Addressing the challenges resulting from the longer life expectancy1 in high-income countries requires that we reinforce the resilience of health systems.2 In the context of increasing financial constraints, the identification of the most frequently treated and costliest health conditions is essential for prioritizing actions to improve resilience, such as preventive actions or the reorganization of care, and a better understanding of the mechanisms underlying health care expenditures.3

Over the last decade, national claims data used for health care billing have been increasingly used to describe the morbidity1,4 and expenditures by health condition.5–11 However, the study population is often not representative of the general population if health insurance is not universal. In addition, the lack of individual information sometimes requires the modeling or the grouping of diseases into large categories. Thus, only very few countries have simultaneously described the prevalence of health conditions and the resulting expenditures for their management.12

In France, the National Health Data System (“Système National des Données de Santé, SNDS”) contains extensive individual information for almost the entire population, as public health insurance coverage is mandatory.13 Thus, the SNDS has been increasingly used for research in recent years13 and the development of Healthcare Expenditures and Conditions Mapping (HECM). The objective of this standardized tool is to describe the national annual prevalence of 58 health conditions among health care users and the expenditures reimbursed by the national health insurance for the care of each health condition. Here, ‘health conditions’ refers to treated diseases, chronic treatments (without a specific diagnosis identified), and episodes of care (such as maternity). We present the methodology of the HECM, its main results for 2019, and how they varied between 2015 and 2019 to provide a first overview of the economic burden of disease in France, a country that aims to offer free and universal access to health care.

METHODS Design

The HECM is equivalent to an annually repeated cross-sectional population-based study, without sampling, as all eligible individuals are included. The study has been repeated for all years studied with the release of each new version. The study using the present version was conducted for the years 2015–2019.

Population

The HECM includes all health care users (French residents with at least 1 reimbursement for health care delivery during the year studied). In 2019, they represented ~66.3 million people (97% of French residents).

Data Source: The French National Health Data System

Individual data from administrative forms and reimbursement claims have been prospectively recorded in a common data warehouse, the SNDS, since 2005.13 SNDS contains sociodemographic data, including health care reimbursements covered by complementary universal health insurance, which is free supplemental health insurance that provides free access to health care to low-income earners and was used to measure social precarity. SNDS also contains exhaustive information on pharmacy claims and the type of outpatient services or procedures reimbursed (without their results). In outpatient settings, physician-reported diagnoses are available only for beneficiaries with the “Affection de Longue Durée” status, which waives copayments related to the treatment of specific long-term diseases. SNDS also includes the French Hospital Discharge Database, containing inpatient diagnoses and procedures, from 2006 to 2019 at the time of this study.

All SNDS data are anonymous and individually linkable. Access to data is subject to prior training and authorization and requires approval by the independent French data protection authority (“Commission Nationale Informatique et Libertés”). Our public institution has permanent access to these data in application of the provisions of articles R. 1461‐12 et seq. of the French Public Health Code. Therefore, ethics board approval was not required.

Health Conditions

Algorithms have been developed to identify 58 health conditions (grouped into 15 categories) from the medical information available in SNDS using a lookback period of up to 5 years (Supplemental Digital Contents 1, https://links.lww.com/MLR/C491, and 2, https://links.lww.com/MLR/C492).14

Several health conditions can be identified in the same patient. “Hospitalisations for other reasons” concerned people with at least 1 short stay for a reason other than those considered for the health conditions otherwise identified. In particular, this reason may be related to infection (pneumonia), trauma, surgery (hip prosthesis and appendicectomy), exploratory examinations (colonoscopy), or ill-defined symptoms or conditions.15 For cardiovascular diseases (CVDs), cancer, and end stage renal disease, exclusive algorithms identified different states of the same disease to distinguish expenditures related to active treatment or acute care or to long-term follow-up and chronicity. Finally, several health conditions aimed to attribute expenditures to individuals receiving chronic treatment when they had no specific diagnosis coded as anlong-term disease or during a hospital stay.

The first version of the algorithms was developed in 2012 by AFC, with the help of a coding expert, an epidemiologist, and a health insurance physician and exchanges with other experts. All algorithms were submitted to review by clinicians, epidemiologists, and coding experts, conducted by an independent research team14 and subsequently adapted. Their continuing improvement has also benefited from the work conducted by a network of SNDS expert users who record and compare algorithms.16

Individual Expenditures

All reimbursed individual expenditures directly related to personal health care administered during a given year were calculated by expenditure item for each individual from a national health insurance perspective. Expenditures related to collective services (prevention, public health services, and health administration) and lump-sum financing were not included in HECM. The expenditure items and sources of data are listed in Table 1.

