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World Health Day on 7 April 2023: Health For All<\/strong><\/p>\n On 7 April 2023\u00a0 \u0336\u00a0 World Health Day\u00a0 \u0336\u00a0 The World Health Organization will observe its 75th<\/sup>\u00a0birthday.<\/p>\n Closing the gender data gap in healthcare | McKinsey<\/a><\/p>\n Exhibits noted in Article are accessed on the above website.<\/p>\n Closing the Data Gaps in Women\u2019s Health<\/strong><\/p>\n By\u00a0Delaney Burns,\u00a0Tara Grabowsky,\u00a0Emma Kemble, and\u00a0Lucy P\u00e9rez<\/a><\/p>\n April 3, 2023 – Now more than ever,<\/strong>\u00a0data is at the center of engagement and decision making. Healthcare and life sciences are no exception: data is central to discussions about public health and is core to enabling continued scientific advancement. The accelerated digitalization of healthcare during the pandemic has only expanded the amount of health-related data available\u2014and cemented the key role that it plays in care delivery, disease prediction and diagnosis, biopharma and medtech innovation, and patient outcomes.<\/p>\n Despite the exponential growth in data generated across the healthcare ecosystem, notable gaps remain. One such area is women\u2019s health, in which gaps span the entire data value chain\u2014from defining women\u2019s health (pre\u2013data generation) to diagnosing (data generation) to tracking at the national level (data collection) to translating data into insights at the global level through epidemiological studies (data analysis). These data disparities ultimately influence health outcomes for women globally by creating blind spots in the insights that drive research design, investment decisions, and pipeline priorities. Certain subsets of women, such as those of different backgrounds, sexual orientations, and gender identities, are more vulnerable to the gaps and negative effects of these blind spots.1<\/sup>\u00a0Furthermore, insufficient availability and analysis of women-specific health data undermine advancements in disease-state understanding and limit asset discovery opportunities across medical conditions with meaningful unmet need.<\/p>\n This article highlights these disparities and explores options to remedy them.<\/p>\n Understanding the data value chain<\/strong><\/p>\n Health-related data has numerous sources and is analyzed for different applications. This article explores examples of data gaps in women\u2019s health at key moments across the health data value chain (Exhibit 1).<\/p>\n Exhibit 1<\/p>\n We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at:\u00a0McKinsey_Website_Accessibility@mckinsey.com<\/a><\/p>\n The following sections examine these examples in greater detail.<\/p>\n Pre\u2013data generation: Defining women\u2019s health<\/strong><\/p>\n Good data sets begin with good definitions. Without clear definitions, the metrics to track and the conclusions to draw remain murky. However, at present there is no one definition of \u201cwomen\u2019s health.\u201d2<\/sup><\/p>\n Historically, women\u2019s health was largely defined as \u201creproductive health.\u201d3<\/sup>\u00a0More recently, academics and clinicians have used a more expansive lens, recognizing that sex is a significant factor in the development and progression of many diseases.4<\/sup>\u00a0The National Academy on Women\u2019s Health Medical Education defines women\u2019s health as \u201cdevoted to facilitating the preservation and wellness of and prevention of illness in women and includes screening, diagnosis and management of conditions that are unique in women, are more common in women, are more serious in women, and have manifestations, risk factors or interventions that are different in women.\u201d5<\/sup><\/p>\n For the purposes of this analysis, we define \u201cwomen\u2019s health\u201d as encompassing both female-specific conditions, including those tied to female reproduction or female biology, and general health conditions that may affect women differently, such as cardiovascular diseases, or disproportionately, such as autoimmune diseases (Exhibit 2).6<\/sup>\u00a0It is crucial to understand sex-driven differences, as today\u2019s care models often ignore those differences, resulting in health outcomes that can vary by sex (often to women\u2019s disadvantage).7<\/sup><\/p>\n Exhibit 2<\/p>\n We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at:\u00a0McKinsey_Website_Accessibility@mckinsey.com<\/a><\/p>\n Data generation: Documenting women\u2019s diagnoses in claims data<\/strong><\/p>\n Insurance claims data\u2014particularly in the United States\u2014provides critical insight into the nature of health conditions and how they are treated. These data sets have inherent limitations, as their primary use for physicians is for billing purposes. However, this data is high quality and widely used, and thus the analysis for this article uses diagnosis codes on claims as a proxy for overall diagnosis rates.<\/p>\n According to US claims data from January 2019 through August 2022, the prevalence of women\u2019s health conditions (estimated by epidemiological data sources8<\/sup>) is roughly five times that of their documented diagnoses. In other words, for every one woman diagnosed with a women\u2019s health condition, roughly four go undiagnosed. In comparison, the difference between epidemiological prevalence and documented diagnoses for men\u2019s health conditions narrows to roughly 1.5 times (Exhibit 3).9<\/sup><\/p>\n Exhibit 3<\/p>\n We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at:\u00a0McKinsey_Website_Accessibility@mckinsey.com<\/a><\/p>\n While this disconnection between prevalence and diagnosis indicates an inconsistency in women\u2019s health data across various sources, such sex-based differences may also reflect structural drivers, such as biases in care delivery. Implicit biases, defined as \u201cattitudes and beliefs about race, ethnicity, age, ability, gender, or other characteristics that operate outside our conscious awareness and can be measured only indirectly,\u201d have been found to be associated with \u201cdiagnostic uncertainty.\u201d10<\/sup>\u00a0This phenomenon is corroborated by surveyed patients: 20 percent of women say a healthcare provider has ignored or dismissed their symptoms, compared with 14 percent of men.11<\/sup><\/p>\n Furthermore, we found that the sex of the diagnosing physician appears to be correlated with the likelihood of being diagnosed with a condition. In other words, in the claims data we analyzed, women appear to be more likely to be diagnosed with a female-specific condition, including menopause, polycystic ovary syndrome (PCOS), and endometriosis, if their physician is a woman. In our analysis of claims data, women represent approximately 40 percent of primary care physicians (PCPs) but nearly 50 percent of PCPs documenting diagnoses of these key women\u2019s health conditions (Exhibit 4).<\/p>\n Exhibit 4<\/p>\n