Introduction

In early 1993 media reports alleged clusters of anophthalmia and microphthalmia in England, and postulated that these might be linked to exposure to the pesticide (fungicide) Benomyl (DuPont, Wilmington, DE).¹ This was not the first time that alleged clusters of anophthalmia and microphthalmia had been reported in relation to environmental exposure: earlier reports dating from 1986 investigated clusters of microphthalmia in connection with two high temperature waste incinerators in Wales and Scotland. ^{2 3}

Immediate assessment of the English clusters was complicated by two factors. Firstly, there was a lack of diagnostic detail in the reports, which meant that an expected prevalence of the conditions could not be confidently asserted.⁴ Secondly, there was the so called Texas sharpshooter problem (a Texan fires randomly at a barn door and subsequently draws a bull's eye in the densest cluster of bullet holes), a usual dilemma in the post-hoc assessment of clusters. Even on a random, and therefore non-uniform, spatial pattern it is possible to draw boundaries around apparent clusters of cases in such a way that the density of the cases in that area far exceeds expectation.⁵ However, this has no statistical validity without reference to the background pattern of all cases from whom the clusters were identified.

We report on the results of a study set up in response to public concern to establish the presence or absence of any geographical variation in anophthalmia and microphthalmia, including large scale regional variation, excess prevalence in rural areas, or localised clustering. The analysis is based on a register of all cases of anophthalmia and microphthalmia born in England in 1988-94, which was established for the purposes of this study.⁶ The time period was chosen to overlap as far as possible with the period of concern and to collect enough cases for geographical analysis, but not to go back too far and risk major underascertainment of cases in earlier years.

Subjects and methods

Data
Our study is based on a register of all cases of anophthalmia and microphthalmia born in England in 1988-94. The methodology by which the register was established is described elsewhere.⁶ There were 444 cases registered, excluding cases of trisomy 13 and holoprosencephaly or cyclops.⁶

Statistical analysis
Comparison of prevalence of anophthalmia and microphthalmia across regions and groups defined by population density and deprivation was informed by the Pearson and Armitage ² tests, and where necessary adjusted for confounding using Poisson regression. Variation in underlying prevalences across regions and across districts was estimated by the Martuzzi-Hills method,⁸ which removes the random sampling variation expected in small numbers. This variation is expressed as the 5th to 95th centile range of prevalence ratios, relative to the overall prevalence. Expected numbers were calculated in each district of residence (for all cases and severe cases), stratified for region and population density fifth.

Results

The overall prevalence of anophthalmia and microphthalmia in England was 1.0 per 10 000 births. Table 1 shows the regional prevalence of all cases. Regional variation in prevalence did not reach statistical significance (P=0.07 for all cases, P=0.76 for severe cases). The 5th to 95th centile range of regional to national prevalence ratios for all cases estimated by the Martuzzi-Hills method was 0.83 to 1.22. There was no statistically significant heterogeneity in prevalence of all cases across districts (P>0.20), with an estimated 5th to 95th centile range of the ratios of observed to expected numbers of cases of 0.83 to 1.19.

The prevalence of anophthalmia and microphthalmia increased with decreasing population densitythat is, it was higher in rural areas (table 2). The relative risk was 1.79 (95% confidence interval 1.15 to 2.81) in the most rural group compared with the most urban group for all cases, and 2.37 (1.38 to 4.08) for severe cases (table 2). Among the 22 most rural cases, 36% (eight cases) were bilateral compared with an average of 35% (157 cases) overall. Poisson regression showed that urban and rural variation was reduced after controlling for region, both for all cases (relative risk for most rural fifth compared with most urban fifth was 1.61 (95% confidence interval 1.01 to 2.58)) and for severe cases (relative risk for most rural fifth compared with most urban fifth was 2.25 (1.29 to 3.95)). The validation analysis using the Office for National Statistics' urban and rural indicator showed that 20 out of 22 cases within the areas of lowest population density lived in enumeration districts classified as rural. Forty (9%) cases were classified as rural. The odds ratio for living in a rural enumeration district was 1.21 (0.85 to 1.72).

