New York State Department of Health Center for Environmental Health

Transcription

1 New York State Department of Health Center for Environmental Health March 2002 Evaluation of Asthma and Other Respiratory Hospital Admissions among Residents of ZIP Codes and 14227, Cheektowaga, Erie County, Background and Objectives In July of 2000, New York State Department of Health (NYS DOH) epidemiologists reviewed a list of reported conditions provided by residents living in the Bellevue neighborhood of Cheektowaga, Erie County. Bellevue residents, concerned about unusual numbers of people with health problems and the community s proximity to the Buffalo Crushed Stone quarry, had conducted a survey asking neighborhood residents about health conditions. Residents also voiced concerns about potential exposures to silica dust, hydrogen sulfide, and diesel truck exhaust from the quarry operations, as well as potential exposures that might occur from landfills and inactive hazardous waste sites in the area. NYS DOH environmental investigators, in cooperation with the New York State Department of Environmental Conservation (NYS DEC) reviewed available information about the community s environmental concerns. Hydrogen sulfide odors were a periodic problem at the quarry, and NYS DEC requested that Buffalo Crushed Stone implement a treatment system to control hydrogen sulfide releases. In response to concerns about diesel exhaust, dust and traffic from the quarry, truck traffic was limited to Como Park Boulevard. NYS DOH concluded that the list of health conditions provided from the community survey did not appear unusual, but it was not possible to draw definite conclusions. Community health surveys are difficult to interpret because the people who choose to respond may not be representative of the community as a whole. For example, people who are experiencing health problems may be more likely to respond than those without health problems, or those who are concerned about potential exposures may be more likely to recall health problems. In order to help address the community s concerns about respiratory health problems, NYS DOH proposed a screening evaluation of available hospital admissions data for respiratory problems among area residents. This type of screening evaluation uses information based on medical diagnostic records, gathered comprehensively for all hospital admissions and maintained in a statewide database. The hospital admissions data allow a comparative evaluation of admissions due to respiratory problems for the entire population in a specific area. This type of screening evaluation can suggest whether additional research is warranted. The screening evaluation compared hospital admission rates for respiratory problems among residents of the two ZIP codes (14043 and 14227) surrounding the Buffalo Crushed Stone quarry to hospital admission rates among residents of Erie County as a whole, for the years 1994 through In addition, the residential addresses of the individuals living in the study area

2 who were hospitalized for respiratory conditions were located on maps in order to assess whether there was unusual clustering near the Buffalo Crushed Stone Quarry. Methods Study Population and Data Sources: The populations residing in ZIP Codes and near the Buffalo Crushed Stone quarry are the study population in this investigation. The comparison population is the population residing in Erie County, New York. Erie County inpatient hospitalization data for the years 1994 through 1998 and United States Census data for 1990 and 2000 were used in the analysis. As can be seen on the attached map, the two ZIP Codes are primarily in the town of Cheektowaga, with ZIP Code extending into the Town of Lancaster. ZIP 14227, which includes the Buffalo Crushed Stone Quarry, covers the south-central portion of the Town of Cheektowaga. ZIP borders on the east and extends further north than ZIP ZIP contains the Village of Depew. The hospitalization data were from the Statewide Planning and Research Cooperative System (SPARCS) database and contain information on 99% of all hospital discharges in New York State. The system uses two sources of data, the Discharge Data Abstract and the Uniform Billing Form. New York State Office of Real Property Services data for the year 2000 and telephone directories were used to find nursing homes and senior living centers in the study area. Case Ascertainment: The International Classification of Disease Codes for the Ninth Revision (ICD9) was used to identify cases for which admissions data were extracted from the SPARCS computerized data files for the five-year study period. An ICD9 Code 493 in the primary diagnosis field was used to select asthma admissions. The other respiratory admissions were selected using primary diagnoses codes 490 (bronchitis, not specified as acute or chronic), 491 (chronic bronchitis), 492 (emphysema), 496 (chronic airway obstruction, not elsewhere specified), 502 (pneumoconiosis due to other silica or silicates), 506 (respiratory conditions due to chemical fumes and vapors), and 508 (respiratory conditions due to other and unspecified external agents). Analysis: Gender, ethnicity, and age group distributions were calculated for the study ZIP Codes and Erie County using 1990 and 2000 Census data. The Census data also provided median household income levels for the study and comparison populations. Population estimates for 1994 through 1998 were calculated by performing a linear interpolation of the 1990 and 2000 Census data. Numbers and rates of asthma admissions and other respiratory admissions per 10,000 population were calculated. The number of cases of each specific type of respiratory admission was also calculated. Using race and age-specific hospitalization rates for respiratory admissions among residents of the comparison area, Erie County, expected numbers of hospitalizations for each type of respiratory health outcome were calculated for the study area ZIP Codes. The actual observed numbers of admissions in the two study ZIP Codes were then compared to the numbers expected. Repeat admissions for the same individual were removed by using SPARCS ID and date of birth for the study population and the comparison population. The comparisons between observed and expected numbers of admissions are shown as age-specific hospitalization ratios, calculated by dividing the observed number of admissions in a certain age group by the expected number of admissions in that same age group. Age-adjusted 2

