A Woman Goes to the Doctor’s Office: Gender Data Gap in Health


This research paper is aimed to explore the gender data gap in the health area. Women face daily inconveniences when getting diagnosed and face problems in health-seeking. There are both problems in the structure of data collecting in health science and obstacles in admission to health research, which lead to the perception of the male body considered a universal model that can speak on behalf of every patient’s health issues. The reasons behind this data gap are discussed, with a particular emphasis on the COVID-19 pandemic. In the end, possibilities of increasing women’s inclusion in health data are explored.

Key Words: Gender Data Gap, Health Research, Woman, Health Issues, COVID-19.


A woman goes to the doctor’s office, only to be sent back home, told “not to stress much and it will go away, to take a painkiller if she feels the need” (Perez, 2019). She goes to another doctor and hears that her symptoms are “atypical”, was she possibly overanalyzing her symptoms? It takes her a few tries to actually get diagnosed if she is lucky and adamant. Her insistence depends on the availability of her free time to visit the office and allocate money for examination. Similar instances where women face an invisible barrier of sex-specific consideration are not only limited to individual instances but, in fact, encompass many areas we live in as an asymmetrically structured society.

This paper examines the gender data gap in the health area and aims to highlight the problems that are deemed micro and not noticed at times; however, grounding of these various inconsiderations and sexist practices solidify themselves in the public life and set the prejudiced examples for further applications.


Before further examining several examples from public and private life, this author would like to provide and elaborate on several limitations of this paper. Firstly, the author does not have any license in health science; the text is not a mimicry to speak on behalf of scientists but to point out slipped-through inconsistencies and inconsiderations many women face regularly. Secondly, the paper provides the argumentation through the gender binary. Because of the limited scoop, many LGBTQ+ misrepresentations in the health area are not discussed. Moreover, the same issue was not discussed on this paper’s resources, so the author would like to underline a lack of general health data gap for LGBTQ+, which is beyond the subject matter for this paper. The intention is not to reinforce currently existing gender norms but to criticize inclusivity and health data diversity. The author warns that the call for inclusion should not be understood as a demand for women-specific data to be added next to the norm (men). Rather, it calls for a re-examination of biased procedures that (re)births men as the stereotypical figure for health studies.

Gender Data Gap

“Gender data gap” is the lack of knowledge existing in research that stems from women’s exclusion when conducting said research. White, able-bodied, middle-aged men are the typical candidates for surveys. The provided results are thought to be universal, but recent studies show that they only portray male-specific results (at times, not applicable for all of them) and cannot speak on behalf of those not considered when conducting the research. Perez, the author of the book Invisible Women, underlines that the data gap is so prevalent in various aspects of scientific research that it is not even malicious or deliberate; it’s just the way things are thought to be. “It is simply the product of a way of thinking that has been around for millennia and is, therefore, a kind of not thinking” (Perez, 2019: 10). Of course, similarities exist because humans are initially similar creatures but ignoring sex and gender-specific data results in discrimination. Even when women are considered, their varied symptoms are portrayed as deviant characteristics from the “common”. “Gender-neutral” applications have a long way to go, and today, especially in the health area, the discharging of sex-specific, gender-specific data would only serve the hidden male bias.

Why do we have a gender gap in health data? Arguably, there are both involuntary and voluntary reasons. Out of habit, women are not explicitly sought when producing data. “A UN Statistics Division survey in 2012 of 126 countries found that around two-thirds regularly generate statistics on sexual and reproductive health, but only about one-third track other gender-related topics like informal employment or violence against women” (Temin and Roca, 2016: 265). Certainly, it could be argued that more men than women visit the doctor; that is why the prevalent data is centered more around men. The previous story of a woman struggling to get diagnosed implies that scheduling an appointment does not ensure the correct diagnosis. However, there is accuracy in the comment that men possibly have more time than women to pay a visit to the doctor. Whether they are formally working or not, women for a fact devote time for their unpaid labor in the household, which in turn diminishes the time they can allocate for their individual concerns and needs. Buvinic, Furst-Nichols, Koolwal (2014) argue that women lose life years to cardiovascular and cerebrovascular diseases but delay or even refrain from seeking treatment possibly because they do not want to be perceived as a “burden”. Although it sounds hyperbolic, the statement holds some sort of truth in it. A woman who does not do chores but lays down due to sickness is regarded as inconvenient. This argument will be developed within the coming parts of the paper.