TABLE 1 - List of Expenditure Items, Sources of Data, Methods to Attribute Expenditures to Health Conditions, and Whether “Usual Care” Were Considered Category of Expenditure Expenditure Item Sources of Data Attribution to Health Conditions Expenditure Attributed to Usual Care Ambulatory care General practitioners Derived directly from individual SNDS reimbursement data Indirect Yes Specialists Dental care Nursing care Physiotherapists Other paramedical care Drugs Medical devices and associated care Laboratory tests Other ambulatory care Midwifery No (not related to usual care) Transportation Hospital care Public hospital outpatient care Hospital discharge database Indirect Short-stay hospitalization (DRG) Direct Drugs and medical devices out of DRG* Hospitalization in rehabilitation care Hospitalization in psychiatry Hospital at home Indirect Cash benefits Maternity leave† Derived directly from individual SNDS reimbursement data Direct (maternity) Disability pension‡ Indirect Sick leave† Indirect Yes

*Within health care institutions, the “supplementary list” (“liste en sus”) allows the payment of pharmaceutical specialties by the health insurance system, for some of their therapeutic indications, in addition to the costs related to the hospital stay derived from DRG, when these indications are innovative. This list is set by order of the minister responsible for health and social security and specifies the indications concerned, in accordance with Article L. 162-22-6 of the Social Security Code.

†Daily allowances paid by national health insurance in case of work stoppage due to disease or maternity. Also comprise compensation for work accidents or occupational disease.

‡Compensation of loss of salary as a result of disability.

DRG indicates diagnosis-related group; SNDS, Système national des données de santé (French national health database).

Attribution of Expenditures to Health Conditions

Overall annual reimbursed expenditures were distributed between the 58 identified conditions, with no possibility of double counting. The sum of expenditures attributed to a health condition across all expenditure items constituted its estimated economic burden. On the basis of a comprehensive and standardized methodology rather than multiple disease-specific cost-of-illness studies, which tend to overestimate the total expenditure, this general cost-of-illness approach has been considered conceptually more relevant for a political objective.17

Two methods were used for attribution according to the expenditure item (Table 1). For items for which diagnoses are systematically recorded (eg, hospital stays), we used this information to directly attribute expenditures to a single health condition (Supplemental Digital Content 3, https://links.lww.com/MLR/C493). This approach is similar to a “bottom-up” method, as individual-level expenditures are summed to obtain a population-level condition-specific estimation.18 Short-stay hospitalization expenditures not attributable to a specific health condition were attributed to hospitalizations for other reasons.

For items not associated with information based on a diagnosis, we used an indirect attribution approach, similar to a “top-down” method, as portions of resource aggregates were assigned to specific health conditions.18 The indirect method was applied for each concerned item and observed combination of health conditions in the study population (~450,000 combinations). When only 1 health condition was identified for the year, expenditures were attributed exclusively to this condition. For other combinations, the total expenditure was assigned to the constituent conditions pro rata to the mean expenditure previously calculated in groups with only 1 of these conditions. For example, the mean drug expenditures for people with diabetes (A) or epilepsy (B) as unique identified conditions were used to calculate the proportions for distributing drug expenditures for people with these 2 conditions [A/(A+B) for diabetes and B/(A+B) for epilepsy]. The indirect method was applied for each individual after subtracting expenditures for so-called “usual” care. For concerned items, this amount corresponded to the second decile of expenditures for people of the same age and sex who had used this type of care but had no health condition identified other than hospitalizations for other reasons.

Statistical Analyses

Sociodemographic characteristics of the study population and the prevalence of each health condition were described for 2019. Annual expenditures (in current euros) and mean expenditures (in current euros per patient) attributed to the various health conditions were calculated, globally and by expenditure item. The mean annual growth rates of expenditures and the number of patients were calculated for the 2015–2019 period. As the analyses were not performed on a random sample of the population, no confidence interval was estimated. Statistical analyses were performed using SAS Enterprise Guide 4.3 software (SAS Institute Inc., Cary, NC).

RESULTS Population Characteristics and Health Conditions in 2019

In 2019, the HECM comprised 66.3 million people, including 52% women and 21% people aged 65 years or older, with a median age of 42 years (interquartile range: 21–61). Social precarity concerned 9.1% of the population. People with no health condition identified (55%) were younger than the general population (median age: 29 y, interquartile range: 14–46), and 9.5% of them were in a socially precarious situation. Sociodemographic characteristics by health condition are presented in Supplemental Digital Content 4, https://links.lww.com/MLR/C494.

The most prevalent categories of health conditions were “CVD and chronic treatments” (21%), and “psychiatric diseases and chronic treatments” (12%) (Table 2). The most prevalent health conditions were hospitalization for other reasons (14%) and chronic treatments without a specific diagnosis identified, in particular antihypertensive treatment (11%). Diabetes (6.0%) and chronic respiratory disease (5.5%) were the most frequent diseases, followed by coronary disease (3.0% for chronic coronary disease without an acute event, 3.2% when combined with acute coronary syndrome).

