- Methodology
- Open Access
Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York
- Pierre Goovaerts^{1}Email author and
- Geoffrey M Jacquez^{1}
https://doi.org/10.1186/1476-072X-3-14
© Goovaerts and Jacquez; licensee BioMed Central Ltd. 2004
- Received: 14 June 2004
- Accepted: 23 July 2004
- Published: 23 July 2004
Abstract
Background
Complete Spatial Randomness (CSR) is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern is present. This paper presents a geostatistical approach to filter the noise caused by spatially varying population size and to generate spatially correlated neutral models that account for regional background obtained by geostatistical smoothing of observed mortality rates. These neutral models were used in conjunction with the local Moran statistics to identify spatial clusters and outliers in the geographical distribution of male and female lung cancer in Nassau, Queens, and Suffolk counties, New York, USA.
Results
We developed a typology of neutral models that progressively relaxes the assumptions of null hypotheses, allowing for the presence of spatial autocorrelation, non-uniform risk, and incorporation of spatially heterogeneous population sizes. Incorporation of spatial autocorrelation led to fewer significant ZIP codes than found in previous studies, confirming earlier claims that CSR can lead to over-identification of the number of significant spatial clusters or outliers. Accounting for population size through geostatistical filtering increased the size of clusters while removing most of the spatial outliers. Integration of regional background into the neutral models yielded substantially different spatial clusters and outliers, leading to the identification of ZIP codes where SMR values significantly depart from their regional background.
Conclusion
The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the background variation extant in real-world systems. In particular, this new methodology allows one to identify geographic pattern above and beyond background variation. The implementation of this approach in spatial statistical software will facilitate the detection of spatial disparities in mortality rates, establishing the rationale for targeted cancer control interventions, including consideration of health services needs, and resource allocation for screening and diagnostic testing. It will allow researchers to systematically evaluate how sensitive their results are to assumptions implicit under alternative null hypotheses.
Keywords
- Neutral Model
- Semivariogram Model
- Complete Spatial Randomness
- Sequential Gaussian Simulation
- Spatial Outlier
Background
Cancer mortality maps are important tools in health research, allowing the identification of spatial patterns, clusters and disease 'hot spots' that often stimulate research to elucidate causative relationships [1, 2]. In most spatial analysis software a statistical pattern recognition approach has been implemented whereby a statistic (e.g. spatial cluster statistic, autocorrelation statistic, etc.) quantifying a relevant aspect of spatial pattern is calculated. The value of this statistic is then compared to the distribution of that statistic's value under a null spatial model. This provides a probabilistic assessment of how unlikely an observed spatial pattern is under the null hypothesis [3]. Waller and Jacquez [4] formalized this approach by identifying five components of a test for spatial pattern.
1. The test statistic quantifies a relevant aspect of spatial pattern (e.g. Moran's I, Geary's c, LISA, a spatial clustering metric, etc.)
2. The alternative hypothesis describes the spatial pattern that the test is designed to detect. This may be a specific alternative, such as clustering near a focus, or it may be the omnibus "not the null hypothesis".
3. The null hypothesis describes the spatial pattern expected when the alternative hypothesis is false (e.g. Complete Spatial Randomness, often called CSR).
4. The null spatial model is a mechanism for generating the reference distribution. This may be based on distribution theory, or it may use randomization (e.g. Monte Carlo) techniques.
5. The reference distribution is the distribution of the test statistic when the null hypothesis is true.
CSR is the null hypothesis employed by most, if not all, statistical tests for spatial pattern, and is the workhorse of almost all spatial statistical software. Examples of statistics used in these tests include spatial autocorrelation (e.g. Moran's I and Geary's c); its local counterpart (e.g. LISA); geographic boundary statistics (e.g. boundary count and mean length), and a host of techniques for identifying hot spots, cold spots and foci. While CSR is useful in some situations, it often is not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences [5, 6]. For such fields CSR may be not relevant because spatial randomness rarely, if ever, occurs – some spatial pattern is almost always present. Hence in many situations rejecting CSR has little scientific value because CSR does not describe any plausible state of the system. As emphasized by Ord and Getis [7], Type I errors may abound when statistical tests are applied without regard to the global autocorrelation structure. For example, locations would be identified as hot spots simply because they lie in areas of generally high (or low) values, which would lead one to blend together local peaks and clusters of high (low) values. Even when health professionals are interested in identifying areas with generally high (or low) disease rates, it is still important to account for spatial autocorrelation to avoid an over-identification of the number of significant spatial clusters or outliers. In summary what are needed are realistic models that incorporate background pattern – the spatial and multivariate structure found when the null hypothesis is true.
