Respiratory-related Disease in Vilnius City Districts and Relationships to CO, NO₂, SO₂ Levels and the Air Pollution Index (API)

By Randolph B. Flay, Fulbright Fellow &

Irena Taraškevičiene,Vilnius Public Health Center

Introduction & Background:

In a time of intensive city development throughout Lithuania and in many other countries of central and eastern Europe, protecting air quality for city residents is of vital importance to protecting public health. Although pollution from stationary sources has declined in the past few years, an increase in pollution from mobile sources yielded an air pollution total of 107,900 tons in Vilnius in 1996 (Zickus, 1999). Carbon monoxide (CO) accounted for about 77% of this total by weight, while nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and other pollutants comprised the remainder. Currently mobile sources, mostly automobiles, account for 88% of air pollution in Vilnius.

In addition to an aesthetic influence on air quality in the commonly known forms of smog and smell, these air quality indicators are an indicator of a real influence on public health. Epidemiological studies prove that increased levels in CO, NO₂, and SO₂, increase the instances of several types of respiratory infections, heart disease, and other respiratory-related diseases (Pershagen G, 1995; Wojtyniak B, 1997). Given this conditions there is a need to set air quality standards, monitor air quality, and meet air quality standards in order to protect public health. Further people should be educated about the influence of air quality on public health and how they can help reduce their exposure to unhealthy conditions and reduce their contribution to air pollution.

This study examines the relationship between air quality and public health in Vilnius by comparing air quality to various diseases in Vilnius city districts. CO, NO₂, SO₂, and the Air Pollution Index (API) are compared in relation to a group of respiratory-related diseases and other diseases that are known to be influenced by air quality. Results of the analysis show a positive relationship between air quality indicators and several diseases. This study will hopefully highlight the need to meet standards, seek ways to improve quality, and develop other ways of minimizing the health risk from air pollution.

Data & Methods of Analysis:

Public Health Data

Public health data were obtained from the public health database maintained by the Vilnius Public Health Center or Vilniaus Visuomenes Sveikatos Centras (VSC). Each time a patient visits a polyclinic in Vilnius, a record is sent to the Vilniaus VSC recording the diagnosis, the patient’s age, residence, the date, and other useful information. The data spanned the years 1991 to 1995 for ages 0-19 and years 1991 and 1992 for all ages. After 1992, only records for those under age 20 were kept. The data set contains over 1 million records and diseases are recorded utilizing the International Classification of Diseases, 9^th Revision (ICD) codes (Министерство здравоохранения СССР).

Raw data from the Vilniaus VSC was collected in a DB4 database format. In order to analyze the information, a Microsoft Access database was designed and the information imported. Also developed in the database was a feature to analyze the data geographically by city district. To accomplish this, each street in the public health database was assigned to the city district in which it is located. This allowed for the creation of queries based on the city districts; i.e. one could then calculate the number of diagnoses of a particular illness by city district.

Utilizing a data table that also included the population living on each street in Vilnius, annual disease rates were calculated per 100,000 residents. In total, two public health data sets were composed; one contained all ages for years 1991 to 1992 and the second contained ages 0-19 but spanned from 1991 to 1995. (This is again because health data after 1992 was only collected for children). Since to analyze the data for years 1991 to 1995 we only are looking at patients aged 0-19, we had to multiply by the portion of the population represented by the age-group 0-19. This was roughly calculated as 0.261 (Vilnius Statistical Office). The diseases evaluated for this study are shown in Table 1.

Air Quality Data

In 1995 a new project by the name of "Air Quality Management in Vilnius City" was initiated by the Environmental Protection Ministry of Lithuania and the Swedish government. This project saw the implementation of a computerized air pollution management system know as "Airviro." This system consists of three automatic pollutant monitoring stations in Vilnius which monitor concentrations of CO, NO₂, NO, NO_X, SO₂, and O₃. These three stations are located in Senamiestis, Žirmūnai, and Žverynas. Measurement data is sent hourly to a central database and is available via the Internet (http://vilnair.gamta.lt/).

