Annual age-adjusted preventable hospitalization rate measured with Prevention Quality Indicators (PQIs)
Geographic Unit of Analysis:
|Age-adjusted, annual preventable hospitalization rate measured with Prevention Quality Indicators (PQIs), San Francisco, CA (2009-2011)|
|94102 - Downtown Civic Center, Western Addition||1,874.8|
|94103 - South of Market, Mission, Financial District, Mission Bay||1,645.8|
|94104 - Financial District||--|
|94105 - Financial District, South of Market||235.0|
|94107 - Potrero Hill, South of Market, Mission Bay||750.7|
|94108 - Nob Hill, Chinatown, Financial District, Downtown Civic Center||728.2|
|94109 - Russian Hill, Nob Hill, Downtown Civic Center, Pacific Heights, Western Addition||922.2|
|94110 - Mission, Bernal Heights||1,099.4|
|94111 - Financial District, North Beach||463.4|
|94112 - Outer Mission, Crocker Amazon, Ocean View, Excelsior, West of Twin Peaks, Bernal Heights||903.8|
|94114 - Castro/Upper Market, Noe Valley, Twin Peaks||612.3|
|94115 - Western Addition, Pacific Heights||1,176.8|
|94116 - Parkside, Outer Sunset, West of Twin Peaks, Inner Sunset||675.7|
|94117 - Haight Ashbury, Western Addition||743.4|
|94118 - Inner Richmond, Presidio Heights||489.5|
|94121 - Outer Richmond, Seacliff||592.9|
|94122 - Outer sunset, Inner Sunset, Golden Gate Park||691.3|
|94123 - Marina, Pacific Heights||465.0|
|94124 - Bayview||1,931.1|
|94127 - West of Twin Peaks, Ocean View, Outer Mission||513.9|
|94129 - Presidio||--|
|94130 - Treasure Island||1,373.0|
|94131 - Diamond Heights/Glen Park, Twin Peaks, Noe Valley, Inner Sunset, Outer Mission||481.4|
|94132 - Lakeshore, Ocean View||737.7|
|94133 - North Beach, Russian Hill, Nob Hill, Chinatown||775.3|
|94134 - Visitacion Valley, Excelsior, Bayview||1,139.5|
|94158 - Mission Bay, Potrero Hill||231.4|
|* EPC = early prenatal care
Only ZIP Codes with five events or more are listed in this report.
|Source: California Department of Public Health, 2010 Birth Records. Available at:
“Preventable hospitalizations” are hospitalizations that most likely could have been avoided if the patient had received proper outpatient care earlier. They are not an indication of hospital performance, but rather the accessibility and quality of primary care services for the community. Hospital care is far more resource intensive than outpatient care; thus, for both ethical and financial reasons it is important to monitor and reduce the rate of preventable hospitalizations.a The Agency for Healthcare Research and Quality has developed a list of Prevention Quality Indicators (PQIs) which are a set of ambulatory sensitive conditions (treatable in outpatient settings) which can be used a measure for preventable hospitalizations. The PQIs used in the composite measure for overall preventable hospitalizations include:
PQI #01 Diabetes Short-Term Complications Admission Rate
PQI #03 Diabetes Long-Term Complications Admission Rate
PQI #05 Chronic Obstructive Pulmonary Disease (COPD) or Asthma in Older Adults Admission Rate
PQI #07 Hypertension Admission Rate
PQI #08 Heart Failure Admission Rate
PQI #10 Dehydration Admission Rate
PQI #11 Bacterial Pneumonia Admission Rate
PQI #12 Urinary Tract Infection Admission Rate
PQI #13 Angina without Procedure Admission Rate
PQI #14 Uncontrolled Diabetes Admission Rate
PQI #15 Asthma in Younger Adults Admission Rate
PQI #16 Rate of Lower-Extremity Amputation Among Patients With Diabetes
The zip codes in San Francisco with the highest age-adjusted preventable hospitalizations rates are 94124, 94102, 94103, 94130, and 94115. These correspond to the neighborhoods of Bayview, Downtown/Civic Center, South of Market, Treasure Island, and Western Addition. In general the eastern side of the city has higher rates than the western side. The zip codes with the lowest hospitalization rates are 94158, 94105, 94111, 94123, and 94131, which include the neighborhoods of Mission Bay, Financial District, Marina, Pacific Heights, Diamond Heights/Glen Park, Twin Peaks, and Noe Valley. The zip codes 94129 (Presidio), and 94104 (Financial District) had to be excluded because they did not have sufficient numbers of hospitalizations.
Public patient discharge data can be obtained from the state department of public health by county public health departments. Using three consecutive years of data, select patients with zip codes falling within the city. Adults that meet the criteria for 12 of the Agency for Healthcare Research and Quality’s Prevention Quality Indicators (PQIs) will be extracted as those with a preventable hospitalization. Follow the guidance here to code patients with the appropriate PQI: http://www.qualityindicators.ahrq.gov/Modules/PQI_TechSpec.aspx. It is generally easiest to use a statistical program such as SAS or STATA to create PQI codes because numerous criteria must be met to fit into a PQI category (e.g. specific age, primary diagnosis, secondary diagnoses or procedures, etc.). Once PQIs have been applied, extract those adults with PQIs: 1, 3, 5, 7, 8, 10, 11, 12, 13, 14, 15, and 16 to create a composite measure for preventable hospitalizations (details on this procedure here: http://www.qualityindicators.ahrq.gov/Downloads/Modules/PQI/V44/Composite_User_Technical_Specification_PQI%20V4.4.pdf).
The next step is to age adjust the rates to the US population. To do this determine the percent of US adults that fall into the following age groups from the most recent census: 18-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, 85+. Then determine the number of people that fall into those age groups within each zip code and the city as a whole. Then take the number of PQI hospitalizations for each of those age groups in each zip code and the city as a whole and divide it by, three and the number of residents in that age group and multiply by 100,000 to come up with an annual unadjusted rate per 100,000 for that age group. Multiply the age group unadjusted rates by the US population proportion for that age group. Sum the products for each zip code and the city to produce age adjusted rates of PQI hospitalizations. Map, and present in a table.
To protect patient confidentiality, records with unique combinations of certain demographic variables will have one or more of those variables masked to make sure the files are de-identified. In most cases masking involves defaulting the variable. Each unique record will have the minimum number of fields masked to an asterisk “*” or missing to ensure it is no longer unique. Due to the need to mask records, some hospitalizations may be excluded from analyses because the patient’s zip code or age was missing.
PQIs are an indirect measure of the accessibility or quality of primary care available to a city’s population. However, there are factors outside of primary care that will affect an area’s PQI rate, such as environmental conditions like air quality and healthy food access, as well as patient compliance to treatment regimens.
California Office of Statewide Health Planning and Development, Public Patient Discharge Data, 2009-2011.
California Office of Statewide Health Planning and Development. Preventable Hospitalizations in California: Statewide and County Trends in Access to and Quality of Outpatient Care, Measured with Prevention Quality Indicators (PQIs), 1999-2008. 2010.