A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2016-2020 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents. On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021. Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset. Dataset is cumulative and covers cases going back to 3/2/2020 when testing began. Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents. C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time. D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000 Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology. A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website. Row included for Citywide case counts, incidence rate, and deaths A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongo

Vital Statistics tables that contain aggregate metrics on the mortality (deaths) of Philadelphia residents. Included in these datasets are mortality metrics by planning district or citywide. You can find natality (births) metrics, and social determinants of health metrics at the city and planning district levels of geography as well. Population metrics are provided at the city, planning district, and census tract levels of geography. Please refer to this technical notes document to access detailed technical notes and variable definitions.

A. SUMMARY This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2016-2020 American Community Survey (ACS) population estimates are included to calculate the cumulative rate per 10,000 residents.

Dataset covers cases going back to 3/2/2020 when testing began. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily.

Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date.

The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date).

COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 05:00 Pacific Time.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset can be used to track the spread of COVID-19 throughout the city, in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date.

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. Cases are dropped altogether for areas where acs_population < 1000 4. Deaths data are not included in this dataset for privacy reasons. The low COVID-19 death rate in San Francisco, along with other publicly available information on deaths, means that deaths data by geography and day is too granular and potentially risky. Read more in our privacy guidelines

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the cumulative case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website.

Rows included for Citywide case counts Rows are included for the Citywide case counts and incidence rate every day. These Citywide rows can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling bases.

Related dataset See the dataset of the most recent cumulative counts for all geographic areas here: https://data.sfgov.org/COVID-19/COVID-19-Cases-and-Deaths-Summarized-by-Geography/tpyr-dvnc

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by geography over time are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our case data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/31/2023 - removed the “multipolygon” column. To access the multipolygon geometry column for each geography unit, refer to COVID-19 Cases and Deaths Summarized by Geography.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

Social determinants of health metrics at the city and planning district levels of geography. Please refer to the metadata links below for variable definitions and this technical notes document to access detailed technical notes and variable definitions. You can find related vital statistics tables that contain aggregate metrics on vital events, including natality (births) metrics and mortality (deaths) by planning district or citywide. Population metrics are provided at the city, planning district, and census tract levels of geography.

Abstract Background: Acute myocardial infarction (AMI), the leading cause of death in Brazil, has presented regional disparities in mortality rate time trends in recent years. Previous time trend studies did not correct for cause-of-death garbage codes, which may have skewed the estimates. Objective: To analyze regional and gender-based inequalities in the AMI mortality trend in Brazil from 1996-2016. Methods: A 21-year time series study (1996-2016). Data are from the Mortality Information System and population estimates from the Brazilian Institute of Geography and Statistics. Corrections of deaths due to ill-defined causes of death, garbage codes, and underreporting were made. The time series broken down by major geographic regions, gender, capital cities, and other municipalities was analyzed using the linear regression technique segmented by Jointpoint. Statistical significance level was set at 5%. Results: In the period, mortality decreased more sharply in women (−2.2%; 95% CI: −2.5; −1.9) than in men (−1.7%; 95% CI: - 1.9; −1.4) and more in the capital cities (−3.8%; 95% CI: - 4.3; −3.3) than in other municipalities (−1.5%; 95% CI: - 1.8; −1.3). Regional inequalities were observed, with an increase for men living in other municipalities of the North (3.3; 95% CI: 1.3; 5.4) and Northeast (1.3%; 95% CI: 1.0; 1.6). Statistical significance level was set at 5%. Mortality rates after corrections showed a significant difference in relation to the estimates without corrections, mainly due to the redistribution of garbage codes. Conclusions: Although AMI-related mortality has decreased in Brazil in recent years, this trend is uneven by region and gender. Correcting the numbers of deaths is essential to obtaining more reliable estimates.

This dataset contains model-based county estimates for drug-poisoning mortality.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8).

REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm.

1. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

2. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013.

3. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014.

4. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf.

5. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e.

6. National Center for Health Statistics. County geography changes: 1990–2012. Available from: http://www.cdc.gov/nchs/data/nvss/bridged_race/County_Geography_Changes.pdf.

