In Los Angeles County, the number of deaths among people experiencing homelessness (PEH) had an overall increase when comparing the 12 months pre- and post-COVID-19. Among the leading death causes, drug overdose reported the biggest increase of 78 percent. Additionally, COVID-19 was the third leading cause of death from April 1, 2020 to March 31, 2021, resulting in 179 deaths during that time. This statistic depicts the number of deaths among people experiencing homelessness, 12 months pre- and post-COVID-19 pandemic, in Los Angeles County, by cause of death.

The number of deaths of homeless people in England and Wales, by sex, five-year age group and underlying cause of death, 2013 to 2021 registrations. Experimental Statistics.

From July 1, 2021 to June 30, 2022, New York City's Department of Social Services/Department of Homeless Services (DHS) and Office of the Chief Medical Examiner (OCME) reported 684 deaths among persons experiencing homelessness. Furthermore, during this period the NYC Department of Social Services/Human Resources Administration (HRA) reported an additional 131 deaths among persons experiencing homelessness. This statistic depicts the number of deaths among persons experiencing homelessness in New York City between 2005 and 2022, by Reporting Agency.

From July 1, 2021 to June 30, 2022, New York City's Department of Social Services/Department of Homeless Services (DHS) and Office of the Chief Medical Examiner (OCME) reported 684 deaths among individuals experiencing homelessness. Among these, around 329 were attributed to drug-related causes, making this the primary cause of death within this demographic. This statistic depicts the leading causes of death among persons experiencing homelessness in New York City between 2021 and 2022.

People experiencing homelessness have historically had high mortality rates compared to housed individuals in Canada, a trend believed to have become exacerbated during the COVID-19 pandemic. In this matched cohort study conducted in Toronto, Canada, we investigated all-cause mortality over a one-year period by following a random sample of people experiencing homelessness (n = 640) alongside matched housed (n = 6,400) and low-income housed (n = 6,400) individuals. Matching criteria included age, sex-assigned-at-birth, and Charlson comorbidity index. Data were sourced from the Ku-gaa-gii pimitizi-win cohort study and administrative databases from ICES. People experiencing homelessness had 2.7 deaths/100 person-years, compared to 0.7/100 person-years in both matched unexposed groups, representing an all-cause mortality unadjusted hazard ratio (uHR) of 3.7 (95% CI, 2.1–6.5). Younger homeless individuals had much higher uHRs than older groups (ages 25–44 years uHR 16.8 [95% CI 4.0–70.2]; ages 45–64 uHR 6.8 [95% CI 3.0–15.1]; ages 65+ uHR 0.35 [95% CI 0.1–2.6]). Homeless participants who died were, on average, 17 years younger than unexposed individuals. After adjusting for number of comorbidities and presence of mental health or substance use disorder, people experiencing homelessness still had more than twice the hazard of death (aHR 2.2 [95% CI 1.2–4.0]). Homelessness is an important risk factor for mortality; interventions to address this health disparity, such as increased focus on homelessness prevention, are urgently needed.

Note: This Dataset is updated nightly and contains all downloadable Medical Examiner-Coroner records, January 1, 2018 to current, related to deaths that occurred in the County of Santa Clara under the Medical Examiner-Coroner’s jurisdiction and those deaths reportable to the Medical Examiner-Coroner (non-jurisdictional cases/NJA) but in which the office did not assume jurisdiction.

The Santa Clara County Medical Examiner- Coroner’s Office determines cause and manner of death for those deaths that fall under the jurisdiction of the Medical Examiner-Coroner, as defined by California Government code 27491.

The Medical Examiner-Coroner will not be responsible for data verification, interpretation or misinformation once data has been downloaded and manipulated from the dashboard.

Refer to the following document to know more of which deaths are reportable: https://medicalexaminer.sccgov.org/sites/g/files/exjcpb986/files/Reportable%20Death%20Chart%202018.pdf.

In Los Angeles County, methamphetamine accounted for the highest share of overdose deaths among people experiencing homelessness (PEH) in the 12 months before and after the COVID-19 pandemic onset, contributing to approximately three-quarters of all overdose deaths in both years. Fentanyl ranked as the second leading cause of overdose death in both periods, but showed the largest increase in its contribution over the analyzed timeframe. This statistic depicts the percentage of deaths among people experiencing homelessness by overdose pre- and post-COVID-19 pandemic in Los Angeles County, by drug type.

Decedents over whom the Pierce County Medical Examiner assumed jurisdiction, who are presumed to have been experiencing homelessness at the time of their death.

