Geographical differences of cancer incidence in Costa Rica in relation ...

© International Epidemiological Association 1999

International Journal of Epidemiology 1999;28:365–374

Printed in Great Britain

Geographical differences of cancer incidence in Costa Rica in relation to environmental and occupational pesticide exposure Catharina Wesseling,a,b,c Daniel Antich,a Christer Hogstedt,c,d Ana Cecilia Rodrígueza,e and Anders Ahlbomb,f

Background This study describes geographical differences in cancer incidence in Costa Rica, and investigates if some of these differences may be related to pesticides. Methods

Data were combined from the cancer registry (1981–1993), the 1984 population census, the 1984 agricultural census, and a national pesticide data set. The 81 counties of Costa Rica were the units for the ecological analyses. Adjacent counties were grouped into 14 regions (3 urban and 11 rural) with relatively similar socioeconomic characteristics. County indices for population density and agricultural variables were constructed and categorized. Differences across regions and categories were assessed by comparing observed numbers of incident cases to expected values derived from national rates. Within the tertile of most rural counties, rate ratios between categories of high and low pesticide use were calculated.

Results

In urban regions, excesses were observed for lung, colorectal, breast, uterus, ovary, prostate, testis, kidney, and bladder cancers; and in rural regions for gastric, cervical, penile, and skin cancers. Skin cancers (lip, melanoma, non-melanocytic skin and penile cancer) occurred in excess in coffee growing areas with extensive use of paraquat and lead arsenate. In the most rural subset, heavy pesticide use was associated with an increase of cancer incidence overall and at a considerable number of specific sites, including lung cancer (relative risk [RR] 2.0 for men and 2.6 for women) and all female hormone-related cancers (RR between 1.3 and 1.8).

Conclusions Regions and populations at high risk for specific cancers were identified. Several hypotheses for associations between pesticides and cancer emerged. The findings call for studies at the individual level. Keywords

Cancer, developing country, environmental exposure, epidemiology, occupational exposure, pesticides

Accepted

21 October 1998

Excess cancer risks have been reported in relation to various pesticides,1–4 but few associations between pesticides and cancer have been sufficiently studied in human populations. To date, only arsenic and arsenical compounds, which cause lung and skin cancers, are classified as carcinogens in humans

a Central American Institute of Studies on Toxic Substances, Universidad

Nacional, Box 86-3000, Heredia, Costa Rica. b Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden. c National Institute of Working Life, Solna, Sweden. d Department of Occupational Health, Karolinska Hospital, Stockholm, Sweden. e National Tumor Registry, Ministry of Health, San José, Costa Rica. f Department of Epidemiology, Karolinska Hospital, Stockholm, Sweden.

Reprint requests to: Catharina Wesseling, Division of Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Box 210, 171–77 Stockholm, Sweden.

(Class 1) by the International Agency for Research on Cancer.5,6 Epidemiological studies of associations between pesticides and cancer could be particularly informative in those Third World countries where high exposures coexist with acceptable cancer research conditions, as is the case in Costa Rica. In Costa Rica, working conditions and environmental emissions from traffic, industry, and agriculture did not receive serious attention until recently. Considerable occupational and environmental exposures to carcinogens may have occurred. Agriculture was the principal economic activity for decades, and pesticides are the only widespread chemical pollutants in rural areas. Average pesticide use during the 1980s was 16 kg/ha of arable land, higher than in areas with intensive agriculture in developed countries.7 Inappropriate handling of pesticides and contamination of workers8 as well as the environment7 have been documented. On the other hand, Costa Rica (population

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INTERNATIONAL JOURNAL OF EPIDEMIOLOGY

2.4 million in 1984 and 3.2 million in 1993) has reasonable infrastructures, a health care system that covers the entire population, good health indicators (life expectancy 76 years in 1993), population registries, and a cancer registry (National Tumor Registry, RNT). The RNT has operated since 1978 with many of the limitations characteristic of a developing country. In the early 1990s, a Swedish-Costa Rican research collaboration was initiated. The programme addressed, among other matters, the improvement of RNT with the long-term goal of meeting international quality standards. The use of RNT for research purposes during the improvement process is essential for its continued evaluation and consolidation. In a recent study, an excess of certain cancers was observed in a cohort of banana plantation workers, a group highly exposed to pesticides.9 So far, no data on cancer risk are available for any other subgroup of the population of Costa Rica. This study describes geographical differences in cancer incidence in Costa Rica during 1981–1993, and explores if some of these differences may be related to pesticide use.