7. National Center for Health Statistics. County geography changes: 1990–2015. Available from: https://www.cdc.gov/nchs/nvss/bridged_race/county_geography-_changes2015.pdf.

Abstract

The KHDS 2010 was designed to provide data to understand changes in living standards of the sample of individuals originally interviewed 16-19 years ago. The KHDS 2010 attempted to re-interview all respondents ever interviewed in the KHDS 91-94 – irrespective of whether the respondent had moved out of the original village, region, or country, or was residing in a new household.

Geographic coverage

Kagera region of Tanzania

Analysis unit

Households and individuals

Universe

The KHDS attempts to re-interview all respondents interviewed in the original KHDS 1991-1994, irrespective of whether the respondent had moved out of the original village, region or country or was residing in a new household.

Kind of data

Sample survey data [ssd]

Sampling procedure

KHDS 1991-1994 Household Sample: First Stage

The KHDS 91-94 household sample was drawn in two stages, with stratification based on geography in the first stage and mortality risk in both stages. A more detailed overview of the sampling procedures is outlined in "User's Guide to the Kagera Health and Development Survey Datasets." (World Bank, 2004).

In the first stage of selecting the sample, the 550 primary sampling units (PSUs) in Kagera region were classified according to eight strata defined over four agronomic zones and, within each zone, the level of adult mortality (high and low). A PSU is a geographical area delineated by the 1988 Tanzanian Census that usually corresponds to a community or, in the case of a town, to a neighbourhood. Enumeration areas of households were drawn randomly from the PSUs in each stratum, with a probability of selection proportional to the size of the PSU.

Within each agronomic zone, PSUs were classified according to the level of adult mortality. The 1988 Tanzanian Census asked a 15 percent sample of households about recent adult deaths. Those answers were aggregated at the level of the "ward", which is an administrative area that is smaller than a district. The adult mortality rate (ages 15-50) was calculated for each ward and each PSU was assigned the mortality rate of its ward.

Because the adult mortality rates were much higher in some zones than others and the distribution was quite different within zones, "high" and "low" mortality PSUs were defined relative to other PSUs within the same zone. A PSU was allocated to the "high" mortality category if its ward adult mortality rate was at the 90th percentile or higher of the ward adult mortality rates within a given agronomic zone.

The KHDS 91-94 selected 51 communities as primary sampling units (also referred to as enumeration areas or clusters). In actuality, two pairs of enumeration areas were within the same community (in the sense of collecting community data on infrastructure, prices or schools). Thus, for community-level surveys, there are 49 areas to interview.

KHDS 1991-1994 Household Sample: Second Stage

The household selection at the second stage (with enumeration areas) was a stratified random sample, where households which were expected to experience an adult death were oversampled. In order to stratify the population, an enumeration of all households was undertaken.

Between March 15 and June 13, 1991, 29,602 households were enumerated in the 51 areas. In addition to recording the name of the head of each household, the number of adults in the household (15 and older), and the number of children, the enumeration form asked:

1. Are any adults in this household ill at this moment and unable to work? If so, the age of the sick adult and the number of weeks he/she has been too sick to work were also noted.
2. Has any adult 15-50 in this household died in the past 12 months? If so, the age of each adult and the cause of death (illness, accident, childbirth, other) were also noted.

The enumeration form asked explicitly about illness and death of adults between the ages of 15-50 because this is the age group disproportionately affected by the HIV/AIDS epidemic; it is the impact of these deaths that was of research interest. Out of over 29,000 households enumerated, only 3.7 percent, or 1,101, had experienced the death of an adult aged 15-50 caused by illness during the 12 months before the interview and only 3.9 percent, or 1,145, contained a prime-age adult too sick to work at the time of the interview. Only 77 households had both an adult death due to illness and a sick adult. This supports the point that, even with some stratification based on community mortality rates and in an area with very high adult mortality caused by an AIDS epidemic, a very large sample would have had to have been selected to ensure a sufficient number of households that would experience an adult death during the two-year survey.