3 hospitalization ratios were calculated by summing the number of age-specific admissions and dividing by the sum of the number of age-specific expected admissions. The expected number of admissions for each age group in the study ZIP Codes was calculated by multiplying the number of people in a that age group in the study population by the rate of admissions in Erie County in that same age group. 95% percent confidence intervals were calculated around the hospitalization ratios, using the methods of Rothman and Boice (1979). Residential addresses of cases in the study ZIP Codes were located on computerized maps using MapMarker and MapInfo. Nursing homes, residential care facilities, and senior living centers were also mapped to determine if respiratory cases were residents of these facilities. Distance from the Buffalo Crushed Stone quarry and clustering of cases were visually assessed. Numbers of respiratory cases were summed by age group for each half-mile increment from the site and percentages of cases living in nursing homes, residential care facilities, or senior living centers were calculated for each half-mile increment. Results Tables 1 and 2 show descriptive statistics of selected demographic and economic variables from the 1990 Census data for Erie County. The population of the two study ZIP Code areas was less than 2% African-American compared to 11.3% African-American in the Erie County population. The 1989 median household income levels in the study ZIP Codes were approximately 16% higher than in Erie County as a whole. It was not possible to assess income levels for specific ethnic groups within the study area because this type of data is not available at the ZIP Code level. Race-specific income is provided at the level of the town and village however. Table 3 shows that median household incomes for the Cheektowaga and Depew white populations are very similar to the median among the white population of Erie County. Because the population of the study ZIPs was more than 98% white, and the white median income for Erie County was comparable to the median income for the study area s population as a whole, the analyses were conducted using Erie County hospitalization rates for whites only. There were fewer than six total respiratory admissions in the African-American population in the two study ZIP Codes over the five-year study period. These very small numbers do not allow separate analyses for these admissions to be presented. According to Real Property data for the year 2000 and the telephone directory, there are six nursing homes, one retirement community, and one senior housing complex within the two study ZIP Codes. In addition, there is one Veterans Administration Hospital in Erie County, in ZIP Code within 6 miles of the study area. Veterans Administration Hospitals do not participate in the SPARCS data program. Table 4 shows the numbers and crude (not adjusted for age or other factors) rates of asthma and other respiratory admissions for the white population. These admissions include repeat admissions for the same individual. Over the five-year study period, there were 163 asthma admissions and 339 other respiratory admissions in the study ZIP Codes. The rates for asthma and other respiratory admissions in the study ZIP Codes are lower than the Erie County rates. Table 5 shows the distribution of diseases within the other respiratory admissions category. Approximately 80% of the other respiratory admissions category in the study ZIP Codes were attributed to chronic bronchitis. None of the admissions in the study area were due to silicosis. 3

