The forgotten drought of 1765-1768: Reconstructing and re-evaluating historical droughts in the British and Irish Isles.

Historical precipitation records are fundamental for the management of water resources, yet rainfall observations typically span 100-150 years at most, with considerable uncertainties surrounding earlier records. Here, we analyse some of the longest available precipitation records globally, for England and Wales, Scotland and Ireland. To assess the credibility of these records and extend them further back in time, we statistically reconstruct (using independent predictors) monthly precipitation series representing these regions for the period 1748-2000. By applying the Standardized Precipitation Index at 12-month accumulations (SPI-12) to the observed and our reconstructed series we re-evaluate historical meteorological droughts. We find strong agreement between observed and reconstructed drought chronologies in post-1870 records, but divergence in earlier series due to biases in early precipitation observations. Hence, the 1800s decade was less drought prone in our reconstructions relative to observations. Overall, the drought of 1834-1836 was the most intense SPI-12 event in our reconstruction for England and Wales. Newspaper accounts and documentary sources confirm the extent of impacts across England in particular. We also identify a major, "forgotten" drought in 1765-1768 that affected the British-Irish Isles. This was the most intense event in our reconstructions for Ireland and Scotland, and ranks first for accumulated deficits across all three regional series. Moreover, the 1765-1768 event was also the most extreme multi-year drought across all regional series when considering 36-month accumulations (SPI-36). Newspaper and other sources confirm the occurrence and major socio-economic impact of this drought, such as major rivers like the Shannon being fordable by foot. Our results provide new insights into historical droughts across the British Irish Isles. Given the importance of historical droughts for stress-testing the resilience of water resources, drought plans and supply systems, the forgotten drought of 1765-1768 offers perhaps the most extreme benchmark scenario in more than 250-years.

No association between soil constituents and amyotrophic lateral sclerosis relative risk in Ireland.

INTRODUCTION: We have recently mapped ALS spatial risk in Ireland using Bayesian and cluster analysis methods at electoral division (ED) and small area (SA) levels. As a number of metal elements (both minerals and toxins) have been proposed as risk factors for ALS, here we extend this analysis to include soil constituents from the Irish National Soils Database as Bayesian conditional auto-regression covariates to determine associations with small area ALS risk. METHODS: Data on 45 different soil parameters were obtained under license from National Soils Database (via Irish EPA). We interpolated average values of each soil constituent for each small area using ordinary kriging. All cases of ALS in Ireland from January 1995 to December 2013 were identified from the Irish ALS register and observed and age and gender standardised expected cases were calculated for each SA. Besag-York-Mollié (BYM) models were then built including each parameter from the national soils database in turn as a Bayesian covariate in the BYM model. Models were compared using the deviance information criterion (DIC) and separate models were built for ALS subtypes. RESULTS: 1701 ALS patients were included - 959 (56%) were male, 938 (55%) had limb onset ALS. 315 Bayesian models were built in total. Of the 315 models built, only one resulted in a coefficient that did not overlap zero. For limb onset cases, total magnesium had a mean coefficient of 0.319 (credible interval 0.033-0.607). DISCUSSION: We report the first spatial analysis of potential association between ALS and soil minerals using a population-based dataset collected over 18 years. Our sole non-zero finding is likely a random finding due to the high number of models built. We did not find any evidence to support soil mineral and toxin levels as risk factors for ALS. However as soil parameters are an ecological assessment of exposure in a given area, individual level measures of exposure are required.

Social deprivation and population density are not associated with small area risk of amyotrophic lateral sclerosis.