Prevalence of anophthalmia and microphthalmia varied little by socioeconomic deprivation of enumeration districts (table 3). Relative risk for the most deprived group compared with the least deprived group was 0.92 (0.68 to 1.24). Tests for both heterogeneity and trend showed that the variation was easily explained by chance (P>0.20).

Neither the Cuzick-Edwards (table 4) nor the Diggle-Chetwynd tests showed overall evidence of localised clustering at a national level (after removal of one of each of two pairs of siblings) whether or not controls were frequency matched to cases by population density fifth. Both tests were also performed for three regions separately: Trent, Northern, and Oxford. These regions had been selected a priori as having probably the most complete case ascertainment, or being areas of prior interest for clustering. The Cuzick-Edwards test showed no statistically significant clustering in these regions, while the Diggle-Chetwynd test showed statistically significantly more cases than expected within 2 km of an index case in Trent and within 50 km of an index case in the Oxford region. There was no evidence of clustering by the Cuzick-Edwards test within the most rural fifth or the two most rural fifths (table 4). The Diggle-Chetwynd test showed significant clustering in the most rural fifth only in the subgroup of severe cases of unknown aetiology, with more cases than expected within 7-9 km of other cases but based on only one case-case pair in Yorkshire. There was no significant clustering by this test when the two most rural fifths were considered.

Table 5 gives an empirical idea of the number of cases that occurred close together. Results are given for the most rural fifth and second most urban fifth. Altogether, 48 cases had another case within 1 km and one case had two cases within 1 km. The maximum number of cases within 5 km of another case was eight, with 15 cases having 6-8 other cases within 5 km. The maximum number of cases within a 20 km radius urban area was 50. We may infer from the results of the tests for clustering that these numbers are close to those expected given the distribution of births and the prevalence of anophthalmia and microphthalmia.

Discussion

Aetiological significance of the findings
We found little or no evidence of large scale geographical variation or localised clustering in anophthalmia and microphthalmia during 1988-94. However, we did find a gradient in prevalence from urban to rural, with a 80% excess prevalence (95% confidence interval 15% to 181%) in the least densely populated wards compared with the most densely populated. Further analysis, whether by controlling for region or using a dichotomous indicator of urban or rural only, tended to reduce the strength of this association, but given the a priori hypothesis of a rural excess that we set out to test we cannot dismiss this finding.

Data quality and potential artefacts
The geographical analysis that we performed depends for its validity on the quality of the underlying data. As reported elsewhere,⁶ we believe that about 15% (80) of cases may not have been reported to the register that we established, and that it is probable that these included mainly mild microphthalmia and children who did not survive their first year of life. It is difficult to establish a register retrospectively although our use of multiple sources of notification overcame this to a large extent. No source, even paediatricians who had seen the majority of cases, reported more than one quarter of cases to the register.⁶ Underascertainment can have two main effects: to reduce the overall case number and thus statistical power (where 15% underascertainment is not a serious problem), and to introduce spurious geographical variation due to geographical variation in ascertainment. Since we found little or no regional or district variation or clustering we doubt if these results would essentially have changed with complete ascertainment. The theoretical possibility that underascertainment might obscure geographical variation and clustering seems unlikely.

Implications of the study
A geographical and clustering analysis such as that reported here is very much a first stage in looking at the environmental epidemiology of a congenital anomaly. Generalised clustering analyses may not be sensitive to some particular geographical trends, as we showed with the urban-rural gradient, and it is therefore necessary to move forward to hypothesis testing about specific exposures, whether within an ecological or case-control context. Our results are reassuring in terms of the absence of strongly localised environmental exposures causing clusters of children to be born with anophthalmia and microphthalmia, but further study is needed of the aetiology of anophthalmia and microphthalmia to prevent this condition in future generations.