The way data are collected and interpreted by gender is subject to fundamental biases ¹1. Shansky RM, Murphy AZ. Considering sex as a biological variable will require a global shift in science culture. Nat Neurosci 2021; 24:457-64.. This generates a gender health data gap that impacts all branches and levels of health research, including disease prevention and diagnosis, medical treatment for cancer, cardiovascular and Alzheimer’s disease ²2. Polack FP, Thomas SJ, Kitchin N, Absalon J, Gurtman A, Lockhart S, et al. Safety and efficacy of the BNT162b2 mRNA COVID-19 vaccine. N Engl J Med 2020; 383:2603-15.^,33. Dusenbery M. Doing harm: the truth about how bad medicine and lazy science leave women dismissed, misdiagnosed, and sick. San Francisco: HarperOne; 2018..

The gender health data gap is characterized by ¹1. Shansky RM, Murphy AZ. Considering sex as a biological variable will require a global shift in science culture. Nat Neurosci 2021; 24:457-64.^,44. Perez CC. Invisible women: data bias in a world designed for men. New York: Abrams Press; 2019.^,55. McGregor AJ. Sex matters: how male-centric medicine endangers women's health and what we can do about it. Paris: Hachette Go; 2020.: (1) missing or incomplete evidence for diseases that disproportionately impact women because of lack of funding or inclusion of women in clinical trials (henceforth called “type 1 problem”); (2) existing evidence is interpreted in light of men’s symptoms as default or textbook (henceforth called “type 2 problem”). The two problems are interrelated and stem from the perspective that male disease etiology and progression is the standard threshold and that differences between women and men are restricted to reproductive health ⁶6. Beery AK, Zucker I. Sex bias in neuroscience and biomedical research. Neurosci Biobehav Rev 2011; 35:565-72.^,77. Hay K, McDougal L, Percival V, Henry S, Klugman J, Wurie H, et al. Disrupting gender norms in health systems: making the case for change. Lancet 2019; 393:2535-49.. Because these issues are influenced by societal gender norms, I call this gender health data gap, and not only sex data gap, as further detailed as follows.

The type 1 problem is more prevalent in clinical research, as for over half a century the default model organism is based on male rodents ¹1. Shansky RM, Murphy AZ. Considering sex as a biological variable will require a global shift in science culture. Nat Neurosci 2021; 24:457-64.^,66. Beery AK, Zucker I. Sex bias in neuroscience and biomedical research. Neurosci Biobehav Rev 2011; 35:565-72.. The lack of a female-based model contributed to the incomplete understanding of the etiology, symptomatology and treatment of a series of diseases in women ¹1. Shansky RM, Murphy AZ. Considering sex as a biological variable will require a global shift in science culture. Nat Neurosci 2021; 24:457-64.. For example, women experience twice as much adverse drug reactions as men for over 86 different medications approved by the United States’ Federal Drug Administration (FDA), including antidepressants, analgesics, cardiovascular and anti-seizure drugs ⁸8. Zucker I, Prendergast BJ. Sex differences in pharmacokinetics predict adverse drug reactions in women. Biol Sex Differ 2020; 11:32.^,99. Tamargo J, Rosano G, Walther T, Duarte J, Niessner A, Kaski J, et al. Gender differences in the effects of cardiovascular drugs. Eur Heart J Cardiovasc Pharmacother 2017; 3:163-82.. However, most drugs are approved based on clinical trials that are either conducted solely on men or only include women in the first or second trial steps ¹⁰10. Steinberg JR, Turner BE, Weeks BT, Magnani CJ, Wong BO, Rodriguez F, et al. Analysis of female enrollment and participant sex by burden of disease in US clinical trials between 2000 and 2020. JAMA Netw Open 2021; 4:e2113749.. As a result of such missing data on women, they face higher levels of over medication, adverse reaction, susceptibility for drug-induced liver injury, and dosage inaccuracy ¹¹11. Dekker MJHJ, de Vries ST, Versantvoort CHM, Drost-van Velze EGE, Bhatt M, van Meer PJK, et al. Sex proportionality in pre-clinical and clinical trials: an evaluation of 22 marketing authorization application dossiers submitted to the European Medicines Agency. Front Med (Lausanne) 2021; 8:643028.. Likewise, despite important differences between women and men in cognitive deterioration and brain atrophy rates, gender is rarely considered in the design and analysis of clinical trials ¹²12. Ferretti MT, Martinkova J, Biskup E, Benke T, Gialdini G, Nedelska Z, et al. Sex and gender differences in Alzheimer's disease: current challenges and implications for clinical practice. Eur J Neurol 2020; 27:928-43.. Lastly, women’s diseases are underfunded when compared with the burden of disease they experience and the lethality of conditions ¹³13. Mirin AA. Gender disparity in the funding of diseases by the U.S. National Institutes of Health. J Womens Health (Larchmt) 2021; 30:956-63., including ovarian and cervical cancers ¹⁴14. Spencer RJ, Rice LW, Ye C, Woo K, Uppal S. Disparities in the allocation of research funding to gynecologic cancers by Funding to Lethality scores. Gynecol Oncol 2019; 152:106-11., Alzheimer’s ¹⁵15. Castro-Aldrete L, Moser MV, Putignano G, Ferretti MT, Schumacher Dimech A, Santuccione Chadha A. Sex and gender considerations in Alzheimer's disease: The Women's Brain Project contribution. Front Aging Neurosci 2023; 15:1105620., and cardiovascular disease ¹⁶16. Baggio G, Corsini A, Floreani A, Giannini S, Zagonel V. Gender medicine: a task for the third millennium. Clin Chem Lab Med 2013; 51:713-27..

