Dilemma of missing specific disease codes: an approach to assess the incidence and prevalence of a rare nephrology disease

Abstract

Background

Estimating disease epidemiology using health insurance claims data is challenging due to the lack of specific diagnostic codes. This study demonstrates an approach to estimate the epidemiology of primary immunoglobulin A nephropathy (IgAN) using German Statutory Health Insurance (SHI) claims data.

Methods

A retrospective observational study was conducted using data from January 1^st, 2015 to December 31^st, 2022. Two coding algorithms – one restrictive and one inclusive – were developed to identify primary IgAN cases based on ICD-10-GM codes. The restrictive algorithm identified cases based on histologically confirmed diagnoses, while the inclusive algorithm included a broader range of related diagnoses. Annual incidence and prevalence rates were calculated and extrapolated to the German population.

Results

The mean prevalence rate from 2017–2022 ranged from 4.5 (restrictive codes) to 38.3 (inclusive codes) cases per 100,000 individuals, demonstrating the orphan nature of primary IgAN. Mean incidence rates ranged from 0.2 to 0.6 cases per 100,000 individuals. In 2022, this corresponded to an estimated 4,043 to 32,229 prevalent cases and 164 to 538 incident cases in Germany.

Conclusion

This study presents an approach for estimating disease epidemiology in the absence of specific diagnostic codes, using primary IgAN as an example. While our dual-algorithm method provides valuable insights into the incidence and prevalence of primary IgAN in Germany, it also highlights the need for consistent coding practices and specific diagnostic codes for accurate epidemiological assessments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-025-04271-4.

Background

Estimating disease epidemiology often relies on health insurance claims data due to its ability to provide information on extensive populations across diverse demographics [1].

Diagnostic, pharmaceutical, and procedure codes are typically used to identify diseases within these datasets [1]. However, challenges arise when specific disease codes are absent and coding practices by clinicians are inconsistent. This study presents an approach to improve disease epidemiology assessments in contexts where specific diagnostic codes are absent and coding practices by clinicians are inconsistent. This case study addresses these challenges using primary immunoglobulin A nephropathy (IgAN) as an example. In Germany, there is currently no specific diagnostic code for primary IgAN, leading clinicians to use various codes to document the disease [2].

IgAN is a rare kidney disease, yet the most prevalent primary glomerular disease and one of the most common reasons for kidney failure in younger adults [3–5]. IgAN is characterized by proteinuria, persistent microhematuria or episodes of macrohematuria, edema and arterial hypertension [4]. A distinct diagnosis can only be made via a kidney biopsy. The progression of the disease varies from spontaneous resolution to progressive loss of kidney function leading to kidney failure and the need for dialysis or a kidney transplant usually around the age of 30–50 years [5]. About 20–40% of all patients with IgAN experience kidney failure over the course of 20 years from the time of diagnosis [6, 7].

According to a literature review conducted in 2011, which incorporated data from prospective and retrospective studies, the incidence of IgAN varies greatly between 0.2 and 2.9 per 100,000 inhabitants depending on region, respective study designs and renal biopsy rates [8]. The average global incidence rate was reported to be approximately 2.5 per 100,000 [8]. A recent literature review of national kidney biopsy registry data from 10 European countries presented a pooled incidence of 0.8 per 100,000 and a prevalence of 25.3 per 100,000 for biopsy-confirmed IgAN occurrence [9]. For Germany, only two regional studies are available that reported an annual IgAN incidence of 1.7 per 100,000 between 2003 and 2008 in Northern Germany [10] and 1.9 per 100,000 between 2006 and 2013 in the Western German city of Aachen [11]. However, both studies were conducted in tertiary hospitals with a high reputation in IgAN care, likely leading to an increase in patient referral to those centres from the geographical surrounding network of physicians but also from other regions in Germany. Consequently, the published incidence rates are potentially overestimated.

Given the current lack of a specific diagnostic code for IgAN and the inconsistency in clinicians’ coding practices for patients with primary IgAN, developing an optimal strategy for identifying these patients in claims data is challenging. Therefore, two algorithms were developed for this purpose, one of which sets a lower bound (LB) and the other an upper bound (UB) for the definition of primary IgAN. The LB considered specific diagnosis codes which rely on biopsies. The UB included a broader field of specific and unspecific codes that are used by physicians to code IgAN.