The Air Quality Management Group at the Environmental Protection Ministry constructed models based on these measurements, taking into account meteorological conditions in order to simulate pollutant cover throughout Vilnius's city districts. CO, NO₂, and SO₂, concentrations are plotted on a map of Vilnius, allowing for the pollutant level in each district to be estimated. Also, an API is calculated in Vilnius taking into account government standards for CO, NO₂, and SO_2. This is the formula for calculating the API:

API = C(_CO)/LV(_CO) + C(_NO2)/LV(_NO2) + C(_SO2)/LV(_SO2)

Where, C(_CO) = CO concentration

LV(_CO) = Hourly Limit Value for CO

C(_NO2) = NO₂ concentration

LV(_NO2) = Hourly Limit Value for NO₂

C(_SO2) = SO₂ concentration

LV(_SO2) = Hourly Limit Value for SO₂

Based on the API, each Vilnius city district was classified as unpolluted, semi-polluted, and very polluted, and then set to numbers 1,2, and 3 respectively. In this way "1" denotes clean air and "3" denotes very polluted air. Table 2 shows Lithuania Hygienic Norm 33-1998 standards for selected air pollutants.

Analysis

Based on the results for air quality indicators and public health diagnosis rates calculated in each of the 20 city districts, regression analysis was performed to look for relationships utilizing Systat 7.0. Air pollution indicators were held as the independent and the disease rates were held as the dependent. The results of these linear regression analyses are presented in Table 6. The regression analysis is a unique tool because it tells us how much of the variability in our dependent variable is explained by our independent variable. This is enumerated in the R-squared (R²) value in Table 6. The R² value is equal to 1.0 minus the ratio of the residual variability of the Y variable to the original variance (StatSoft, Inc.). If X and Y are perfectly related the residual variance will be equal to 0.0 and thus the R² value equal to 1.0. For example, if we have an R² value of 0.55, then we know that the relationship explains 55% of the variability in the data, and we have 45% residual variability. In short, the closer the R² value is to 1.0, the more of the variability we have explained. The closer R² value is to 0.0, the less we have explained.

Also included is the p-value of the relationship. The p-value is a measure of the significance of the relationship in the sense that the samples under examination are representative of the total population. The p-value is essentially based on the sample size, a larger sample size being more representative of the whole population. A p-value of 0.093 means that there is a 9.3% chance that the relationship between the variables is a complete “fluke.” For the purpose of this study a p-value < 0.05 has been chosen for values that we will accept, which corresponds to "confidence level" of α > 95%. Although the R² value and the p-value are related, one often refers to the R² value as a measure of strength or magnitude and the p-value as a measure of significance.

Sources of Error

In this study, socioeconomic and other factors may also influence public health in addition to environmental conditions and be confounders in the analysis (Anderson R, 1997). Age distribution throughout city districts may not be uniform and results may be affected if a given disease afflicts a certain age group to a greater extent than other age groups.

Results:

Public Health Data Tables

There are two tables presenting public health data. In these tables, each city district in Vilnius is listed along with a group of diseases that were examined. Table 3 shows the average annual number of diagnoses per 100,000 people averaged over years 1991 and 1992 and including all ages, 0-100 years. Table 4 shows the average annual number of diagnoses per 100,000 people as well. However, this table is averaged over years 1991 to 1995 and includes only persons aged 0-19 years. After 1992, polyclinics only collected this data for patients aged 0-19 years. The total number of diagnoses are also listed in the tables.

The vast majority of diseases chosen for examination were respiratory-related infections of various locations. Also chosen was Acute Myocardial infarction (410) due to a known relationship in previous epidemiological studies to CO levels. For the sake of brevity, only data for diseases which showed a relationship to air pollutants are presented in the tables.

The tables can be referred to for the identification of city districts having the highest and lowest disease rates. As a highlight of the findings, Naujamiestis, Rasos, and Zirmunai, have the highest rates of Acute Myocardial infarction (410). Chronic bronchitis (491) is most prevalent in the city districts of Zverynas, Vilkpedes, Senamiestis, and Naujamiestis for the 0-100 year age group.