4 Table 6 shows the age-specific and age-standardized asthma hospitalization ratios for the two study ZIP Codes, restricted to one admission per person. When repeat admissions by the same individual were excluded, there were 127 asthma cases. Overall, for all age groups combined, there were fewer than expected asthma admissions in both ZIP Codes, using the Erie County white population as the comparison group. The difference (fewer cases than expected) was statistically significant in ZIP Code 14043, but not statistically significant in ZIP Code In ZIP Code 14043, among 0-4, 5-19, and year-olds, the observed number of asthma admissions was statistically significantly lower than the number of expected admissions. For these younger age groups in ZIP Code and for the 55 and older age groups in both ZIP codes, the observed numbers of asthma admissions were not statistically significantly different from the expected numbers. Among people in the 75 and older age group in ZIP Code 14227, there was a non-statistically significant elevation of observed asthma admissions compared to expected, with 13 asthma admissions observed compared to 8 expected. This difference is not statistically significant which means that this magnitude of difference is likely due to chance or random variation. A few of the cases in this older age group listed an address of a senior housing complex as their residential address, and a few also listed a retirement community. (Exact numbers, when fewer than six, are not shown, in order to help protect people s privacy regarding health information.) None of the 20 asthma hospitalizations in adults age 75 and older in both ZIP Codes lived within ½ mile of the quarry. Most of the cases within one mile of the quarry lived in a retirement community. Table 7 shows age-specific and age-adjusted hospitalization ratios for other respiratory admissions in the study area, restricted to one admission per person. When repeat admissions by the same individual were excluded, there were 259 other respiratory cases. Using the Erie County white population as the comparison group, the numbers of other respiratory admissions for all age groups combined were not statistically significantly different from the numbers expected in either ZIP Code. There were no statistically significant differences between observed and expected numbers of hospital admissions for other respiratory conditions in any of the six age groups in either ZIP Code. In both ZIP Codes combined, fewer than six other respiratory admissions were seen in the 0-19 year-old group. The observed number of other respiratory admissions in the year-old age group in ZIP was not statistically significantly elevated, which means that the difference between the 51 observed cases and 41 expected cases is likely due to chance or random variation. In ZIP Code 14227, twelve cases in the year-old group and ten cases in the 75 and older group listed a retirement community or a senior housing complex as their residential address. Sixteen cases in the 75 and older age group listed a nursing home address as the residential address. Approximately, 97% of the asthma admission data and 95% of the other respiratory admission data in the study ZIP Codes had residential address information that could be mapped at the street address level. Additional visual assessment of exact address locations was conducted to look for evidence of spatial clustering of the residences of childhood asthma admission cases and adult asthma and other respiratory admission cases. The visual assessment of maps of childhood asthma hospitalization cases residences did not show patterns of addresses in close proximity to each other or in close proximity to the quarry. Among adults, residential address clustering was observed, but it was due to multiple cases residing in nursing homes and other retirement or senior citizen housing. Three nursing 4

5 homes are within a half-mile of the Buffalo Crushed Stone quarry, and a retirement community is located within one mile of the site. For asthma hospitalizations for adults age 65 and over, none lived within the first ½ mile of the quarry. Among the small number (fewer than six) living in the next ½ mile, almost all resided in the same retirement community. Of the 184 hospitalizations for other respiratory conditions among study area residents age 65 and older, 14 (8%) lived within ½ mile and 35 (19%) within the next ½ mile from the quarry. Seven of the 14 people (50%) residing with ½ mile of the quarry lived in nursing facilities (two separate facilities). Of the 35 hospitalized respiratory cases living in the next ½ mile, 14 (40%) lived in one retirement community. Summary of Results Comparative hospital admission ratios (observed compared to expected cases) for asthma and other respiratory hospital admissions were assessed for the population of the two-zip Code study area, using Erie County as a whole, adjusting for age, sex, and ethnicity (African- American/white) as the comparison area. Hospital admission ratios were assessed in a separate analysis for black residents of the study area. The population of the study area is less than two percent African-American. The numbers of admissions among black residents was very small, fewer than six over the five-year period, which is fewer than expected. These small numbers are not presented. The evaluation showed no statistically significant elevations of hospital admissions due to asthma for any separate age group, nor for all age groups combined. For age groups 0-4, 5-19, and in ZIP code 14043, observed numbers of asthma admissions were statistically significantly lower than expected. Additionally, visual assessment of maps of asthma hospitalization cases residences did not show evidence for clustering near the facility. For respiratory admissions other than asthma, there were no statistically significant elevations or deficits in either ZIP Code for any separate age group, nor for all age groups combined. Assessment of maps showing the residential addresses of people hospitalized for other respiratory conditions showed that the appearance of clustering was associated with multiple cases living in nursing facilities. Strengths and Limitations Available health outcome data: This investigation used existing SPARCS hospitalization data to track chronic health problems such as asthma and bronchitis. One limitation of using hospitalization data is that the numbers of people with chronic conditions such as asthma and other respiratory disease are usually much higher than the numbers of people hospitalized for these conditions. People with these conditions who receive regular preventive care from health care providers or who have family members to coordinate home care may never be hospitalized. Household income, health insurance status and family situation are examples of factors that could affect the likelihood that someone with one of these conditions will be hospitalized. This investigation was unable to directly control for these possible influences on respiratory hospitalization rates. By comparing hospitalizations among the predominantly white population of the study area to hospitalizations for the white population of Erie County, which has a similar median income level, this analysis attempted to correct to some degree for the possible influence of income on hospitalization. 5