The term "Neutral Model" captures the notion of a plausible system state that can be used as a reasonable null hypothesis (e.g. "background variation"). The problem then is to identify spatial patterns above and beyond that incorporated into the neutral model, enabling, for example, the detection of cancer clusters beyond background or regional variation in the risk of developing cancer. Neutral models can be generated using simulation techniques developed in the field of geostatistics [8] which provides a set of statistical tools for analyzing and mapping data distributed in space and time. In particular, sequential Gaussian simulation (SGS) allows one to generate realizations of the spatial distribution of rates that reproduce the sample histogram and spatial patterns displayed by the data, and also account for any auxiliary data or information on the local trend [9].
The objective of this paper is to present geostatistical approaches to generating neutral models that account for the spatial dependence of cancer rates, their regional background and spatially heterogeneous population sizes. These models are then used for the detection of local clusters and anomalies in cancer rates. The new methodology is applied to the analysis of the geographical distribution of lung cancer in three counties of Long Island, New York, which have been investigated under the CSR hypothesis in a previous issue of this journal [10, 11]
Methods
Data
The use of neutral models in cluster analysis will be illustrated using the lung cancer data analysed in [10, 11]. This section briefly summarizes the salient features of this dataset, and readers are referred to the above papers for a detailed description.
The New York State Department of Health (NYSDOH) published the cancer incidence data online as part of their Cancer Surveillance Improvement Initiative, http://www.health.state.ny.us/nysdoh/cancer/csii/nyscsii.htm. Data have been released on the following four cancers: breast (female only), colorectal (female and male), lung (female and male), and prostate. These data represent newly diagnosed cancer cases in the period 1993–7 assigned to the patient's residence at diagnosis, and they are calculated as the number of cancers for each 100,000 people in the population.
To protect patient privacy, the NYSDOH data provided case counts referenced to ZIP codes rather than individual residences. While ZIP codes are somewhat arbitrary spatial units of analysis with respect to potential health and environmental factors, they provide a convenient way to group the population and preserve confidentiality. The methods presented here do not depend on the spatial unit of aggregation and the reader may use census geography if that is their preference. As in the earlier analysis [10, 11], the focus of this study is on the 214 ZIP codes within Nassau, Queens and Suffolk County on Long Island.
Because cancer incidence is related to age, NYSDOH calculated the expected cancer incidence for each ZIP code using the ZIP code's age structure and the average incidence by age class for New York State (direct adjustment). We thus are using an external standard (the state average) rather than an internal standard (the average for Long Island), to calculate the expected incidence. A standardized morbidity ratio (SMR) has been calculated by dividing the observed value by the age-adjusted expected incidence. An SMR value of 1.0 indicates that the observed incidence is the same as expected, lower than 1.0 indicates that fewer than expected cases of cancer occurred, and greater than 1.0 indicates that more than expected occurred.
Local cluster analysis under CSR
Jacquez and Greiling [10] identified significant clustering and spatial outliers in SMR using Anselin's local Moran test [12] in the ClusterSeer™ Software http://www.terraseer.com/products/clusterseer.html. The local Moran test evaluates local clustering or spatial autocorrelation by computing the contribution of each location to the Moran's I statistics for the whole study area. Its null hypothesis is that there is no association between SMR values in neighboring ZIP codes. The working (alternative) hypothesis is that spatial clustering exists. For each ZIP code, referenced geographically by its centroid with the vector of spatial coordinates u = (x, y), the LISA (Local Indicator of Spatial Autocorrelation) statistic is computed as:
where z(u) is the SMR for the ZIP code being tested, which is referred to as the "kernel" hereafter. z(u_{j}) are the values for the J(u) neighboring ZIP codes that are here defined as units sharing a common border or vertex with the kernel u (1-st order queen adjacencies). All values are standardized using the mean m and standard deviation s of the SMR data (here 214 values). Since the standardized values have zero mean, a negative value for the LISA statistics indicates a spatial outlier where the kernel value is much lower or much higher than the surrounding values (e.g. SMR is below the global zero mean while the neighborhood average is above the global zero mean, or conversely). Cluster of low or high values will lead to positive values of the LISA statistics (e.g. both kernel and neighborhood averages are jointly above zero or below zero).
