Geographic Variation of ALS Incidence in New Jersey
Geographic Variation of ALS Incidence in New Jersey
To our knowledge, this is the first statewide population-based study completed in the United States to have assessed geographic clustering of ALS and to have described the relative risk of ALS by geography and area-based SES. The geographic cluster analysis did not identify any places in New Jersey where the relative risk of ALS (observed number of cases vs. expected number of cases) was statistically significantly high; however, the smoothed relative risk maps did identify distinct regional differences in ALS risk throughout the state. The results from the multivariable regression models indicated a statistically significant association between area-based median income and ALS risk in New Jersey independent of age, sex, and race. ALS risk was highest among persons living in areas of the state with the highest median income compared with those living in areas with the lowest median income.
Not finding any statistically significant geographic clustering of ALS cases in New Jersey is in contrast to many population-based studies completed in other parts of the world, including one in the United States that found geographic clustering of ALS. There are several possible reasons why there was no evidence of geographic clustering of ALS in New Jersey. First, environmental exposures or other risk factors could be present in the other study regions that are not present in New Jersey. Second, the ALS cases in our study were more contemporary incident cases diagnosed between 2009 and 2011, while many other studies used ALS cases or deaths from earlier time periods. It is possible that there may be period and cohort effects where the risk of ALS may vary by time period or year of birth, which could coincide with shifts in exposure to risk factors over time. For example, in Guam, rates of ALS were extremely high among its indigenous people, the Chamorro, in the 1950s (estimated to be 50–100 times higher than worldwide rates), followed by decades of significant decline, with rates today that are only slightly higher than those worldwide. It has been suggested that the decline in ALS rates in Guam was related to modernization, which resulted in the elimination of the environmental factors that had triggered the high rates.
The positive association between area-based SES and ALS risk is an intriguing finding, but the underlying factors that may account for this association are unknown. It is possible that area-based median income could be correlated with specific behavioral risk factors that modify the risk of ALS. It is also possible that differences in residential location based on income could account for differential exposures to environmental risk factors that are hypothesized to be associated with ALS, including pesticides or the cyanobacteria-produced neurotoxin β-methylamino-l-alanine (BMAA), which is most commonly found in marine and freshwater environments. More research is needed to understand what it is about census-tract median income that produces variability in risk of ALS in New Jersey. Because both individual and environmental factors have been suggested as possible causes of ALS, future studies should include both individual and area-level measures of SES, behavior, and environment.
Of the previous 3 studies that examined SES and ALS risk in the United States, Italy, and Finland, our findings were consistent with only 1: the nationwide study that examined mortality rates for motor neuron disease in the United States. In that study, Bharucha et al. found that SES, as defined by county-level education, was positively associated with motor neuron disease. In the study in Italy, Bracco et al. found that the incidence of ALS was highest among manual workers, but they did not find significant differences based on educational level. Finally, in Finland, Palo and Jokelainen reported that social class was negatively associated with ALS incidence, in contrast with our findings. Variations between the studies carried out in the United States, Italy, and Finland—countries whose social, economic, and demographic profiles vary greatly—make comparisons difficult, and the differences in how SES was operationalized in each one exacerbate the problem. These issues point to the need for additional studies to assess the SES-ALS relationship, as well as to determine whether our finding of a positive association with SES is unique to New Jersey.
A rigorous case ascertainment approach was employed to capture ALS cases for the New Jersey ALS surveillance project; however, it is important to consider potential study biases related to any unreported ALS cases during the study period. As part of the case capture strategy in New Jersey, which has been previously described, neurologists in New Jersey and in parts of the surrounding states of New York, Pennsylvania, and Delaware submitted 109% of the expected number of cases for the 3-year reporting period. New Jersey death certificate and hospital discharge data were also used to identify possibly unreported cases. Although approximately 10% of neurologists who said they diagnosed or cared for ALS patients did not submit case reports, the majority of ALS patients are treated at ALS referral centers, and all ALS referral centers in the region reported cases. However, it is possible that a small number of cases may have gone unreported. If the unreported cases were not missing at random or differential by SES, the direction of the potential bias would depend on whether the high- or low-SES groups were more likely to be captured.
