Epidemiological Study PHM

8/4/2019 Epidemiological Study PHM

1/47

Public Health Monitoring of the Metro Manila Air Quality Improvement Sector Development Program

70Main Report. March 2004

6. Epidemiological study

The study was undertaken by prospectively collecting environmental and health data over the

period January to May 2003. The components of the study are (i) ambient air monitoring in sixsites, (ii) identification of high, medium and low-exposure areas to air pollution, (iii) householdsurvey, (iv) child health monitoring, (v) adult health monitoring, (vi) health center monitoring, (vii)indoor and outdoor air monitoring, (viii) biological monitoring for blood lead, (viii) hospital ERmonitoring, (ix) private clinic monitoring, and (x) GIS mapping.

6.1 Identification of exposure risk areas

The ambient PM10 monitoring data obtained in 2002 from six sites for the health riskassessment (HRA) component of the project were modeled using raster GIS technique tocategorize the level of risk of exposure associated with the ambient PM levels in Metro Manila.The categories of exposure risks to air pollution are shown in Table 5.32. Further, the results

of the application of classification scheme are shown in Figures 5.15a and 5.15b. Antipoloconsistently showed the lowest ambient PM levels therefore it was designated the lowexposure risk area.

Eight (8) sentinel barangays were randomly chosen in each of the high, medium, and lowexposure risk areas. A total of 24 Barangays were selected representing sentinel communitiesall over Metro Manila and Antipolo. The barangays randomly selected in the exposure areas aresummarized in Table 6.1

Table 6.1 Sentinel barangays in the exposure areasExposure Area Barangays City/Municipality

High Bagbaguin KalookanMapulang Lupa ValenzuelaPaso de Blas ValenzuelaParada ValenzuelaPaltok Quezon CityNayong Kanluran Quezon CityBungad Quezon CityPinyahan Quezon City

Medium Bambang TaguigCalzada TaguigLidig Tipas TaguigIbayo Tipas TaguigPalingon Taguig

Tuktukan TaguigPineda PasigMartirez/Aguho Pateros

Low Sta. Cruz AntipoloMayamot AntipoloMambugan AntipoloSan Isidro AntipoloDela Paz AntipoloSan Luis AntipoloInarawan AntipoloBagong Nayon Antipolo


2/47



6.2 Socio-demographic and health profile of sentinel households

A total of one hundred (100) households were randomly selected in each sentinel barangay.

The sentinel household should meet the following requirements:

The household should have been a resident of the barangay for at least 2 years prior tothe study;

Should have at least one member who is a child with age ranging from 6 to 10 years old;and,

Willing to participate in the study.

A total of two thousand four hundred (2400) households were randomly selected toparticipate in the study. The household survey component of the study was conductedto obtain vital socio-economic, demographic, environmental exposure and healthoutcome data among sentinel households randomly selected in barangays located in

the predicted low, medium and high exposure risk areas.

This section of the report provides descriptive information of the characteristics and profile ofthe study households, potential exposures to indoor and outdoor air pollution and possiblehealth outcomes. Aside from the descriptive section of the report, analysis of the relationshipsbetween potential environmental exposures and specific health outcome variables using across-sectional study approach was also performed.

Baseline data from the household survey were further used in the analysis of the prospectivemonitoring of health outcomes from the sentinel household as well as in the assessment ofexposure to air pollution.

The main data collection tool used in the survey is an interviewer-administered questionnaire(Appendix 6-1) developed and pre-tested in communities not included in the study scope toensure appropriateness and accuracy. Interviewers were provided training by the consultantsand hands-on experience prior to the commencement of the actual data collection.

Data accuracy and completeness were insured by thorough review of the completedquestionnaire by the interviewers and conducting a re-visit or repeat interview in cases whereinformation provided is unclear or conflicts with other responses

The following assumptions were made in this study:

The minimum requirement of 2-year residency in the community as inclusion criteria was

based on the assumption that health impacts of air pollution (PM10) can be possiblyobserved with a minimum of 2 years community exposures. Furthermore it is assumedthat on the average it takes 2 years for a household informant to have detailedawareness and information on the true state of affairs of health in their community.

The classification into high, medium and low exposure risk areas of Metro Manila werebased on air pollution models using actual ambient air monitoring data that are robust.


3/47



6.2.1 Results of the household survey

6.2.1.1 Study household distribution across study area

Table 6.2 shows the frequency distribution of study households by air pollution exposure riskcategory.

Table 6.2 Frequency distribution of study households by air pollution exposure categoryHousehold locat ion Number Percent

Low air pollution area 800 33.7

Medium air pollution area 800 33.7

High air pollution area 777 32.7

Total 2,377 100

6.2.1.2 Socio- demographic characteristics

Age

The study communities are relatively young with majority of the members 15 years old andlower. No significant differences in age distribution can be recognized between the various airpollution exposure risk areas. This is shown in Figure 6.1 .

Fig. 6.1 Percentage age distribution of study HH by exposure area

0

5

10

15

20

25

30

1 to 5 6 to

10

11 to

15

16 to

20

21 to

25

26 to

30

31 to

35

36 to

40

41 to

45

46 to

50

51 to

55

56 to

60

61 to

65

66 +

Age

Percent Low

Medium

High

Educational profile

Majority of the survey informants have finished high school education. A high school graduaterespondent is assumed to provide assurance that they have understood and respondedappropriately to the study questions as compared to a respondent with a lower educationalbackground. Educational profile of respondents across various air pollution exposure risk areasdoes not vary significantly as shown by Figure 6.2 .


4/47



Fig. 6.2 Education profile of survey respondents by location

0

10

20

30

40

5060

Low Medium High

Air Pollution Exposure Areas

Percent

No Formal Education

Elemantary Level

High School Level

College Level or Higher

Vocational

Figure 6.3 shows that respondents in the medium exposure risk areas in Metro Manila have thehighest average level of formal schooling (9.5 years) as compared to high and low exposurestudy households.

Fig. 6.3 Average years of formal schooling of respondents by location

8

8.5

9

9.5

10

1Air Pollution Exposure Areas

Years

Low

Medium

High

Household income

Majority of the study households have monthly income of P 8,000.00 and below, Table 6.3shows the distribution of households across various income categories.

Table 6.3 Household distribution by monthly incomeIncome level (Pesos) Number Percent

Less than 5,000 pesos 920 38.75,001 to 8,000 914 38.58,001 to 15,000 405 17.1Greater than 15,000 136 5.7

Total 2,375 100

Available data show slight but significant (p =. 000) differences in the income profile acrossvarious air pollution exposure groups. Households in the low exposure areas have reportedlower incomes (higher proportion of families earning less than PhP 5,000) as compared to theother exposure risk areas. Households in the medium exposure category appear to have betterincome profile than the rest as shown in Figure 6.4.


5/47



Fig. 6.4 Average household income distribution of barangays by location

0

10

20

30

4050

< 5,000 5,001 - 8,000 8,001 - 15,000 > 15,000

Pesos per month

Percent Low

Medium

High

Housing

Aside from income, proxy indicators of socio-economic status commonly used incommunity surveys include the construction material used in building the house and the currentcondition of repair of the house. Households were classified as to type of construction materialsas follows:

Temporary house predominantly made of temporary materials such as recycledboards and cardboards, commonly observed amongst informal settlers in Metro-Manila

Semi-permanent houses are usually made of wood with concrete for flooring orwalls, which can easily be dismantled and transferred to another site.

Permanent house commonly made of concrete.

Classification based on the state of repairs of house, yielded the following categorization:

Good no repairs needed

Fair house needs minor repair

Poor signs indicating that no repair has been done on many aspects of the house.House in danger of breaking down and generally unsafe to live in requiring majorrenovation and repair.

Table 6.4 and Table 6.5 below indicate the frequency distribution of households by buildingmaterial type and state of repair respectively.

Table 6.4 Household classification by construction materials used in building

Building material Number Percent

Temporary 634 26.7Semi-permanent 817 34.5Permanent 920 38.8

Total 2,371 100


6/47



Table 6.5 Household classification by house conditionHouse condition Number Percent

Good 678 28.8Fair 1,212 51.5Poor 464 19.7

Total 2,354 100

Majority of the houses surveyed are mostly constructed of permanent and semi-permanentmaterials, typical of houses that can be found in Metro Manila although a significant portion aretemporary in nature. Furthermore, majority of the houses only need minor repairs. Thereappears to be a small but statistically significant (p = .000) difference in the type of housingamongst the households located in the pollution exposure risk areas. Although comparable in

proportion to houses made of permanent materials, there are a higher proportion of housesmade of temporary material in the low exposure areas as shown in Figures 6.5 and 6.6.