TABLE 2 - Frequency, Prevalence, and Distribution of Total Expenditures by Health Condition in 2019 Expenditures Health Condition n Prevalence* (%) In Billion € Proportion of Total (%) Whole population 66,266,685 100 166.97 100 No health condition identified 36,194,044 55 11.14 6.7 Cardiovascular diseases and chronic treatments 13,605,415 21 23.54 14  Acute coronary syndrome 103,648 0.16 1.03 0.62  Chronic coronary disease without acute event 1,983,957 3.0 3.40 2.0  Acute stroke 124,733 0.19 1.55 0.93  Sequelae of stroke 833,164 1.3 2.24 1.3  Acute heart failure 197,856 0.30 1.59 0.95  Chronic heart failure without acute event 662,875 1.0 1.50 0.90  Peripheral vascular diseases 714,256 1.1 1.87 1.1  Heart arrhythmia or conduction disorders 1,785,941 2.7 2.65 1.6  Valvular heart diseases 461,016 0.70 1.28 0.77  Pulmonary embolism 44,805 0.068 0.23 0.14  Other cardiovascular diseases 351,056 0.53 0.59 0.35  Antihypertensive treatments† 7,329,506 11 4.26 2.6  Lipid-lowering treatments† 3,056,830 4.6 1.34 0.80 Diabetes 3,964,561 6.0 8.58 5.1 Cancers 3,297,155 5.0 20.10 12  Active female breast cancer 228,165 0.66 2.92 1.8  Follow-up for female breast cancer 495,965 1.43 0.58 0.35  Active colorectal cancer 150,434 0.23 1.67 1.0  Follow-up for colorectal cancer 220,811 0.33 0.22 0.13  Active lung cancer 98,603 0.15 2.28 1.4  Follow-up for lung cancer 54,018 0.082 0.09 0.057  Active prostate cancer 220,797 0.70 1.54 0.92  Follow-up for prostate cancer 314,728 0.99 0.25 0.15  Other active cancers 836,056 1.3 9.59 5.7  Follow-up for other cancers 934,542 1.4 0.95 0.57 Psychiatric diseases and chronic treatments 8,103,919 12 22.73 14  Psychotic disorders 475,244 0.72 4.76 2.8  Neurotic and mood disorders 1,405,133 2.1 6.21 3.7  Mental impairment 132,039 0.20 0.56 0.34  Addictive disorders 440,883 0.67 1.59 0.95  Psychiatric disorders having begun in childhood 172,358 0.26 1.30 0.78  Other psychiatric disorders 427,305 0.64 1.67 1.0  Antidepressant or mood-regulating treatments‡ 2,994,072 4.5 2.88 1.7  Neuroleptic treatments‡ 325,742 0.49 0.28 0.17  Anxiolytic treatments‡ 3,077,668 4.6 2.53 1.5  Hypnotic treatments‡ 1,244,966 1.9 0.95 0.57 Neurological diseases 1,673,904 2.5 7.66 4.6  Dementia (including Alzheimer disease) 760,673 1.1 2.57 1.5  Parkinson disease 271,583 0.41 1.09 0.65  Multiple sclerosis 115,498 0.17 1.20 0.72  Paraplegia 97,635 0.15 1.12 0.67  Myopathy or myasthenia 48,334 0.073 0.26 0.15  Epilepsy 342,385 0.52 0.61 0.36  Other neurological conditions 175,486 0.26 0.82 0.49 Chronic respiratory diseases§ 3,656,804 5.5 3.49 2.1 Chronic inflammatory diseases 964,753 1.5 3.36 2.0  Inflammatory bowel diseases 273,098 0.41 0.99 0.59  Rheumatoid arthritis and related diseases 299,918 0.45 0.96 0.58  Ankylosing spondylitis and related diseases 228,949 0.35 0.94 0.56  Other chronic inflammatory diseases 215,598 0.33 0.47 0.28 Rare diseases 180,599 0.27 1.52 0.91  Hereditary metabolic diseases or amyloidosis 120,196 0.18 0.64 0.38  Cystic fibrosis 8796 0.013 0.32 0.19  Hemophilia or severe hemostasis disorders 52,021 0.079 0.57 0.34 HIV infection 151,346 0.23 1.22 0.73 End stage renal disease 98,427 0.15 4.10 2.5  Chronic dialysis 54,566 0.082 3.30 2.0  Kidney transplant 3514 0.0053 0.24 0.14  Follow-up for kidney transplant 40,347 0.061 0.56 0.34 Liver or pancreas diseases§ 604,162 0.91 1.54 0.92 Other long-term diseases 1,975,489 3.0 4.30 2.6 Maternity 1,265,621 3.7 8.48 5.1 Hospitalization for other reasons 9,417,185 14 37.41 22 Analgesic or NSAI treatment∥ 1,308,126 2.0 1.52 0.91 Usual care¶ NA NA 6.28 3.8

*Among men or women only when appropriate.