Materials and Methods Four data sources were used: the cancer registry, the population census, the agricultural census, and a data set on pesticide use in Costa Rica.

Cancer data Over 50 000 cases of cancer were reported to the RNT during 1981–1993. The standard RNT record contains personal identification, gender, age at diagnosis, cancer site and histology (International Classification of Diseases for Oncology, first edition), year of diagnosis, and residence at diagnosis. Basal cell skin and in situ cervical cancers were excluded from the study, and data on residence or age were missing for 834 other cancers (2.1%). The remaining 39 647 cancers were included.

Population data Population data for geographical regions by gender and 5-year age strata for the year 1987 were used. These were the projections of the Latin American Center of Demography (CELADE), based on the 1984 census. The numbers of workers by economic branch were obtained from the 1984 census files.

Agricultural data The agricultural census of 1984 provided data on total land surface, cropping in hectares, and number of hectares treated with pesticides per crop in geographical areas.

Pesticide use and exposure Data on types of pesticides, frequency of applications, and spray methods for specific crops according to geographical areas were obtained from the Central American Institute of Studies on Toxic Substances at the Universidad Nacional, Costa Rica. A pesticide exposure indicator (PEI) was calculated for each county: k PEIcounty = ∑ hiniai / population i =1

PEI = pesticide exposure indicator i = crop (1,2,...k) hi = hectares treated with pesticides (1984)

ni = estimated average number of applications per year (1980–1984) ai = aerial spraying or presence of large fraction of workers in sprayed fields: low = 1; medium = 2; high = 4 (1980–1984). The numerator of the formula is an estimate of the total pesticide load. With the size of the total population of the county in the denominator, the PEI provides an estimate of pesticide used per inhabitant. The structure of the databases corresponded to the administrative geographical division of Costa Rica. The 81 counties of the country were the basic units for the ecological analyses. Computer programs were developed to enumerate cancer cases by population and agricultural variables within the counties, as well as in aggregations of counties. Adjacent counties were aggregated into 14 regions with a reasonable population size (.74 000), and relatively similar socioeconomic attributes, and geological and climatic characteristics. Furthermore, county indices were constructed for population density, proportion of workers by economic branch, total arable land, specific crops, and pesticide use. The indices were categorized into quartiles. Table 1 displays socioeconomic and agricultural characteristics of the 14 regions. Differences in cancer incidence across the 14 regions and across categories of the various indices were assessed by comparing the observed number of all cancers and of specific sites to the expected numbers. The expected numbers were derived for gender- and 5-year age-specific strata from the person-years in the regions or categories (1987 population times 13 years) and the national incidence rates. The standardized incidence ratios (SIR) were calculated as ratios of observed and expected numbers of cases. The 95% CI were calculated with the normal approximation of the Poisson distribution; for observed numbers below 20, the exact distribution was used. Overrepresentation of cases in counties with a hospital was anticipated. Preliminary analyses to evaluate the influence of the hospitals on the risk estimates showed that in the central county with the capital San José, where the four most specialized hospitals of Costa Rica are concentrated, SIR were increased for both genders, for all sites combined as well as for nearly all specific sites. In the remaining counties with hospitals, the SIR for all cancers combined correlated weakly with the total number of specialist services at the rural hospitals (r = 0.5 for male cancers and 0.3 for female cancers), but no pattern was observed for SIR of specific cancers and the existence or absence of specific medical services. Consequently, we excluded the central county of San José from all analyses. Many counties had both urban (unexposed to agricultural use of pesticides) and rural (potentially exposed to agricultural use of pesticides) populations. To diminish the within-county heterogeneity of exposure, and to achieve a more comparable distribution of extraneous risk factors across the counties,10,11 we performed analyses restricted to the tertile of most rural counties, i.e. the 27 counties with the lowest population density. After ranking these counties according to their PEI-value (range 7–243), we divided them into categories of low and high PEI with roughly the same population size (cutpoint 50; mean PEI 16 [SD 8.2] and 137 [SD 69.2]). Relative risks (RR) along with the 95% CI were calculated for men and women as ratios of the age-adjusted incidence rates in high versus low PEI categories.