Fig.6.5 Distribution of type of household construction in study

barangays by location

0

10

20

30

40

50

Temporary Semi-permanent Permanent

House Construction

Percent

Low

Medium

High

Fig. 6.6 Distribution of household in housing condition by exposure

area

0

10

20

30

40

5060

Good (little or no evidence of

dilapidation

Fair (needs minor repair) Poor (dilapidated, needs major

repair

House Condition

Percent Low

Medium

High


7/47



6.2.1.3 Exposure factors

There are several environmental factors that can contribute to pollution exposures to members

of the household. These can include any of the following sources: Exposure from air pollution generated by mobile sources (jeepneys, buses, tricycles,

etc.). Potential exposure of the study households from these sources has beenestimated using proxy variable of house location vs. major/minor traffic routes.

Major determinants of indoor air pollution were also covered to include: type of cookingfuel used, frequency and duration of cooking activity, location of kitchen within andoutside the house, and smoking behavior among household members

The level of household congestion as reflected in the crowding index indicates furthersusceptibility of household members to respiratory infection

House location vs. traffic

Table 6.6 shows that majority of the houses (88.8%) are located along major traffic roads whereambient air pollution from mobile sources are expected to be higher compared to residents inminor routes. Contributors to air pollution along major traffic routes include public utility vehicles(buses and jeepneys) and private vehicles (trucks and cars). Along minor traffic routes privatevehicles and emissions coming from tricycles are the main contributors.

Table 6.6 Frequency distribution of study household location by traffic route

Household locat ion Number Percent

Along minor traffic route 264 11.2

Along major traffic route 1,097 88.8

Total 2,361 100

Available data shown in Figure 6.7 below indicate a small but statistically significantdifference (p = .02) in the location of study households to major/minor roads acrossthe different air pollution exposure areas.

Fig. 6.7 Proximity of study HH to roads by exposure area

0

20

40

60

80

100

Major traffic route Minor traffic route

Location of Study Households (p = 0.02)

Percent

Low

Medium

High


8/47



Household cooking fuel and cooking activ ities

The type of cooking fuel used has been identified in the literature as one of theprimary determinants of indoor air pollution. Amongst the fuels used in Filipinohouseholds, electricity followed by liquefied petroleum gas (LPG) are two of the lowestgenerators of indoor air pollution as compared to kerosene and wood.Table 6.7 showsthe profile of cooking fuel used in the study households.

Table 6.7. Profile of cooking fuel used in the study households Type of cooking fuel No. of households using Percent

LPG 1,766 74.5

Wood 289 12.2

Kerosene 498 21.0

Note: Category are not mutually exclusive, households may use multiple cooking fuel types

Majority (74.5 %) of the study households use LPG for cooking. A small fraction (21%)use kerosene and with wood only occasionally used (12.2%). This is an expectedprofile amongst households in Philippines urban areas.

Figure 6.8 below shows that LPG remain the most commonly used cooking fuelamongst the three exposure clusters

Fig. 6.8 Distribution of HH by cooking fuel used and by location

0

10

20

30

40

50

60

70

8090

LPG Wood Kerosene Others

Cooking Fuel Used

Percent Low

Medium

High

The average cooking duration per day is highest among households located in mediumexposure areas with an average of 2.3 hours per day. Households located in the highand low exposure areas have comparable cooking duration of 2.1 hours per day. Thisis shown in Figure 6.9.


9/47



Fig. 6.9 Average cooking duration of households per day by

exposure area

1.95

2

2.05

2.1

2.15

2.2

2.25

2.3

2.35

1Air Pollution Exposure Area

Hours

Low

Medium

High

Table 6.8 shows that most study households do their cooking inside the house. This is acommon observation among households located in highly urbanized areas like Metro Manila.The location of the kitchen is an important indoor air pollution determinant. Cooking inside thehouse exposes occupants to higher levels of indoor air pollution due to inadequacy ofappropriate ventilation and exhaust systems especially in the lower social strata of society.

Table 6.8 Frequency distribution of study households by kitchen locat ion

Kitchen location Number Percent

Inside the house 2,077 87.7

Outside the house 291 12.3 Total 2,361 100

Figure 6.10 shows the location of kitchen in a household is equally distributed across thevarious air pollution exposure areas.

Fig. 6.10 Distribution of HH by location of kitchen by exposure areas

0

20

40

60

80

100

Inside the House Outside the House

Location of Kitchen

Percent Low

Medium

High


10/47



Household crowding index

The crowding index is a composite variable composed of the number of household members asthe numerator and the number of rooms used for sleeping as the denominator. The higher theindex, the more congested is the household putting the household members at risk for indoor airpollution exposure as well as cross-infection of communicable diseases specially respiratoryillnesses. The crowding index is highest (4.8) among households located in low air pollutionexposure areas, and lowest (4.3) amongst households in the medium exposure area.Households in the high pollution areas have an average crowding index of 4.75 as shown inFigure 6.11.

Fig. 6.11 Average crowding Index of HH by exposure area

4

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.84.9


CongestionIndex

Low

Medium

High

Smoking profile

Smoking is another major contributor to indoor air pollution. Second-hand smoke is a potentialsource of multiple chemical exposure that among other things can cause respiratory symptomsamong the vulnerable members of the households. Figure 6.12 shows the average number ofsmokers per household across the various pollution exposure areas. Households located in themedium exposure areas have the highest number of smokers (0.95). Households in the highexposure risk areas have the lowest number of smokers (0.83).

Fig. 6.12 Average number of smokers per HH by exposure areas

0.75

0.8

0.85

0.9

0.95

1


AverageNumberof

Smokers Low

Medium

High


11/47



Other environmental considerations

Finally, other environmental factors such as drinking water quality, waste disposal and

sanitation contribute to the overall health of a particular household. Although they do not havedirect impact on respiratory conditions, these variables may affect indirectly the respiratoryhealth of people through weakening of the resistance, leading to increased vulnerability torespiratory infections.

A significant proportion of households in the low exposure area have access to commonsources of drinking water (Figure 6.13 ) as compared to the other households.

Fig. 6.13 Source of drinking water of HH by exposure area

0

10

20

30

40

50

60

7080

Faucet Inside the

House

Source Inside the

House

Public

Faucet/Well

Neighbor's

Faucet/Well

Other Sources

Drinking Water Source

Percent Low

Medium

High

Available data indicate that a significant majority of houses have access to their own toilet(Figure 6.14). There is no difference in toilet access amongst households located in variousexposure risk areas.

Fig. 6.14 Proportion of houses with toilet by exposure area

0

10

20

30

405060

70

80

90

100

With Own Toilet Without Own Toilet

Toilet Ownership

Percen

t

LowMedium

High


12/47



Of the 10% study households (n = 238) without toilet, dispose of their wastes in alternativeways, commonly through the used of their neighbors toilet. Waste disposal strategy iscomparable across the various households in the exposure risk areas. This is illustrated in

Figure 6.15 .

Fig. 6.15 Type of waste disposal by exposure area

0

10

20

30

40

50

60

70

Discarded in

Public Toilet

Discarded in

Neighbors Toilet

Discarded Wrap

and Throw

Discarded in

River, Canals

Streams

Discarded

Elsewhere

Percent Low

Medium

High

6.2.1.4. Health profile and access to health resources of study households

In order to assess the respiratory health status of household members, the respiratorysymptoms experienced by household members and consultations made for the year 2002 wereobtained from the survey.

Utilization of health resources

A significant number of households (21.4%) reported that at least one member of the householdwas brought to the hospital in 2002, while 30.6% have at least one member consulting the localhealth center or being brought to a doctor for consultation. Statistical analysis shows nosignificant differences in the proportion of household members being brought to the hospital orconsulting the health center/private doctor in 2002 in the various air pollution exposure areas.Data on access to health resources are shown in Table 6.9.

Table 6.9 Household consultation to a health facility for 2002Facility accessed Number Percent

Hospital 509 21.4Health Center/Private MD 725 30.6

Note: Categories are not mutually exclusive

Sub-analysis conducted in the same section of the population showed some interesting findingsas follows:

1. There is a statistically significant finding that less number of household members werebrought to a hospital in 2002 amongst households which use LPG as cooking fuel. The tablebelow shows this relationship:


13/47



Household member brought to the hospital in 2002HH using LPG No N (%) Yes N (%) Total

No 438 (72.3) 168 (27.7%) 606Yes 1,424 (80.7) 340 (19.3) 1,764

Total 1,862 508 2,370Note: p value = 0.000

2. There is a statistically significant finding that more number of household members werebrought to the hospital in 2002 amongst households which are using wood as the maincooking fuel as shown in the table below.

Household member brought to the hospital in 2002HH using wood No N (%) Yes N (%) Total

No 1,652 (79.4) 429 (20.6) 2,081Yes 210 (72.7) 79 (27.3) 289


3. There is a statistically significant finding that more household members were brought to thehospital in 2002 amongst households which are using kerosene as the main cooking fuel.The table below shows this finding.

