In 2023, approximately 10.4 percent of Massachusetts' population lived below the poverty line. This accounts for persons or families whose collective income in the preceding 12 months was below the national poverty level of the United States.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Middlesex County, MA (S1701ACS025017) from 2012 to 2023 about Middlesex County, MA; Boston; MA; poverty; percent; 5-year; population; and USA.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Suffolk County, MA (S1701ACS025025) from 2012 to 2023 about Suffolk County, MA; Boston; MA; poverty; percent; 5-year; population; and USA.

10.80 (%) in 2018.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Norfolk County, MA (S1701ACS025021) from 2012 to 2023 about Norfolk County, MA; Boston; MA; poverty; percent; 5-year; population; and USA.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Plymouth County, MA (S1701ACS025023) from 2012 to 2023 about Plymouth County, MA; Boston; MA; poverty; percent; 5-year; population; and USA.

Thematic map of Massachusetts cities and towns 1999: education, poverty, and income. Thematic map of Massachusetts cities and towns by percent of the 25 and older population with a high school graduate degree or higher. Thematic map of the percent of families below the poverty level in 1999. Thematic map of 1999 median household income

This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

The included document uses GIS to investigate and compare Medicare and Medicaid provider infrastructure in Massachusetts. Provider addresses were geocoded and then compared to the geospatial locations of each insurance programs' eligible patient populations (percent of population of each census tract over 65 for Medicare and percent population for each census tract below the Federal Poverty Line for Medicaid). Massachusetts (MA) was picked for the comparison because Medicaid provider data, unlike Medicare provider data, is only available on cms.gov's website going back to 2011 and 2010, before the ACA was implemented in most states. However, MA had enacted "An Act Providing Access to Affordable, Quality, Accountable Health Care" in 2006, which had similar provisions to the subsequent ACA. The included maps used direct comparisons, buffers, and kernel density. Provider addresses obtained from: CMS' MAX Provider Characteristics and Provider of Services Current Files.

This data originally comes from the US Census and is illustrated by margin of error, percent, and rank of families with related children below the poverty line.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Nantucket County, MA (S1701ACS025019) from 2012 to 2023 about Nantucket County, MA; MA; poverty; percent; 5-year; population; and USA.

This data comes from the US Census and is illustrated through margin of error, percent of those below the poverty line, and rank of states with the worst senior poverty.

The poly shapefile is a subset of data that was derived from HRSA data that shows medically underserved ares within each county. Out of nearly 2,650 counties with such areas, there are only 96 counties that have areas that have incomes that are below the poverty level. The top five counties that are medically underserved and are poor are: 1. Radford, VA (52.7% pop below poverty level) 2. Autauga, AL (51.3% pop below poverty level) 3. Baldwin county, AL (50.2% pop below poverty level) 4. Washington D.C., (41.5% pop below poverty level) 5. Montgomery County, VA (40.4% pop below poverty level)

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Worcester County, MA (S1701ACS025027) from 2012 to 2023 about Worcester County, MA; Worcester; MA; poverty; percent; 5-year; population; and USA.

Data on the percentage of people who are living under the poverty line in their respective country

BackgroundThe World Health Organisation (WHO) estimates that about 3.2 billion people which is nearly half of the world’s population are at risk of malaria. Annually about 216 million cases and 445,000 deaths of malaria occur globally. Africa accounted for 90% and 91% of the malaria cases and deaths respectively. Zambia has earmarked malaria elimination on its path to Universal Health Coverage (UHC). This paper aims to determine the incidence of Out-of-Pocket Payments (OOP) and Catastrophic Health Expenditures (CHE) and impoverishment among households with malaria patients in Zambia. The paper focusses on the incidence of OOP and impoverishment for malaria in a setting without user fees for accessing primary malaria health care services and virtually no user fees at all levels of care if referred through the referral system. The results of this study will also serve as a baseline for tracking Zambia’s path towards achieving malaria financial access on its path towards UHC among patient with malaria.MethodsThe study uses a nationally representative cross-sectional survey of households in both rural and urban areas of Zambia. The study employed probability sampling procedures. A two-stage stratified cluster sample design was used. We analyse a total of 2,005 households that had at least one member suffering from malaria with a recall period of four weeks for out-patients and six months for the in-patient respectively. A logistic regression model was estimated with a Categorical Dependent variable being CHE (CHE = = 1, or otherwise = = 0). A household is considered impoverished if it fell below the poverty line due to OOP. All data was analyzed using Stata version 2013.Results and discussionThe results show that although the country has a free malaria policy at primary care level and virtually at all levels if referred through the health system process, households are still incurring costs in accessing health care services. Incidence of CHE and impoverishment were reflected at all levels. In terms of CHE, the poorest contributed almost 30% while the wealthier quintile contributed about 10%. Similarly, impoverishment effects of OOPs are more pronounced in the poorest quintile. The OOP composed mainly of transport, followed by diagnosis and medicines and was lowest for Insecticide-treated bed nets (ITNs) payments. The high costs of transport that the households had to incur when accessing health services could be due to the long distance that the households have to face as they travel to the health facilities as most of the facilities in Zambia are still outside the 5 km radius. The drug expenditure could be explained by the drugs running out of stock. Low expenditure on ITNs could be due to the country’s strategy of mass distribution working to give the country’s universal financial protection on ITNs for malaria.Conclusion and policy implicationsThis study sought to address gaps in OOP and the associated incidence of CHE and impoverishment for malaria, distribution of OOP among Social Economic Status (SES) setting and determinants of OOP in Country that has earmarked malaria elimination in the UHC agenda. Understanding household’s costs related to malaria will enable targeting intervention to accelerate Zambia’s path towards elimination of malaria and therefore contribute to attainment of the Sustainable Development Goals of household’s financial access to UHC. Thus, the study will also serve as a baseline for tracking UHC for household financial access to malaria care that the country has embarked on.