119 466

192 643 164 086 225 813

12. North-west Costa Rica

13. Mid-south Costa Rica

14. East Costa Rica (Atlantic Region)

17

27

19

17

30

17

87

41

167

116

227

333

1002

2432

Rural

Rural

Rural

Rural

Rural

Rural

Rural

Rural

Rural

Rural

Rural

Urban

Urban

Urban

Population Urban/ density rural (per km2) status

59

68

50

59

39

65

33

55

34

30

18

9

6

3

4

4

5

3

10

5

14

7

12

15

19

22

20

23

92 538

67 296

145 081

108 723

44 130

62 159

36 903

35 976

10 255

41 983

5773

4404

5039

2120

Agricultural Industrial workers workers Arable (% of EAPb) (% of EAPb) land (ha)

Coffee

Coffee, sugarcane

Coffee

Coffee

Coffee

Coffee

Main crops (1984)

Banana,corn, beans, rice

Corn, beans, coffee

Rice, sugarcane

Rice, banana, corn, beans

Rice

Corn, beans, rice

Coffee, sugarcane

Coffee, corn, beans

c Pesticide exposure indicator (mean pesticide load per inhabitant, see Method). d OP: organophosphates; CB: carbamates; OC: organochlorines. Fungicides: copper compounds, mancozeb, chlorothalonil, benomyl, tridemorph.

a Central county of San José excluded. b Economically active population.

136 031

11. Southern Central Pacific and South Pacific Region

137 225

9. Mid-north Costa Rica

10. Northern Central Pacific Region

320 884

8. Eastern Central Valley and east mountain chain (Cartago)

297 363

5. Western Central Valley

105 924

112 537

4. Northern mountain chain of Central Valley

7. Southern mountain chain of Central Valley

138 579

3. Northern Central Valley

74 574

207 723

2. Southern Central Valley

6. South-west Central Valley

296 319

1. Central Valleya

Region

Population (1987)

Table 1 Regional socioeconomic and agricultural characteristics

143

15

48

92

18

10

10

16

9

12

6

4

2

1

PEIc

OP, DBCP, paraquat, fungicides, formaldehyde, OC, phenoxyacids

Paraquat, lead arsenate, copper, OP, CB

OC, OP, phenoxyacids, propanil, pyrethroids

OC, OP, phenoxyacids, propanil, DBCP, paraquat, fungicides, formaldehyde

OP, OC, phenoxyacids, propanil

Paraquat, OP, CB, OC, phenoxyacids, propanil

Paraquat, lead arsenate, copper, OP, CB, 2,4-D, pyrethroids



Paraquat, lead arsenate, copper, OP, CB, 2,4-D, pyrethroids





Main pesticidesd

CANCER INCIDENCE IN COSTA RICA

367

368


Results For the 14 regions, SIR for all sites ranged from 73 to 123 in men and from 84 to 116 in women (Figure 1). With the exception of lymphohaematopoietic cancers, the geographical differences were more than twofold for all major tumour sites (Tables 2 and 3). Standardized incidence ratios .120 with

narrow CI were observed in at least two of the three most urban regions (regions 1–3) for lung, colorectal, breast, prostate, testis, kidney, and bladder cancers. Standardized incidence ratios .120 with narrow CI were observed, in at least two of the 11 rural regions, for gastric, cervical, penile, and skin cancers (Tables 2 and 3). The southern suburbs of San José (region 2) showed the highest risk for all cancers combined for men and

Figure 1 Regional distribution of cancer risk (*SIR: standardized incidence ratio), all sites, Costa Rica, 1981–1993

Site

122 113–132


137 122–153


c 95% confidence interval. d Lymphohaematopoietic system.

a Central county of San José excluded. b Standardized incidence ratio.