Household member brought to the hospital in 2002HH using wood No N (%) Yes N (%) Total

No 1,490 (79.6) 382 (20.4) 1,872Yes 372 (74.7) 126 (25.3%) 498


4. Logistic regression analysis using members of the household brought to the hospital in 2002as dependent variable against independent variables such as: air pollution exposure area,household income, cooking fuel used (LPG, wood and kerosene), crowding index,household size, and number of smokers in the household resulted in the protective effect ofLPG used as cooking fuel and negative effect of increasing household size resulting in morehospital consultations in 2002 as shown in Table 6.10

Table 6.10 Determinants of hospital admissions, 2002Determinants Beta S.E. Significance

LPG as cooking fuel - .431 .111 .000Household size +.053 .024 .025


14/47



5. Logistic regression analysis using members of the household consulting the health center in2002 as dependent variable and independent variables such as: air pollution exposure,

household income, cooking fuel used (LPG, wood and kerosene), crowding index,household size, and number of smokers in the household resulted in the following variableas main determinants as shown in Table 6.11.

a) Negative effect of increasing household size resulting in more health centerconsultations in 2002.

b) Living in medium and high air pollution exposure areas were not significant butshowed trends of increasing health center consultations

Table 6.11 Determinant of health center consultations, 2002Determinant Beta S.E. Sig.

Household Size .084 .021 .000.Residing in moderate air pollution area .309 .111 .06Residing in high air pollution area .198 .113 .08

Health symptoms and status of households, 2002

The most common reported respiratory symptom amongst household members for 2002 is theoccurrence of severe cough. The proportion of respondents reporting severe cough for 2002appear to be highest in high pollution exposure areas. This relationship, however, was notobserved for reports on other air pollution-related symptoms such as chest tightness, wheezing,and chest pain. Figure 6.16 below summarizes the symptoms profile of household members for

2002.

Fig. 6.16 Symptoms expereinced by HH members in 2002 by exposure areas

0

10

20

30

40

50

60

Chest Tightness Wheezing Severe Cough Chest Pain

Symptoms

Percent Low

Medium

High

Available interview data indicate that respiratory diseases often account for a significantproportion of hospitalization amongst study households in 2002. There appears to be nocorrelation between the percentages of hospital admission for respiratory diseases across thevarious air pollution exposures areas as shown Figure 6.17:


15/47



Fig. 6.17 History of hospitalization of HH members in 2002 by exposure areas

0

10

20

30

40

50

60

70

Respiratory Diseases Cardiovascular Diseases Other Diseases (Accidental,

Non-Accidental)

Symptoms Causing Hospitalization

PercentofHH

Low

Medium

High

Deaths in the study households in 2002 due to respiratory diseases appear to be higher in highexposure area compared to households in the middle and low air pollution exposure areas asshown in Figure 6.18. Death due to cardiovascular diseases is highest among households inthe medium exposure category. Majority of mortality reports were due to other accidental andnon-accidental illnesses. Difference in death rates across air pollution exposure categories,however, is not statistically significant.

Fig. 6.18 Cause of death amongst HH reporting death in the family for

2002

0

10

20

30

40

50

60

70

Respiratory Diseases Cardiovascular Diseases Other Diseases (Accidental,

Non-Accidental)

Percent Low

Medium

High

Access to health service providers, 2002

All study households across the various exposure categories have nearly universal access tolocal health care center services, traditional healers, and private doctors. Available data indicatethat access to traditional healers are higher compared to private doctors. Differences on healthseeking behavior maybe influenced heavily by cost, and accessibility of the health resource.This is reflected in Figure 6.19 below:


16/47



Fig. 6.19 Access to specific health providers of study HH by exposure

location

0

20

40

60

80

100

120

Health Center Traditional Hilot Private Doctor

Health Provider

Percent Low

Medium

High

Health perception of respondents

An inquiry was made on the perception of respondents with regards to the general health of thecommunity residents in their area. Majority of respondents rated the health of the communityresidents as good and fair while some 15% estimate that community members have a low levelof health as shown in Table 6.12.

Table 6.12 Respondents perception of community health statusHealth status Number Percent

Good 1,011 43.1

Fair 1,020 43.5

Poor 315 13.4

Total 2,346 100

Although not statistically significant, there is a higher perception among respondents of poorcommunity health as the exposure to air pollution becomes higher. This is shown in Figure 6.20below:


17/47



Fig. 6.20 Respondent's estimate of health status in their barangay by

exposure area

0

10

20

30

40

50

Good Fair Poor

Health Status

Percent Low

Medium

High

On the other hand, estimates on the health of their own households tend to be better comparedto estimates of community health status. Majority of respondents (59.4%) have rated their familyhealth to be good. Less than 5% have rated the health of their household members to be pooras shown in Table 6.13 below.

Table 6.13 Respondents perception of own household health statusHealth status Number Percent

Good 1,409 58.7

Fair 881 36.7

Poor 83 3.5

Total 2,373 100

Although not statistically significant, there is a higher perception among respondents of poorhealth of household members as the exposure to air pollution becomes higher as demonstratedin Figure 6.21.

Fig. 6.21 Respondent's estimate of health status of their household by

exposure area

0

10

20

30

40

50

60

70

Good Fair Poor

Health Status

Percent Low

Medium

High


18/47



6.3 Exposure to air pollution

The assessment of exposure to air pollution involves the (i) ambient air monitoring for 24-hr PM

in six sites within the study areas , (ii) indoor air measurements of 24-hr PM, 8-hr nitrogendioxide, and 24-hr carbon monoxide (iii) outdoor air measurements of 24-hr PM10, (iv)measurements of indoor and outdoor lead levels, and (v) biological monitoring of blood lead.

6.3.1 Ambient air monitoring

Ambient 24-hr PM10 samples were collected January to May 2003 from six sites (2 stations ineach defined high, medium and low exposure areas) as presented in Section 5.1 of this report.The results of the ambient monitoring are described in Figure 5.1 of this report.

The exposure assessment for the prospective study aims to determine the exposure of thesentinel households to ambient air pollution. Thus, the ambient PM10 levels obtained from the

six sites stated above were extrapolated using the geographic information system to the specificbarangays where the sentinel households are located. The results of the extrapolation areshown in Appendix 6-2.

The results are graphically illustrated in Figure 6.22 in terms of mean levels of ambient PM10 inthe high-, medium-, and low exposure risk areas. It can be seen that the levels obtained for thehigh exposure areas are consistently higher than those in the medium- and low-exposure areas.It should be noted that these levels are only the average values for January to May 2003. If thistrend would continue, then the long term levels in the high and medium exposure risk areaswould exceed the national guideline of 60 ug/m3.

Fig. 6.22 Ambient PM 10 levels in the exp osure areas

0

50

100

150

High Medium Low

Risk Areas

PM10g/m3

25th % tile

m in

max

mean

75th % tile


19/47



6.3.2 Indoor /Outdoor Air Monitoring

A total of 120 households were selected from the 2400 sentinel households for the indoor and

outdoor air monitoring. The selection was undertaken through the following procedures. Thereare 100 sentinel households in each of the 24 study barangays. The 100 sentinel households ina study barangay were mapped out using GIS and the centerpoint determined. From this pointand within 500 meter-radius, the households for indoor/outdoor air monitoring were randomlyselected. The assumption is that ambient air quality particularly of PM10 is highly stable withinthis distance and the levels obtained from the sampled households could represent the otherhouseholds within the radius.

Parameters measured in the indoor air were PM10, NO2 , CO, and environmental lead. For theoutdoor air monitoring, only PM10 and environmental lead were determined. 24-hr PM10samples were collected using personal air sampling equipment at a flow rate of 3 liters perminute. The sampling filters were pre-weighed and analyzed by PNRI. For the 8-hr NO2, the

same sampling equipment were used but using a flow rate of 0.4 liters per minute. NO2 wasanalyzed in the laboratory using the Griess Saltzman method. 24-hr CO was measured usinglow range passive dosimeter tubes. Environmental lead was determined from the PM filtersamples using atomic absorption spectrophotometry.

PM10 Levels

Table 6.14 shows the indoor and outdoor air 24-hr PM10 levels at the exposure areas (high-,medium- and low-risk areas).

Table 6.14 Indoor and outdoor 24-hr PM10 levels in exposure areas

N Mean SD Range

Indoor:High 40 111.04 116.43 7.54 730.53Medium 40 141.25 96.92 60.63 442.95Low 40 183.65 456.35 13.37 2,906.56

Overall 120Outdoor:

High 39 102.6 55.31 16.6 246.45Medium 40 128.46 70.95 29.52 317.28Low 40 99.24 134.00 17.74 866.16

Overall 119

Correlation analysis between indoor and outdoor PM10 levels indicate that the two parametersare highly correlated (0.847) and statistically significant at p=.000. This indicates that as theoutdoor PM10 level increases, the indoor air PM10 level likewise increases. It should be notedfurther that the mean indoor PM10 level is statistically higher than outdoor air PM10 level in all theexposure risk areas. Factors that may have influenced this result is the quality of ventilationinside the house and the lack of regular dusting practices of the households allowingaccumulation of dust indoors.


