Poverty

The HEDSS system was implemented in 2004 to monitor disease activity.1 In all, 18 of 32 emergency departments (ED) and urgent care clinic provide data. Chief complaints are routinely categorized into eight syndromes. The fever/flu syndrome is used for early detection and monitoring of influenza in the community. Area-based measures, such as zip code, enable linkage to area-based socioeconomic census data. Neighborhood poverty, defined as the percentage of persons living below the federal poverty level in a geographic area, predicts a wide range of disease outcomes.

Objective

To describe the relationship between neighborhood poverty and emergency department visits for fever/flu syndrome illnesses reported through the Connecticut Hospital Emergency Department Syndromic Surveillance (HEDSS) system.

About 60% of Nairobi residents live in slums with higher poverty, population density prevalence diseases and lower health access than the city average. Some residents own livestock or in are in contact with its products. Most slums dwellers work outside slums. Thus, health surveillance in slum area is vital because of potential disease outbreaks and spread. Yet, little is known on practice/challenges of health surveillance in resource-limited slums.

Objective

Disseminate field lessons from a zoonotic disease study nested on the Nairobi Urban Health and Demographic Surveillance System (NUHDSS). The study investigates the emergence and introduction of zoonoses in urban areas

 

The burdens of poverty and disease continue to affect the livelihoods of pastoralists in Tanzania. Their knowledge of seasons and the ecosystems has evolved over years to manage human and animal health problems, including food insecurity. But, both local and global factors are putting pressure on their knowledge base and their capacity to manage health issues, this conflict has not been adequately explored nor have the synergies between indigenous and exotic knowledge.

Objective

To collect and assess indigenous knowledge and practices to manage diseases of food security as well as create opportunities to disseminate results for improving self-help.

Geographic Information System (GIS) technology provides visual tools, through the creation of computerized maps, graphs, and tables of geographic data, which can assist with problem solving and inform decision-making. One of the GIS tools being developed by KFL&A Public Health is the Social Determinants of Health (SDOH) Mapper. The SDOH Mapper consists of layers of information related to deprivation and marginalization indices across Ontario. The SDOH Mapper facilitates the inclusion of information related to vulnerable populations with the use of both age and social determinants of health data into the GIS portal. This is useful for observing trends in marginalization and deprivation across dissemination areas in Ontario, and for examining health inequities in an area over time. The SDOH mapper will, in this way, improve knowledge transmission on the effects of poverty and marginalization on outcomes.

Objective

To describe how the Social Determinants of Health (SDOH) Mapper is used by KFL&A Public Health to enhance real-time situational awareness of vulnerable populations across Ontario by facilitating the inclusion of information relating to marginalization and deprivation indices.

Obesity and related chronic diseases cost Canadians several billion dollars annually. Dietary intake, and in particular consumption of carbonated sweetened drinks (soda), has a strong effect on the incidence of obesity and other illness. Marketing research suggests that in-store promotion, and more specifically price discounting, has a strong effect on the purchase of energy-dense products such as soda. Attempts by public health authorities to monitor price discounts are currently limited by a lack of data and methods. Although rarely used in public health surveillance, electronic retail sales data collected around the world by marketing companies such as the Nielsen Corporation have an immense potential to measure dietary choices at high geographical resolution. These scanned sales data are recorded in real-time and they include a detailed product description, price, purchased quantity, store location, and product-specific advertising activities.

Objective

To assess the influence of in-store price discounts on soda purchasing by neighborhood socio-economic status in Montreal, Canada using digital grocery store-level sales data.

Health inequalities are major global public health problem and varies within and between countries. LMICs particularly India, are undergoing a phase of rapid economic development leading to an increase in informal settlements or urban slums. These settlements exhibits extreme poverty and suffers from adverse health outcomes. The worst affected are the adolescents because it is a crucial and most vulnerable age when health behaviours and lifestyle choices are established which affects their current and future health. The current health system in many of the developing countries are outdated and have either rudimentary health statistics or none. There is lack of standardized and reliable questionnaires to capture various behavioural aspects of subjective health of the population in India. Thus, we aim to identify various measures of determinants of social inequalities relevant to the Indian adolescent population context.

Objective

To identify and validate methods and scales measuring determinants of social inequalities in health in context to Indian adolescents. 

Most public health surveillance systems in the United States do not capture individual-level measures of socioeconomic position. Without this information, socioeconomic disparities in health outcomes can be hidden. However, US Census data can be used to describe neighborhood-level socioeconomic conditions like poverty and crowding. Place matters. Neighborhood affects health independently of personal characteristics. Thus, important trends may be elucidated by linking geocoded public health surveillance data to area-based measures of socioeconomic position, such as the percentage of residents with incomes below the federal poverty level.

Objective

The panel will describe applying the methods of Harvard’s Public Health Disparities Geocoding Project to a diverse collection of infectious disease surveillance data from 14 US states and New York City. This session will demonstrate the feasibility and utility of using US Census data to reveal sub-populations vulnerable to infectious diseases.

Evidence from over 100 years of epidemiological study demonstrates a consistent, negative association between health and economic prosperity. In many settings, it is clear that causal links exist between lower socioeconomic status and both reduced access to healthcare and increased disease burden. However, our study is the first to demonstrate that the increased disease burden in at-risk populations interacts with their reduced access to healthcare to hinder surveillance.

Objective

Improve situational awareness for influenza by combining multiple data sources to predict influenza outbreaks in at-risk populations.