The type 2 problem presents itself more prominently among healthcare providers, where evidence is used inconsistently or influenced by subjective judgements. Some scholars call this “healthcare gender bias”, where women’s symptoms are dismissed or neglected, leading to delays in diagnosis ³3. Dusenbery M. Doing harm: the truth about how bad medicine and lazy science leave women dismissed, misdiagnosed, and sick. San Francisco: HarperOne; 2018.^,55. McGregor AJ. Sex matters: how male-centric medicine endangers women's health and what we can do about it. Paris: Hachette Go; 2020.. For example, women are less likely to receive the same care recommended by guidelines defined by the European Society of Cardiology (ESC) and the Acute Cardiovascular Care Association (ACCA), such as procedures like timely reperfusion therapy in the case of ST-elevation myocardial infarction (STEMI) and coronary angiography in case of non-ST-elevation myocardial infarction (NSTEMI) ¹⁷17. Wilkinson C, Bebb O, Dondo TB, Munyombwe T, Casadei B, Clarke S, et al. Sex differences in quality indicator attainment for myocardial infarction: a nationwide cohort study. Heart 2019; 105:516-23.^,1818. Khera S, Kolte D, Gupta T, Subramanian KS, Khanna N, Aronow WS, et al. Temporal trends and sex differences in revascularization and outcomes of ST-segment elevation myocardial infarction in younger adults in the United States. J Am Coll Cardiol 2015; 66:1961-72.^,1919. Mainz J, Andersen G, Valentin JB, Gude MF, Johnsen SP. Disentangling sex differences in use of reperfusion therapy in patients with acute ischemic stroke. Stroke 2020; 51:2332-8.. Women are also less likely to receive or be referred to cardiac rehabilitation or be prescribed a statin or an angiotensin-converting-enzyme inhibitors (ACE inhibitor), which increase their 30-day mortality risk after a heart attack ¹⁷17. Wilkinson C, Bebb O, Dondo TB, Munyombwe T, Casadei B, Clarke S, et al. Sex differences in quality indicator attainment for myocardial infarction: a nationwide cohort study. Heart 2019; 105:516-23.^,2020. Nguyen JT, Berger AK, Duval S, Luepker RV. Gender disparity in cardiac procedures and medication use for acute myocardial infarction. Am Heart J 2008; 155:862-8.. As a consequence, deaths among women with acute myocardial infarction could be significantly prevented if the quality of care received was the same as for men ¹⁷17. Wilkinson C, Bebb O, Dondo TB, Munyombwe T, Casadei B, Clarke S, et al. Sex differences in quality indicator attainment for myocardial infarction: a nationwide cohort study. Heart 2019; 105:516-23., which could also be the case for conditions such as stroke, attention deficit/hyperactivity disorder (ADHD) and arthritis ¹1. Shansky RM, Murphy AZ. Considering sex as a biological variable will require a global shift in science culture. Nat Neurosci 2021; 24:457-64..

The consequences of the gender health data gap are not limited to clinical settings. Gender bias can impact the quality and representativeness of cause of death statistics in civil registration and vital statistics (CRVS), due to misdiagnosis made by physicians and misreport of certain conditions ²¹21. Peralta A, Benach J, Borrell C, Espinel-Flores V, Cash-Gibson L, Queiroz BL, et al. Evaluation of the mortality registry in Ecuador (2001-2013) - social and geographical inequalities in completeness and quality. Popul Health Metr 2019; 17:3.. Research into how gender bias in the health system influences the quality of cause of death statistics remains limited ²²22. Hamberg K. Gender bias in medicine. Womens Health (Lond) 2008; 4:237-43.. These biases can also impact population-level summary indicators, which are often used to develop and monitor progress in health, set targets, and develop strategies in national health plans ²³23. Spitzer S, Weber D. Reporting biases in self-assessed physical and cognitive health status of older Europeans. PLoS One 2019; 14:e0223526.. Biases can also impact rapidly growing and emerging fields such as digital health, precision medicine, and the use of artificial intelligence (AI), as these technologies still do not account for gender bias detection ²⁴24. Cirillo D, Catuara-Solarz S, Morey C, Guney E, Subirats L, Mellino S, et al. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. npj Digit Med 2020; 3:81..

