Evidence map›Paper›PMID 40038705›Full record

ArticleBMC health services research2025

Epidemiological disease burden and annual, nationwide health insurance treatment cost of female infertility based on real-world health insurance claims data in Hungary.

Dalma Pónusz-Kovács, Tímea Csákvári, Luca Fanni Sántics-Kajos, Diána Elmer, Róbert Pónusz, Bettina Kovács, Ákos Várnagy, Kálmán Kovács, József Bódis, Imre Boncz

Abstract read
In one paragraph

Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Dalma Pónusz-KovácsFaculty of Health Sciences, Institute for Health Insurance, University of Pécs, 7621 Vörösmarty Utca 3, Pécs, Hungary. dalma.kovacs@etk.pte.hu.
Tímea CsákváriFaculty of Health Sciences, Institute for Health Insurance, University of Pécs, 7621 Vörösmarty Utca 3, Pécs, Hungary.
Luca Fanni Sántics-KajosFaculty of Health Sciences, Institute for Health Insurance, University of Pécs, 7621 Vörösmarty Utca 3, Pécs, Hungary.
Diána ElmerFaculty of Health Sciences, Institute for Health Insurance, University of Pécs, 7621 Vörösmarty Utca 3, Pécs, Hungary.
Róbert PónuszFaculty of Health Sciences, Institute for Health Insurance, University of Pécs, 7621 Vörösmarty Utca 3, Pécs, Hungary.
Bettina KovácsFaculty of Health Sciences, Institute for Health Insurance, University of Pécs, 7621 Vörösmarty Utca 3, Pécs, Hungary.
Ákos VárnagyNational Laboratory On Human Reproduction, University of Pécs, 7624 Ifjúság Utca 20, Pécs, Hungary.
Kálmán KovácsNational Laboratory On Human Reproduction, University of Pécs, 7624 Ifjúság Utca 20, Pécs, Hungary.
József BódisFaculty of Health Sciences, Institute for Health Insurance, University of Pécs, 7621 Vörösmarty Utca 3, Pécs, Hungary.
Imre BonczFaculty of Health Sciences, Institute for Health Insurance, University of Pécs, 7621 Vörösmarty Utca 3, Pécs, Hungary.

Funding

"Project no. RRF-2.3.1-21-2022-00012, titled National Laboratory on Human Reproduction, has been implemented with the support provided by the Recovery and Resilience Facility of the European Union within the framework of Programme Széchenyi Plan Plus." RRF-2.3.1-21-2022-00012
6 · The paper itself

Abstract

backgroundInfertility affects more than 50 million couples worldwide, resulting in a significant burden on individuals and society. Its prevalence ranges from 8-12% among developed countries. The growing number of patients poses an increasing challenge to the healthcare system and its funding. Our quantitative, descriptive, and cross-sectional study aimed to analyze the prevalence and annual nationwide health insurance treatment cost of female infertility in Hungary in 2019.

methodsWe used claims data obtained from the Hungarian National Health Insurance Fund Administration (NHFIA). The number of patients, total and age-specific prevalence, annual health insurance expenditure, and the distribution of costs by age group were evaluated. Infertility was determined according to the World Health Organization International Classification of Diseases codes (N.97.0, N97.1, N97.2, N97.3, N97.4, N97.8, and N97.9) and the utilization of each healthcare service type. During the study descriptive statistics, correlation analysis and t-test were used.

resultsIn 2019, the NHIFA spent a total of 7.2 billion HUF (22.2 million EUR) on female infertility treatment in Hungary (33,151 women in outpatient care). The most significant costs were related to inpatient care (4.1 billion HUF, 12.7 million EUR). The highest number of patients and prevalence (650.4 per 100,000 women) were found in outpatient care. In inpatient care, the prevalence is substantially lower (206.7 per 100,000 women). Regardless of its type, female infertility mainly affects patients in the 30-39 years age group (number of patients: 18,156 women). The average annual health insurance expenditure per capita was 1,083 EUR.

conclusionsReproductive health education, prevention, and medical screening play inevitable roles in the early stages of reproductive life to reduce the risk of infertility and decrease treatment costs.

Indexed as

Cost of IllnessHealth Care CostsHealth ExpendituresInfertility, FemaleInsurance, HealthNational Health ProgramsAdultCross-Sectional StudiesFemaleHumansHungaryInsurance Claim ReviewMiddle AgedPrevalenceYoung AdultCostEpidemiologyFemale infertilityHungaryPrevalence

Identifiers

PMID40038705
PMCPMC11881250

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.