6 While calculations based on hospitalization data do not estimate the total burden of chronic disease, they do help us estimate the burden of severe disease in the population. Since the hospitalized cases represent the most severe cases, evaluation of these cases is important. In addition, the SPARCS hospitalization data allow a comparative evaluation of medically diagnosed cases, reported to a statewide database from hospital records. This type of evaluation avoids the problems typically encountered in health surveys, for example, selective participation, with many people not responding, and inconsistent recall of health histories among the people who do respond. SPARCS data are not collected specifically for this type of geographic health tracking investigation, so there may be data quality problems, address accuracy for example, that have not been evaluated by the collectors of the data. By comparing hospitalization rates in the study area ZIP Codes with rates within the same county, problems due to regional differences in reporting practices are minimized, but differences among hospitals could influence the data in ways that have not been controlled in this analysis. Since Veterans Administration Hospitals are not part of the SPARCS data system, this may have resulted in an undercounting of respiratory admissions for Erie County as a whole and/or an undercounting of admissions for the ZIP Code study areas. This would not have affected the analyses for admissions of children, however. Small area analyses: Age-specific population estimates are needed to assess disease rates for geographic areas. For areas defined by proximity to a point source such as a quarry, the census data boundaries for census tracts, block groups or ZIP Codes usually do not coincide very well with the boundaries for the area of concern. Another problem with evaluating health outcomes for relatively small areas is that if the health outcomes being evaluated are relatively rare, such as hospitalizations for childhood asthma, the numbers will be too small for reliable analyses to be conducted. This is an important reason for using ZIP Codes, with relatively larger populations than census tracts, for this comparative evaluation. Another reason for using ZIP Codes rather than smaller areas is that because the ZIP Code is part of every individual s address, the ZIP Code is available as part of the address record in the SPARCS database. To get to the level of census tract or block group, all individuals within the ZIP Codes need to be located at exact street addresses to determine whether they live in the census tract of concern. This additional step can be difficult because some addresses are incomplete, missing information on street number for example. An additional reason for using ZIP Codes is that age-specific populations need to be estimated for the decade between census counts, and for areas smaller than ZIP Codes these estimates are less reliable. The approach used in this evaluation was to assess hospitalization data for the two ZIP Codes containing the population closest to the site of concern. The ZIP Code data were compared to data for the entire County. Evaluation of hospitalization data for the entire ZIP Code could fail to detect a very localized elevation, so the addresses of hospitalized cases were geocoded to check visually for spatial clustering near the quarry. Because the area of greatest concern is smaller than the ZIP Code, spatial clustering evaluation is based on very small numbers. Available analytic methods for assessing spatial clustering of age-related health outcomes are not generally useful for this type of evaluation of one small area. An analytic technique has to take account of the spatial variation and changes over time in population size and age distribution, and these data are not routinely available for small areas. Evaluation of maps showing residential addresses was conducted to visually assess whether there was obvious clustering of cases. While no type of spatial evaluation allows for conclusions to be drawn about potential causes for particular cases of disease, visual spatial assessment of residential addresses may provide useful information about whether the distribution of cases near the quarry is unusual. 6

