a medical code, called DOT code. Based on those DOT codes newly diagnosed HF-patients were identified in the present study. In addition, HF-patients need to be diagnosed in a clinical setting in one of the four hospitals involved in the NHN and patients had to be insured by health insurance company CZ in the Netherlands. For that reason data on patients’ characteristics originated from the Electronic Medical Records (EMRs) of the involved hospitals and data regarding the healthcare costs were gathered from the health insurance’ database for both primary, secondary, and tertiary care.

Procedure

In order to answer the research questions data extraction was performed between February 2018 and July 2018 from the hospitals’ EMRs. Data were collected at the time of diagnosis (T0), at 12 months (T12) and 24 months after diagnosis (T24). All patients that met the inclusion criteria have an insurance number that matches with the database of CZ. For that reason, data on relevant outcomes and healthcare costs can be extracted independently. Health insurance company CZ receives invoices for healthcare procedures and activities throughout the year. Based on these data, distinctions can be made which costs have been made for what type of care (e.g. drugs, hospital care, or primary care). Similarly to patients’ outcomes, healthcare costs were determined for the first and second year after diagnosis using CZ’s database.

The protocol of the present study was submitted for approval to the Medical research Ethics Committee United (MEC-U) in the Netherlands (reference number: W17.156). The MEC-U confirmed that the Medical Research Involving Human Subjects Act did not apply to this research and that therefore an official approval of this study by the MEC-U is not required.

Measurements

The measurements for the present study consist of patient relevant outcomes and healthcare costs. Below the measurements are outlined in more detail.

Patient relevant outcomes

In order to assess the health status, relevant outcomes for HF-patients consisting of a validated national indicator set were used. In the present study the following patient relevant outcomes are included: New York Heart Association (NYHA) class [6] a scale to indicate the severity of HF was measured by ‘1= NYHA I no limitations in normal physical activity’; ‘2= NYHA II mild symptoms only in normal activity’; ‘3= NYHA III marked symptoms during daily activities, asymptomatic only at rest’; ‘4= NYHA IV severe limitations, symptoms even at rest’, long-term survival (1= deceased; 0= non-deceased), and left ventricle ejection fraction (LVEF) (indicated by a percentage). 

Healthcare costs

For the present study the costs (in Euros (€)) made for physiotherapy, consultations with the GPs, drugs, and hospital care are merged, leading to an indication of the overall costs HF-patients consume. However, for physiotherapy and hospital costs only invoices specifically destined for care related to HF-patients were included.

To provide an overview of healthcare costs, this study presents costs for the first and second year of diagnosis combined (i.e. overall costs), and for the first and second year after diagnosis, separately.

Statistical Analyses

In order to assess the baseline characteristics, descriptive analyses are performed for HF-patients’ age, smoking status, type of HF, NYHA class, LVEF, and potential comorbidities. These analyses are performed for the overall sample and separately for males and females. T-test and chi-square tests are performed to indicate potential differences between both sexes. Since a correction for complexity of patient groups was not possible, all presented patient relevant outcomes should be interpreted as raw values.

To indicate patient relevant outcomes, NYHA class and LVEF are dummy coded in order to assess changes in both outcomes. For both measures an improvement in outcomes after 24 months was indicated by ‘1’, the variables were indicated by a ‘0’ when the outcome decreased or remained stable after 24 months of follow-up. Furthermore, in the analyses differences between males and females are assessed by means of t-test analyses. For all analyses concerning baseline characteristics and patient relevant outcomes of the HF-patients, SPSS 25.0 was used and differences were indicated to be significant when the P-value is ≤0,05.

In addition, healthcare costs are separately indicated for males and females based on the average, minimum, and maximum costs separately for the first and second year after diagnosis and overall for the first and second year together. Subsequently potential differences between healthcare costs for males and females are assessed using SAS Enterprise Guide 6.1.

RESULTS

Baseline characteristics

In table 1 the baseline characteristics of the study sample (N= 398) are shown. Males (n= 199) and females (n= 199) are equally distributed in the present analyses. It is shown that males are significantly younger than females, mean age is 73 years (p<0,01), have less hypertension (43,2%; p<0,01) and obesity (13,6%; p<0,01) in contrast to females. Furthermore, males are significantly more often diagnosed with anemia (53,8%; p<0,01) and HFrEF (33,7%; p<0,01) and their LVEF is significantly lower (39,5%; p<0,01) than females.