INTRODUCTION: Evidence of an association between areal ALS risk and population density has been previously reported. We aim to examine ALS spatial incidence in Ireland using small areas, to compare this analysis with our previous analysis of larger areas and to examine the associations between population density, social deprivation and ALS incidence. METHODS: Residential area social deprivation has not been previously investigated as a risk factor for ALS. Using the Irish ALS register, we included all cases of ALS diagnosed in Ireland from 1995-2013. 2006 census data was used to calculate age and sex standardised expected cases per small area. Social deprivation was assessed using the pobalHP deprivation index. Bayesian smoothing was used to calculate small area relative risk for ALS, whilst cluster analysis was performed using SaTScan. The effects of population density and social deprivation were tested in two ways: (1) as covariates in the Bayesian spatial model; (2) via post-Bayesian regression. RESULTS: 1701 cases were included. Bayesian smoothed maps of relative risk at small area resolution matched closely to our previous analysis at a larger area resolution. Cluster analysis identified two areas of significant low risk. These areas did not correlate with population density or social deprivation indices. DISCUSSION: Two areas showing low frequency of ALS have been identified in the Republic of Ireland. These areas do not correlate with population density or residential area social deprivation, indicating that other reasons, such as genetic admixture may account for the observed findings.

Spatial cluster analysis of population amyotrophic lateral sclerosis risk in Ireland.

OBJECTIVE: Few spatial cluster analyses of amyotrophic lateral sclerosis (ALS) incidence have been conducted on prospective incident population-based cohorts; we report results of a formal cluster analysis of the Irish ALS cohort from January 1, 1995, to December 31, 2013. METHODS: We identified 1,684 incident cases from the Irish ALS register. Population data from 4 census years were used to calculate age- and sex-standardized expected ALS cases for 3,355 areas. Spatial cluster analysis was performed to identify high-risk clusters using both SaTScan and FleXScan software. Poisson-based, time period-stratified statistics and time-stratified Bayesian smoothed risk mapping were used to audit completeness of case ascertainment of the register. RESULTS: No significant high-risk clusters of incident ALS were identified. However, SaTScan revealed 2 significant areas of lower-than-average ALS risk-one centered on County Kilkenny (relative risk 0.53, p = 0.012) and a smaller area in County Clare (relative risk 0.0, p = 0.029). Audit of case ascertainment did not indicate any failure to detect cases in these areas. CONCLUSIONS: The absence of high-risk ALS clusters in Ireland contrasts with previous studies. Our study has several advantages, notably the use of a long-running prospective ALS register with nationwide case ascertainment. The presence of 2 low-risk areas was unexpected. No obvious ascertainment, demographic, or common environmental factors explain this finding. However, we postulate that historical factors may have led to altered genetic admixture in these regions, possibly contributing to lower rates.

An exploratory spatial analysis of ALS incidence in Ireland over 17.5 years (1995-July 2013).

INTRODUCTION: There has been much interest in spatial analysis of ALS to identify potential environmental or genetically caused clusters of disease. Results to date have been inconclusive. The Irish ALS register has been recently geocoded, presenting opportunity to perform a spatial analysis on national prospectively gathered data of incident cases over an 18-year period. METHODS: 1,645 cases of ALS in Ireland from January 1995 to July 2013 were identified from the Irish ALS register. 1,638 cases were successfully geocoded. Census data from four censuses: 1996, 2002, 2006 & 2011 were used to calculate an average population for the period and standardized incidence rates (SIRs) were calculated for 3,355 areas (Electoral Divisions). Bayesian conditional auto-regression was applied to produce smoothed relative risks (RR). These were then mapped for all cases, males & females separately, and those under 55 vs over 55 at diagnosis. Bayesian and linear regression were used to examine the relationship between population density and RR. RESULTS: Smoothed maps revealed no overall geographical pattern to ALS incidence in Ireland, although several areas of localized increased risk were identified. Stratified maps also suggested localized areas of increased RR, while dual analysis of the relationship between population density and RR of ALS yielded conflicting results, linear regression revealed a weak relationship. DISCUSSION: In contrast to some previous studies our analysis did not reveal any large-scale geographic patterns of incidence, yet localized areas of moderately high risk were found in both urban and rural areas. Stratified maps by age revealed a larger number of cases in younger people in the area of County Cork--possibly of genetic cause. Bayesian auto-regression of population density failed to find a significant association with risk, however weighted linear regression of post Bayesian smoothed Risk revealed an association between population density and increased ALS risk.