Deaths of despair in England: an observational study of local authority mortality data.

BACKGROUND: Deaths of Despair (DoD) are socially patterned causes of death encompassing drug and alcohol misuse and suicide. DoDs are strongly associated with socioeconomic disadvantage. England has high levels of inequalities, so we hypothesised the existence of marked geographical variations in DoD. We aimed to yield new knowledge on the spatial distribution of DoD, and area-level socioeconomic factors that predict DoD risk in England. METHODS: This observational study was conducted using ICD-10 coded deaths for 307 local authorities in England during 2019-21. Deaths were grouped to non-overlapping categories of drug-related death, alcohol-specific death, and suicide. The mean contributions of each of these causes to the total number of DoD in England were calculated with Poisson exact confidence intervals. Standardised mortality ratios (SMRs) for DoD were generated for each local authority population. A multivariable regression model for DoD risk was developed using 25 socioeconomic variables. FINDINGS: An estimated 46 200 people lost their lives due to DoD between Jan 1, 2019, and Dec 31, 2021. Regional SMRs ranged from 57·4 (SD 16·1) in London to 144·1 (SD 26·8) in the northeast of England (p<0·0001). Alcohol-specific deaths were the largest contributor of DoD, accounting for 44·1% of DoD (95% CI 43·5-44·8), followed by drug-related death (28·1%, 27·7-28·6) and suicide (27·7%, 27·2-28·2). Living in the North, living alone, White British ethnicity, lower inward migration, economic inactivity, income deprivation in older people, employment in elementary occupations, unemployment, and education deprivation in adults were significantly associated with higher DoD rates in England. INTERPRETATION: DoD in England are spatially patterned, with northern regions experiencing a considerably higher burden of mortality from these avoidable causes. A key limitation is ecological bias. This study provides novel insights into area-level risk factors for DoD in England. FUNDING: National Institute for Health and Care Research (NIHR) Applied Research Collaboration Greater Manchester (ARC-GM).

Histopathology-guided mass spectrometry differentiates benign nevi from malignant melanoma.

PURPOSE: Distinguishing benign nevi from malignant melanoma using current histopathological criteria may be very challenging and is one the most difficult areas in dermatopathology. The goal of this study was to identify proteomic differences, which would more reliably differentiate between benign and malignant melanocytic lesions. METHODS: We performed histolpathology - guided mass spectrometry (HGMS) profiling analysis on formalin-fixed, paraffin embedded tissue samples to identify differences at the proteomic level between different types of benign nevi and melanomas. A total of 756 cases, of which 357 cases of melanoma and 399 benign nevi, were included in the study. The specimens originated from both biopsies (376 samples) and tissue microarray (TMA) cores (380 samples). After obtaining mass spectra from each sample, classification models were built using a training set of biopsy specimens from 111 nevi and 100 melanomas. The classification algorithm developed on the training data set was validated on an independent set of 288 nevi and 257 melanomas from both biopsies and TMA cores. RESULTS: In the melanoma cohort, 239/257 (93%) cases classified correctly in the validation set, 3/257 (1.2%) classified incorrectly, and 15/257 (5.8%) classified as indeterminate. In the cohort of nevi, 282/288 (98%) cases classified correctly, 1/288 (0.3%) classified incorrectly, and 5/288 (1.7%) were indeterminate. HGMS showed a sensitivity of 98.76% and specificity of 99.65% in determining benign vs malignant. CONCLUSION: HGMS proteomic analysis is an objective and reliable test with minimal tissue requirements, which can be a helpful ancillary test in the diagnosis of challenging melanocytic lesions.

Necrotic Keratinocytes Are Common in Psoriasis and Have a Predilection to the Upper Epidermis: A Quantitative and Comparative Analysis.

BACKGROUND: The most salient histopathological features of psoriasis are epidermal hyperplasia, hypogranulosis, parakeratosis, dilated capillaries in dermal papillae, and intraepidermal and intracorneal neutrophils. Several additional "nonclassic" features of psoriasis have recently been reported, including necrotic keratinocytes (NK). To determine the diagnostic utility of NK, we characterized NK in a large cohort of psoriasis cases compared with psoriasiform spongiotic dermatitis (PSD) and normal skin. METHODS: NK were quantified in 101 cases of psoriasis, 20 cases of PSD, and 20 cases of normal skin. The location of NK within the lower, middle, or upper thirds of the epidermis was recorded. RESULTS: NK were identified in 77/101 (76%) of psoriasis cases. By comparison, NK were seen in 8/20 (40%) cases of PSD and 4/20 (20%) cases of normal skin. The linear concentration of NK was significantly higher in psoriasis (0.36 NK/mm) compared with PSD (0.12 NK/mm) and normal skin (0.03 NK/mm) (P = 0.0009). NK were preferentially located in the upper (58%) and middle (31%) epidermis in psoriasis. CONCLUSIONS: NK are a common feature of psoriasis and have a predilection to the upper layers of epidermis. Superficial NK may provide additional diagnostic support for psoriasis in challenging or borderline cases.