In this study, we also investigated the potential for bias to affect estimates of SES and ALS due to the exclusion of 27 cases that either had unknown race or were coded as other race. Of the 27 cases, 38.1% and 26.7% were in the 2 highest SES groups, respectively. The inclusion of these cases would have resulted in a stronger association between SES and ALS, and therefore their exclusion in our analysis biased the result toward the null. We also conducted 2 additional regression analyses by including only whites and mutually exclusive race/ethnicity groups in the models. Both analyses found a significant positive association between SES and ALS risk.
There are several limitations to be noted regarding this investigation. First, the geographic locations of the cases might not have been their addresses at the time of diagnosis, as neurologists were instructed to report the most recent address that was in the medical record. Therefore, if patients moved after diagnosis and neurologists updated the medical records, it is possible that some addresses might not have represented the case's address at the time of diagnosis. Second, using only residential address at the time of diagnosis limits a study's ability to assess clustering based on past exposures, which may be measured through previous residential locations. This is especially important for a disease such as ALS, where some studies have suggested a lag time between exposure to a causative agent and the development of clinical symptoms. To better assess geographic exposures over time, future investigators should consider lifetime residential histories when conducting spatial analysis.
In conclusion, no statistically significant geographic clusters of ALS were found in this study. However, ALS risk did vary geographically throughout New Jersey and appeared to be associated with area-based SES and race. Men and women living in the wealthiest or highest-income areas of the state had a higher risk of ALS than those living in lower-income areas. The variations we observed in incidence rates by race, area-based SES, and geography could be indicative of differential exposures to relevant environmental factors, with respect to person or place. Further research is needed to clarify the area-based SES-ALS relationship and to determine what other factors, including behavioral, environmental, or occupational health risk factors, might be contributing to the observed geographic variation in ALS in New Jersey.
Discussion
To our knowledge, this is the first statewide population-based study completed in the United States to have assessed geographic clustering of ALS and to have described the relative risk of ALS by geography and area-based SES. The geographic cluster analysis did not identify any places in New Jersey where the relative risk of ALS (observed number of cases vs. expected number of cases) was statistically significantly high; however, the smoothed relative risk maps did identify distinct regional differences in ALS risk throughout the state. The results from the multivariable regression models indicated a statistically significant association between area-based median income and ALS risk in New Jersey independent of age, sex, and race. ALS risk was highest among persons living in areas of the state with the highest median income compared with those living in areas with the lowest median income.
Not finding any statistically significant geographic clustering of ALS cases in New Jersey is in contrast to many population-based studies completed in other parts of the world, including one in the United States that found geographic clustering of ALS. There are several possible reasons why there was no evidence of geographic clustering of ALS in New Jersey. First, environmental exposures or other risk factors could be present in the other study regions that are not present in New Jersey. Second, the ALS cases in our study were more contemporary incident cases diagnosed between 2009 and 2011, while many other studies used ALS cases or deaths from earlier time periods. It is possible that there may be period and cohort effects where the risk of ALS may vary by time period or year of birth, which could coincide with shifts in exposure to risk factors over time. For example, in Guam, rates of ALS were extremely high among its indigenous people, the Chamorro, in the 1950s (estimated to be 50–100 times higher than worldwide rates), followed by decades of significant decline, with rates today that are only slightly higher than those worldwide. It has been suggested that the decline in ALS rates in Guam was related to modernization, which resulted in the elimination of the environmental factors that had triggered the high rates.