73 65–82

61 53–69



72 61–84

90 79–103



83 71–96

129 113–146



95 80–111


119 104–136

4. Northern mountain chain of Central Valley 94 87–103

105 92–118



140 127–154

89b 81–98c

Stomach


1. Central Valleya

Region

78 51–107

78 48–121

65 40–91

72 42–118

76 44–110

85 52–132

79 57–103

118 70–167

93 52–154

139 110–170

85 50–137

128 86–172

113 78–148

135 104–167

Colon

62 36–89

265 199–332

38 21–66

81 47–133

74 44–118

109 65–155

91 65–118

98 57–158

143 87–221

106 79–135

39 16–81

103 62–145

114 77–152

107 78–137

Liver

63 37–90

144 95–195

100 67–134

121 73–170

85 49–122

77 44–126

119 89–149

86 49–146

115 67–185

97 71–123

74 40–127

110 68–153

116 78–154

90 63–118

Pancreas

127 104–150

56 37–76

68 52–86

91 65–117

109 84–135

46 28–65

118 100–137

55 34–77

60 34–85

72 59–87

100 72–130

128 100–157

137 112–163

146 125–168

Lung

82 54–111

122 81–165

58 36–91

70 39–116

49 26–87

89 51–128

106 79–134

148 92–204

127 74–204

112 84–141

100 61–157

118 75–163

115 79–151

106 77–135

Brain

92 78–107

106 88–125

79 62–98

94 76–113

97 81–113

82 65–100

106 94–120

81 62–101

104 79–130

111 98–125

94 74–116

97 79–117

107 91–124

107 94–122

LHd

99 69–129

80 47–114

34 19–56

57 31–96

67 39–96

50 27–87

107 82–133

105 63–149

69 37–120

95 73–119

127 80–175

128 89–169

137 101–175

171 138–205

Bladder

61 44–79

135 103–167

76 57–96

54 33–77

149 117–181

77 52–103

79 63–96

71 45–98

153 112–196

121 102–141

50 31–78

169 134–205

87 66–110

114 94–135

Skin

121 105–139

82 65–101

90 77–105

78 60–97

101 83–119

66 50–83

76 65–87

76 58–96

86 65–108

97 86–109

99 78–121

141 119–163

110 93–128

137 122–154

Prostate

Table 2 Standardized incidence ratios and 95% confidence intervals of all and selected cancer sites by region, male population of Costa Rica, 1981–1993

123 72–198

216 130–337

103 56–173

118 54–224

82 36–163

85 34–176

54 27–97

175 91–307

34 4–126

88 53–138

99 40–206

96 44–184

105 56–180

87 50–142

Penis

49 26–84

43 19–86

71 41–117

79 40–142

79 42–135

62 30–121

116 82–151

55 22–115

134 69–234

103 71–136

143 87–224

80 43–137

154 105–205

164 121–207

Testis

87 82–92

111 104–118

73 69–78

79 73–85

97 91–103

77 71–83

102 98–107

102 95–110

98 91–107

102 98–107

98 91–105

117 110–124

123 117–129

114 110–120

All sites


369

Sites

86 71–102 85 67–104 100 88–113 105 82–129 147 122–174 114 102–128


4. Northern mountain chain of Central Valley





c 95% confidence interval. d Lymphohaematopoietic system.

a Central county of San José excluded. b Standardized incidence ratio.