In Search of a Universal Model

The current positivist trend of producing facts based on a universal human model without a more individual-accounted approach misses the sex-specific and gender-specific variations humans inevitably have. Without exposure to inclusive health data, variations in patients would turn to be portrayed as disparities. Furthermore, those disparities, more often, are attributed to the female body and mind. Perez (2019: 164) says: “A 2008 analysis of a range of textbooks recommended by twenty of the ‘most prestigious universities in Europe, the United States and Canada’ revealed that across 16,329 images, male bodies were used three times as often as female bodies to illustrate ‘neutral body parts’.”

Doctors who are trained to recognize male bodies as the sample, by no surprise, would not be adept at understanding the female body’s operating principles. Without data collected gender-specifically, the resources available would always be lacking in comprehensive portrayal whether it is about arthritis or depression. It could be argued that the universal subject of health science is not a neutral body but a predominantly male one.

In a world most data are designed by men for men, inconveniences for women are an after-thought. Leaving the aspect of social shame aside, men with a penis can urinate publicly, but a person with a vagina would feel the need to go inside a secluded installation to be safe from not only infections but also scare of being assaulted (Perez, 2019). Public loos and portable stalls are necessary to ensure standard hygiene and privacy. The problem of lack of public toilets should be considered as a necessity, not a demand. Absence of carefully planned facilities equipped with locks and toilet seats show that women’s health and safety at public space is neglected by urban planners with no advice from health scientists either. This failure in sex and gender-specific consideration while planning public toilets may even be a cause of urinary tract diseases in the long run.

Male Based Approach

If a “gender ambiguous” model hinders apprehensive data, then male-biased research would produce counterproductive results for female patients. It should be noted that not all areas of health portray significant differences in data on different sexes, but ones that do so would result in misdiagnosing or, at times, malpractice due to lack of sex-disaggregated data. One of the most common issues with health surveys is the use of Adult Male Equivalent (AME) approach, which calculates consumption or analyses behavioral traits based on collected data of an average adult male (Coates, Rogers, Blau, Lauer, and Roba, 2017). Failing to comprehend the issue with calculating compared to men means failure to grasp the core problem of male-centrism in health data. We can consider food consumption as an example. Certainly, the estimation of per capita consumption could be done parallel to the weights of household members, but it should also be underlined that meal patterns within the same household vary. Beyond the point of intake-fixation of each household member, the AME-based approach overlooks traditional paternal roles and patriarchal dominance of men over household decisions. Age, gender and roles certainly influence the food expenditures and consumption in a household (Buse and Salathe, 1978). In a house where the father and the son get the meatier parts of a chicken, or the mother eats less to give more to her children would eventually fall short on their estimated calorie intake on paper and downsize the extent of female malnutrition.

“The formula to determine standard office temperature was developed in the 1960s around the metabolic resting rate of the average forty-year-old, 70 kg man” (Perez, 2019: 100). This statement can explain why more women are seen wearing more layered clothes at the office where men walk with short-sleeved shirts. These kinds of supposedly “minor” inconveniences, in fact, show any inconvenience for women is regarded as an after-thought, only to be taken into consideration if or after a complaint arises. Why aren’t women’s metabolism taken primarily into account while regulating a few degrees at the office? The issue with miscalculations like these is they reflect the general tendency to treat women as second class.