†Without a diagnosis of cardiovascular disease, diabetes, or end stage renal disease.

‡Without a diagnosis of mental illness.

§Excluding cystic fibrosis.

∥Excluding diseases, treatments, maternity care, or hospitalization.

¶Prevalence not reported because it is not a group of people, only aggregated expenditures.

NA indicates not applicable; NSAI, nonsteroidal anti-inflammatory.

Social precarity was more frequent among people with addictive disorders (19%), psychiatric disorders that began in childhood (24%), maternity (18%), and chronic analgesic or non-steroidal anti-inflammatory (NSAI) treatment (24%) than those with no health condition identified (9.5%). Nevertheless, it was less frequent among people receiving care for CVD (3.9%), cancer (3.3%, in particular in long-term follow-up), diabetes (6.7%), dementia (1.2%), and Parkinson disease (1.7%).

Expenditures Attributed to Health Conditions in 2019

In 2019, €167.0 billion was reimbursed by the national health insurance for the entire population (Table 2). Hospitalizations for other reasons accounted for the highest expenditure (22% of total expenditures), followed by CVD and treatments (14%), psychiatric diseases and treatments (14%), and cancer (12%). At a more detailed level, expenditures attributed to the management of these categories of health conditions were primarily related to neurotic and mood disorders (3.7% of total expenditures), psychotic disorders (2.8%), chronic coronary heart disease (2.0%), and “other cancers during the active treatment phase” (5.7%). Among the 4 specific cancer sites (breast, lung, colorectal, and prostate), breast cancer was associated with the highest expenditure (2.1% of total expenditures, 1.8% when considering only cancer in the active treatment phase). In addition, at this detailed level, diabetes seemed to be the third most expensive health condition (5.1% of total expenditures).

Furthermore, the predominant component of expenditures differed according to health condition (Fig. 1). As mentioned above, CVD and psychiatric diseases were the most frequent health conditions, but the mean expenditures per patient were low (1334 and 2125 €/patient, respectively) relative to other health conditions, including cancer (5656 €/patient). By contrast, the highest mean expenditures concerned, by far, end stage renal disease (41,701 €/patient, n=98,427; not shown in Fig. 1), mostly related to chronic dialysis (60,557 €/patient, n=54,566).

FIGURE 1:

Expenditures by health condition category in 2019 and their components: number of patients and mean expenditure per patient. Excluding cystic fibrosis. Excluding diseases, treatments, and maternity care or hospitalization. The size of the bubbles is proportional to the expenditure. Bubbles for end stage renal disease (n=98,427, 41,701 €/patient), people with no identified health condition (n=36 million, 308 €/patient), and usual care were not represented because of the very high mean expenditure or very large number of people. CVD indicates cardiovascular diseases; LTD, long-term disease; NSAI, nonsteroidal anti-inflammatory.

Finally, the expenditures were mostly related to hospital care for acute CVD, valvular heart diseases, cancer in the active treatment phase (except prostate cancer), and psychiatric diseases (Supplemental Digital Content 5, https://links.lww.com/MLR/C495). By contrast, expenditures were mostly related to ambulatory care for chronic CVD, follow-up for cancer, diabetes, HIV infection, and rare diseases. Finally, cash benefits represented an important proportion of expenditures for maternity (5.5%), chronic analgesic and NSAI treatment, and chronic psychiatric treatments.

Evolution of Expenditures Between 2015 and 2019

Between 2015 and 2019, health care expenditures increased by €14.5 billion (+9.5%, +2.3%/y on average). The growth in expenditures was the highest for cancer (+€3.8 billion, +5.3%/y) (Fig. 2). Apart from the heterogeneous group of other cancers during the active treatment phase (+€1.7 billion), lung cancer during the active treatment phase was the specific cancer site associated with the highest increase in expenditures (+€797 million; +11.4%/y) during the study period. Except in 2016, the largest share of this increase can be attributed to the mean expenditure per patient (Fig. 3), which sharply increased by 13% in 2017 and continued to increase during the following years due to drugs and medical devices out of the diagnosis-related group of the stay, for which the expenditures increased dramatically from 1351 €/patient in 2015 to 5161 €/patient in 2019 (+282%) (Supplemental Digital Content 6, https://links.lww.com/MLR/C496).