Annual Experimental Statistics on the number of deaths of homeless people in England and Wales at local authority level. Deaths registered in 2013 to 2017.

Experimental Statistics showing the number of deaths of homeless people in England and Wales, by underlying cause of deaths.

https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsofhomelesspeopleinenglandandwales

Starting in January 2017, Toronto Public Health (TPH) began tracking the deaths of people experiencing homelessness to get a more accurate estimate of the number of deaths and their causes. TPH leads the data collection, analysis and reporting. The Shelter, Support and Housing Administration (SSHA) and health and social service agencies that support people experiencing homelessness share information about a death with TPH and the Office of the Chief Coroner of Ontario (OCCO) verifies some of the data. For this data collection initiative, homelessness is defined as “the situation of an individual or family without stable, permanent, appropriate housing, or the immediate prospect, means and ability of acquiring it”.

From July 1, 2021 to June 30, 2022, New York City's Department of Social Services/Department of Homeless Services (DHS) and Office of the Chief Medical Examiner (OCME) reported 684 deaths among persons experiencing homelessness. Of this total, around 345 deaths occurred in hospitals, while 155 occurred in shelters. This statistic depicts the number of deaths among persons experiencing homelessness in New York City as reported by the DHS and the OCME between 2021 and 2022, by location of death

IntroductionCertain living conditions, such as homelessness, increase health risks in epidemic situations. We conducted a prospective observational cohort study to investigate the impact of the COVID-19 pandemic on morbidity and mortality in adult people who were homeless.MethodsThe study population comprised around 40% of the entire population experiencing homelessness in Marseille. They were enrolled at 48 different locations during the first pandemic wave (June to August 2020) and were followed up 3 and 6 months later. Rapid serological screening for SARS-CoV-2 was performed by community outreach teams at each follow-up, who also conducted interviews. Death registers and hospital administrative databases were consulted.ResultsA total of 1,332 participants [mean age 40.1 years [SD 14.2], women 339 (29.9%)] were enrolled in the cohort. Of these, 192 (14.4%) participants were found positive for COVID-19 and were propensity score matched (1:3) and compared with 553 non-COVID-19 cases. Living in emergency shelters was associated with COVID-19 infection. While 56.3% of the COVID-19-infected cohort reported no symptoms, 25.0% were hospitalized due to the severity of the disease. Presence of three or more pre-existing comorbidities was associated with all-cause hospitalization. Among COVID-19 cases, only older age was associated with COVID-19 hospitalization. Three deaths occurred in the cohort, two of which were among the COVID-19 cases.ConclusionThe study provides new evidence that the population experiencing homelessness faces higher risks of infection and hospitalization due to COVID-19 than the general population. Despite the efforts of public authorities, the health inequities experienced by people who are homeless remained major. More intensive and appropriate integrated care and earlier re-housing are needed.

In 2021, it was estimated that there were 188 deaths among those who were homeless in King County, Washington. From 2012 to 2021, the total number of deaths among those presumed to be homeless was 1,429, with the majority (1,000; 70%) of these deaths occurring in Seattle. This statistic shows the number of deaths among those presumed to be homeless in King County, Washington from 2012 to 2021.

Context

Disasters include all geophysical, meteorological and climate events including earthquakes, volcanic activity, landslides, drought, wildfires, storms, and flooding. Decadal figures are measured as the annual average over the subsequent ten-year period.

Content

Thanks to Our World in Data, you can explore death from natural disasters by country and by date.

Acknowledgements

https://www.acacamps.org/sites/default/files/resource_library_images/naturaldisaster4.jpg" alt="Natural Disasters">