Methods

Study design

A retrospective observational study design was applied based on German Statutory Health Insurance (SHI) claims data. Data from January 1^st, 2015 through December 31^st, 2022 was taken into account.

Data source

The analysis made use of the research database from the Institute for Applied Health Research Berlin [Institut für angewandte Gesundheitsforschung Berlin (InGef)]. The overall InGef database comprises anonymized claims data from over 50 different SHIs including approximately 8.8 million individuals. The InGef research database, a subset of the overall database, is an adjusted dataset of approximately 4 million individuals, which corresponds to approximately 5.0% of the German population [12] and 5.7% of the SHI population as of 2022 [13]. It represents the German population in terms of age and sex, and provides a good reflection of the morbidity, mortality, drug use and regional distribution [14]. All analyses for this study were performed by InGef staff based on a study protocol. Only aggregated results were provided due to national data protection regulations.

Study population

Annual primary IgAN incidence and prevalence rates were determined for the years 2017 to 2022. The following patient selection steps are described for the year 2022 but also apply to the previous years. The underlying analysis sample comprised individuals from the InGef research database who were continuously observable from January 1^st, 2020 until December 31^st, 2022. It included individuals who were born in this timeframe or deceased within the year 2022 but individuals who deceased in the years 2020 or 2021 were not included in the analysis sample.

Prevalent primary IgAN patients were identified based on ICD-10-GM codes (International Statistical Classification of Diseases and Related Health Problems, 10th revision, German Modification) using two different algorithms to capture a LB and UB of patients. The LB considered the specific diagnosis codes that rely on biopsies to diagnose IgAN and the UB considered a broader field of specific and unspecific codes that are used by physicians to diagnose IgAN, as identified through market research (see Fig. 1).

Fig. 1.

Diagnosis codes used for the two identification algorithms LB and UB. Abbreviations: UB, upper bound; LB, lower bound; ICD-10-GM codes, International Statistical Classification of Diseases and Related Health Problems, 10th revision, German Modification

Individuals with at least one documentation of one of the relevant ICD-10-GM codes in the inpatient sector (primary or secondary discharge diagnosis) and/or at least two documentations in two different quarters or by different physicians in the same quarter (M2Q criterion) in the outpatient sector (verified diagnosis, Zusatzkennzeichen Diagnosensicherheit “G”) in the timeframe from January 1^st, 2022 to December 31^st, 2022 were flagged as potential IgAN patients.

To ensure identified patients were actual primary IgAN patients, patients with ICD-10-GM codes indicating diseases other than IgAN or comorbidities/diseases leading to secondary IgAN in the timeframe from January 1^st, 2022 to December 31^st, 2022 were excluded (see Additional file (Supplementary Table 1 & 2)).

To identify incident primary IgAN patients in 2022, two additional inclusion criteria were applied. First, patients were excluded if they had a documented ICD-10-GM code for IgAN between January 1^st, 2020, and December 31^st, 2021. Second, patients were required to have undergone a renal biopsy within a period spanning up to four quarters prior to and up to three quarters following the initial diagnosis of IgAN. The latter criterion could not be applied to prevalent patients due to the limitations of the available database, which covers a period of six years, with an additional two years for inclusion and exclusion criteria. Consequently, it was not possible to retrospectively determine the date of the initial IgAN diagnosis for all prevalent cases.

Outcomes

The annual primary IgAN prevalence and incidence per 100,000 individuals were determined for the years 2017 to 2022 using the following formula (example for 2022):

The prevalence and incidence results were extrapolated to the German overall population. The underlying analysis sample from the InGef research database is adjusted for age and sex of the German population according to the Federal Office of Statistics (DESTATIS) [12]. Therefore, no further adjustment steps were necessary. The following formula was used for the extrapolation (example for 2022):

The size of the German overall population was extracted from the data of (DESTATIS). According to DESTATIS, there were 83,237,124 inhabitants as of December 31^st, 2022 [12].