72 58–87

164 137–192



73 60–87

96 72–121

82 66–99




83 63–104

120 105–137



90b 80–101c

Stomach

1. Central Valleya

Region

91 59–124

104 61–149

57 34–82

64 31–124

80 47–115

69 37–118

67 46–88

84 49–135

89 51–146

150 119–181

57 31–98

135 97–175

114 83–146

126 101–153

Colon

85 47–144

190 116–294

80 45–133

102 44–202

83 42–149

95 44–182

95 60–131

50 17–119

69 26–152

123 83–164

99 50–178

110 65–175

132 83–181

77 49–107

Liver

78 45–128

103 55–177

92 55–130

169 97–276

85 48–141

78 36–148

118 84–153

92 48–162

84 41–165

88 59–118

61 28–118

76 45–123

89 54–125

137 104–170

Pancreas

150 105–195

107 65–168

53 32–85

75 36–147

134 88–181

49 22–98

101 74–129

73 39–127

63 31–123

92 66–118

45 21–87

97 62–133

128 93–165

125 99–153

Lung

51 26–90

100 59–162

69 39–114

108 58–185

84 45–144

92 49–158

93 62–125

93 46–167

122 61–219

94 61–127

99 53–169

127 74–181

127 84–172

130 95–167

Brain

78 61–95

101 79–124

86 62–112

93 71–115

94 76–113

74 53–95

90 76–104

128 99–159

83 57–111

106 91–123

104 79–130

87 67–107

133 112–154

112 97–128

LHd

93 40–185

76 21–195

57 21–124

0 0–94

80 30–176

0 0–77

100 57–163

54 11–160

60 12–175

116 70–183

114 46–235

127 66–223

136 78–222

150 98–203

Bladder

49 31–69

214 164–265

38 23–54

120 77–164

137 104–172

78 46–110

49 35–63

154 112–196

132 92–173

142 119–166

60 36–86

173 140–208

81 61–103

82 67–98

Skin

78 66–91

63 50–77

80 69–93

62 46–78

90 75–105

57 44–72

88 79–99

83 66–100

100 80–121

117 105–129

87 71–103

111 96–126

120 107–134

142 131–154

Breast

Table 3 Standardized incidence ratios and 95% confidence intervals of all and selected cancer sites by region, female population of Costa Rica, 1981–1993