7 Geographical evaluations: Many types of factors can lead to spatial clustering of health outcomes. The assessment of mapped locations has to also consider where the population density is higher, either due to apartment complexes, nursing homes, or simply a more densely settled area of houses. This information is not always readily available or accurate for small areas. In addition, neighborhoods, even from street to street, often differ according to social, economic, cultural, and age characteristics that are associated with risk for a particular disease. Mobility of the population is another important issue in geographical investigations. Some neighborhoods have more families who have recently moved in, while others are much more stable. The elderly in nursing homes may not have resided in the area for very long. The hospitalization data used in this investigation provided addresses of residences at the time of the hospital admission, but no information was available on how long the individual had resided at that address. As part of the geographical evaluation, this investigation included information on the location of group residences. Because the health outcomes being studied occur much more frequently among older people, this additional information about the locations of senior housing and nursing homes was needed in order to assess whether the distribution of cases appeared unusual near the quarry. Health Information Common causes of asthma are allergies to pets, mold, dust and dust mites. Occupational exposure to high dust concentrations and fumes (as in underground construction, heavy construction and coal mining) may also cause asthma. For people who have asthma there are certain exposures that can trigger asthma attacks. These triggers are allergies (dust mites, pets, etc.), respiratory infections, exercise, weather changes and exposure to smoke. Chronic bronchitis, defined as a mucus-producing cough most days of the months lasting for three months of a year for 2 consecutive years without other underlying disease to explain the cough, affects 9 million Americans annually. It is considered to be the ninth ranking chronic condition in the United States. It is one of the diseases, along with emphysema, that make up the category of disease called chronic obstructive pulmonary disease (COPD). In 1997 COPD was determined to be the fourth leading cause of death after cardiovascular diseases, tumors, and cerebrovascular diseases in the United States. The principal risk factor for chronic bronchitis is exposure to smoke from tobacco products. Other risk factors include bacterial or viral infections and occupational exposure to high amounts of dust or fumes as found in coal mining, grain handling, metal molding, and heavy or underground construction. Chronic bronchitis may occur at any age but is more commonly found in people over the age of 45. Conclusions The available hospitalization data for 1994 through 1998 provide no evidence of unusual elevations of asthma or other respiratory conditions among children or adults residing in the two ZIP Codes near Buffalo Crushed Stone Quarry. Looking more closely at specific addresses, no spatial clustering of childhood asthma or other respiratory conditions near Buffalo Crushed Stone was observed. For the elderly adult asthma hospitalization cases and the other respiratory cases, the cases living in close proximity to 7

8 Buffalo Crushed Stone were frequently residents of nursing homes and other residential facilities, as well as retirement communities. Elderly residents of nursing homes may be at greater risk for respiratory disease hospitalizations than other elderly people of the same age. People with preexisting health problems are more likely to reside in nursing homes and are also more likely to be hospitalized for respiratory problems. The conclusions drawn from this evaluation must be considered cautiously. This type of evaluation cannot assess if there are links between particular environmental factors and health outcomes. The approach of this evaluation was to track outcomes using a database that records hospital admissions for the entire study population and comparison population. These comprehensive data are needed in order to make comparisons that include the entire population in both the study and comparison area. This type of surveillance or tracking evaluation will be improved when data for hospital emergency department admissions are also available comprehensively in New York State. A bill adding emergency department admissions to the SPARCS system was signed by the Governor on September 4, 2001, with the new data mandated to be collected starting in September As discussed above in the Limitations section, this tracking evaluation used hospitalization data to assess potential elevations of respiratory problems among residents of the study area in comparison to the larger County area. It is important to emphasize that the numbers and rates shown for hospitalized asthma and other respiratory diagnoses are not estimates of how frequently these conditions occur in the population. The hospitalization data represent the most severe cases, but assess disease experience only to the extent that the comparative hospitalization rates reflect the comparative rates of these diseases in the population. This hospitalization data has the advantage that the information is gathered comprehensively from medical diagnostic records for hospital admissions and maintained in a statewide database. The hospital admissions data allow a comparative evaluation of admissions due to respiratory problems for the entire population in a specific area. This type of evaluation avoids the problems encountered with health surveys such as low response rates and inconsistent recall of health histories. This screening evaluation used existing data to check for unusual elevations in relevant health outcomes. The analysis showed no elevations of hospitalizations for respiratory problems among children or adults. This type of screening evaluation assesses health outcomes for a group of individuals and determines if the numbers for the group appear unusual. This kind of evaluation does not allow any conclusions to be drawn about the causes of particular individuals diseases or health status. 8