Confidence intervals (CI) with a 95% confidence level were calculated by applying the Clopper-Pearson interval. The Clopper-Pearson method is based on an exact binomial distribution and the resulting CI is considered very conservative [15]. Microsoft Excel was used for the technical execution, applying the following formula:

Calculation of the lower limit:,

Calculation of the upper limit:  

with n = underlying InGef research database analysis sample, k = number of subjects, α = 0.05.

Further, the distribution of age, sex, and documented diagnostic codes for IgAN by healthcare setting and 4-digit ICD-10-GM code was analyzed for prevalent and incident patients in 2022.

Results

Incidence

The patient selection process for incident primary IgAN patients in the InGef research database for the year 2022 is displayed in Fig. 2.

Fig. 2.

Patient selection for incident primary IgAN patients in the InGef research database in 2022. Abbreviations: InGef, [Institut für angewandte Gesundheitsforschung Berlin] Institute for Applied Health Research Berlin; ICD-10-GM codes, International Statistical Classification of Diseases and Related Health Problems, 10th revision, German Modification; IgAN, immunoglobulin A nephropathy

In 2022, the 1-year incidence of primary IgAN in Germany ranged from 0.2 (LB) to 0.6 (UB) per 100,000 individuals, equating to 164 to 538 newly diagnosed cases. Estimates for the LB and UB from 2017 to 2021 showed minimal fluctuation, with the mean incidence rate across all analyzed years remaining at 0.2 (LB) to 0.6 (UB) per 100,000 individuals, as shown in Table 1.

Table 1.

1-year incidence of primary IgAN in Germany

Year	Lower bound				Upper bound
	per 100,000	Absolute number			per 100,000	Absolute number
		n	95 % CI lower limit	95 % CI upper limit		n	95 % CI lower limit	95 % CI upper limit
2017	0.3	253	131	442	0.7	549	358	804
2018	0.3	215	103	395	0.6	494	313	741
2019	0.2	153	61	314	0.6	523	335	778
2020	0.2	155	62	320	0.5	399	237	631
2021	0.3	208	95	395	0.8	624	411	908
2022	0.2	164	66	337	0.6	538	341	807

In 2022, the majority of newly diagnosed patients were male, comprising 85.7% in the LB and 82.6% in the UB. The mean age was 35.6 years for the LB and 43.6 years for the UB (see Table 2).

Table 2.

Demographic characteristics of incident patients with primary IgAN in 2022

Patient characteristics		Lower bound			Upper bound
		Female	Male	Total	Female	Male	Total
Age (in years)	Mean ± SD	-	34.5 ± 18.7	35.6 ± 17.3	-	42.7 ± 22.1	43.6 ± 22.4
	Median (IQR)	-	38 (29)	42 (27)	-	48 (32)	48 (36)
Age groups (in years)	0–17 (n%)	0 (0.00)	≤4a (≤66.7)	≤4a (≤57.1)	≤4a (≤100.0)	≤4a (≤21.1)	≤4a (≤17.4)
	18–29 (n, %)	0 (0.00)	≤4a (≤66.7)	≤4a (≤57.1)	0 (0.00)	5 (26.3)	5 (21.7)
	30–39 (n, %)	0 (0.00)	0 (0.00)	0 (0.00)	0 (0.00)	≤4a (≤21.1)	≤4a (≤17.4)
	40–49 (n, %)	≤4a (≤100.0)	≤4a (≤66.7)	≤4a (≤57.1)	≤4a (≤100.0)	≤4a (≤21.1)	≤4a (≤17.4)
	50–59 (n, %)	0 (0.00)	≤4a (≤66.7)	≤4a (≤57.1)	0 (0.00)	5 (26.3)	5 (21.7)
	60–69 (n, %)	0 (0.00)	0 (0.00	0 (0.00	≤4a (≤100.0)	≤4a(≤21.1)	≤4a (≤17.4)
	70–79 (n, %)	0 (0.00)	0 (0.00	0 (0.00	0 (0.00)	0 (0.00)	0 (0.00)
	≥80 (n, %)	0 (0.00)	0 (0.00	0 (0.00	0 (0.00)	≤4a (≤21.1)	≤4a (≤17.4)
	Total (n, %)	≤4a (≤57.1)	6 (85.7)	7 (100.0)	≤4 a (≤17.4)	19 (82.6)	23 (100.0)