In addition to the sign of the LISA statistics, its magnitude informs on the extent to which kernel and neighborhood values differ. To test whether this difference is significant or not, a Monte Carlo simulation is conducted, which traditionally consists of sampling randomly and without replacement the global distribution of observed rates (i.e. sample histogram), then computing the corresponding simulated neighborhood averages. This operation is repeated many times (e.g. L = 999 draws) and these simulated values are multiplied by the kernel value to produce a set of L simulated values of the LISA statistics at location u:
with z^{(l)}(u_{j}) = F^{-1}[p^{(l)}(u_{j})], F[.] is the sample cumulative distribution function (cdf), and p^{(l)}(u_{j}) is a random number uniformly distributed within 0 and 1. This set represents a numerical approximation of the probability distribution of the LISA statistics at u, under the assumption of spatial independence. The observed LISA statistics, LISA(u), can then be compared to the probability distribution, allowing the computation of the probability of not rejecting the null hypothesis (so-called p-value). Following Jacquez and Greiling [10], we used an adjusted significance level α = 0.01101 to account for the fact that the multiple tests (i.e. 214 in this study) are not independent since near ZIP codes share similar neighbors. This significance level was obtained using the Bonferroni adjustment which amounts at dividing the significance level α = 0.05 by the average number of neighbors in each test. Thus, every ZIP code where the p-value is lower than 0.01101 will be classified as a significant spatial outlier (HL: high value surrounded by low values, and LH: low value surrounded by high values) or cluster (HH: high value surrounded by high values, and LL: low value surrounded by low values).
A typology of neutral models
The use of CSR as the null hypothesis means that the distribution of cancer rates is assumed to be spatially random (no autocorrelation) with uniform risk over the study area. In most cases, however, mortality rates are spatially correlated while the risk of developing cancer varies regionally as a result of changes in environmental exposure or other demographic, social, and economic factors. Another weakness of the above test is that it does not consider whether ratio data are based on many or a few cases, thereby ignoring the instability of rates computed from small population sizes. The basic idea of the proposed approach is to generate neutral models that are more realistic in the sense that they incorporate presence of spatial autocorrelation, non-uniform risk, and account for spatially heterogeneous population sizes.
Typology of neutral models. Models differ according to the reproduction of spatial correlation, the incorporation of non-uniform risk, and the filtering of noise caused by spatially varying population sizes.
Risk | Accounting for Population size | |
---|---|---|
No | Yes | |
Uniform, spatially random | I | IV |
Uniform, spatially correlated | II | V |
Heterogeneous, spatially correlated | III | VI |
Normal score transform of SMR data
The simulation techniques used in this paper assume a multiGaussian distribution for the variable under study, which requires a prior normal score transform of SMR data to ensure that at least their univariate distribution (histogram) is normal. Normal score transform is a graphical transform that allows one to normalize any distribution, regardless of its shape. It can be seen as a correspondence table between equal p-quantiles z_{p} and y_{p} of the z-cdf F(z) (cumulative histogram) and the standard Gaussian cdf G(y). In practice, the normal score transform proceeds in three steps:
1. The N original data z(u_{ α }) (i.e. SMR data) are first ranked in ascending order. Since the normal score transform must be monotonic, ties in z-values must be broken, which has been done randomly as implemented in GSLIB software [13].