138 124–154

123 107–141

131 117–146

197 172–223

124 108–141

111 94–129

77 69–86

85 70–102

61 47–77

79 70–88

81 68–96

75 64–87

100 89–112

85 77–94

Cervix

127 94–161

69 40–99

61 38–84

63 35–107

84 53–116

80 46–115

91 70–113

56 31–95

112 67–159

108 85–133

107 69–146

115 81–149

113 85–142

129 105–153

Ovary

93 88–99

110 103–118

84 79–89

111 103–120

100 94–107

84 78–91

87 83–91

103 96–111

92 85–100

103 100–108

88 82–95

102 96–108

116 111–122

110 106–114

All sites

370 INTERNATIONAL JOURNAL OF EPIDEMIOLOGY


women; men had SIR .100 for 22 out of 29 sites and women for 26 out of 30 sites; a particularly high risk for cancer of the nasopharynx was detected in women (SIR = 236, 95% CI : 114–459). Several rural regions had markedly high risks for specific cancers. In one area with predominantly smallholder agriculture growing grains, coffee, and sugarcane (region 9), excesses were found for cancer of the eye (men: SIR = 175, 95% CI : 83–337; women: SIR = 262, 95% CI : 131–470) and of the oropharynx among women (SIR = 395, 95% CI : 145–860, based on six cases). In an aggregation of seven small counties south of the Central Valley (parts of regions 6 and 7, contiguous to region 2), with mainly coffee production, elevated SIR (between 123 and 281) with rather narrow CI were observed for cancer of the tongue and oral cavity (men), oesophagus (particularly women), stomach (both genders), and liver (men). Similarly elevated rates were also observed for lymphohaematopoietic cancers (women), skin melanoma (men), penile cancer, eye (men), and brain cancers (particularly men). Excess cancer of the lip occurred in both genders in the four regions with the largest coffee production areas (use of, for example, lead arsenate and paraquat), and excess cancer of the oesophagus and stomach in three of them. High risks for penile cancer, non-melanocytic skin cancer and skin melanoma were also observed in several of these coffee regions. In the rice producing north-west area of Costa Rica (use of, for example, chlorophenoxy herbicides), the rates for most sites, except cervical cancer, were remarkably low. In the banana producing Atlantic Region (use of, for example, dibromochloropropane, chlorothalonil, and mancozeb) SIR were high for several cancers usually not seen in excess in rural areas: respiratory cancers in both genders, and ovary and prostate cancers. With regard to population density, excesses were predominantly observed for the highest and deficits for the lowest quartile. Risks of invasive cervical and penile cancer were inversely related to population density. The analyses by quartiles of proportions of industrial workers were similar to those by population density, while those by agricultural workers showed an opposite tendency. No or irregular trends were obtained for areas of arable land, specific crops and PEI. In the tertile of most rural counties, the RR of all sites combined for counties with high versus low PEI was slightly elevated in men, and more clearly in women (Table 4). The risk of lung cancer was twofold in both genders. Increases between roughly 25% and 80% were observed for hormone-related cancers in women, but in men the RR of prostate cancer was just over unity (RR = 1.1) and of testicular cancer well below (RR = 0.6). Relative risks >1.5 with at least 15 cases in the high PEI category were observed for skin melanoma and cancers of the oesophagus, gallbladder, larynx, bone, and bladder in men; and for colorectal, liver, gallbladder and brain cancer in women.

Discussion This study showed considerable geographical differences in the incidence of several cancers. Cancer patterns contrasted between rural and urban areas, and population density was related to many sites. Within the subset of most rural counties, heavy pesticide use was associated with an increase in overall cancer occurrence and at a substantial number of specific sites,

371

including a twofold risk of lung cancer among both genders, and excesses of all hormone-related cancers in women. The results may have been affected by a number of potential systematic errors. The quality of the cancer data can be considered in general acceptable.12 Cancer reporting is compulsory for public and private health and pathology facilities, where trained technicians are in charge, but the completeness of coverage of RNT has not been evaluated. Before 1994 cancers reported on death certificates that could not be traced in any health centre were not included. Any underregistration is more likely in remote areas, and would have caused a dilution of the SIR in the most rural regions. Registration of the county of treatment as the county of residence inflated the incidence rates in the hospital counties. This bias was reduced by excluding the central county of San José with its specialized hospitals, but it may partly explain the overall increased cancer risk in the nearby, densely populated, suburbs (region 2). The incidence rates in the other regions are not affected by a concentration of cases in the hospital counties, since patients come from surrounding areas. Some confidence in the validity of the results can be derived as the findings are in accordance with current epidemiological knowledge. Excesses of nasopharyngeal cancers (SIR = 175, 95% CI : 105–247, based on 23 cases) and nasal cancers (SIR = 149, 95% CI : 77–261, based on 12 cases) occurred in men in the west part of the Central Valley (region 5), where the district of Sarchí with the main wood carving and furniture industry of Costa Rica is located. The residence of most cases was registered as the hospital county, Alajuela. Conversely, in categories of non-adjacent counties, overrepresentation of cases in hospital counties may have biased the results into either direction. The impact of this potential bias on the risk estimates in the tertile of most rural counties was examined by excluding the counties with a hospital of a substantial size (one in the high and one in the low PEI category), one or both at a time. Concentration of cases in hospital counties did not seriously affect the results for this subset; the high and low PEI hospital counties respectively increased and decreased the rate ratios somewhat, for all as well as most specific sites, but when eliminating both counties the net changes were very small. Population data for 1987 were used. Recent estimates from the Central American Population Program at the University of Costa Rica show that in 1995 the largest population change over the 14 regions was a 1.6% increase in the Atlantic Region, due to expansion of banana plantations after 1990. The data on pesticide use have several limitations. The complexity of pesticide and crop patterns did not allow assessment of specific pesticides, and the PEI is an indicator for the combined use of different pesticides with varying carcinogenic properties. Furthermore, the PEI is based on pesticide use in 1984, whereas the relevant exposure period for cancers may extend one to two decades further back. In the rural subset, the high and low PEI categories distinguished well between counties with a commonly known history of early, heavy pesticide use and other areas. Finally, migration, particularly related to the banana plantations in the Atlantic Region, may make geographical comparisons of cancer risk more difficult. The use of aggregated data instead of the joint distributions of exposure, outcome, and covariates at the individual level, may