20/47



Further statistical analysis show that no significant association can be established betweenoutdoor PM10 and the high, medium and low exposure risk areas (p=0.193). The same isobserved in the case of indoor PM10 levels (p=0.274). Another correlation analysis was also

performed to determine if there is a significant association between indoor and outdoor PM10levels and the ambient PM10 concentrations. The results of analysis indicated that both indoorPM10 (Beta= -.068) and outdoor PM10 (Beta=.045) levels are weakly correlated with ambientPM10 levels.

The indoor air PM10 levels were regressed against several contributory variables such as thelocation of the household along near major and minor roads; the use of various cooking fuelsuch as LPG, kerosene and wood; location of cooking facility inside or outside the house; thenumber of smokers in the household; the number of smokers who smoke inside the house; andthe outdoor PM10 levels. The significant results of regression are shown in Table 6.15. Thecontributory factors significantly correlated with indoor PM10 levels are the location of the house(using the minor road as reference group), the number of household members who smoke

(using number of non-smokers as reference group), the number of household members whosmoke inside the house, and the outdoor PM10 levels. These findings concur with that of thebaseline health study in 2000 (6).

Table 6.15 Regression analysis of indoor PM10 against contributory factors

Independent variables coefficient p-value coefficient p-value

No. of HH members who smoke insidehouse

20.778 0.006 21.703 0.003*

Outdoor PM10 levels 0.433 0.000 0.425 0.000*Dependent variable: indoor PM10*Significant @ p = 0.05

Comparative statistics of indoor PM10 levels obtained from other studies are presented in Table6.16. It can be seen from the results of these studies that the Metro Manila households inurban slums and along high traffic density areas have higher indoor exposure levels to PM10than those in randomly selected households in the entire Metro Manila. The indoor PM levels inthe urban slums were highly correlated with number of smokers in the households. Ruralhouseholds are not spared as well to the risk of elevated exposure to indoor PM10, These levelsat rural households are highly associated with the type of fuel used for cooking and lighting,particularly wood and kerosene. In the present study, the higher indoor PM10 levels in thebarangays of Antipolo which are supposed to be in the low exposure area, may be due to poorventilation in the house or accumulation of road dusts inside the house.

Table 6.16 Comparative statistics of 24-hr indoor PM10, 1993 2003N Mean

g/m3Study characteristics

186 117* Metro Manila in randomly selected households, 1993 (2)60 221 Metro Manila in urban slum households,1995 (5)

108 209 Metro Manila in households along high density traffic, 2000(6)80 126 Metro Manila in high and medium exposure areas, 200363 196 Rural households in Quezon 1999

100 132 Rural households in Pangasinan 200140 183 Antipolo City, 2003

*Estimated from TSP @ 55% of TSP is PM10


21/47



Environmental lead (Pb)

The lead content of the particulate matter obtained from indoor and outdoor air samples wasdetermined to assess if the elimination of lead from gasoline has resulted to improvement in airquality. The results are shown in Table 6.17. The mean outdoor lead ranged from 0.11 to0.37g/m3 which are much lower than the National Air Quality Guideline of 1.0 g/m3. Althoughthere are high values for outdoor lead(1.48 and 4.28 g/m3) obtained, the frequency ofoccurrence is only once in both cases. The mean lead level is highest in the high exposure riskarea and lowest in the low exposure risk areas. However, the differences are not statisticallysignificant.

Table 6.17 Environmental lead in exposure areas

Exposure area Indoor OutdoorN Mean Range N Mean Range

High 40 0.21 0.01 0.87 39 0.27 0.005 1.48Medium 40 0.18 0.01 0.57 40 0.37 0.003 4.28Low 40 0.15 0.01 0.77 40 0.11 0.01 0.67Overall 120 0.18 0.01 0.87 119 0.25 0.005 4.28

Statistical test was done to verify if outdoor lead levels are correlated with indoor lead. Asshown in Table 6.18, the correlation is very weak. Nevertheless, these results indicate that leadis still in the environment and may continue to pose serious threat to health particularly tochildren.

Table 6.18 Result of paired samples correlationN Correlation Significance

Indoor Lead

Outdoor Lead

116

1160.210 0.023

Comparative statistics for environmental lead exposures (24-hr) in Metro Manila are shown inTable 6.19. The WHO study in 1990 showed that the exposure of study populations toenvironmental lead in Metro Manila on the average was 1.2 g/m 3 among commuters and 1.4g/m3 among air-conditioned bus drivers which are almost three times the health guideline setby WHO at 0.5 g/m3. The jeepney drivers were the highest risk group in 1990. These elevatedlevels were attributed then to the high lead content of gasoline of 0.84 g/liter up to 1991 and 0.6g Pb per liter of gasoline in 1992.

The reduction of ambient lead levels in 1994 may be attributed to the further reduction of lead in gasolineto 0.15 g/liter in mid -1993 and unleaded gasoline was introduced in early 1994 to key urban centers in

the country. The unleaded fuel policy was implemented nationwide in 2000 with the passing of the Clean

Air Act of 1999. In the 2000 and 2003 studies, the effect of this reduction is apparent.


22/47



Table 6.19 24-hr environmental lead exposures in Metro ManilaYear N Values

(g/m3)Reference

1990 71 3.6 WHO-UP Manila1990 38 1.4 WHO-UP Manila1990 37 1.2 WHO-UP Manila1992 74 1.0 2.3 DENR*

1993 No data 0.3 0.6 DENR*1994 51 0.214 DOH-WHO2003 80 0.195 DOH-ADB-WHO

*Source: URBAIR-Metro Manila, 1996 (4)

Blood lead

A total of 450 children (6-10 year-old) in the sentinel barangays participated in thedetermination of blood lead. Figure 6.23 shows the blood lead levels of children in the high-,medium-, and low-exposure areas. Statistical analysis indicate significant differences betweenthe exposure areas (p=.000). On the average study children from the high-risk areas haveblood lead levels 2.5 times higher than those from medium exposure areas and 1.5 timeselevated compared with those from the low exposure areas.

Regression analysis was conducted to determine what child or household attributeshave significant association with blood lead. Among these attributes are age and sex of thestudy child, location of the house, and indoor/outdoor lead levels. The results of regressionindicate that all these variables tend not to exert influence on the levels of blood lead. Althoughthe blood lead levels, on the average, has significantly reduced from 2000 to 2003, the lingeringpresence of lead in children is still a health concern. Considering that no risk factors can beidentified from the regression analysis, the levels of blood lead may be explained by pastexposures of children to lead from the air and from those deposited in the soil. In such case,the deposited lead in the bones tend to equilibrate with the lead in blood. Another possibility isthat there are other sources of lead to which the present study children are exposed to, forexample, lead in soil and food.

Fig. 6.23 Blood lead levels in exposure areas

0.0

10.0

20.0

30.0

40.0

50.0

60.0

High Medium Low

Risk Areas

BloodPbLevels

(g/dl)

25th % tile (g/dl)

min

max

mean (g/dl)

75th % tile (g/dl)

High (N = 146)Medium (N = 152)

=


23/47



These recent findings on blood lead are compared with the results of previous studies on bloodlead among children in Metro Manila. For the 2003 children group, those from Antipolo City (N=152) have been excluded in the computation. Comparative statistics of blood lead levels among

children in Metro Manila since 1993 are shown in Table 6.20. It should be noted that the studychildren in 1993 were school children randomly selected all over Metro Manila while the 2000study children were selected from study households that reside along major routes of landtransportation and heavy traffic. This could explain for the higher mean values of blood lead in2000 as compared with the 1993 result. Apparently, the blood lead levels have significantlyreduced by more than fifty percent since the implementation of unleaded gasoline in 2000.

Table 6.20 Mean blood lead (Pb) levels among children in Metro Manila, 1993 - 2003Population N Mean Blood Pb,

g/dlStudy Characteristics

Children(6 14 y.o.) 488 14.8

Schoolchildren and street child vendors, July 1993

Children6 10 y.o.) 207 16.3 Children living along heavy traffic densityApril 2000Children6 10 y.o) 298 9.3

Children in high, medium exposure areasApril May 2003

Figure 6.24 below shows that a considerable reduction of blood lead levels amongchildren is obtained with the reduction and elimination of lead in gasoline. Before June1993, the lead content of gasoline was 0.6 g/liter. After this period the lead content wasreduced to 0.15 g/liter. The 1993 children study was conducted in July of that yeartherefore the effect of lead reduction in gasoline cannot be ascertained. The actualvalues are presented below.