2. The sample cumulative frequency of the datum z(u_{ α }) with rank k is then computed as = k/N - 0.5/N.
3. The normal score transform of the z-datum with rank k is matched to the -quantile of the standard normal cdf:
y(u_{ α }) = φ(z(u_{ α })) = G^{-1}[F(z(u_{ α }))] = G^{-1}[ ]
Local cluster analysis under spatial neutral model (Model II)
Model II aims to reproduce the pattern of spatial correlation displayed by the data that is here quantified using the normal score semivariogram [8, 14] which plots the average squared difference between normal score transformed SMR data as a function of the separation distance and direction between ZIP codes:
Here |h| corresponds to the Euclidian distance between two ZIP codes. Note the following discussion can be readily generalized to other distance measures that could be more appropriate to capture contiguity of entities of complex shape: our methodology is general and does not depend on a particular formulation of the distance measures. Following previous simulation studies [9] and in order to account for the noise induced by small population sizes, each pair has been assigned a weight proportional to the square root of the population size, , where n(u_{ α }) is the size of the population at risk in the ZIP code with centroid u_{ α }. Following an earlier analysis of the data [10], the population in ZIP codes was estimated using 2000 US census numbers.
Spatial neutral models are generated using Sequential Gaussian Simulation (SGS) which proceeds as follows (see [8] p. 380 for more details):
1. Fit a permissible function [9] to the experimental semivariogram (Equation 3). The modeling was here performed using least-square regression [15]. All semivariogram models were bounded, that is a sill is reached for a given distance referred to as the range of influence. The covariance models were then derived by subtracting the semivariogram model from the sill value.
2. Define a random path (i.e. using a random number generator) visiting each ZIP code location u_{ α }only once.
3. At each location u_{ α }determine the mean and variance of the Gaussian probability distribution of y-values as:
where y^{(l)}(u_{i}) are normal scores simulated at locations previously visited along the random path and located within a search radius from u_{ α }, m_{Y} is the stationary mean of the variable Y (which is zero following the normal score transform), and C(u_{i}-u_{ α }) is the covariance function of the normal score variable Y for the separation vector h_{iα}= u_{i}-u_{ α }. λ_{i} are kriging weights obtained by solving the following system of linear equations (simple kriging, SK):
4. Draw a simulated value from the conditional cumulative distribution function (ccdf) of probability and add it to the data set. In other words, the simulated value at u_{ α }is , where p^{(l)} is a random number between 0 and 1.
5. Proceed to the next location along the random path, and repeat the two previous steps.
6. Loop until all N locations (i.e. N = 214 here) are simulated.
7. Transform the simulated normal scores {y^{(l)}(u_{ α }); α = 1,..., N} so that the target histogram (in this case the global distribution of observed rates, F[.]) is reproduced: z^{(l)}(u_{ α }) = F^{-1}[p^{(l)}(u_{ α })] with p^{(l)}(u_{ α }) = G[y^{(l)}(u_{ α })]
The procedure is repeated using a different random path and set of random numbers to generate another realization. Note that these realizations account for only the histogram and semivariogram model of the SMR data (global conditioning), but they are non-conditional to the SMR data themselves (e.g. location of zones of high or low SMR values).
Once the L sets of N simulated SMR values, {z^{(l)}(u_{ α }); α = 1,..., N} have been generated, Equation (2) is applied to each member of this set to compute the simulated values of the LISA statistics at each location u. The simulated LISA values form the empirical distribution of the LISA statistics, allowing the calculation of the p-value for the test of hypothesis.
Local cluster analysis under a locally constrained spatial neutral model (Model III)
The simulation of neutral model II is conducted using a stationary mean for SMR values, which is unrealistic for situations where environmental exposure or other factors make the risk non-uniform. In this instance the researcher wishes to detect spatial pattern above and beyond this non-uniform risk. For example, one might want to detect clusters of melanoma beyond those that are explained by the north-south gradient in solar radiation. Non-uniform risk can easily be accounted for in the simulation procedure by replacing the stationary mean m_{ Y }in Equation 4 by locally varying means m_{Y}(u_{ α }), that is using the following estimate for the mean and variance of the Gaussian ccdf:
where C_{R}(u_{i}-u_{ α }) is the covariance function of the residual normal score variable [Y(u_{ α }) - m_{Y}(u_{ α })] for the separation vector h_{iα}= u_{i}-u_{ α }, and the kriging weights are obtained by solving the following system of linear equations (simple kriging with local means, SKlm):
The first step in the generation of model III is the computation of the local means m_{Y}(u_{ α }), which define the reference background risk the user wants to consider for the null hypothesis. In this paper a smooth model of background risk values was obtained by using the following kriging estimator of the local means of observed SMR data:
The kriging weights are calculated in two-steps. First, the following "kriging of the local mean" system [8] is solved:
Then, to incorporate data reliability due to spatially varying population size directly into the geostatistical filter the kriging weights are rescaled, following [9], as:
This rescaling is applied separately to the negative and positive kriging weights, keeping constant the overall contribution of these two sets of weights; that is the sum of positive (negative) kriging weights is the same before and after rescaling, which ensures that the unbiasedness constraint in system (11) is still satisfied. Note that although the population size is incorporated in the estimation of the local means, it is not accounted for directly into the test of hypothesis, which will be achieved using Models IV through VI introduced below.