^aDue to data protection regulations patient counts ≤ 4 and corresponding percentages cannot be reported

The distribution of documented diagnostic codes for IgAN in 2022 by 4-digit ICD-10-GM code and healthcare setting is shown in Table 3. Applying the UB definition for incident patients in 2022, up to 73.9% received an inpatient IgAN diagnosis and 47.8% at least two outpatient IgAN diagnoses. The two most frequent 4-digit ICD-10-GM codes using the UB IgAN definition were N02.8 (Recurrent and persistent haematuria: other) with 60.9% and N02.3 (Recurrent and persistent haematuria: diffuse mesangial proliferative glomerulonephritis) with 30.4%. In the LB, all patients exclusively received the code N02.3. Supplementary Table 3 (see Additional file) displays how the distribution of 4-digit ICD-10-GM codes developed from 2017–2022. In the years prior to 2022, the code N00.3 (Acute nephritic syndrome: diffuse mesangial proliferative glomerulonephritis) was also used for patients from the LB, i.e. not all patients exclusively received the code N02.3 as in 2022. For the UB, the ICD-10-GM codes N02.8 and N02.3 were consistently used from 2017–2022, while the use of the other codes fluctuated to a greater extent, e.g., the codes N00.3 and N02.7 were not recorded in some years but for over 20.0% of incident patients in other years. The code N06.3 (Isolated proteinuria with indication of morphologic changes: diffuse mesangial proliferative glomerulonephritis) was not documented at all for patients from the LB and UB in all years.

Table 3.

Distribution of documented diagnostic codes for IgAN in 2022 by healthcare setting and 4-digit ICD-10-GM code among incident primary IgAN patients in 2022

Healthcare setting/4-digit ICD-10-GM code		Lower bound	Upper Bound
At least one inpatient primary diagnosis		≤57.1%a	39.1%
At least one inpatient secondary diagnosis		≤57.1%a	34.8%
At least two outpatient diagnoses		≤57.1%a	47.8%
N00.3	Acute nephritic syndrome: diffuse mesangial proliferative glomerulonephritis	0.0%	0.0%
N02.3	Recurrent and persistent haematuria: diffuse mesangial proliferative glomerulonephritis	100.0%	30.4%
N02.5	Recurrent and persistent haematuria: diffuse mesangiocapillary glomerulonephritis	-	≤17.4%a
N02.7	Recurrent and persistent haematuria: diffuse crescentic glomerulonephritis	-	0.0%
N02.8	Recurrent and persistent haematuria: other	-	60.9%
N02.9	Recurrent and persistent haematuria: unspecified	-	≤17.4%a
N06.3	Isolated proteinuria with indication of morphologic changes: diffuse mesangial proliferative glomerulonephritis	0.0%	0.0%
N06.8	Isolated proteinuria with indication of morphologic changes: Other morphological changes	-	≤17.4%a

^aDue to data protection regulations patient counts ≤ 4 and corresponding percentages cannot be reported

Prevalence

Figure 3 displays the patient selection process for prevalent primary IgAN patients in the InGef research database for the year 2022. According to our methodological approach, the incident population is a subset of the prevalent population, as the first two selection steps resulted in the same patients counts as for the incident population.

Fig. 3.

Patient selection for prevalent primary IgAN patients in the InGef research database in 2022. Abbreviations: InGef, [Institut für angewandte Gesundheitsforschung Berlin] Institute for Applied Health Research Berlin; ICD-10-GM codes, International Statistical Classification of Diseases and Related Health Problems, 10th revision, German Modification; IgAN, immunoglobulin A nephropathy

The 1-year prevalence of primary IgAN ranged from 4.8 (LB) to 38.2 (UB) per 100,000 individuals, corresponding to 4,043 up to 32,229 affected patients in Germany in 2022. Both the LB and UB remained stable from 2017 to 2022 (see Table 4). The average 1-year prevalence across all analyzed years was 4.5 (LB) and 38.3 (UB) per 100,000 individuals in Germany.