 TABLE 1: Differences in baseline characteristics among males and females

Gender and patient relevant outcomes

Patient relevant outcomes after 24 months of follow-up are shown in table 2. In the study period of 24 months a total of 68 HF-patients were lost to follow-up (17,1%), regarding the patient relevant outcomes. For that reason table 2 presents data of the complete cases (i.e. illustrated by the amount of patients (N)) for the patient relevant outcomes. In the total study sample 107 HF-patients deceased (26,9%), which was equally distributed between males (n=54; 27,1%) and females (n=53; 26,6%).

Besides the patients that were lost to follow-up after 24 months, missing data and registration errors were present after 12 and 24 months of follow-up. This resulted in a completeness of the NYHA class of 17,1% (n=68) and for the LVEF 24,9% (n=99) after 24 months of follow-up. Only the complete cases are used for the follow-up analyses. Regarding the NYHA class, of the 68 HF-patients of which the data was complete after 24 months the NYHA class worsened or remained stable in 58 HF-patients (85,3%). Similarly, the LVEF remained equal or decreased for 59 HF-patients during 24 months of follow-up (59,6% of the complete cases). As indicated in the table, no significant differences between males and females are present regarding their NYHA class (p=0,41), the LVEF (p=0,63), and the number of HF-patients that passed away (p=0,91) within 24 months of follow-up.

 TABLE 2: Differences in patient relevant outcomes among males and females (after 24 months of follow-up)

Gender and healthcare costs

As indicated in table 3 males cause, especially in the first year after diagnosis, more healthcare costs (€7.070,44) in contrast to females (p<0,01). Although not significant (p=0,21), females consume €3.103,69 in the second year after diagnosis compared to €2.918,70 for males. Nevertheless, in the total study period males consume on overage significantly more healthcare costs (€9.402,47; p=0,03) than females.

TABLE 3: Average healthcare costs for males and females

¹Min= Minimum; ²Max= Maximum; ³SD= Standard Deviation

DISCUSSION

Interpretation of findings

The present study aimed to assess differences in patient relevant outcomes and healthcare costs among males and females diagnosed with HF. The results show that no significant differences are observed in patient relevant outcomes between both sexes. However, males show a more complex medical profile and consume significantly more healthcare costs as compared to females, especially in the first year after diagnosis and regarding the overall healthcare costs.

Even though the debate concerning differences in medical profile between males and females diagnosed with HF is still ongoing [13-15], similar to the conclusions of recent Dutch reports [4,8] females with HF are known to be older and more often diagnosed with hypertension compared to men with HF. This may be explained by the fact that women in general get older than males in the Netherlands. However, numerous prior studies [9,14,21] demonstrated that males diagnosed with HF have a more complex medical profile and higher mortality rates due to their HF. This severity in risk profile among males may be explained since their lifestyle is inferior compared to that of females [22-24].

Overall healthcare costs after 24 months of follow-up, presented in this study, also showed to be significantly higher among males diagnosed with HF as compared to females. In addition, males with HF produce more healthcare costs in the first year after diagnosis, which may represent a more instable medical profile of males for whom the treatment is escalated more quickly with additional medical examinations or medical interventions compared to females. Prior research showed that hospitalizations constitute a huge burden among HF-patients [25-27]. As described in a study by Fang et al. (2008) [28], hospitalizations among males and females diagnosed with HF have increased similarly during the past two decades; however, the hospitalization rate is significantly higher among males. It is of interest for future research to assess which aspects in the treatment of males and females diagnosed with HF are responsible for the differences in healthcare costs.

Data in the present study can be marked as a preliminary measurement in which both patient relevant outcomes and healthcare costs of HF-patients are assessed. Based on the VBHC equation [17,18], patient value is calculated by dividing patient relevant outcomes and the healthcare costs spent. Since healthcare costs for males diagnosed with HF are significantly higher compared to females and no significant differences are observed between both genders, the patient value of males with HF is lower compared to female HF-patients. Although the results provide valuable insight in differences in patient value among both genders and guidance for improvement projects, for future research it is crucial by calculating patient value to take the correction for complexity of patient groups into account. Until now such method is not available yet.