Limited Data

There are also data collected preserving gender differences, albeit limited. Data that consider women may be short of precise representation of women under different hormonal conditions. For example, there is a vast data gap about the effect of drugs on pregnant women (Macklin, 2010). The effects of drugs on women are assumed the same but fail to account for pregnancy, where hormone levels change significantly. At times when pregnant women are included in the data, the effects on fetuses and newborns are studied more but not the mothers themselves. Shields and Lyerly (2013) accentuate the fact that current data on diseases seen in pregnant women are obstetric; non-obstetric diseases are seen as problems that should be delayed to post-partum. Why should anybody’s health problems be trivialized to the point of prolonging numerous months? Moreover, people who experience menstruation are not tested periodically depending on their cycles to include in data. The early follicular phase, which shows low hormonal activity, is preferred to keep the data samples more “arguably neutral”. This demand on certain and predictable data exhibits the carelessness of health scientists, not their efficiency. If collecting more sex and gender-specific data is seen as burdensome, the researcher’s sexist mindset should be questioned instead of estimating costs of broadening and deepening the data set.

Another reason women’s data is limited is the deficiency in considering women’s traditional roles within society and household labor (Boerma, Hosseinpoor, Verdes and Chatterji, 2016). Analyzing people who take an active part in social life and perform cultural roles as untouched cells in a petri dish would lead to aberrated results. Especially women, with their attributed roles and expected responsibilities, spend more energy, are more vulnerable to the exposure of chemicals and diseases for example. The undocumented impact of household labor drives the width of the gender data gap. Temin et al. (2016: 267) provide a fitting example of Ebola contamination among women. Women in West Africa were more vulnerable to be affected than men because women took active participation in funeral rites traditionally, where they were in contact with numerous people at once as well as the deceased. Women are also the primary caregivers of their household and are at more risk if one of the members gets ill because they are in charge of bathing, feeding, clothing the sick. Although Ebola virus infection risk would not vary on sex on controlled research, the social cues give the reason why women were disproportionally more affected.

Research on exposure to chemicals of nail salon workers or housekeepers do not consider the exposure of the people in question while doing housework. The same woman who involuntarily inhales hazardous substances at work also goes to their home the same day and does house cleaning, maybe even duplicating the daily exposure. The office worker who sits for hours behind a desk and reports little time for physical activity, in fact, spends energy doing house chores and tending to household members, which are not counted when calculating daily movement. Once again, the author would like to remind the paper’s emphasis is not on comparing men’s availability of leisure time to women socially, but to stress the fact that an apprehensive understanding of women as both biologically and socially constructed beings is missing, and that is the reason behind the gender data gap.

COVID-19 Pandemic

The Novel Coronavirus Disease (COVID-19) outbreak of the last two years has resulted in global-scale cases and numerous accompanying symptoms. The enormity of casualties and the global restrictions have indeed led to a community-wide trauma. Still, few are known about women who constitute around half of the global community regarding sex-disaggregated data. Most of the available research in regard to sex and gender specific data are on pregnant women and about the safety of child delivery. Although the research on pregnancy and contamination possibility of babies is crucial especially considering the fast transmission of the disease, there is no excuse why women should be considered as a priority only when child-bearing status is in interest. However, follow-up research on post-partum mothers seems to be missing, which also raises suspicion on why women in certain conditions are deemed more study-worthy. It is known that men have a higher rate of morbidity from the virus, but more prolonged-term effects on women are underrepresented. Rodriguez-Rey, Garrido-Hernansaiz, and Collado (2020) highlight that young people and women are more vulnerable to the pandemic’s negative psychological impacts. Imagine the intersection of these two groups. Moreover, most working women who were used to splitting their day in half -job and house- found themselves working in house full-time while still being employed, or most lost their jobs, as Turquet and Koissy-Kpein (2020) underline that half of the working women globally were employed in service jobs in 2019. The stress, combined with the sense of being stuck, has deteriorated women’s mental wellbeing, but we do not have a quantitative report as of now. Women also occupy the primary caregiver position within the household, not only taking care of the elderly but also the children (Gausman and Langer, 2020). In the absence of a daycare center (whether due to economic difficulties or simply lockdown due to the pandemic), women take on the extra work to provide unpaid labor at home and the vulnerability and exposure increases. Moreover, data on women subjected to domestic violence is expected to increase as crisis-shelters, hotlines, health and support centers at times halt their activities due to imposed curfews (Roesch et al., 2020). These data are neither included as an indirect result of the pandemic, nor they are collected apprehensively. Health data on the indirect effects of the pandemic on women is still missing.