Table 4.

1-year prevalence of primary IgAN in Germany

Year	Lower bound				Upper bound
	per 100,000	Absolute number			per 100,000	Absolute number
		n	95 % CI lower limit	95 % CI upper limit		n	95 % CI lower limit	95 % CI upper limit
2017	4.3	3525	3010	4102	37.8	31,278	29,706	32,912
2018	4.4	3629	3102	4219	38.2	31,672	30,077	33,331
2019	4.6	3814	3270	4423	39.1	32,477	30,849	34,169
2020	4.3	3570	3040	4166	37.7	31,313	29,701	32,990
2021	4.4	3700	3149	4320	38.9	32,353	30,680	34,093
2022	4.8	4043	3463	4693	38.2	32,229	30,551	33,976

In 2022 using the LB definition, 72.8% of primary IgAN patients were male and mean age was 52.7 years. The UB included 57.4% male patients, and the average age was 59.1 years (see Table 5).

Table 5.

Demographic characteristics of prevalent patients with primary IgAN in 2022

Patient characteristics		Lower bound			Upper bound
		Female	Male	Total	Female	Male	Total
Age (in years)	Mean ± SD	52.6 ± 15.6	52.8 ± 16.6	52.7 ± 16.3	59.8 ± 19.0	58.7 ± 18.2	59.1 ± 18.6
	Median (IQR)	54 (21)	54 (25)	54 (23)	62 (25)	60 (25)	61 (25)
Age groups (in years)	0–17 (n, %)	≤4a (≤8.5)	≤4a (≤3.2)	5 (2.9)	24 (4.1)	25 (3.2)	49 (3.6)
	18–29 (n, %)	0 (0.00)	8 (6.3)	8 (4.6)	22 (3.7)	32 (4.0)	54 (3.9)
	30–39 (n, %)	≤4a (≤8.5)	16 (12.7)	20 (11.6)	32 (5.5)	70 (8.8)	102 (7.4)
	40–49 (n, %)	12 (25.5)	21 (16.7)	33 (19.1)	71 (12.1)	84 (10.6)	155 (11.2)
	50–59 (n, %)	13 (27.7)	35 (27.8)	48 (27.7)	112 (19.1)	167 (21.1)	279 (20.2)
	60–69 (n, %)	10 (21.3)	19 (15.1)	29 (16.8)	123 (21.0)	151 (19.1)	274 (19.9)
	70–79 (n, %)	5 (10.6)	18 (14.3)	23 (13.3)	118 (20.1)	162 (20.5)	280 (20.3)
	≥80 (n, %)	≤4a (≤8.5)	6 (4.8)	7 (4.0)	85 (14.5)	101 (12.8)	186 (13.5)
	Total (n, %)	47 (27.2)	126 (72.8)	173 (100.0)	587 (42.6)	792 (57.4)	1,379 (100.0)

^aDue to data protection regulations patient counts ≤ 4 and corresponding percentages cannot be reported

Among prevalent patients in 2022, more than 90% of the LB and UB received at least two outpatient IgAN diagnoses in 2022 and up to 11.0% (LB)/9.5% (UB) were hospitalized due to or with IgAN. Using the LB IgAN definition, most prevalent patients (77.5%) received the ICD-10-GM code N02.3 (Recurrent and persistent haematuria: diffuse mesangial proliferative glomerulonephritis) in 2022, followed by N00.3 (Acute nephritic syndrome: diffuse mesangial proliferative glomerulonephritis) with 20.8% (see Table 6). Using the UB definition, most prevalent patients received the unspecific codes N02.9 (Recurrent and persistent haematuria: unspecified) with 48.7% and N02.8 (Recurrent and persistent haematuria: other) with 38.2%. The development of the 4-digit ICD-10-GM code distribution from 2017–2022 is shown in Supplementary Table 4 (see Additional file). For the LB, the use of the code N02.3 increased and N06.3 decreased over the years, while the use of N00.3 went up and down. The distribution in the UB did not change significantly from 2017–2022.

Table 6.