Once the local means of the normal score transformed SMR data have been estimated, they are subtracted from the SMR values and the semivariogram of residuals is estimated and modelled. Then, the simulation is performed using SGS and SKlm. Last, the L realizations are used to derive the empirical probability distribution of the LISA statistics and the p-value of the test is computed.
Accounting for population size in local cluster analysis (Models IV to VI)
The neutral models introduced so far ignore the fact that cancer rates estimated over small areas, such as United States ZIP code areas or census tracts, tend to be less reliable [16, 17], hence larger fluctuations among simulated rates are expected at these locations. If ignored, large differences in population size decrease the ability of Moran's I to detect true clustering. There are essentially three approaches to incorporate population sizes in cluster detection: 1) randomly shuffle the cases rather than the rates (i.e. under a heterogeneous Poisson model the cases are allocated to each area using hypergeometric sampling [18]), 2) use a modified version of the test statistics (i.e. Oden's I pop [19] or Waldhör's I [20]), and 3) transform or standardize the rates first, then compute the LISA statistics on the results (i.e. Empirical Bayes Index [21], Cressie's transform [14 p.385–402], or any other smoothing algorithm [17, 22]). In this paper, the third approach has been adopted and the noise caused by small population sizes was filtered using a variant of the estimator introduced in equation 10:
The kriging weights are calculated in two-steps. First, the following system is solved:
with g_{0} = b_{0} × (1-δ(u_{ i }-u_{ α })) where b_{0} is the nugget variance in the weighted semivariogram model of SMR data, and δ(u_{ i }-u_{ α }) = 0 if u_{ i }= u_{ α }and 1 otherwise. Then, to incorporate data reliability (i.e. population size) directly into the geostatistical filter the kriging weights are rescaled according to Equation 12. The ability of the proposed approach to reconstruct the underlying disease risk from observed mortality rates has been tested in extensive simulation studies [9].
Results and discussion
Generating spatial neutral models
Accounting for population size in spatial neutral models
Local cluster analysis under various neutral models
Female
Number of significant zip codes for the different types of cluster/outliers and neutral models. Results are reported for female lung cancer. Numbers between parentheses indicate zip codes that have similar classification under the reference Model I (CSR). Summary statistics for the p-values are also provided.
Neutral Model Type | ||||||
---|---|---|---|---|---|---|
Model I | Model II | Model III | Model IV | Model V | Model VI | |
High-High | 7 | 0(0) | 4(2) | 10(5) | 0(0) | 3(2) |
High-Low | 1 | 0(0) | 2(0) | 0(0) | 0(0) | 1(0) |
Low-High | 2 | 0(0) | 4(0) | 2(2) | 0(0) | 1(0) |
Low-Low | 18 | 1(0) | 2(2) | 31(18) | 4(4) | 6(4) |
P-value | ||||||
Mean | 0.178 | 0.230 | 0.405 | 0.166 | 0.237 | 0.394 |
CV | 85.9% | 62.5% | 71.3% | 95.3% | 62.2% | 76.9% |
Under the CSR model (Model I), results similar to the ones reported in [11] were found. First, the local Moran test identified a single, large cluster of low SMR extending through portions of Flushing in the north and Jamaica in the south. Next to this cluster is the only high-low outlier: Oakland Gardens (11364) with a SMR of 1.116. Sayville (11782) is a significant spatial outlier with low SMR (72% of the New York average), though its SMR has a wide confidence interval resulting from the small number of observed cases there (15,896 habitants). Thus, while statistically distinct from its neighbors, it does not have significantly reduced risk. This is also the case for the second low-high outlier, Manorville (11949, 11,384 habitants), which has a SMR close to one but is located in the western part of Long Island where background rates are higher. Several local clusters of high SMR values occurred in the more central portions of Long Island. There is a cluster in north-mid Long Island, made up of two significant local clusters centered on Bayville (11709) and Mill Neck (11765). This cluster has about 60–70% higher SMR than the New York state average. A large cluster in south central Long Island is composed of four local clusters centered on Ronkonkama (11779), Central Islip (11722), Islip Terrace (11752), and East Islip (11730). This cluster has an SMR about 40% higher than the New York state average. Further east is a third cluster of high female lung cancer incidence centered on Mastic (11950) and including several adjacent ZIP codes. Its SMR is about 60% higher than the New York state average.