The children study of 2000 was conducted in April 2000 just when the unleadedgasoline policy of the government was then enforced. In the 2003 study, the meanblood level among children in Metro Manila of 9.3 g/dl is significantly much lower thanthe 2000 level. However, all the studies were cross-sectional in nature and there wasno regular blood lead monitoring program in between the years of implementing thelead elimination policy. Therefore, the decreasing trend of blood lead through the yearscannot be ascertained.

Fig. 6.24 Lead content of gasoline and mean blood lead levels among

children in Metro Manila, 1993-2003

0

4

8

12

16

20

1991 1992 1993 2000 2003

N.B. lead in gasoline not actual values but highlighted for illustration purpose only

bloodleadlevel(ug/dl)

lead gasoline

blood lead0.84

0.6

School children

Children

Children in hightraffic areas


24/47



Although there are other sources of lead exposure among children, it should be noted thattogether with the complete elimination of lead in gasoline, the distribution of children having

blood lead levels below the biological limits have considerably increased and those exceedingthe limits have reduced. These results are presented in Table 6.21 below using the biologicallimit for blood lead of US-CDC at 10g/dl.

Table 6.21 Frequency distribution of blood lead levels of children in Metro Manila, 2003Population Percent of children with blood lead levels

< 10 g/dl >10g/dl6 14 y.o. children, 1993(N = 488) 17.8% 82.2%6 10 y.o. children, 2000(N = 207) 9.67% 90.33%6 10 y.o. children, 2003

(N = 298) 65.4% 34.6%

Although this project did not look at the effects of elevated blood levels among children,previous studies have estimated effects of lead on intelligence quotient (IQ) of study children. AWorld Bank assessment in 1996 have estimated an average loss of 5 IQ points among allchildren due to exposure to lead levels in 1990(PEHAS) (3). Eventually, the IQ increment ofchildren due to exposure to lead in gasoline will be drastically reduced with the elimination oflead in gasoline. However, other sources of lead (industrial, soil, food) still remain a concern inreducing exposure to lead.

Nitrogen dioxide and carbon monoxide

Table 6.22 provides information pertaining to levels of indoor nitrogen dioxide (NO2). The levelsare way below the national air quality guidelines . A major source of indoor NO2 is combustionof cooking fuel, particularly LPG. However, result of correlation analysis indicated that indoorNO2 is weakly associated with LPG (p= 0.82) as well as with kerosene (p=0.3)

Table 6.22 Levels of indoor 8-hr NO2

Exposure area N Mean SD Range

High 39 4.93 4.31 0.57 20.53Medium 40 6.29 5.9 0.3 173.87Low 40 10.15 9.04 0.58 42.3

Levels of indoor carbon monoxide(CO) were measured for 24 hours. The results shown in

Table 6.23. indicate that the CO levels are way below the guideline. CO levels are weaklycorrelated with the number of household members who smoke (Beta=.095). CO is likewiseweakly correlated with the number of household members who smoke inside the house (Beta=0.013)

Table 6.23 Levels of indoor 24-hr COExposure area N Mean SD Range

High 40 2.24 0.78 1.04 5.21Medium 40 1.25 0.52 0.52 2.60Low 40 0.9 0.57 0.26 2.60

0.15

0.00.0


25/47



6.4 Health Impact of Air Pollution

The impact of air pollution to health was assessed by prospectively collecting health information

from January to May 2003. For each sentinel household, a child (6-10 years old) and an adult(more than 18 years old) were identified to participate in the health monitoring. In the case ofchild health, a profile of the respiratory health is likewise presented. This profile was obtainedfrom the survey of the sentinel households and by health monitoring using a health calendar forstudy children (Appendix 6-3).

For monitoring health of the general population, a health center from each city and municipalityin Metro Manila were identified to participate in the pilot study on recording, reporting andmonitoring daily consultations of air-pollution related symptoms and diseases. In addition,emergency room consultations in hospitals and consultations in private clinics located in theexposure areas were likewise monitored during the same period.

Additional information required for the assessment are the mean 5-month (January to May)ambient PM10 in exposure areas, environmental (indoor and outdoor measurements) andbiological monitoring results, and certain household and child attributes obtained from thehousehold survey.

6. 4.1 Child health

To better assess the impact of air pollution on the health of children, the respiratory healthprofile of the children as obtained from the household survey is presented together with thehealth monitoring results .

6.4.1.1 Respiratory health profile of study children

The results presented herein were obtained from the household survey where more than 75 %of the respondents are the mothers of the study children . The mean age of study children inthe low air pollution exposure area is slightly higher (8.15 years) compared with those in themedium and low air pollution exposure areas (8.05 years).

Frequent cough

Available data indicate that there is a higher prevalence of frequent cough amongchildren residing in high air pollution areas in the study; however, differences didnot reach statistical significance (p = .083). These results are presented inFigure 6.25 below.


26/47



Fig. 6.25 Distribution of study child with frequent cough by study area

24

25

26

27

28

29

30

3132


Percent Low

Medium

High

A closer look at the data further indicates that the children reported to have frequent coughexperience it less than once a month (>60%) in all exposure groups as shown in Figure 6.26 .

Fig. 6.26 Distribution of frequency of cough by exposure area

0

10

20

30

40

50

60

70

Less than once a month Once to twice a month More than twice a month

Percent Low

Medium

High

Reports of frequent cough amongst the study children has been shown tobe associated with the type of cooking fuel used. Table 6.24 below shows thatthere is a statistically significant lower prevalence of frequent cough amongsthousehold which use LPG as cooking fuel (p = .005).

Table 6.24 Frequency distribution of children with reported frequent cough among household using LPGvs. other cooking fuels

Children reported to have frequent coughUse of LPG as cooking fuel No N (%) Yes N (%) Total

Yes 1,279 (72.9) 476 (27.1) 1,755No 404 (57.2) 197 (32.8) 601

Total 1,683 673 2,356


27/47



Although not reaching statistical significance (p=.208), the distribution ofchildren reporting frequent cough among household using wood as cooking fuel(30.8%) are higher as compared to households using other fuel types (28.3%)as shown in Table 6.25. As presented in Table 6.26, similar trend isobserved amongst household using kerosene as cooking fuel (31.5%) ascompared to households using other type of fuels (27.8%), although thedifference between the two groups did not reach statistical significance (p =.058).

Table 6.25 Frequency distribution of children with reported frequent cough among householdusing wood as cooking fuel vs. other fuels

Children reported to have frequent coughUse of wood as cooking fuel No N (%) Yes N (%) Total

Yes 198 (69.2) 88 (30.8) 286No 1,485 (71.7) 585 (28.3) 2.070

Total 1,683 673 2,356

Table 6.26 Frequency distribution of children with reported frequent cough among householdusing kerosene as cooking fuel vs. other fuels

Children reported to have frequent cough

Use of kerosene as cooking fuel No N (%) Yes N (%) Total

Yes 339 (68.5) 156 (31.5) 495No 1,344 (72.2) 517 (27.8) 1,861

Total 1,683 673 2,356

It should be noted that smoking practice of parents, and location of the house in a majorroad network have not been identified to be significantly associated with episodes offrequent cough amongst the study children.

However the results of the logistic regression analysis (Table 6.27) conducted on variouspossible predictors of frequent cough in children have yielded the following as majorpredictors:

Living in an area designated as high pollution exposure area, and

Use of LPG as household cooking fuel showing a statistically significant lower cough

prevalence compared to other household fuel types such as kerosene and wood.

Table 6.27 Predictors of prevalence of frequent cough among study childrenPredictor Beta Standard Error Sig.

Living in high pollution area .245 .123 .036LPG as cooking fuel -.385 .180 .004


28/47



Tightness of the chest

Difficulty of breathing as indicated by episodes of chest tightness is a symptom whichcan be related to acute effects of air pollution. Available data indicate a comparable prevalenceon occurrence of chest tightness among children in the high and low pollution exposure areas(Figure 6.27). Furthermore, the most common occurrence of difficulty of breathing as reportedby the respondents is less than once per month.

Fig. 6.27 Percentage of children with frequent chest tightness by exposure

area

0

2

4

6

8

10

12

1Exposure Area

Percent Low

MediumHigh

As shown in Table 6.28, available data indicate a trend for higher prevalence of chesttightness among children residing in high pollution areas in Metro Manila, although notstatistically significant (p=.099)

Table 6.28 Frequency distribution of children with reported frequent tightness of the chest by airpollution exposure area.