Distribution of documented diagnostic codes for IgAN in 2022 by healthcare setting and 4-digit ICD-10-GM code among prevalent primary IgAN patients in 2022

Healthcare setting/4-digit ICD-10-GM code		Lower bound	Upper Bound
At least one inpatient primary diagnosis		8.1%	5.7%
At least one inpatient secondary diagnosis		2.9%	3.8%
At least two outpatient diagnoses		91.9%	93.6%
N00.3	Acute nephritic syndrome: diffuse mesangial proliferative glomerulonephritis	20.8%	2.7%
N02.3	Recurrent and persistent haematuria: diffuse mesangial proliferative glomerulonephritis	77.5%	9.9%
N02.5	Recurrent and persistent haematuria: diffuse mesangiocapillary glomerulonephritis	-	1.2%
N02.7	Recurrent and persistent haematuria: diffuse crescentic glomerulonephritis	-	3.6%
N02.8	Recurrent and persistent haematuria: other	-	38.2%
N02.9	Recurrent and persistent haematuria: unspecified	-	48.7%
N06.3	Isolated proteinuria with indication of morphologic changes: diffuse mesangial proliferative glomerulonephritis	6.9%	0.9%
N06.8	Isolated proteinuria with indication of morphologic changes: Other morphological changes	-	1.4%

Discussion

This case study illustrates an approach to estimate disease epidemiology in health insurance claims data when clinicians pick varying diagnostic codes due to the absence of a specific code, using primary IgAN as an example. In Germany, while there is no specific ICD-10-GM code for IgAN, several codes are used to define the condition, each associated with different leading symptoms, such as proteinuria or hematuria. However, the primary challenge is that these codes are inconsistently and infrequently used in cases of IgAN.

A dual algorithm strategy was applied: a restrictive algorithm focused on a narrow set of codes linked to histologically confirmed IgAN, establishing a lower bound for primary IgAN prevalence. In contrast, a comprehensive algorithm included a broader range of codes potentially associated with IgAN or related symptoms, identified through market research. This approach enabled the delineation of a range of incident and prevalent primary IgAN cases.

Overall, the results on the incidence of primary IgAN in Germany align with findings from existing literature, although the data from the existing literature is based on different study designs that are not directly comparable with our own. Compared with incidence rates between 0.2 and 2.9 per 100,000 reported in a global literature review from 2011 [8], the LB and UB (0.2/0.6) of this case study lie at the lower end of this range. A recent literature review for Europe on biopsy confirmed IgAN found an incidence rate of 0.8 per 100,000 [9], closely resembling the UB of this study. Higher incidence rates of 1.7 and 1.9 per 100,000 were reported in two regional studies from Germany [10, 11]. However, as described above, both studies likely overestimated the incidence rate for Germany due to an increased patient referral to the participating tertiary hospitals. Published prevalence estimates are scarce in the literature. The aforementioned European literature review reported a point prevalence of 25.3 per 100,000 [9], ranging in-between the LB and UB (4.5/38.3) determined by this study. The identified prevalence ranges are large.

The epidemiology of IgAN shows notable geographic variation, with higher incidence reported in several Asian countries compared to Western countries. For example, a targeted literature review found that incidence rates in Japan, Taiwan, and Australia range from 0 to 10.7 per 100,000 people per year [16].

These differences are driven by genetic and healthcare differences. Genetically, Asians are more predisposed to IgAN than Europeans. Additionally, routine urine screening in many Asian countries enables earlier detection. Physicians are also more likely to perform kidney biopsies on patients with mild symptoms, such as microscopic hematuria. In contrast, European practices typically reserve biopsies for patients with more severe kidney disease [17], which may lead to underdiagnosis and a lower reported prevalence.

Furthermore, there are some differences in the patient compositions between the LB and UB, which are also present in the current literature. For example, the prevalent population showed a female/male ratio of 1:2.7 for the LB and 1:1.3 for the UB. Additionally, the age distribution showed an older population for the UB, suggesting that more non-glomerular causes of hematuria in older men entered the analyses. The age difference was even greater for the incident populations, whereas a similar sex distribution was observed for the incident LB and UB.