The map of significant ZIP codes at the bottom of Figure 7 bears little resemblance with the maps obtained under the neutral models I and II. This is expected since Model III addresses a different question, namely the detection of local departures from the regional background. Thus, in general, one would expect HL and LH outliers to be more frequent than spatial clusters HH or LL. Also the local constraining of the neutral models to the regional background causes less variation among realizations, leading to the J neighboring values being consistently either small or large across the realizations. Thus the distribution of 999 simulated LISA values is expected to be narrower than for the two previous models with a shift in the mean. This is illustrated for the ZIP code #11364 in Figure 10 (left bottom graph). Because this unit is located in a low-valued area, the use of neutral models reproducing the regional background yields smaller simulated LISA values (average = -0.14 instead of 0.0). In high-valued areas, the shift is expected to be in the opposite way, leading to a larger range of p-values observed across the area, see the scattergram in Figure 9 (right graph). Table 2 and Figure 9 indicate that the p-values are of larger magnitude (average: 0.405 versus 0.23 for Model II) and weakly correlated with the ones obtained under CSR.
For female lung cancer, the same numbers of ZIP codes (6) were classified as significant outliers or clusters under neutral model III. The two low-low clusters are Springfield Gardens (11413) and Saint Albans, which was the only significant unit under neutral model II. These ZIP codes are both located in the western part of Long Island with low background SMR values, and in the same area the following three low-high outliers are found: Bellerose (11426), Little Neck (11362), and New Hyde Park (11040) surrounding the high-high cluster Glen Oaks (11004). The last low-high outlier is found in Shelter Island Heights (11965) in the eastern part of Long Island, though its SMR (72% of the New York average) has a wide confidence interval resulting from the small number of observed cases there (1,080 habitants). The two high-low outliers are found in central Long Island characterized by a low SMR background level: Ridge (11961) and Bayport (11705) with SMR values 20 to 40% higher than the New York state average. Three more clusters of high SMR (1.15 to 1.20) are found in the North western part of Long Island, next to the large group of low SMR recorded in Flushing and Jamaica: Bayville (11709), Mill Neck (11765), and Glen Cove (11542).
For all three types of model, accounting for population size through geostatistical filtering leads to a larger number of ZIP codes classified as clusters and fewer outliers, see Figure 8 and Table 2. This result can be explained by the smoothing of local fluctuations, in particular the ones recorded in sparsely populated ZIP codes, yielding larger and more compact clusters, such as for Model IV. Figure 10 (right column) also shows that this smoothing halves the standard deviation of the distributions of simulated LISA statistics. Major differences between Models I and IV include bigger and more compacts clusters of low and high SMR values, the disappearance of two sparsely populated high-high clusters (Bayville and Mill Neck, with 7,134 and 732 habitants, respectively), and the classification of a former high-low cluster into a low-low cluster (Oakland Gardens) since the filtered rate becomes slightly lower than the global mean. A similar trend is observed for spatially correlated neutral models where the filtering increases the number of significant low-low clusters from one to four, all located in the eastern part of Long Island. The comparison of Models III and IV indicates the disappearance of a few sparsely populated ZIP codes which were classified as spatial outliers prior to filtering: HL (Bayport, 8,006 habitants), LH (Shelter Island Heights, Bellerose and Little Neck, with 1,080, 18,726 and 17,502 habitants). The only remaining LH cluster is New Hyde Park which has 39,156 habitants. The HH cluster (Glen Oaks, 14,682 habitants) also disappeared. Conversely, three other ZIP codes with populations ranging from 776 to 21,282 became significant LL clusters under Model IV: East Marion (11939), Woodbury (11797), and Cambria Heights (11411).