Air Quality Data Table

Table 5 shows the estimated concentrations of selected air pollution indicators for 1996. Perhaps it would have been more conducive to use data from 1995 or slightly earlier, but the data were not available given the 1996 start date of the air quality monitoring program. However, the air quality conditions in Vilnius have not drastically changed over the time period of the study and it is believed that air quality data among districts should still be quite representative.

Based on the API, the worst air conditions are found in the city districts of Zverynas, Vilkpedes, Snipiskes, Senamiestis, and Naujamiestis. The best air conditions are found in Antakalnis, Fabijoniskes, Justiniskes, Karoliniskes, Lazdynai, N. Vilnia, Pasilaiciai, Rasos, and Verkiai.

Analysis Table of Results

Table 6 shows the results of regression analyses comparing air quality indicators to these disease rates. Where there appear numbers in a particular comparison, the p-value of the regression was less than 0.05 and the α or confidence level was greater than 95%. This essentially means that there is only a 5% chance that the relationship we found was a statistical "fluke." These relationships were accepted as being statistically significant. Presented adjacent to the p-value is the R-squared value, the measure of strength. Again, the R-squared value tells us how much of the variation in a disease rate can be explained by the air pollution indicator.

Where there appear only an "X" in the table the p-value of the relationship was greater than 0.05 but less than 0.10. Although these relationships were not considered significant in this study, there does seem to be some grounds for a relationship that might be clarified with further study. Where there is simply a blank spot in the chart, p-values were greater than 0.10 and there seems to be no significant statistical relationship.

Discussion:

This results of the study showed several interesting relationships, none perhaps stronger than the relationship of air pollution indicators to several chronic respiratory infections. In particular, Chronic pharyngitis and nasopharyngitis (472), Chronic laryngitis and laryngotracheitis (476), Bronchitis, not specified as acute or chronic (490), Chronic bronchitis (491) showed very low p-values and high R-squared values. These findings were true for both age groups, and across all air pollution indicators.

Although this finding is documented in other cities, this study helps highlight the problem in Vilnius. CO, NO₂, and SO₂ lead to an increase in disease, especially when they are present in concentrations above Hygienic Norm standards. To illustrate the extent of the influence, an average rate of chronic bronchitis (491) was calculated for city districts with an API of "unpolluted" and for those with an API of "very polluted." The average rate for the unpolluted districts was 125 per 100,000 for the 0-19 year age group, and in the polluted districts the rate was 216. That means that residents of the very polluted city districts have a 72% greater chance of coming down with chronic bronchitis than residents of the unpolluted districts. A similar percentage increase was also found for the 0-100 year age group.

Another interesting finding is the apparent relationship between Acute myocardial infarction (410) and levels of CO (R-squared = 0.23) and NO₂ (R-squared = 0.22) in the 0-100 year age group. This finding holds consistent with prior epidemiological studies.

CO, NO₂, and SO₂ act in a variety of ways to weaken the body's ability to function properly. CO is known to lead to these health effects: "aggravation of angina pectoris and other aspects of coronary heart disease, decrease exercise tolerance in persons with peripheral vascular disease and lung disease, impairment of central nervous system functions, and possible increase risk to fetuses (South Coast Air Quality Management District, 1999)." NO₂ is known to lead to these health and other effects: "potential to aggravate chronic respiratory disease and respiratory symptoms in sensitive groups, risk to public health implied by pulmonary and extra-pulmonary biochemical and cellular changes and pulmonary structural changes, and contribution to atmospheric discoloration." SO₂ is known to lead to these health effects: "bronchoconstriction accompanied by symptoms which may include wheezing, shortness of breath and chest tightness, during exercise or physical activity in persons with asthma."

Although this is a preliminary study, the relationships found show that a strong influence is being exhibited by air quality on public health. These findings merit further examination and bring attention to the need for cities such as Vilnius to take strong steps to bring air quality into compliance with government norms. It is further hoped that other cities in Lithuania will begin to take the same steps toward more effective monitoring and management of air quality.

