The launch of the sustainable development goals (SDGs) recognised gender-sensitive and disaggregated data as essential and integral to the attainment of the SDGs. Gender intersects with various factors such as poverty, geographical location, age and ethnicity. The data that is segregated by sex can highlight how the intersection of gender with each of these variables gives differential results. Recognising this fact, WHO’s Global Health Statistics have been disaggregated by sex for the first time in 2019.
With the advancement of medical science, it is now evident that disease outcomes, occurrences and health systems differ for women and men. Research has indicated that Ebola, SARS and influenza viruses affect men and women differently. Diverse experiences, however, are often homogenised by assuming that data and studies involving men are equally relevant for women. There is also little examination of sex differences and gender disparities in health data, and women are often underrepresented in many scientific and clinical studies. According to the Global Health 50/50 2020 report, most organisations fail to sex-disaggregate data or even if they do, they do not use it for gender-based analysis. Only about 35 percent of the reported data is segregated by gender.
The negative consequences of data non-segregation are not only limited to women’s health concerns. Men too face rigid gender norms associated with poor health outcomes. Often traditional notions around masculinity make them less inclined to seek healthcare when needed, increasing their vulnerability to serious health risks. For example, men are less likely than women to take an HIV test, less likely to access antiretroviral therapy and more likely to die of AIDS-related illnesses than women. Gender diverse people also tend to face more discrimination and stigma in healthcare settings. Therefore, to bridge the gender gap in health and development, data must be sex-disaggregated and analysed through a gender lens. This will recognise the role of sex as a biological determinant, and gender as a social construct. A gender-based analysis will consider how societal norms, expectations and roles of women and men affect their health differently.
Why is sex-gender-responsive health data crucial?
It is not uncommon to find that clinical definitions of symptoms for many diseases are based only on the characteristics of those perceived in men. As the symptoms perceived by women remain unrecognised, it results in higher cases of misdiagnosis amongst them. For instance, a study found that women are seven times more likely than men to be misdiagnosed of having a heart attack. Data segregation also becomes crucial in healthcare as it is used to allocate research funding in the specified field. For example, the effect of a new medication can be different based on the physiology of men and women. In the United States, eight of the ten drugs that were recalled between 1997 and 2000, posed greater health risks to women than to men. A seemingly neutral default data can prove to be fatal for women. Such a conclusion cannot be drawn if the data remains unsegregated.
Apart from biological differences, gender norms in different countries also influence the health status of men and women differently. For instance, in South East Asia, a stark difference between male (48.2 percent) and female smokers (8.2 percent) was observed. It was speculated that the higher incidence of male smokers led to higher case fatalities from SARS. On the other hand, a large proportion of families in South East Asia and South Asia use solid fuel for indoor cooking. The exposure to such a high level of indoor pollution puts women at a greater risk of pneumonia, obstructive pulmonary disease and other ill-health conditions like asthma, tuberculosis, etc. Additionally, since women play the role of the primary caregiver in most societies, exposure to sick persons makes them more vulnerable to pathogens. Caring for a sick family member was one of the significant factors witnessed during Ebola and Nipah virus epidemics.
Gender differences in norms, behaviours and access to resources can affect disease transmission and outcome for emerging infectious diseases. Therefore, sex-disaggregated data must be utilised for gender analysis within different regions and socio-economic conditions.
How can disaggregated data help in a pandemic?
Infection and morbidity rates for COVID-19 indicate that more men are likely to contract and die from the virus. However, data also indicates that women are more prone to deteriorating emotional and mental health. Women are also overall less likely than men to be covered by health insurance. Along with playing the role of frontline healthcare workers, COVID-19 has caused many women to carry most of the burden of unpaid care responsibilities. Additionally, about 60 percent of the people who are food insecure are women and girls, putting them at a greater risk of malnutrition during public health emergencies. Studies, however, found that only six countries (China, France, Germany, Iran, Italy, South Korea) have provided sex-disaggregated data for numbers of confirmed COVID-19 cases and deaths.
Disaggregating outbreak-related data is crucial to effectively guide the response and prevention efforts of the international community. Disaggregating data on the basis of sex can help in achieving equity, while addressing the ongoing pandemic. Gender health analysis could disclose how women will be particularly affected by it. It can further enable governments across the world to monitor the trends in the transmission of COVID-19 and consequently, tailor their resources and preventive measures to achieve better outcomes for everyone.
Disaggregated data in policy-making
The burden of diseases is not only affected by biological differences, but also by social factors such as gender norms, behaviours and roles. Global governments and health institutions require sex-disaggregated data to identify gender inequalities to allocate suitable resources to build capacity and implement equitable health systems. Gender-responsive programmes and policies are necessary to ensure health equality, evaluate risk and to improve people’s health and well-being.
The data should be able to document different health needs and experiences to include the voices of those affected. Contextualising the gendered data within cultural norms and socio-economic conditions is also important, to estimate the person’s social location within the wider health system. The gender data gaps must be regularly filled to ensure informed and evidence-based policy-making. It can also be utilised to create an enabling environment to effectively monitor gender inequalities and build gender-sensitive national strategies.
Countries can further financially support specialised surveys and other forms of data collection, as well as train data producers on disseminating gender data. Timely and reliable sex-disaggregated data is vital to evaluate the measures taken to narrow the gender gap. Understanding data, such as the infection and mortality rates in men and women, will uncover data gaps and help policymakers find more effective solutions in the fight against COVID-19.
The article first appeared in ORF.