Across all six types of neutral models, only one out of 214 ZIP codes is consistently classified into the same category: the low-low cluster at Saint Albans (11412) which has a SMR = 0.82 and a population of 37,452. The stability of this cluster under alternative specifications of the statistical null hypothesis can be used by cancer surveillance and control efforts to quantify the degree of confidence associated with this cancer cluster.
Male
As for female lung cancer, accounting for spatial autocorrelation (i.e. Model II) leads to fewer significant ZIP codes compared to the common CSR assumption. Only two units are now significant: high-high cluster at Shirley (11967) and a low-high outlier at Rockaway Park (11694). Changes are also substantial when looking at results obtained under neutral model III. We found two high-high clusters: Shirley (11967) and Queens Village (11429), one low-low cluster: Corona (11368), and one low-high outlier: Elmont (11003).
Accounting for population size in the cluster analysis (Model IV) enhances the size and compactness of the two major clusters of low and high SMR values, while the classification of three sparsely sampled ZIP codes (Cutchogue, Moriches and Rockaway Park with 3,444, 2,652 and 19,278 habitants respectively) changed from spatial outliers to clusters. A new cluster of high SMR values (SMR = 1.29) is also found in Lindenhurst (11757). Using spatially correlated neutral models the geostatistical filtering (Model V) reveals a new cluster of low SMR values in Port Washington (11050) and Great Neck (11024) with lung cancer SMR values that are 70% of the New York State average. Comparison of Models III and VI indicates that besides increasing the size of clusters identified under Model II geostatistical filtering leads to the identification of a new low-low cluster: Cold Spring Harbor (11724, with a SMR 63% below the New York state average) and one low-high outlier: Springfield Gardens (11413, SMR = 0.80).
Across all six types of neutral models, only one out of 214 ZIP codes is consistently classified into the same category: the high-high cluster at Shirley (11967) which has a SMR = 1.157 and a population of 24,942.
How many realizations are needed?
Conclusions
Cancer mortality maps are used by public health officials to identify areas of excess and to guide surveillance and control activities. Maps of incidence as well as mortality are used as input to disease clustering procedures whose purpose is to identify local areas of excess and deficit. While some controversy revolves around the utility of these techniques, it is indisputable that the finding of a confirmed cancer cluster is often of considerable concern. The accurate quantification of local excesses and deficits, as well as regional trends and differences in cancer incidence and mortality, is therefore a problem of considerable practical importance.
Arguably one of the biggest problems facing spatial epidemiology and exposure assessment is that of identifying geographic pattern (e.g. hotspots, coldspots, clusters, etc) above and beyond background variation. Most, if not all, environmental contaminants and diseases with potential environmental causes occur at a background level in the absence of a pollution- or disease-generating process. Nonetheless, this background pattern is typically ignored in spatial analyses that employ null hypotheses of complete spatial randomness. Because some spatial dependency is expected at background levels, CSR often is an inappropriate null hypothesis.
When should the different types of neutral models be employed? The 6 types of neutral models presented here represent permutations of whether or not population size is accounted for, and 3 types of underlying risk models. As a rule of thumb one should employ that neutral model or those neutral models that most closely correspond to the spatial pattern expected in the absence of the alternative spatial process. So, for a cluster study one would select those neutral models that specify the risk function deemed most likely in the absence of spatial clustering. When working with rates spatial heterogeneity in the size of the at-risk population should always be accounted for, and selections from neutral models of types IV through VI are appropriate. When in doubt about which neutral model to employ, it makes sense to use several in order to determine how sensitive the results are to specification (and misspecification) of the null hypothesis. To the authors' collective knowledge, CSR is rarely if ever encountered in real-world biological systems. It is an apt descriptor of the "snow" that used to appear on late-night television when the programming day was over. It thus seems that neutral model types I and IV will seldom be appropriate. They perhaps will prove most useful for evaluating the extent of bias in past studies that employed CSR.