Conclusions:

· Higher CO, NO₂, and SO₂ levels have been proven in past studies to lead to higher rates of respiratory and other illnesses.

· Air pollutant levels are not evenly distributed throughout Vilnius, thereby exposing residents of certain districts to air of poor quality.

· Disease rates for respiratory-related, and other diseases are not evenly distributed throughout city districts in Vilnius.

· Districts with more polluted air closely correspond to those with higher respiratory-related disease rates.

· Residents in city districts with an API of "very polluted" had on average a 72% higher rate of Chronic bronchitis (491) than residents of districts with an "unpolluted" API. Similar increases were noted with other respiratory-related diseases in this study.

Recommendations:

· There needs to be a plan for coming into compliance with air pollutant standards.

· Monitoring should be continued in Vilnius and expanded to other cities in Lithuania with potential air pollution problems.

· Residents should be supplied with information about air quality conditions, how to minimize their health risk from poor air quality, and how to reduce their contribution to air pollutant levels.

References:

Air Quality Management Group, Ministry of Environmental Protection, Lithuania. Vilnius Air Quality Management. Internet: Ministry of Environmental Protection, 1999.

Anderson R, Sorlie P, Backlund E, Johnson N, & Kaplan G. Mortality Effects of Community Socioeconomic Status. Epidemiology. January 1997, Volume 8, Number 1: 42-47.

Brunekreef B, Janssen N, Hartog J, Harssema H, Knape, M, & Vliet P. Air Pollution from Truck Traffic and Lung Function in Children Living near Motorways. Epidemiology. May 1997, Volume 8, Number 3: 298-303.

Jedrychowski W, Maugeri U, Flak E, Mroz E, & Bianchi I. Predisposition to Acute Respiratory Infections Among Overweight Preadolescent Children: An Epidemiologic Study in Poland. European EpiMarker. April 1999, Volume 3, Number 2: 1-7.

Ministry of Health, Lithuania. Hygienic Norm 33-1998, Air Quality Standards. Vilnius: Ministry of Health, 1998.

Pershagen G, Rylander E, Norberg S, Eriksson M, & Nordvall SL. Air Pollution Involving Nitrogen Dioxide Exposure and Wheezing Bronchitis in Children. International Journal of Epidemiology 1995; 24:1147-1153.

South Coast Air Quality Management District, the State of California. 1997 Air Quality Management Plan. Internet: Air Quality Management District, 1999.

Statistical Office of Vilnius. Vilnius in Figures, 1996. Vilnius: Vilniaus Apskrities Statistikos Valdyba, 1998.

Statistikos Departmentas, Prie Leituvos Respublikos Vyriausybės. Mirties Priežastys 1997. Vilnius: Statistikos Departmentas, 1998.

Statistikos Departmentas, Prie Leituvos Respublikos Vyriausybės. Natural Resources and Environmental Protection 1997. Vilnius: Statistikos Departmentas, 1998.

StatSoft, Inc. Electronic Textbook. Internet: StatSoft, Inc., 1998.

Van Leeuwen F.X.Rolaf. WHO Air Quality Guidelines for Europe. European EpiMarker. April 1997: 1-3.

Wojtyniak B, Rabczenko D & Piekarski T. Short-term Effect of Air Pollution on Mortality in Poland. European EpiMarker. July 1997: 1-3.

World Bank, Environment Department. Initial Draft of Industrial Pollution Prevention and Abatement Handbook. Internet: World Bank, 1995.

World Resources Institute. Why the Increase in Asthma? Internet: World Resources Institute, 1999.

Zickus, Mindaugas. Influence Of Meteorological Parameters On The Urban Air Pollution And Its Forecast. Internet: Ministry of Environment, 1999.

Министерство здравоохранения СССР. Статистическая классификация болезней, травм и причин смерти. Москва: Министерство здравоохранения СССР, 1986.