Implication of findings

In accordance with prior research, the findings of the present study indicate no significant differences between males and females diagnosed with HF in patient relevant outcomes. However, based on the healthcare costs significant differences are observed between both sexes in which male HF-patients generate more costs compared to female patients. Additional research is needed to measure the cause of the higher healthcare costs in males, since this may be related to the diagnostic trajectories, (re)hospitalizations, or other aspects in HF care. Knowledge in those fields may provide concrete directions for improvements. In the future this may result in individual trajectories (i.e. diagnostic and treatment trajectories) for males and females diagnosed with HF. Tailored diagnostics and treatments for males and females may result in improved value such as increased patient relevant outcomes and reduced healthcare costs regarding the HF epidemic. Therefore, it is also crucial for future research to assess the consequences of tailored gender approaches in HF care since information on this topic is currently lacking. Furthermore, this research illustrates that studies in the field of VBHC should take gender differences into account when implementing VBHC improvement strategies. Currently, prior VBHC research most often focuses on either patient relevant outcomes or healthcare costs [16,29,30]. Nevertheless, a method to include the correction for complexity of patient groups needs to be developed to improve the correctness of the calculated patient value.

In the present study data on patient relevant outcomes were extracted from hospitals’ EMRs and data on healthcare costs from the health insurance’ database. Digital health solutions would be of assistance in combining data on outcomes and costs in accordance with applicable laws and regulations. Especially for an organization as the NHN it would be beneficial if digital solutions were developed to link the current EMR systems in both primary, secondary, and tertiary care. Moreover, digital health solutions (i.e. eHealth) have already shown to impact both outcomes and healthcare costs positively. Those solutions may also impact the treatment opportunities for males and females diagnosed with HF.

Limitations

Besides the interesting findings of the present study, it also has some limitations. First, a filtered set of patients was used for the study sample since only HF-patients were included when they were insured with health insurance company CZ. Since CZ is one of the largest health insurance companies in the Netherlands and covers approximately 40 – 50% of the population included in the present study, this can be marked as a representative sample. Also, the 4 participating centers, of which one is a heart center, can be considered representative for routine outpatient HF-care.

Lost to follow-up, registration errors, and underreporting of the required data is often a problem in cohort studies, and especially in retrospective research. For the present study mainly the NYHA class (17,1% completeness after 24 months) and LVEF (24,9% completeness after 24 months) suffered from missing data. For that reason the results in table 2 only present complete data. Although differences between males and females regarding those variables showed non-significant results it may have affected the interpretation of the results. Therefore, it is advised to replicate the present findings in other prospective studies.

A third limitation may be that in calculating the improvement of patient relevant outcomes (i.e. NYHA class and LVEF after 24 months of follow-up) no differentiation is made in the order of improvement. Additionally, it was not possible to take the correction for complexity of patient groups into account in assessing improvements in patient relevant outcomes, which may affect the interpretation of HF-patients’ value.

Finally, a limitation of the present study may be that the analyses regarding the medical profile and the healthcare costs are separately performed by the NHN and CZ due to prevailing privacy legislations. This may has affected the interpretation of the presented data. However, beforehand registration numbers of the included HF-patients were matched in both datasets. Furthermore, validation checks (i.e. age, gender, date of death, amount of included HF-patients per hospital) were performed to ensure similar HF-patients in NHNs’ and CZs’ dataset used for the analyses. However, for future studies digital health solutions may be helpful in combining the required data in accordance with the applicable laws and regulations.

Conclusion

Based on the findings of the present study we conclude that specific attention towards gender in the diagnosis and treatment of HF may improve patient relevant outcomes and healthcare costs when implementing VBHC improvement trajectories. For that reason, future research is advised to put effort in assessing the possibility and applicability of tailored management of HF between males and females in order to increase patient value for HF-patients.

FUNDING

The NHN is supported by various medical industries (i.e. Medtronic, BMS Pfizer, Bayer, Boehringer Ingelheim, St. Jude Medical, Abbott Medical, Novartis, Servier, and Vifor Pharma). For the present study the NHN was supported by Roche Diagnostics. However, the sponsors of the NHN were not in any way involved in the present study or in analyzing and writing this manuscript.

CONFLICT OF INTERESTS

Besides the funding of the medical industries, the authors declare there are no conflicts of interest.