A rare presentation of seizures in a not-so-rare disease: Henoch-Schönlein purpura presenting with repeated seizures.

Henoch-Schönlein purpura represents the most common form of systemic vasculitis in children. Although a very common cause of vasculitis, seizures are a very rare complication of this disorder. We report a 5-year-old boy who presents with no other clinical symptoms of the disorder other than a seizure. By presenting this case, we hope to expand the differential diagnosis of repeated seizures to include diseases in which the pathogenesis of diseases with small vessel vasculitis such as Henoch-Schönlein purpura is considered.

Risk factors for deaths of despair in England: An ecological study of local authority mortality data.

Deaths of Despair (DoD) are socially patterned fatalities encompassing those attributable to drug and alcohol misuse and suicide. DoD occur much more frequently in socially deprived communities. This ecological study aimed to yield new knowledge on the spatial distribution of DoD, and socioeconomic factors that predict DoD risk in England. Via ICD-10 coding, deaths nationally during 2019-2021 were classified to non-overlapping categories of drug-related death, alcohol-specific death, and suicide. The proportion of DoD from each of these causes was calculated and age standardised DoD rates were generated for local authorities. A multivariable regression model for DoD risk was developed using 25 socioeconomic indicators. In 2019-2021, an estimated 46,200 people lost their lives due to DoD. Rates were higher in the North and in coastal areas (p < 0.001), ranging regionally from 25.1/100,000 (SD 6.3) in London to 54.7/100,000 (SD 9.5) in the North East. Alcohol-specific deaths were the largest contributor of DoD, accounting for 44.1% (95%CI 43.5-44.8%) of all such deaths. Living in the North, unemployment, White British ethnicity, living alone, economic inactivity, employment in elementary occupations, and living in urban areas were significantly associated with elevated DoD risk. DoD in England are spatially patterned, with northern regions experiencing a much higher burden of mortality from these avoidable causes. This study provides novel insights into the area-level factors associated with DoD in England. Potential ecological error is a key limitation.

Spatial proteomics reveals human microglial states shaped by anatomy and neuropathology.

Microglia are implicated in aging, neurodegeneration, and Alzheimer's disease (AD). Traditional, low-plex, imaging methods fall short of capturing in situ cellular states and interactions in the human brain. We utilized Multiplexed Ion Beam Imaging (MIBI) and data-driven analysis to spatially map proteomic cellular states and niches in healthy human brain, identifying a spectrum of microglial profiles, called the microglial state continuum (MSC). The MSC ranged from senescent-like to active proteomic states that were skewed across large brain regions and compartmentalized locally according to their immediate microenvironment. While more active microglial states were proximal to amyloid plaques, globally, microglia significantly shifted towards a, presumably, dysfunctional low MSC in the AD hippocampus, as confirmed in an independent cohort (n=26). This provides an in situ single cell framework for mapping human microglial states along a continuous, shifting existence that is differentially enriched between healthy brain regions and disease, reinforcing differential microglial functions overall.

Development of a predictive risk model for school readiness at age 3 years using the UK Millennium Cohort Study.

OBJECTIVES: The aim of this study is to develop a predictive risk model (PRM) for school readiness measured at age 3 years using perinatal and early infancy data. DESIGN AND PARTICIPANTS: This paper describes the development of a PRM. Predictors were identified from the UK Millennium Cohort Study wave 1 data, collected when participants were 9 months old. The outcome was school readiness at age 3 years, measured by the Bracken School Readiness Assessment. Stepwise selection and dominance analysis were used to specify two models. The models were compared by the area under the receiver operating characteristic curve (AUROC) and integrated discrimination improvement (IDI). RESULTS: Data were available for 9487 complete cases. At age 3, 11.7% (95% CI 11.0% to 12.3%) of children were not school ready. The variables identified were: parents' Socio-Economic Classification, child's ethnicity, maternal education, income band, sex, household number of children, mother's age, low birth weight, mother's mental health, infant developmental milestones, breastfeeding, parents' employment, housing type. A parsimonious model included the first six listed variables (model 2). The AUROC for model 1 was 0.80 (95% CI 0.78 to 0.81) and 0.78 (95% CI 0.77 to 0.79) for model 2. Model 1 resulted in a small improvement in discrimination (IDI=1.3%, p<0.001). CONCLUSIONS: Perinatal and infant risk factors predicted school readiness at age three with good discrimination. Social determinants were strong predictors of school readiness. This study demonstrates that school readiness can be predicted by six attributes collected around the time of birth.