Distribution and Incidence of catastrophic OOP by wealth quintile.

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Essex County, MA (S1701ACS025009) from 2012 to 2023 about Essex County, MA; Boston; MA; poverty; percent; 5-year; population; and USA.

BackgroundThe World Health Organisation (WHO) estimates that about 3.2 billion people which is nearly half of the world’s population are at risk of malaria. Annually about 216 million cases and 445,000 deaths of malaria occur globally. Africa accounted for 90% and 91% of the malaria cases and deaths respectively. Zambia has earmarked malaria elimination on its path to Universal Health Coverage (UHC). This paper aims to determine the incidence of Out-of-Pocket Payments (OOP) and Catastrophic Health Expenditures (CHE) and impoverishment among households with malaria patients in Zambia. The paper focusses on the incidence of OOP and impoverishment for malaria in a setting without user fees for accessing primary malaria health care services and virtually no user fees at all levels of care if referred through the referral system. The results of this study will also serve as a baseline for tracking Zambia’s path towards achieving malaria financial access on its path towards UHC among patient with malaria.MethodsThe study uses a nationally representative cross-sectional survey of households in both rural and urban areas of Zambia. The study employed probability sampling procedures. A two-stage stratified cluster sample design was used. We analyse a total of 2,005 households that had at least one member suffering from malaria with a recall period of four weeks for out-patients and six months for the in-patient respectively. A logistic regression model was estimated with a Categorical Dependent variable being CHE (CHE = = 1, or otherwise = = 0). A household is considered impoverished if it fell below the poverty line due to OOP. All data was analyzed using Stata version 2013.Results and discussionThe results show that although the country has a free malaria policy at primary care level and virtually at all levels if referred through the health system process, households are still incurring costs in accessing health care services. Incidence of CHE and impoverishment were reflected at all levels. In terms of CHE, the poorest contributed almost 30% while the wealthier quintile contributed about 10%. Similarly, impoverishment effects of OOPs are more pronounced in the poorest quintile. The OOP composed mainly of transport, followed by diagnosis and medicines and was lowest for Insecticide-treated bed nets (ITNs) payments. The high costs of transport that the households had to incur when accessing health services could be due to the long distance that the households have to face as they travel to the health facilities as most of the facilities in Zambia are still outside the 5 km radius. The drug expenditure could be explained by the drugs running out of stock. Low expenditure on ITNs could be due to the country’s strategy of mass distribution working to give the country’s universal financial protection on ITNs for malaria.Conclusion and policy implicationsThis study sought to address gaps in OOP and the associated incidence of CHE and impoverishment for malaria, distribution of OOP among Social Economic Status (SES) setting and determinants of OOP in Country that has earmarked malaria elimination in the UHC agenda. Understanding household’s costs related to malaria will enable targeting intervention to accelerate Zambia’s path towards elimination of malaria and therefore contribute to attainment of the Sustainable Development Goals of household’s financial access to UHC. Thus, the study will also serve as a baseline for tracking UHC for household financial access to malaria care that the country has embarked on.

The poly shapefile shows income levels needed to support a family of four living in low-cost areas withing the states. It also shows percent income above the average poverty levels (Fed poverty level $20,650)for the U.S.