Using data from the enumeration survey, households were stratified according to the extent of adult illness and mortality. It was assumed that in communities suffering from an HIV epidemic, a history of prior adult death or illness in a household might predict future adult deaths in the same household. The households in each enumeration area were classified into two groups, based on their response to the enumeration:

1. Sick" households: Those that had either an adult death (aged 15-50) due to illness in the past 12 months, an adult too sick to work at the time of the survey, or both (n=2,169).
2. "Well" households: Those that had neither an adult death (aged 15-50) due to illness nor an adult (aged 15-50) too sick to work (n=27,433).

In selecting the sixteen households to be interviewed in each enumeration area, fourteen were selected at random from the "sick" households in that enumeration area and two were selected at random from the "well" households. In one enumeration area, where the number of "sick" households available was less than fourteen, all available sick households were included in the sample; the numbers were balanced using well households. The final sample drawn for the first passage consisted of 816 households in 51 enumeration areas.

KHDS 2004 and 2010 Household Samples

The sampling strategy in KHDS 2004 and KHDS 2010 was to re-interview all individuals who were household members in any wave of the KHDS 91-94, a total of 6,353 people. The Household Questionnaire was administered in the household in which these PHHMs lived. If a household member was alive during the last interview in 1991-1994, but found to be deceased by the time of the fieldwork in 2004 and 2010 then the information about the deceased was collected in the Mortality Questionnaire. The next sections provide statistics of the KHDS 2004 and 2010 households.

KHDS 2004 Households

Although the KHDS is a panel of individuals and the concept of a household after 10-19 years is a vague notion, it is common in panel surveys to consider re-contact rates in terms of households. Table 4 shows the rate of re-contact of the baseline households in KHDS 2004, where a re-contact is defined as having interviewed at least one person from the household. In this case, the term household is defined by the baseline KHDS survey which spans a period of 2.5 years. Due to movements in and out of the household, some household members may have not, in fact, lived together in the household at the same time in the 1991-1994 waves (for example, consider one sibling of the household head moving into the household for one year and then moving out, followed by another sibling moving into the household).

Excluding households in which all previous members are deceased (17 households and 27 respondents), the KHDS 2004 field team managed to re-contact 93 percent of the baseline households. Not all 915 households received four interviews. Unsurprisingly, households that were in the baseline survey for all four waves had the highest probability of being reinterviewed. Of these 746 households, 96 percent were re-interviewed.

Turning to re-contact rates of the sample of 6,353 respondents, Table 5 shows the status of the respondents by age group (based on their age at first interview in the 1991-1994 waves). Reinterview rates are monotonically decreasing with age, although the reasons (deceased or not located) vary by age group. The older respondents were much more likely to be located if alive. Among the youngest respondents, over three-quarter were successfully re-interviewed. Excluding people who died, 82 percent of all respondents were re-interviewed.

KHDS 2010 Households

The re-contact rates in the KHDS 2010 are in line with the ones achieved in KHDS 2004. Table 4 of the Basic Information Document shows the KHDS 2010 re-contacting rates in terms of the baseline households. Excluding the households in which all PHHMs were deceased, 92 percent of the households were recontacted.

As in KHDS 2004, households that were interviewed four times at the baseline were more likely to be found in 2010. Excluding the households in which all members had died, 95 percent of these households were re-interviewed in 2010.

The KHDS 2010 re-contact rates in terms of panel respondents are provided in Table 5 of the Basic Information Document. As in 2004, the older respondents, if alive, were much more likely to be re-contacted than younger respondents. In the oldest age category, 60 years and older at the baseline, the interview teams managed to re-contact almost 98 percent of all survivors. The length of the KHDS survey starts to be seen in this age category however, as almost three quarters of the respondents had passed away by 2010.

Table 6 of the Basic Information Document provides the KHDS 2010 re-contact rates by location. More than 50 percent of the reinterviewed panel respondents were located in the same community as in KHDS 91-94. Nearly 14 percent of the re-contacted respondents were found from