9 Table 1. Demographics of study ZIP Codes and Erie County, 1990, U.S. Census Data Population Erie County N (%) N (%) N (%) Total 26,501 (100) 25,189 (100) 968,532 (100) Gender Male 12,931 (48.8) 12,059 (47.9) 461,204 (47.6) Female 13,570 (51.2) 13,130 (52.1) 507,328 (52.4) Race White 26,154 (98.7) 24,688 (98.0) 831,903 (85.9) Black 129 (0.5) 352 (1.4) 109,852 (11.3) Age Group 0-4 1,769 (6.7) 1,444 (5.7) 66,512 (6.9) ,184 (19.6) 4,628 (18.4) 187,534 (19.4) ,460 (50.8) 13,228 (52.5) 471,047 (48.6) ,787 (10.5) 2,423 (9.6) 96,356 (9.9) ,225 (8.4) 2,201 (8.7) 87,265 (9.0) 75+ 1,076 (4.1) 1,265 (5.0) 59,818 (6.2) Table 2. Household income of study ZIP Codes and Erie County, 1990, U.S. Census Data Census Variable Erie County Median Household Income, 1989 $32,563 $31,383 $28,005 Number of Households 9,554 9, ,994 Table 3. Economic characteristics of study ZIP Codes and Erie County, 1990 by race, U.S. Census Data Cheektowaga Town Depew Village Erie County Census Variable White Black White Black White Black Median Household Income, 1989 $29,207 NA $30,557 NA $30,215 $13,630 Median Family Income, 1989 $34,455 NA $35,065 NA $37,012 $17,324 % Below Poverty Level 3.6% NA 4.0% NA 5.7% 33.9% Table 4. Asthma and other respiratory admission rates: * , white population Admissions Erie County N Rate N Rate N Rate Asthma , Other Respiratory , * Rate per 10,000 population includes repeat admissions for the same individual. Table 5. Distribution of other respiratory admissions: * , white population ** ICD9 Categories ( s) N (%) N (%) Erie County N (%) Bronchitis and Chronic Bronchitis (490,491) 131 (81.8) 148 (82.7) 4,477 (74.1) Emphysema (492) 8 (5.0) 8 (4.5) 413 (6.8) COPD (496) 18 (11.3) 22 (12.3) 1,106 (18.3) * Includes repeat admissions for the same individual. ** Categories with fewer than six admissions not shown on table. 9

10 Table 6. Observed and expected numbers of asthma admissions *, standardized (age-specific and age-adjusted) hospitalization ratios with 95% confidence intervals, , white population Standardized Hospitalization Ratio (obs/exp) 95% Confidence Interval ZIP Code Age Group 1990 ZIP Population Observed Cases Expected Cases*** , [0.24, 0.81]** , [0.24, 0.94]** , [0.33, 0.98]** , [0.35, 1.79] , [0.46, 2.09] 75+ 1, [0.43, 2.24] Total 26, [0.48, 0.82]** , [0.33, 1.07] , [0.29, 1.10] , [0.46, 1.23] , [0.29, 1.75] , [0.45, 2.08] 75+ 1, [0.84, 2.70] Total 24, [0.63, 1.03] * Excludes repeat admissions for the same individual. **Denotes a statistically significant difference from expected. The probability that this finding is due to chance is less than 5%. ***Expected cases have been rounded to whole numbers for presentation only. Table 7. Observed and expected numbers of other respiratory admissions *, standardized (age-specific and age-adjusted) hospitalization ratios with 95% confidence intervals, , white population Standardized Hospitalization Ratio (obs/exp) 95% Confidence Interval ZIP Code Age Group 1990 ZIP Population Observed Cases Expected Cases*** ,609 0 < ,102 0 < , [0.64, 1.88] , [0.80, 1.71] , [0.52, 1.09] 75+ 1, [0.83, 1.51] Total 26, [0.84, 1.21] ,426 0 < ,571 <6 <1 >1 [0.05, 22.57] , [0.40, 1.54] , [0.54, 1.37] , [0.93, 1.64] 75+ 1, [0.82, 1.41] Total 24, [0.91, 1.28] * Excludes repeat admissions for the same individual. ***Expected cases have been rounded to whole numbers for presentation only. 10

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