372


Table 4 Risk of all cancers and specific sites for category of high pesticide use versus category of low pesticide use, tertile of most rural counties (,30 inhabitants/km2), Costa Rica, 1981–1993 Men

Women

Cases in high PEIa category

RRb

95% CI c

Cases in high PEIa category

RRb

95% CI c

1427

1.15

1.07–1.23

1453

1.32

1.23–1.42

Mouth (140–149)

51

1.19

0.81–1.74

27

1.18

0.70–2.00

Oesophagus (150)

32

1.69

0.98–2.93

5

1.20

0.37–3.93

325

1.01

0.87–1.16

133

1.01

0.80–1.27

Colon (153)

42

1.19

0.78–1.81

31

1.65

0.98–2.78

Rectum (154)

35

1.61

0.96–2.69

28

1.86

1.06–3.25

Liver (155)

22

0.61

0.36–1.02

23

2.20

1.16–4.17

Gallbladder (156)

19

1.49

0.77–2.90

41

1.73

1.07–2.78

Pancreas (157)

43

1.28

0.84–1.97

23

0.97

0.57–1.68

Larynx (161)

33

1.88

1.10–3.23

4

0.48

0.11–1.57

107

2.01

1.49–2.72

37

2.63

1.50–5.62

Hodgkin’s disease (965–966)

35

1.23

0.76–1.98

13

1.14

0.55–2.35

Non-Hodgkin lymphoma (959–964, 969, 975)

44

1.17

0.77–1.77

23

1.32

0.73–2.41

101

1.16

0.88–1.52

78

1.17

0.85–1.60

Multiple myeloma (973)

15

0.97

0.50–1.90

10

1.29

0.51–3.28

Bone (170)

15

1.81

0.83–3.95

6

1.31

0.44–3.91

Soft-tissue sarcoma (171)

18

0.58

0.32–1.03

13

1.11

0.52–2.35

Skin (non-melanocytic) (173)

73

1.16

0.85–1.59

35

1.23

0.79–1.92

Skin melanoma (872–879)

17

1.77

0.83–3.79

4

0.29

0.10–0.87

Breast (174)

174

1.25

1.02–1.54

Cervix (invasive) (180)

Site (ICDO-1) All (140–199)

Stomach (151)

Lung (162)

Leukaemia (980–994)

445

1.32

1.16–1.51

Corpus uteri (182)

28

1.78

1.02–3.11

Ovary (183)

50

1.78

1.17–2.71

Prostate (185)

167

1.12

0.90–1.38

Testis (186)

19

0.60

0.33–1.09

Penis (187)

20

1.05

0.57–1.93

Bladder (188)

34

1.71

1.01–2.90

5

0.94

0.29–3.08

Kidney (189)

10

0.58

0.27–1.24

18

1.09

0.58–2.04

Eye (190)

14

1.39

0.67–2.89

6

0.70

0.26–1.83

Brain (191)

35

1.17

0.73–1.86

29

1.64

0.94–2.88

Thyroid (193)

14

1.47

0.65–3.31

50

1.26

0.85–1.86

a Pesticide exposure indicator. b Relative risk. c Confidence interval.

