There’s a small concentration of cases in one Berkeley zip code, but the overall number is too low to draw conclusions about infection rates across the city.
A new, interactive map released by Alameda County shows the geographic spread of COVID-19 cases by zip code.
The new addition to the county’s COVID-19 data dashboard shows a small concentration of cases in one Berkeley zip code, but the overall case number is too low to draw conclusions about rates of infection across the city.
The dataset shows that 94703 — which stretches from the Oakland border to Hopkins Street, mainly between Sacramento Street and Martin Luther King Jr. Way — is the only zip code in the city with more than 10 lab-confirmed coronavirus cases. Eleven of Berkeley’s first 47 cases were in 94703. Exact case numbers are not given for zip codes with fewer than 10, so the figures for the other areas are not available.
The 94703 zip code also has the second-largest population of all Berkeley zip codes (20,294 residents), so the size could contribute to the relative prevalence in virus cases.
The data “doesn’t really say much,” said city spokesman Matthai Chakko. “Our sample size so far is small. You wouldn’t read too much into this data by itself.”
While Berkeley reports geographic and demographic data to the county, the city itself has not released information on the location of any of its positive COVID-19 cases. Berkeley generally provides fewer details about its cases than neighboring jurisdictions do.
“We have to protect the privacy of individual people,” Chakko said. “With small numbers, zip code information can be used with case counts to identify people.”
The city’s 2018 Health Status Report found large disparities by zip code in many health outcomes, however. Rates of hospitalization for asthma, hypertensive heart disease and stroke are highest in the southwest Berkeley zip codes of 94710 and 94702, typically followed by 94703. Those zip codes house Berkeley’s largest black and Latino populations, according to U.S. Census data.
Across the country, black people are dying from the coronavirus at much higher rates than other groups too, and in San Francisco, Latinos have been overrepresented in confirmed virus cases.
“It’s not surprising that we’re seeing reports of inequity in terms of testing, diagnosis and disparities in terms of who will die,” Sandra McCoy, associate professor of epidemiology and biostatistics at UC Berkeley, recently told Berkeleyside. “We’re basically layering a pandemic on top of entrenched inequities defined by race and socioeconomic status.”
There “are deep health inequities in Berkeley,” Chakko said.
He said the city’s Public Health Department is structured to address the systemic disparities with programs like Heart 2 Heart — a hypertension and heart disease program with LifeLong Medical Care — specifically targeted at a small area of South Berkeley.
Berkeley’s coronavirus testing site, also a partnership with LifeLong, has “a specific goal of testing people without access to health care,” Chakko said.
City Councilman Ben Bartlett, who represents a chunk of 94703, said he was “sadly not surprised” to see that the zip code was the first to register in the database.
“People in South Berkeley often have underlying health conditions, they’re poorer so they lack the same access to good food and other wellness regimes,” he said. “They are the delivery drivers and the Uber drivers and the clerks who are working during this while our more affluent neighbors can work from home.”
Bartlett said his office has been calling up constituents, distributing masks and working with churches to connect with congregants and make sure they’re not gathering in person.
“Its inspiring that they’re engaged with us but it’s troubling because it’s safe to assume whatever inequities we had before will become magnified,” he said. “The worst part is the post-coronavirus collapse is likely going to erase the tenuous achievements we’ve made” in health and economic outcomes.
Throughout the rest of Alameda County, several zip codes in East Oakland have the highest rates of infection, according to the dataset.