Regarding the direct effects of the virus as well, sex and gender-specific data are lacking. Spagnolo, Manson and Joffe (2020) argue that sex-based information is needed to analyze response to treatment and health-seeking behaviors “given that sex differences in pharmacokinetics and pharmacodynamics influence therapeutic effects and risk profiles of numerous medications”. Since there is a gender data gap on COVID-19 underestimates and disregards differences in health access. Treating the data as a variable-less two-element survey overlooks the public and private behavior changes across genders. Besides including factors like age, smoking, and latent diseases, the surveys of cases should also include and put emphasis on working hours and household duties of the patients as well as their former habits of health-seeking. Another data accumulation failure concerns health workers, underlined by Womersley, Ripullone, Peters, and Woodward (2020), that existing personal protective equipment (PPE), were not fit for women’s sizes whether it is gowns or masks. The exposure to disease, combined with ill-fitting PPE means women are at great risk of getting infected.

How to Shorten the Gap?

After much complaining and nameless blaming, the reader would eventually ask for a solution to shorten the gender data gap. If one part of data improvement is unbiased education and egalitarian data-collecting practices, the other part ensures and enables more women’s participation in data collection. Buvinic et al. (2014: 18) highlight the fact that “accessibility, affordability, and appropriateness shape women’s demand for services”. In that case, the use of the internet and smartphones could increase the likelihood of women’s health-seeking and research participation. Although the data produced should always be taken with caution because the statements collected without supervision are hard to verify, obviously, it would be much easier for women to access data-collecting within the ease at their homes. Another proposal could be induced from Temin et al.’s (2016) example of Ebola spread in West Africa. Specifically, the women-targeted campaign has led to better consciousness on protection from the virus. Efforts made preserving women’s social roles would have better success in persuading the targets. However, unless the male-centered approach changes, women’s inclusion in the health data would still be read as the deviations and side effects, not common knowledge (Bird and Rieker, 2008).


The gender data gap in the health area is the product of a mixture of ignorance and neglect. Although it would be an exaggeration to say there are no health data regarding women, the current trend shows that examining men only is thought to be enough to conclude universal results, not the truth. One of the biggest reasons why the gender data gap in health science this wide is the failure to account for scientists to regard women as ever-changing subjects. Hormonal variations, menstrual cycles, pregnancy stages; traditional, cultural, social roles of women are not taken into consideration which in turn produce inconclusive and limited data. These criticisms have relevance specifically to the COVID-19 pandemic as well. Although access to technology and gender-targeted information could help increase women’s participation in data, the core problem would not change as long as data on men are seen as sufficient to be able to speak on behalf of everyone. The unconsciously-biased data collection practices should be changed so that the woman who goes to the doctor’s office would get precisely attended, diagnosed, and treated.

Pelin Dengiz

Gender Studies Internship Programme


Bird, C. E., & Rieker, P. P. (2008). Gender and health. The effects of constrained choices and social policies. Cambridge.

Boerma, T., Hosseinpoor, A. R., Verdes, E., & Chatterji, S. (2016). A global assessment of the gender gap in self-reported health with survey data from 59 countries. BMC public health16(1), 1-9.

Buse, R., & Salathe, L. (1978). Adult Equivalent Scales: An Alternative Approach. American Journal of Agricultural Economics, 60(3), 460-468.

Buvinic, M., Furst-Nichols, R., & Koolwal, G. (2014). Mapping gender data gaps. Data2X report, United Nations Foundation, Washington, DC.

Coates, J., Rogers, B. L., Blau, A., Lauer, J., & Roba, A. (2017). Filling a dietary data gap? Validation of the adult male equivalent method of estimating individual nutrient intakes from household-level data in Ethiopia and Bangladesh. Food policy72, 27-42.

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Womersley, K., Ripullone, K., Peters, S. A., & Woodward, M. (2020). Covid-19: male disadvantage highlights the importance of sex disaggregated data. bmj370.

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