Comparisons of the demographic characteristics with the current literature were inconclusive. A UK study from 2023 reported a mean age of 45 years and a male proportion of 69% for a prevalent IgAN population (population 3 in Supplemental Table 3 of [7]), which is closer to the 52.7 years and 72.8% of prevalent patients using the LB primary IgAN definition than the 59.1 years and 57.4% using the UB primary IgAN definition. Research suggests that men are more often affected by IgAN than women in Western countries due to environmental and genetic factors in the pathogenesis of IgAN [18]. In contrast, a retrospective U.S. study [19] identified prevalent IgAN patients from 2016–2020 via Natural Language Processing (NLP) in a dataset that links electronic health record data with administrative claims data and found a sex distribution (57.2% men) similar to the distribution in the prevalent patients (57.4% men) using the UB primary IgAN definition.

While the overall incidence and prevalence estimates fluctuated only slightly from 2017–2022 in our analysis, the recorded ICD-10-GM codes for primary IgAN patients varied over the years, especially for the incidence. This indicates that even though certain codes may appear irrelevant in some years, they may be important in other years because excluding them could reduce the number of identified patients.

Although the demographic profile of patients identified using the LB algorithm aligns with existing literature, the prevalence estimates are lower than anticipated. In contrast, the prevalence estimates from the UB algorithm are more consistent with those reported in the literature, despite the demographic profile of these patients not matching the existing literature. These discrepancies may stem from variations in ethnic, geographic, and operational factors across different study populations and methodologies. Neither approach from each algorithm provides a completely accurate picture, therefore the true epidemiology of primary IgAN in Germany is likely to lie somewhere between the estimates of the two algorithms.

Even the use of the inclusive coding algorithm, which resulted in a mean prevalence of 38.3 cases per 100,000 individuals, emphasizes the orphan status of IgAN. This prevalence is well below the threshold of 50 individuals per 100,000, which reflects the European Medicines Agency’s (EMA) definition of an orphan condition [20].

Regarding comparability of the ICD-10-GM codes we used in our study (three ICD-10-GM codes for LB and eight ICD-10-GM codes for UB) we identified two other studies that also used ICD-10 codes to identify patients with IgAN. One study from the United States used the ICD-10 code N02.8, supplemented by N04.1, to identify cases of IgAN. This coding approach was based on a survey in which 78% of 409 U.S.-based healthcare professionals reported using these codes for IgAN [21].

A second study from Japan relied exclusively on code N02.8 to identify IgAN cases [22]. However, neither study specifically differentiated primary IgAN. Moreover, both studies were conducted outside Germany and were not designed to investigate the epidemiology of IgAN. These differences, limit the direct comparability of the findings across studies.

In terms of limitations, it needs be considered that it was not feasible in this study to identify only biopsy-proven primary IgAN since there is no specific disease code. Instead, the study had to rely on proxies. First, the LB definition only included diagnostic codes that are associated with histologically confirmed IgAN and second, the incident patients were restricted to those with a documented kidney biopsy around the time of first IgAN diagnosis. The latter was not feasible for the prevalent patients due to the limited available data time period. Although a two-year diagnosis-free period was used to identify incident IgAN cases, the possibility of left censoring cannot be ruled out. Patients diagnosed before 2020 without a recorded diagnosis during the look-back period could have been misclassified as new cases in 2022. This limitation is inherent to the use of claims data with a restricted observation period.

An additional limitation of this study is the lack of data on ethnicity and socioeconomic status in German claims data. Although the InGef database is representative of the German population with respect to age and sex, it does not fully capture other demographic or social determinants of health, i.e. ethnicity and socio-economic factors, which may influence healthcare utilization or outcomes. Therefore, residual confounding due to unmeasured variables cannot be ruled out.

Despite their limitations in diagnostic specificity, ICD-coded administrative data represent a viable and scalable approach for population-level surveillance of IgAN, particularly in countries lacking centralized kidney biopsy registries. National biopsy registries, offer high diagnostic precision but are resource-intensive and not universally implemented. In contrast, ICD-based methods enable broader epidemiological monitoring of large, representative populations and can serve as an alternative to or complement for biopsy-based registries. Due to the variability in healthcare infrastructure around the world, using administrative data may be the most practical strategy for improving the international comparability and coverage of IgAN epidemiology.