lead to severe bias in ecological analyses.10,11 Restriction of the analyses to the tertile of least densely populated counties reduced this bias. The populations of this subset are predominantly agricultural and, on the county level, rather homogeneous with regard to farming practices. This considerably diminished the within-county heterogeneity of pesticide exposures, while large differences between the counties remained. Confounding from the grouping variable, due to the differential distribution of other risk factors over the counties, was at least partly solved by this stratification. Population density is an indirect measure of degree of urbanization and a collective

indicator for a complex set of potentially confounding factors related to socioeconomic status and lifestyle. However, residual confounding is feasible. Overall, it is difficult to assess the net impact of the ecological bias. The apparent gradient of cancer occurrence between urban and rural areas is consistent with previous observations.13,14 Lung, colorectal, and breast cancers occurred more frequently in the urban regions where lifestyles in terms of smoking, alcohol intake, diet, and calorie expenditure resemble more western industrialized societies than in rural Costa Rica. The incidence rates of cervix and penile cancers were high in rural areas with low


socioeconomic status, as expected.13,15 There are no easy interpretations of regional differences with regard to crops or specific pesticides, because of large overlap and because most background cancer rates are low. However, a few results stand out. For example, coffee growing regions seemed to be related to increased risks for various skin type cancers. Solar radiation is one plausible explanation, but pesticides could also play a role. Lead arsenate was widely used on coffee during the 1970s and 1980s, and arsenical compounds are acknowledged skin carcinogens.5 An alternative hypothesis is an association of paraquat with skin cancers. Paraquat is extensively used on coffee and banana, and dermal contamination and chemical burns with this herbicide are common.8 An increased risk for melanoma and penile cancer, observed in a cohort study of banana plantation workers, is consistent with this hypothesis.9 Hormone-related cancers occurred in excess among women in the high PEI category, but not among men. The possibility of associations with environmental hormone-disrupting chemicals continue to be a public health concern.16–18 A recent review of occupational and environmental exposures in relation to ovarian cancers concluded that there is little or no evidence for excess risk among agricultural- and pesticide-exposed women, but the authors also pointed out the lack of good quality epidemiological studies.17 A similar recent breast cancer review considered that exposure to chlorinated hydrocarbon pesticides is possibly a risk factor, but that evidence was insufficient and studies among agricultural populations with high exposure would be of interest.18 Lung cancer rates were decreased or close to unity in most rural regions, while a twofold increase was observed for rural counties with high PEI as compared to low PEI. Both findings are consistent with previous studies. Deficits for lung cancer among farmers have been attributed to a lower prevalence of smoking in this population.1,19 Increased risk for lung cancer among pesticide exposed populations has been observed in ecological as well as cohort and case-control studies.20–24 A doubled risk for lung cancer in both genders appears high, even if confounding by smoking occurred. Assuming a 50% higher prevalence of smokers in the high PEI counties and, on top of that, that all are heavy smokers with a tenfold risk, an RR of 1.4 (0.6 × 10 + 0.4 × 1)/(0.4 × 10 + 0.6 × 1) would be obtained due to smoking alone, which is well below the observed excesses. The excesses of lung cancer, as well as a number of other cancers, among both men and women suggest that, if these are true associations, population wide exposures may have occurred, although mainly men work directly with pesticides. Aerial spraying occurred in all counties in the high PEI category, on rice or banana, and drifts and contamination of water supplies have been documented.7,25 In conclusion, there are substantial geographical differences in the incidence of most major cancer sites in Costa Rica. Although ecological bias and other types of systematic error preclude aetiological interpretations, some of the observed excesses may be due to occupational or environmental exposures to pesticides. The excess risks of skin melanoma, lung cancer, and female hormone-related cancers seem particularly interesting topics for epidemiological studies at the individual level. This investigation also permitted an improved insight into quality aspects of the cancer registry and increases the potential of cancer research in Costa Rica.

373

Population and agricultural characteristics of the 81 counties of Costa Rica, grouped by region are available from the author on request.

Acknowledgements This investigation was financially supported by the Department of Research Cooperation (SAREC) and the Health Division of the Swedish International Development Cooperation Agency (Sida). The authors thank Dr Timo Partanen for useful comments on the manuscript.

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