Some efforts have already been set in motion to start addressing these problems, like the policy of female mice inclusion and sex as a biological variable in clinical trials and scientific studies in 2016 by the U.S. National Institutes of Health (NIH) ¹1. Shansky RM, Murphy AZ. Considering sex as a biological variable will require a global shift in science culture. Nat Neurosci 2021; 24:457-64.^,2525. NIH Office of Research on Women's Health. NIH policy on sex as a biological variable. https://orwh.od.nih.gov/sex-gender/nih-policy-sex-biological-variable (accessed on Apr/2023).
https://orwh.od.nih.gov/sex-gender/nih-p... . Group advocates have also focused on raising awareness for specific diseases ¹⁵15. Castro-Aldrete L, Moser MV, Putignano G, Ferretti MT, Schumacher Dimech A, Santuccione Chadha A. Sex and gender considerations in Alzheimer's disease: The Women's Brain Project contribution. Front Aging Neurosci 2023; 15:1105620.^,2626. Vogel B, Acevedo M, Appelman Y, Merz CNB, Chieffo A, Figtree GA, et al. The Lancet Women and Ccardiovascular Disease Commission: reducing the global burden by 2030. Lancet 2021; 397:2385-438.. However, little has been done on the implications of gender health data bias for summary indicators of health. More effort is needed to translate clinical research into population-level statistics ²⁷27. Arnegard ME, Whitten LA, Hunter C, Clayton JA. Sex as a biological variable: a 5-year progress report and call to action. J Womens Health (Larchmt) 2020; 29:858-64.^,2828. White J, Tannenbaum C, Klinge I, Schiebinger L, Clayton J. The integration of sex and gender considerations into biomedical research: lessons from international funding agencies. J Clin Endocrinol Metab 2021; 106:3034-48.. In that regard, a closer collaboration between demographers, population health experts and medical specialists is required. For instance, the framework of compression and expansion of morbidity could be used to evaluate whether age at onset and disease progression patterns in men and women are impacted by those data gaps ²⁹29. Beltrán-Sánchez H, Jiménez MP, Subramanian S V. Assessing morbidity compression in two cohorts from the Health and Retirement Study. J Epidemiol Community Health 2016; 70:1011-6.^,3030. Crimmins EM, Kim JK, Solé-Auró A. Gender differences in health: results from SHARE, ELSA and HRS. Eur J Public Health 2011; 21:81-91.. The gender paradox - or the fact that women live longer than men, but in poorer health - could also be tested to understand if it happens partly due to a lack of knowledge or proper understanding of diseases that mainly impact women ³¹31. Luy M, Minagawa Y. Gender gaps: life expectancy and proportion of life in poor health. Health Rep 2014; 25:12-9.^,3232. Austad SN. Why women live longer than men: sex differences in longevity. Gend Med 2006; 3:79-92.^,3333. di Lego V, Lazarevic P, Luy M. The Male-Female Health-Mortality Paradox. In: Gu D, Dupre ME, editors. Encyclopedia of gerontology and population aging. New York: Springer; 2020. p. 1-8.. Incorporating demographic analysis would allow alternative questions to be asked, such as: are the results upon which research currently builds accurately reflecting the health of populations by age or are they biased due to missing or inconsistent gender evidence? How do those biases, if and when they exist, impact population-level statistics such as healthy life expectancy indicators? If yes, to what extent are policies based on those population-level statistics missing the actual people they target? Lastly, innovative approaches using machine learning and demographic data and technique are a promising field and have already been devised to estimate neonatal mortality, regional disease prevalence estimates, and the role of gender bias on model accuracy ³⁴34. Luo W, Nguyen T, Nichols M, Tran T, Rana S, Gupta S, et al. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset. PLoS One 2015; 10:e0125602.^,3535. Wu Y, Xiang C, Jia M, Fang Y. Interpretable classifiers for prediction of disability trajectories using a nationwide longitudinal database. BMC Geriatr 2022; 22:627.^,3636. Beluzo CE, Alves LC, Martins Arruda N, Sepetauskas C, Silva E, Carvalho T. NeMoR: a new method based on data-driven for neonatal mortality rate forecasting. medRxiv 2021; 25 apr. https://www.medrxiv.org/content/10.1101/2021.04.22.21255916v1.full.
https://www.medrxiv.org/content/10.1101/... ^,3737. Chung H, Park C, Kang WS, Lee J. Gender bias in artificial intelligence: severity prediction at an early stage of COVID-19. Front Physiol 2021; 12:778720..

The gender health data gap is not trivial and has most likely prevented important advances in knowledge, treatment, and diagnosis of several health conditions. Disciplines, funding agencies, stakeholders, academic institutions, health care systems and the pharmaceutical industry must undertake a concerted effort to identify and bridge the gender health data gap.

Acknowledgments

This study was supported by the European Research Council within the EU Framework Programme for Research and Innovation Horizon 2020 - ERC Grant Agreement n. 725187 (LETHE).

References

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