The impact of the coronavirus disease 2019 (COVID-19) pandemic on healthcare systems and patient behaviors may have impacted the study results. However, the estimates of incidence and prevalence appeared to be stable over the entire observation period.

Conclusion

Estimating a range of incidence and prevalence helps addressing the challenges posed by the absence of specific diagnostic codes and variability in coding practices. Nevertheless, the imminent implementation of the new specific ICD-10-GM code for diseases such as IgAN could greatly improve the accuracy of epidemiological studies. This improvement could have implications for health policy and clinical practice. It could enable more informed, evidence-based decisions regarding infrastructure, i.e., the allocation of nephrology services and biopsy capacity. Additionally, it could support broader policy planning, such as assessing the need for targeted screening programs. From a clinical perspective, recognizing the potential underdiagnosis of IgAN could raise awareness among healthcare providers, leading to earlier detection, especially in patients with subtle urinary abnormalities. Together, these findings highlight the essential role of epidemiological research in guiding evidence-based healthcare strategies. Future efforts should focus on implementing specific diagnostic codes to mitigate uncertainties and enhance the robustness of data.

Electronic supplementary material

Below is the link to the electronic supplementary material.

List of abbreviations

CI

Confidence intervals

COVID-19

Coronavirus disease 2019

DESTATIS

[Deutsches Statistik-Informationssystem] Federal Office of Statistics

EMA

European Medicines Agency’s

ICD-10-GM

International Statistical Classification of Diseases and Related Health Problems, 10th revision, German Modification

IgAN

Immunoglobulin A nephropathy

InGef

[Institut für angewandte Gesundheitsforschung Berlin] Institute for Applied Health Research Berlin

LB

Lower bound

M2Q

At least two documentations of ICD-10-GM codes in two different quarters or by different physicians in the same quarter

NLP

Natural Language Processing

SHI

Statutory Health Insurance

UB

Upper bound

Author contributions

All authors took part in designing the study. H.H., C.J., T.S. and J.T. supported the data analysis. All authors took part in interpreting the results. H.H., C.J., T.S. and J.T. wrote the manuscript. All authors critically reviewed and approved the final manuscript.

Funding

This study was funded by Vifor Pharma Deutschland GmbH.

Data availability

All data generated in this study is provided in the results and/or in the supplementary material files. The data used in this study was retrieved from the Institute for Applied Health Research Berlin (InGef) Research Database (http://www.ingef.de) and cannot be made available in the manuscript, the supplemental files, or in a public repository due to data protection regulations. To facilitate the replication of results, anonymized data used for this study are stored on a secure drive at the Institute for Applied Health Research Berlin (InGef) GmbH. Access to the data used in this study can only be provided to external parties under the conditions of the cooperation contract of this research project and can be assessed upon request, after written approval at InGef GmbH (Tel. +49 (30) 21 23 36-471; info@ingef.de), if required.

Declarations

Ethics approval and consent to participate

Claims data from the participating SHIs are joined in a specialized trust centre, anonymized, and transferred to InGef before the data are made available for research. The analysis of claims data from the SHI is fully compliant with German federal law and in accordance with the “GPS – Good Practice in Secondary Data Analysis” (Guideline 1: Ethics), the approval of an ethics committee and informed consent of the patients were not required.

Consent for publication

Not applicable.

Competing interests

Matthias Roll, Amadeus Gladbach, Isabella Selinger and Thomas Hardt are full-time employees of CSL Vifor. Helena Himmelhaus, Christian Jacob, Timotheus Stremel and Julia Theil are a full-time employees of PharmaLex GmbH, part of Cencora Inc., acting as contractors of CSL Vifor for the execution of this study. Jürgen Floege has received consultancy and/or lecture fees from AstraZeneca, Bayer, Boehringer, CSL Vifor, HiBio, Novartis, Roche, Stadapharm, Travere, Vera Therapeutics and serves on data safety monitoring boards of NovoNordisk and Visterra.