Children reported to have frequent chest tightness

Exposure Area No N (%) Yes N (%) Total

Low Pollution 693 (86.7) 106 (13.3) 799Medium Pollution 694 (88.1) 94 (11.9) 788High Pollution 654 (84.4) 121 (15.6) 775

Total 2,041 321 2,362

Wheezing

Occurrence of wheezing may indicate a possible allergic response due to air pollution, which inmany cases is associated with bronchial asthma. Existing data indicate a higher occurrence(approx. 13.2%) of wheezing of the chest among children residing in high air pollution exposureareas (Figure 6.28). A closer look of the data show that the frequency of wheezing commonlyencountered is for less than once up to twice a month across all exposure areas.


29/47



Fig. 6.28 Percentage distribution of study child with frequent wheezing by

study area

10

10.5

11

11.5

12

12.5

13

13.5

1

Exposure areas

Percent Low

Medium

High

Report of frequent wheezing amongst the study children has been shown to be associated withthe type of cooking fuel used. Table 6.29 shows that there is a statistically significant lowerprevalence of wheezing among household which use LPG as cooking fuel (p = .005) .

Table 6.29 Frequency distribution of children with reported frequent wheezing among householdusing LPG vs. other cooking fuels

Children reporting to have frequent wheezingUse of LPG As Cooking

FuelNo N (%) Yes N (%) Total

Yes 1,562 (89.0) 193 (11.0) 1,755No 511 (84.9) 91 (15.1) 602

Total 2,073 284 2,346

Available data further indicates a statistically significant (p = .022) higher prevalence offrequent wheezing among children living in households which use wood as the main type ofcooking fuel (Table 6.30).

Table 6.30 Frequency distribution of children with reported frequent wheezing among householdusing wood vs. other cooking fuels

Children reported to have frequent wheezingUse of wood as cooking fuel No N (%) Yes N (%) Total

Yes 243 (84.1) 46 (15.9) 289No 1,830 (88.5) 238 (11.5) 2,068Total 1,073 284 2,357

The results of the logistic regression analysis of data indicate that the main predictor offrequent wheezing which carries a protective effect is the use of LPG as household cookingfuel (Beta = .670, Standard Error = .201, and Significance = .001).This means that the useof LPG as cooking fuel predicts the lesser frequency of wheezing among the study children.


30/47



Doctor- diagnosed asthma

The prevalence of doctor-diagnosed asthma among study children is higher (18%) in high

pollution exposure areas, gradually tapering off in the medium and low exposure areas. There issignificant difference (p = .013) in the prevalence of doctor-diagnosed asthma between high andlow exposure areas . Additional details are presented in Table 6.31.

Table 6.31 Frequency distribution of doctor-diagnosed asthma in children by air pollutionexposure area.

Children reported to have doctor-diagnosed asthmaExposure Area No N (%) Yes N (%) Total

Low Pollution 688 (87.4) 99 (12.6) 787Medium Pollution 654 (84.5) 120 (15.5) 774High Pollution 631 (82.1) 138 (17.9) 769

Total 1,973 357 2,330

Other respiratory illnesses

The study also inquired about the occurrence of respiratory diseases in the study child. Datafrom the respondents show that measles is the most common respiratory illness encountered bythe child followed by sinusitis and asthma. The profile of past respiratory illness is similar acrossall exposure risk areas as shown in Figure 6. 29.

In particular, the prevalence of bronchitis (


31/47



The respiratory symptoms listed in the health calendar are cough (ubo); cough withoutphlegm production, nasal discharges (sipon), wheezing (paghuni), difficulty of breathing (hirap

sa paghinga), flu, colds and fever. For the analysis, any of the symptoms or any combinationthereof were lumped together into one health outcome category. A second health outcome wasasthma attack or episode for those with doctor-diagnosed asthma.

From the calendar, crude incidence rates were calculated and the individual probable riskfactors were analyzed. The regression analysis tried to identify the significant risk factorscontributing to the incidences of asthma attacks and respiratory symptoms.

Crude incidence rates

Differences in incidences of asthma episodes are shown in Table 6.32. From this table, it isseen that the distribution of the disease incidences follow the pollution exposure pattern. Thus,

the high pollution exposure areas have the highest incidence, the low pollution areas have thelowest incidence. However, in Table 6.33 showing incidence rates of the respiratory symptoms,this gradient is not as obvious. The high pollution areas have the highest incidence ofrespiratory symptoms but the low pollution areas have a slightly higher incidence than themedium pollution areas. The significance of this slight reversal in incidences is tested in thesucceeding analysis.

Table 6.32 Asthma episodes among 6-10 y.o. and incidence rates per 1000 population inexposure areas for January-May, 2003

Pollution exposurelevel

No. of cases No. of person-months Incidence rate per1000 population

HIGH 56 3749 14.94MEDIUM 44 3829 11.49LOW 32 3874 8.26

Total 132 11452 11.53

Table 6.33 Incidence rates per 1000 population of respiratory symptoms among 6-10 y.o. inexposure areas , January-May, 2003

Pollution level (according to apriori barangay classification)

No. of cases No. of person-months Incidence rate per1000 population

HIGH 2057 3748 548.8MEDIUM 1696 3828 443.1LOW 1817 3874 469.0

Total 5570 11450 486.4


32/47



Individual analysis of probable risk factors with the health outcomes

Several probable risk factors were analyzed individually to identify which among these factorscould be significantly associated with the health outcomes of concern. Significant risk factorsaffecting asthma episodes in this study are sex or gender, father smokes, monthly income andthe different pollution exposure areas as shown in Table 6.34. As can be seen in the table,males have higher risk than females and children with fathers who smoke, belong to higherincome families and who live in high pollution areas have higher risk of asthma attacks orepisodes. These four significant factors are further analyzed together in the next section.

Table 6.34 Rate ratios of probable risk factors of asthma episodes among 6-10 year old children,January May, 2003

Probable risk factors Rate Ratio Confidence Interval p-Value

1. Age 0.992 0.873-1.126 0.9032. Sex Female* 0.698 0.494-0.985 0.041**3.Location of Household:

Major Road*0.911 0.540-1.537 0.728

4. Indoor PM10 Level 0.972 0.918-1.028 0.3215. Indoor Nitrogen dioxide level 1.075 0.927-1.247 0.3376. LPG use* 0.711 0.494-1.024 0.0677. Kerosene use* 1.417 0.966-2.079 0.0748. Wood use* 0.648 0.349-1.202 0.1699. Location of Cooking Facility:

Inside the House*1.349 0.846-2.151 0.208

10. Congestion Level*** 0.936 0.856-1.023 0.14711. Father Smokes* 1.606 1.112-2.319 0.011**

12. Mother Smokes* 0.659 0.334-1.297 0.22813. Caretaker Smokes* 0.426 0.055-3.277 0.41314. Number of Smokers 0.930 0.746-1.158 0.51815. Monthly Income*

a. Middleb. Middle- lowc. Low

0.2950.5740.453

0.140-0.6200.325-1.0120.253-0.811

0.001**0.055

0.008**16. Education Level*

a. High School Levelb. Elementaryc. No Formal Schooling

1.1510.8332.087

0.751-1.7640.485-1.4310.498-8.747

0.5180.5100.314

17. Exposure Level Area*a. Medium Pollution Area

b. High Pollution Area

1.391

1.808

0.882-2.193

1.171-2.791

0.155

0.008***Reference Groups :Male, Minor Road, Other Cooking Fuels, Location of Cooking Facility-Outside,Father/Mother/Caretaker do not smoke,Middle- Upper income class, College and Higher/Vocational level, Lowexposure level area** Statistically Significant***Crowding Index Number of persons per sleeping room

For the respiratory symptoms, the probable risk factors that came out significant are age, indoornitrogen dioxide level, types of cooking fuel, location of cooking facility, monthly income levels,educational levels and the different exposure pollution areas as shown in Table 6.35.