Inspiration

List of variables for inspiration: Number of deaths from drought Number of people injured from drought Number of people affected from drought Number of people left homeless from drought Number of total people affected by drought Reconstruction costs from drought Insured damages against drought Total economic damages from drought Death rates from drought Injury rates from drought Number of people affected by drought per 100,000 Homelessness rate from drought Total number of people affected by drought per 100,000 Number of deaths from earthquakes Number of people injured from earthquakes Number of people affected by earthquakes Number of people left homeless from earthquakes Number of total people affected by earthquakes Reconstruction costs from earthquakes Insured damages against earthquakes Total economic damages from earthquakes Death rates from earthquakes Injury rates from earthquakes Number of people affected by earthquakes per 100,000 Homelessness rate from earthquakes Total number of people affected by earthquakes per 100,000 Number of deaths from disasters Number of people injured from disasters Number of people affected by disasters Number of people left homeless from disasters Number of total people affected by disasters Reconstruction costs from disasters Insured damages against disasters Total economic damages from disasters Death rates from disasters Injury rates from disasters Number of people affected by disasters per 100,000 Homelessness rate from disasters Total number of people affected by disasters per 100,000 Number of deaths from volcanic activity Number of people injured from volcanic activity Number of people affected by volcanic activity Number of people left homeless from volcanic activity Number of total people affected by volcanic activity Reconstruction costs from volcanic activity Insured damages against volcanic activity Total economic damages from volcanic activity Death rates from volcanic activity Injury rates from volcanic activity Number of people affected by volcanic activity per 100,000 Homelessness rate from volcanic activity Total number of people affected by volcanic activity per 100,000 Number of deaths from floods Number of people injured from floods Number of people affected by floods Number of people left homeless from floods Number of total people affected by floods Reconstruction costs from floods Insured damages against floods Total economic damages from floods Death rates from floods Injury rates from floods Number of people affected by floods per 100,000 Homelessness rate from floods Total number of people affected by floods per 100,000 Number of deaths from mass movements Number of people injured from mass movements Number of people affected by mass movements Number of people left homeless from mass movements Number of total people affected by mass movements Reconstruction costs from mass movements Insured damages against mass movements Total economic damages from mass movements Death rates from mass movements Injury rates from mass movements Number of people affected by mass movements per 100,000 Homelessness rate from mass movements Total number of people affected by mass movements per 100,000 Number of deaths from storms Number of people injured from storms Number of people affected by storms Number of people left homeless from storms Number of total people affected by storms Reconstruction costs from storms Insured damages against storms Total economic damages from storms Death rates from storms Injury rates from storms Number of people affected by storms per 100,000 Homelessness rate from storms Total number of people affected by storms per 100,000 Number of deaths from landslides Number of people injured from landslides Number of people affected by landslides Number of people left homeless from landslides Number of total people affected by landslides Reconstruction costs from landslides Insured damages against landslides Total economic damages from landslides Death rates from landslides Injury rates from landslides Number of people affected by landslides per 100,000 Homelessness rate from landslides Total number of people affected by landslides per 100,000 Number of deaths from fog Number of people injured from fog Number of people affected by fog Number of people left homel...

Correction 20 January 2023 - An error was found in the population data used to calculate 2021 rates in this publication. This has been corrected, with the overall Scotland mortality rate for 2021 changing from 60.4 deaths per million population to 60.3 deaths per million. There were small changes to the rates for council areas also. Homeless deaths for the year 2021.

BackgroundOpioid use disorder (OUD) is a growing public health crisis, with opioids involved in an overwhelming majority of drug overdose deaths in the United States in recent years. While medications for opioid use disorder (MOUD) effectively reduce overdose mortality, only a minority of patients are able to access MOUD; additionally, those with unstable housing receive MOUD at even lower rates.ObjectiveBecause MOUD access is a multifactorial issue, we leverage machine learning techniques to assess and rank the variables most important in predicting whether any individual receives MOUD. We also seek to explain why persons experiencing homelessness have lower MOUD access and identify potential targets for action.MethodsWe utilize a gradient boosted decision tree algorithm (specifically, XGBoost) to train our model on SAMHSA’s Treatment Episode Data Set-Admissions, using anonymized demographic and clinical information for over half a million opioid admissions to treatment facilities across the United States. We use Shapley values to quantify and interpret the predictive power and influencing direction of individual features (i.e., variables).ResultsOur model is effective in predicting access to MOUD with an accuracy of 85.97% and area under the ROC curve of 0.9411. Notably, roughly half of the model’s predictive power emerges from facility type (23.34%) and geographic location (18.71%); other influential factors include referral source (6.74%), history of prior treatment (4.41%), and frequency of opioid use (3.44%). We also find that unhoused patients go to facilities that overall have lower MOUD treatment rates; furthermore, relative to housed (i.e., independent living) patients at these facilities, unhoused patients receive MOUD at even lower rates. However, we hypothesize that if unhoused patients instead went to the facilities that housed patients enter at an equal percent (but still received MOUD at the lower unhoused rates), 89.50% of the disparity in MOUD access would be eliminated.ConclusionThis study demonstrates the utility of a model that predicts MOUD access and both ranks the influencing variables and compares their individual positive or negative contribution to access. Furthermore, we examine the lack of MOUD treatment among persons with unstable housing and consider approaches for improving access.