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Allergic asthma and the legacy of structural racism on the African American urban communities Asthma is a chronic inflammatory disease that is characterized by bronchial hyperreactivity (wheezing due to narrowing of the airways) and it disproportionately affects African Americans. Allergic asthma is a chronic inflammatory disease that is characterized by bronchial hyperreactivity and elevation of allergic antibodies. In the United States approximately 25 million people are affected by this disease with a death toll of about 3,500 per year. African American children are at least 10 times more likely to die from asthma than their white counterparts. Collectively, the mortality rate in the African American population is double the rate in Caucasians (21.8 vs 9.5 death rate per million). The 2005 Agency for Healthcare Research and Quality's report on the Disparity in Health Care among African Americans and Ethnic minority reported allergic asthma as the second largest disparity in the quality of health care for them versus Caucasians. Today, health disparity in asthma persists and several hypotheses for this disparity have been proposed. 

Gravitational-wave (GW) radiation from a coalescing compact binary is a standard siren, as the luminosity distance of each event can be directly measured from the amplitude of the signal. One possibility to constrain cosmology using the GW siren is to perform statistical inference on a population of binary black hole (BBH) events. In essence, this statistical method can be viewed as follows. We can modify the shape of the distribution of observed BBH events by changing the cosmological parameters until it eventually matches the distribution constructed from an astrophysical population model, thereby allowing us to determine the cosmological parameters. In this work, we derive the Cramér–Rao bound for both cosmological parameters and those governing the astrophysical population model from this statistical dark siren method by examining the Fisher information contained in the event distribution. Our study provides analytical insights and enables fast yet accurate estimations of the statistical accuracy of dark siren cosmology. Furthermore, we consider the bias in cosmology due to unmodeled substructures in the merger rate and mass distribution. We find that a 1% deviation in the astrophysical model can lead to a more than 1% error in the Hubble constant. This could limit the accuracy of dark siren cosmology when there are more than 10⁴BBH events detected.