33/47



Table 6.35 Rate ratios of probable risk factors of respiratory symptoms among 6-10 year old

children, January May, 2003Probable risk factors Rate Ratio Confidence

Interval

p-Value

1. Age 1.028 1.008-1.048 0.005**2. Sex Female* 0.998 0.947-1.052 0.9643.Location of Household:

Major Road* 1.010 0.929-1.099 0.8034. Indoor PM10 Level 1.00004 0.999-1.0003 0.8105. Indoor Nitrogen dioxide level 1.017 1.004-1.029 0.007**6. LPG use* 0.881 0.831-0.934 0.000**7. Kerosene use* 1.085 1.019-1.156 0.011**8. Wood use* 1.186 1.100-1.279 0.000**9. Location of Cooking Facility:

Inside the House* 1.090 1.008-1.177 0.029**10. Congestion Level** 0.998 0.985-1.011 0.771

11. Father Smokes* 1.014 0.959-1.071 0.61912. Mother Smokes* 0.954 0.872-1.044 0.30813. Caretaker Smokes* 0.845 0.676-1.056 0.14014. Number of Smokers 0.997 0.966-1.030 0.89315. Monthly Income*

a. Middlec. Middle- lowd. Low

1.0211.0861.194

0.893-1.1670.960-1.2291.056-1.349

0.7570.187

0.005**16. Education Level*

a. High School Levelb. Elementaryc. No Formal Schooling

1.0851.1671.517

1.014-1.1611.080-1.2621.168-1.971

0.017**0.000**0.002**

17. Exposure Level Area*a. Medium Pollution Areab. High Pollution Area

0.9441.170

0.884-1.0091.098-1.246

0.0920.000**

*Reference Groups :Male, Minor Road, Other Cooking Fuels, Location of Cooking Facility-Outside,Father/Mother/Caretaker do not smoke, Upper income class, College and Higher/Vocational level, Low exposurelevel area** Statistically Significant***Crowding Index Number of persons per sleeping room

Regression analysis of significant probable risk factors with the health outcomes

Tables 6.36 and 6.37 show the risks posted by the different exposure riskareas in developing asthma episodes and respiratory symptoms, respectively.

Controlling for other factors, it is clear that pollution levels affect theincidences of these two health outcomes. Living in the high pollution areas hasthe highest risk for both health outcomes. With regards the medium pollutionareas, although not statistically significant, risks for developing both asthmaepisodes and respiratory symptoms of children are still higher than the lowpollution areas but still lower than in the high pollution areas.


34/47



Table 6.36 Regression of the exposure areas with the asthma episodes among 6-10 yearold children, January May, 2003

Exposure areas Rate Ratio Confidence Interval p-Value

Low 1.00Medium 1.427 0.886-2.299 0.144High 1.800 1.141-2.840 0.012**

**Statistically Significant

Table 6.37 Regression of the exposure areas with the respiratory symptoms among 6-10year old children, January May, 2003

Exposure areas Rate Ratio Confidence Interval p-Value

Low 1.00Medium 1.214 0.898-1.641 0.207

High 1.435 1.064-1.936 0.018****Statistically Significant

Table 6.38 below shows the effect of the other probable risk factors on asthma episodes.Significant factors are female sex, father smokes and monthly income. Children with fatherswho smoke are shown to have higher risk as compared to those with fathers who do not smoke.Lesser risk is also seen among the female children as compared to the males. Lower incomegroups have also lower risk as compared to the higher income groups. This latter associationmay be due to more cases of doctor-diagnosed asthma reported among the higher incomegroups. These higher income groups may have more means to bring their children to the doctoras compared to the lower income groups. Thus, recognition of asthma attacks or episodes aremore accurate for the higher income groups than the lower income groups.

Table 6.38 Regression of the other significant probable risk factors with the asthmaepisodes among 6-10 year old children, January May, 2003

Probable risk factors Rate Ratio Confidence Interval P Value

1. Sex Female* 0.662 0.464-0.945 0.023**2. LPG Use* 0.660 0.377-1.154 0.1453. Kerosene Use* 1.012 0.569-1.798 0.9674. Father Smokes* 1.598 1.102-2.318 0.013**5. Monthly Income*

a. Middlec. Middle- lowd. Low

0.2100.4970.355

0.093-0.4740.274-0.9000.189-0.665

0.000**0.021**0.001**

*Reference Groups :Male, Other Cooking Fuels, Father do not smoke, Upper income class**Statistically Significant

Apart from the exposure areas as a significant risk factor, other factors are identified in theoccurrence of respiratory symptoms not seen in asthma episodes. Table 6.39 presents thesefactors namely the age of the child, the indoor nitrogen dioxide level and educational level of therespondent. This study shows that the older the child, the risk of developing respiratorysymptoms is also much less. Young children are more sensitive to insults to their respiratorysystem than older children probably because of the young childs level of maturity of the organsystem.


35/47



With regards to nitrogen dioxide levels, the results show that per unit increase in the level of thispollutant inside the house also increases the risk for respiratory symptoms by about 2.8%.Nitrogen dioxide is a known respiratory irritant and the probable sources inside the house could

be the cooking fuel and the inflow of emissions from transport vehicles especially for those livingalong major roads.

Finally, pertaining to educational level as a risk factor, the children in households with lowereducational level of respondents have been shown to have higher risk for experiencingrespiratory symptoms. Educational level is often used as an indicator of socio-economic status.The finding in this study is consistent with other studies which have shown that health ofindividuals is more compromised among those in the lower socio-economic groups. Anexample is the reanalysis of the Pope et al study where the educational attainment had beenshown to be a predictor of mortality in association with air pollution (37).

Table 6.39 Regression of the other significant probable risk factors with the respiratory

symptoms among 6-10 year old children, January May, 2003Probable risk factors Rate Ratio Confidence Interval p-Value

1. Age 0.897 0.826-0.974 0.010**2. Nitrogen dioxide 1.028 1.0123-1.044 0.000**3. LPG Use* 0.967 0.640-1.460 0.8744. Kerosene Use* 1.081 0.696-1.678 0.7275. Wood Use* 0.851 0.565-1.280 0.4406. Location of Cooking Facility:

Inside the House* 0.923 0.605-1.409 0.7137. Monthly Income*

a. Middlec. Middle- low

d. Low

1.1371.061

1.135

0.730-1.7710.709-1.586

0.749-1.720

0.5690.773

0.5498. Education Level*

a. High School Levelb. Elementary

1.2871.819

0.989-1.6741.335-2.478

0.0600.000**

*Reference Groups: Other Cooking Fuels, Father do not smoke, Upper income class, Low exposure level area**Statistically Significant

6.4.2 Adult health

For the qualified adults, the research assistants interviewed them monthly as to any respiratorysymptom and asthma episodes experienced. The monitoring form developed for the interview is

shown in Appendix 6-4. The data were encoded and entered into a spreadsheet for analysis.The analysis of these outcome variables included the different exposure areas and othervariables that may influence the health outcomes. The data pertaining to these variables weretaken from the results of the household survey.

The results of the analysis of the health outcomes among the adult population were lessinteresting than the results of the child health analysis. First of all, very few cases of asthma forthe adults were observed. In addition, the individual analysis of the probable risk factors forasthma did not yield anything significant. Thus, no regression analysis could be done forasthma. Secondly, for the respiratory symptoms, although the individual analysis of probablerisk factors revealed a few significant factors, the results of the regression analysis yielded onlyone significant risk factor age.


36/47



The individual analysis of the probable risk factors for respiratory symptomsshowed the following as significant: age of the adult, cooking fuel used, number

of smokers and educational attainment as shown inTable 6.40. The exposurearea variable, although not statistically significant, show some gradient amongthe three areas. However, as mentioned earlier, only age of the adult came outas significant in the regression analysis. The results of the regression analysisare shown in Table 6.41. The older the subject is, the higher the risk ofdeveloping respiratory symptoms.

Table 6.40 Rate ratios of probable risk factors of the respiratory symptoms among adults insentinel barangays, January May, 2003

Probable risk factors Rate Ratio Confidence Interval p-Value

1.Age 1.012 1.006-1.017 0.000**2. Sex* 1.098 0.990-1.218 0.0743. House location

Major Road* 0.985 0.845-1.148 0.8514. Indoor PM10 level 1.0003 0.999-1.0008 0.2585.Indoor Nitrogen dioxide 0.954 0.915-0.994 0.026**6. LPG Use* 0.846 0.760-0.941 0.002**7. Kerosene Use* 1.122 1.0002-1.259 0.049**8. Wood Use* 1.133 0.983-1.305 0.0839.Location of CookingFacility: Inside thehouse*

1.134 0.985-1.305 0.080

10. Congestion Level*** 1.016 0.992-1.040 0.174

11. Number of Smokers 1.060 1.002-1.121 0.042**12. Monthly Income*a. Middlec. Middle- lowd. Low

1.0901.0621.217

0.853-1.3920.845-1.3340.970-1.527

0.4900.6050.088

13. Education Level*a. High School Levelb. Elementaryc, No Formal Schooling

1.1401.3331.310

1.004-1.2951.155-1.5390.781-2.199

0.043**0.000**0.305

14. Exposure Level Area*a. Medium Pollution Areab. High Pollution Area

1.0991.110

0.976-1.2380.985-1.251

0.1180.086

*Reference Groups :Male, Minor Road, Other Cooking Fuels, Location of Cooking Facility-Outside, Upper incomeclass, College and Higher/Vocational level, Low exposure level area

** Statistically Significant***Crowding Index Number of persons per sleeping room


37/47



Table 6.41 Regression of the significant probable risk factors with the respiratorysymptoms among adults in sentinel barangays, January May, 2003

Probable Risk Factors Rate Ratio Confidence Interval p-Value

1.Age 1.048 1.024-1.071 0.0002. House location

Major Road* 1.854 0.733-4.685 0.1923.Indoor Nitrogen dioxide 0.954 0.910-1.0002 0.0514. LPG Use* 0.616 0.238-1.594 0.3185. Kerosene Use 0.768 0.285-2.065 0.6017. Number of Smokers 1.210 0.850-1.724 0.2898. Educational Level

a. High School Levelb. Elementary

1.0100.829

0.559-1.8250.389-1.765

0.9710.627


38/47



6.4.3 Health center monitoring

A pilot study was conducted among selected health centers in Metro Manila to determine the relationship

between reported morbidity cases from respiratory symptoms and diseases over the period that ambientair monitoring for PM10 was conducted. Eighteen health centers within Metro Manila participated in the

pilot study: one health center from each city/municipality except San Juan which has two. These health

centers were nominated by their respective health offices based on their performance. Standardized

extraction forms and definitions of the illnesses to be monitored were provided to the health center

personnel (Appendix 6-5). Respiratory symptoms were recorded daily by the health center nurse while

illnesses were diagnosed by the health center physician.