The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the spatial correlation and background variation modeled from the observed rates and any ancillary information (i.e. exposure model). An immediate consequence of using more realistic (i.e. spatially correlated) neutral models are larger p-values, leading to a substantial reduction in the number of ZIP codes declared significant outliers or clusters across Long Island. This result confirms earlier findings that CSR often leads to an over-identification of the number of significant spatial clusters or outliers. These false positives have potentially serious consequences in that it can lead to public alarm and demands for investigation by already stretched state health departments. The drop in the number of significant units is however accentuated by the use of an adjusted significance level (Bonferroni adjustment) to account for the correlation between the tests conducted at neighboring ZIP codes. Further research should investigate the redundancy between the use of spatially correlated neutral models and adjusted significance level, which might lead to an "under-identification" of the number of significant spatial clusters or outliers.
When the constraint of local conditioning of neutral models is superimposed to the reproduction of spatial autocorrelation (i.e. model III), the approach allows one to detect local departures from the conditioning background specified by the user. In this paper, this background was identified to the regional variability of SMR data which was estimated geostatistically. Future research will investigate the use of exposure models for local conditioning of neutral models, leading to the detection of clustered or isolated geographical units that depart significantly from the cancer rates expected from exposure data. A similar approach has recently been implemented whereby the regional background observed in the past has been incorporated into the geostatistical simulation of neutral models [24]. This new methodology allowed one to identify geographic pattern above and beyond background variation displayed in prior time intervals for cervix cancer mortality rates.
Another issue, which often impacts the results of cluster analysis, is the lack of reliability of rates inferred from small populations. If ignored, large differences in population size decrease the ability of Moran's I to detect true clustering/departures from spatial randomness. A geostatistical smoother, which accounts for the spatial pattern of SMR data (i.e. anisotropic variability, range of autocorrelation), has been applied to eliminate the random variability that appeared as a nugget effect on the experimental semivariograms. The smoothing of local fluctuations, in particular the ones recorded in sparsely populated ZIP codes, resulted in the detection of larger and more compact clusters of low or high SMR values as well as the disappearance of some unreliable spatial outliers. Geostatistical filters are very flexible and could be used to filter short-range variability in addition to the noise created by small population sizes. In this case, the focus of the analysis would be on the regional background of the data, allowing the detection of regional clusters.
The neutral models and methods in this paper make possible, for the first time ever, evaluation of the sensitivity of the results of cluster or boundary analyses to specification of the null hypothesis. Within a study, this will provide detailed quantification of the reliability of the results, and will identify those areas that are stable (i.e. always classified as a member of a cluster or not) or whose classification is highly sensitive to specification of the null hypothesis. This end result will be a spatially explicit analysis of potential false positives and false negatives.
Number of significant zip codes for the different types of cluster/outliers and neutral models. Results are reported for male lung cancer. Numbers between parentheses indicate zip codes that have similar classification under the reference Model I (CSR). Summary statistics for the p-values are also provided.
Neutral Model Type | ||||||
---|---|---|---|---|---|---|
Model I | Model II | Model III | Model IV | Model V | Model VI | |
High-High | 8 | 1(1) | 2(1) | 15(8) | 2(1) | 4(1) |
High-Low | 1 | 0(0) | 0(0) | 0(0) | 0(0) | 0(0) |
Low-High | 2 | 1(1) | 1(0) | 1(1) | 0(0) | 2(0) |
Low-Low | 14 | 0(0) | 1(1) | 24(14) | 2(1) | 4(2) |
P-value | ||||||
Mean | 0.185 | 0.259 | 0.368 | 0.166 | 0.251 | 0.371 |
CV | 83.4% | 54.8% | 71.8% | 92.9% | 58.9% | 80.5% |
Declarations
Acknowledgements
This research was funded in part by grants R01 CA92669 and 1R43CA105819-01 from the National Cancer Institute and R43CA92807 under the Innovation in Biomedical Information Science and Technology Initiative at the National Institute of Health. The views stated in this publication are those of the authors and do not necessarily represent the official views of the NCI.
Authors’ Affiliations
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