The positive association between area-based SES and ALS risk is an intriguing finding, but the underlying factors that may account for this association are unknown. It is possible that area-based median income could be correlated with specific behavioral risk factors that modify the risk of ALS. It is also possible that differences in residential location based on income could account for differential exposures to environmental risk factors that are hypothesized to be associated with ALS, including pesticides or the cyanobacteria-produced neurotoxin β-methylamino-l-alanine (BMAA), which is most commonly found in marine and freshwater environments. More research is needed to understand what it is about census-tract median income that produces variability in risk of ALS in New Jersey. Because both individual and environmental factors have been suggested as possible causes of ALS, future studies should include both individual and area-level measures of SES, behavior, and environment.
Of the previous 3 studies that examined SES and ALS risk in the United States, Italy, and Finland, our findings were consistent with only 1: the nationwide study that examined mortality rates for motor neuron disease in the United States. In that study, Bharucha et al. found that SES, as defined by county-level education, was positively associated with motor neuron disease. In the study in Italy, Bracco et al. found that the incidence of ALS was highest among manual workers, but they did not find significant differences based on educational level. Finally, in Finland, Palo and Jokelainen reported that social class was negatively associated with ALS incidence, in contrast with our findings. Variations between the studies carried out in the United States, Italy, and Finland—countries whose social, economic, and demographic profiles vary greatly—make comparisons difficult, and the differences in how SES was operationalized in each one exacerbate the problem. These issues point to the need for additional studies to assess the SES-ALS relationship, as well as to determine whether our finding of a positive association with SES is unique to New Jersey.
A rigorous case ascertainment approach was employed to capture ALS cases for the New Jersey ALS surveillance project; however, it is important to consider potential study biases related to any unreported ALS cases during the study period. As part of the case capture strategy in New Jersey, which has been previously described, neurologists in New Jersey and in parts of the surrounding states of New York, Pennsylvania, and Delaware submitted 109% of the expected number of cases for the 3-year reporting period. New Jersey death certificate and hospital discharge data were also used to identify possibly unreported cases. Although approximately 10% of neurologists who said they diagnosed or cared for ALS patients did not submit case reports, the majority of ALS patients are treated at ALS referral centers, and all ALS referral centers in the region reported cases. However, it is possible that a small number of cases may have gone unreported. If the unreported cases were not missing at random or differential by SES, the direction of the potential bias would depend on whether the high- or low-SES groups were more likely to be captured.
In this study, we also investigated the potential for bias to affect estimates of SES and ALS due to the exclusion of 27 cases that either had unknown race or were coded as other race. Of the 27 cases, 38.1% and 26.7% were in the 2 highest SES groups, respectively. The inclusion of these cases would have resulted in a stronger association between SES and ALS, and therefore their exclusion in our analysis biased the result toward the null. We also conducted 2 additional regression analyses by including only whites and mutually exclusive race/ethnicity groups in the models. Both analyses found a significant positive association between SES and ALS risk.
There are several limitations to be noted regarding this investigation. First, the geographic locations of the cases might not have been their addresses at the time of diagnosis, as neurologists were instructed to report the most recent address that was in the medical record. Therefore, if patients moved after diagnosis and neurologists updated the medical records, it is possible that some addresses might not have represented the case's address at the time of diagnosis. Second, using only residential address at the time of diagnosis limits a study's ability to assess clustering based on past exposures, which may be measured through previous residential locations. This is especially important for a disease such as ALS, where some studies have suggested a lag time between exposure to a causative agent and the development of clinical symptoms. To better assess geographic exposures over time, future investigators should consider lifetime residential histories when conducting spatial analysis.
In conclusion, no statistically significant geographic clusters of ALS were found in this study. However, ALS risk did vary geographically throughout New Jersey and appeared to be associated with area-based SES and race. Men and women living in the wealthiest or highest-income areas of the state had a higher risk of ALS than those living in lower-income areas. The variations we observed in incidence rates by race, area-based SES, and geography could be indicative of differential exposures to relevant environmental factors, with respect to person or place. Further research is needed to clarify the area-based SES-ALS relationship and to determine what other factors, including behavioral, environmental, or occupational health risk factors, might be contributing to the observed geographic variation in ALS in New Jersey.