The 18 health centers have a total catchment population of about 479,043. Shown in Table6.42 are the health centers which participated in this study:

Table 6.42 Participating health centers in health monitoring, January to May 2003

Health Center Catchment Population City/Municipality

B. F. Homes 73,430 Paraaque

Bagbaguin 23,817 Kalookan

I. Mendoza HC 30,854 Manila

Ibayo-Tipas 16,014 Taguig

Pamplona 46,908 Las Pias

Ma. Clara 7,047 Mandaluyong

Pugad Lawin 37,000 Marikina

Paso De Blas 15,604 Valenzuela

Pineda HC 16,665 Pasig

Project 7 26,046 Quezon City

Quintin de Borja 12,745 Pateros

San Isidro 40,136 Pasay

Bagong Lipunan 22,983 Navotas

San Juan Main 49,000 San Juan

Santulan 10,132 Malabon

Sucat 52,426 Muntinlupa

West Crame 13,946 San Juan

West Rembo 30,465 Makati

Four symptoms were regularly recorded which included cough with or without any other

symptoms (colds, fever, headache), nasal discharge, wheezing and difficulty of breathing.Respiratory diseases such as sore throat, acute bronchitis, asthma and chronic bronchitis, andischemic heart disease were the health outcomes monitored for this study. Weekly incidences ofthe said symptoms and illnesses were gathered from the health centers by the researchassistants. Data were collected weekly for 24 weeks, January to June, 2003.

For the exposure variable, PM10 levels were monitored for the same period from 6 ambient monitoring

stations around the metropolis. Weekly averages were modeled from these stations using GIS. Each

health centers area of coverage was assigned weekly exposure level for 20 weeks from January to May,

2003.


39/47



Weekly time series technique was used to analyze the association between PM10 and the health outcomes

of concern. Three lag times were evaluated namely, lag 0 exposure and health outcome occur on the

same week, lag 1 health outcome occurs 1 week after the exposure and lag 2 health outcome occurs 2

weeks after exposure. Analysis by age groups was undertaken.

Table 6.43 below shows only the significant findings of this analysis. A complete listing of theresults can be seen in Appendix 6-6. The most significant relative risks were for the followinghealth outcomes:

cough with or without any other symptoms for those less than 15 years old,

nasal discharge among those less than 15 years old

wheezing among less than 15 years old and15-64 years old

difficulty of breathing among less than 15 years old and 15-64 years old

acute bronchitis for those less than 15 years old and15-64 years old

acute respiratory illness for those 1-4 years old and 5-14 years old

asthma for those less than 15 years old

upper respiratory tract illness for those less than 15 years old and 15-64 years old

A less significant relative risk is seen for sore throat among 65 years old and aboveindividuals. The relative risks reported are increases in the health outcome per unitincrease in PM10. For example, an increase of 0.38% is expected in the incidence of acutebronchitis among those who are less than 15 years old for every 1 ug/m 3 increase inPM10. This means an increase of about 4 cases per thousand population is expected forevery ug/m3 increase in PM10 level.

Most of the symptoms and illnesses which were found to be significant tend to affect theyounger age groups with greater magnitude except for wheezing and sore throat. Thisobservation is not surprising because younger children are regarded as a vulnerable part of thepopulation. These acute morbidity findings are generally consistent with international findings.

The table also shows that acute respiratory illness defined as pneumonia and associated with PM10

pollution, affects the older children more than the infants. This result may primarily be due to the

differences in their exposure time. Older children are more exposed to ambient air than infants.

Significant findings for the elderly, >/= 65 years old, are quite limited in this study. Small number of

cases and perhaps less elderly population in the study areas could account for this. Nevertheless, this

study has shown positive associations between health outcomes and PM10 pollution in different age

groups.

Apart from demonstrating the association of PM10 with certain symptoms and illnesses, the main use of

the relative risks calculated in this study is in future health risk assessments. This is until a more rigidtime series analysis study, for example, daily instead of weekly, level of analysis and the control of

variables such as temperature and humidity, could be undertaken.


40/47



Table 6.43 Time series analysis results of air pollution-related symptoms and illness andambient PM levels in 18 health centers in Metro Manila, January-May 2003

Morbidity Endpoints:Symptoms

Relative Risks Confidence Intervals P Value

Cough

a. With other

symptoms+

< 15 y/o***

15-64*

65 />***

b. W/o other

symptoms

< 15 y/o**

15-64***

65 />***

1.0012

1.0002

1.0021

1.0028

0.9994

1.0060

1.0003- 1.0021

0.9977- 1.0027

0.9919- 1.0123

1.0014- 1.0042

0.9844- 1.0028

0.9925- 1.0197

0.10

>0.10

0.10

>0.10

Wheezing< 15 y/o**

15-64***

65 />**

1.0045

1.0066

1.0020

1.0013- 1.0076

1.0005- 1.0127

0.9774- 1.0271

***

1.0077

1.0051

1.0032

1.0049- 1.0103

1.0007- 1.0094

0.9892- 1.0173

0.000@@@

0.10

Nasal Discharge

< 15 y/o***

15-64***

65 />**

1.0034

1.0005

0.9985

1.0004- 1.0026

0.9964- 1.0045

0.9749- 1.0226

0.000@@@

>0.10

>0.10

Morbidity Endpoints:

Illnesses

Relative Risks Confidence Intervals P Value

Sore Throat

< 15 y/o***

15-64**

65 />***

1.0011

1.0023

1.0090

0.9978- 1.0045

0.9967- 1.0079

0.9991- 1.0190

>0.10

>0.10

**

1.0038

1.0025

1.0013

1.0032- 1.0044

1.0004- 1.0045

0.9888- 1.0138

/= 15***

1.0017

1.0014

1.0004- 1.0031

0.9981- 1.0047

0.10

Acute Respiratory Illness

0.10

0.10*lag 0, **lag 1, ***lag 2+symptoms refer to any of the following: colds, headache, fever@ statistically significant at 0.10, @@ statistically significant at 0.05, @@@ highly statistically significant


41/47



6.4.4 Hospital emergency room and private clinic consultations

An attempt to collect data was made to identify associations and trends between monthly air

pollution levels and consultations to private doctors clinics and hospital emergency roomconsultation. A primary assumption made is that consultations done in these health facilities arefrom the population of the barangay where the facility is physically situated. A total of 11 privatepractitioners in private clinics and 5 hospitals in Metro Manila were recruited to provideprospective morbidity statistics data. A pro-forma patient consultation record to beaccomplished by the private practitioner was developedby the project (Appendix 6-7). Projectresearch associates collected hospital emergency room consultations on a monthly interval insentinel hospitals in Metro Manila using a form developed for that purpose (Appendix 6-8)Monthly morbidity summary trends covering respiratory and cardiovascular symptoms andillnesses were prepared and compared with recorded levels of ambient air pollution near theclinic or hospital location.

Analysis of trends between respiratory and cardiovascular illness from private clinics and hospitalemergency room consultations vs. monthly levels of ambient air pollution did not show any correlation.

(Appendix 6-9). Possible explanation for the non-correlation between the private clinic and hospital

emergency room consultation rates and monthly PM10 levels can be attributed to the following:

1. Data limitations in terms of accuracy and completeness of data provided at source, and,2. Inadequate data management by available sentinel hospital emergency room units

resulting in missing records and absence of summary statistics.


42/47



6.5 GIS analysis

Ambient PM10 levels were measured several times in a week from January to May 2003 at eight

monitoring stations, namely Mapulang Lupa in Valenzuela, the National Printing Office inQuezon City, the Manila Observatory in Quezon City, Calzada in Taguig, Martirez del 96 inPateros, Alman

Epidemiological Study PHM

Documents

Transcript of Epidemiological Study PHM