Energy poverty in healthcare facilities: a “silent barrier” to improved healthcare in sub-Saharan Africa

Background: SARS-CoV-2, the virus causing coronavirus disease 2019 (COVID-19), is rapidly spreading across sub-Saharan Africa (SSA). Hospital-based care for COVID-19 is particularly often needed among older adults. However, a key barrier to accessing hospital care in SSA is travel time. To inform the geographic targeting of additional healthcare resources, this study aimed to determine the estimated travel time at a 1km x 1km resolution to the nearest hospital and to the nearest healthcare facility of any type for adults aged 60 years and older in SSA. Methods: We assembled a unique dataset on healthcare facilities' geolocation, separately for hospitals and any type of healthcare facility (including primary care facilities) and including both private- and public-sector facilities, using data from the OpenStreetMap project and the KEMRI Wellcome Trust Programme. Population data at a 1km x 1km resolution was obtained from WorldPop. We estimated travel time to the nearest healthcare facility for each 1km x 1km raster using a cost-distance algorithm. Findings: 9.6% (95% CI: 5.2% - 16.9%) of adults aged 60 and older years had an estimated travel time to the nearest hospital of longer than six hours, varying from 0.0% (95% CI: 0.0% - 3.7%) in Burundi and The Gambia, to 40.9% (95% CI: 31.8% - 50.7%) in Sudan. 11.2% (95% CI: 6.4% - 18.9%) of adults aged 60 years and older had an estimated travel time to the nearest healthcare facility of any type (whether primary or secondary/tertiary care) of longer than three hours, with a range of 0.1% (95% CI: 0.0% - 3.8%) in Burundi to 55.5% (95% CI: 52.8% - 64.9%) in Sudan. Most countries in SSA contained populated areas in which adults aged 60 years and older had a travel time to the nearest hospital of more than 12 hours and to the nearest healthcare facility of any type of more than six hours. The median travel time to the nearest hospital for the fifth of adults aged 60 and older years with the longest travel times was 348 minutes (IQR: 240 - 576 minutes) for the entire SSA population, ranging from 41 minutes (IQR: 34 - 54 minutes) in Burundi to 1,655 minutes (IQR: 1065 - 2440 minutes) in Gabon. Interpretation: Our high-resolution maps of estimated travel times to both hospitals and healthcare facilities of any type can be used by policymakers and non-governmental organizations to help target additional healthcare resources, such as new make-shift hospitals or transport programs to existing healthcare facilities, to older adults with the least physical access to care. In addition, this analysis shows precisely where population groups are located that are particularly likely to under-report COVID-19 symptoms because of low physical access to healthcare facilities. Beyond the COVID-19 response, this study can inform countries' efforts to improve care for conditions that are common among older adults, such as chronic non-communicable diseases.

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Utilization of healthcare services among Chinese migrants in Kenya: a qualitative study

BMC Health Services Research ◽

10.1186/s12913-019-4846-y ◽

2019 ◽

Vol 19 (1) ◽

Author(s):

Jialing Qiu ◽

Duo Song ◽

Juan Nie ◽

Mengyi Su ◽

Chun Hao ◽

...

Keyword(s):

Health Services ◽

Language Translation ◽

Healthcare Services ◽

Sub Saharan Africa ◽

Local Health ◽

Quality Healthcare ◽

Healthcare Facilities ◽

Chinese Migrants ◽

Sub Saharan ◽

Local Healthcare

Abstract Background The number of Chinese migrants in Sub-Saharan Africa (SSA) is increasing, which is part of the south-south migration. The healthcare seeking challenges for Chinese migrants in Africa are different from local people and other global migrants. The aim of this study is to explore utilization of local health services and barriers to health services access among Chinese migrants in Kenya. Methods Thirteen in-depth interviews (IDIs) and six focus group discussions (FGDs) were conducted among Chinese migrants (n = 32) and healthcare-related stakeholders (n = 3) in Nairobi and Kisumu, Kenya. Data was collected, transcribed, translated, and analyzed for themes. Results Chinese migrants in Kenya preferred self-treatment by taking medicines from China. When ailments did not improve, they then sought care at clinics providing Traditional Chinese Medicine (TCM) or received treatment at Kenyan private healthcare facilities. Returning to China for care was also an option depending on the perceived severity of disease. The main supply-side barriers to local healthcare utilization by Chinese migrants were language and lack of health insurance. The main demand-side barriers included ignorance of available healthcare services and distrust of local medical care. Conclusions Providing information on quality healthcare services in Kenya, which includes Chinese language translation assistance, may improve utilization of local healthcare facilities by Chinese migrants in the country.

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Energy Poverty in India

Encyclopedia of Social Work ◽

10.1093/acrefore/9780199975839.013.1298 ◽

2019 ◽

Cited By ~ 1

Author(s):

Praveen Kumar ◽

Smitha Rao ◽

Gautam N. Yadama

Keyword(s):

South Asia ◽

Significant Proportion ◽

Sub Saharan Africa ◽

Poor People ◽

Unified Framework ◽

Indian Government ◽

Energy Poverty ◽

Economic Implications ◽

Non Profit ◽

Sub Saharan

Energy poverty is lack of access to adequate, high-quality, clean, and affordable forms of energy or energy systems. It is a prominent risk factor for global burden of disease and has severe environmental, social, and economic implications. Despite recent international attention to address energy for the poor, there is a limited consensus over a unified framework defining energy poverty, which impacts almost 2.8 billion mostly poor people, especially in Asia, Latin America, and sub-Saharan Africa. Sub-Saharan Africa and South Asia have the largest number of energy poor. India, in South Asia, comprises a significant proportion of energy-impoverished households. There is a continued effort by the Indian government, non-profit agencies, and private organizations to address the needs of energy poor. Social workers have a significant role to play in these interventions addressing energy poverty in India. Emerging research and practice in the energy poverty field in India calls for transdisciplinary collaboration especially between social work practitioners of community development, environmental health, public health, and social policy.

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Rethinking the governance of energy poverty in sub-Saharan Africa: Reviewing three academic perspectives on electricity infrastructure investment

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2019.05.021 ◽

2019 ◽

Vol 111 ◽

pp. 344-354 ◽

Cited By ~ 3

Author(s):

Julian Gregory ◽

Benjamin K. Sovacool

Keyword(s):

Sub Saharan Africa ◽

Infrastructure Investment ◽

Energy Poverty ◽

Sub Saharan ◽

Electricity Infrastructure

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Promising adoption of an electronic clinical decision support system for antenatal and intrapartum care in rural primary healthcare facilities in sub-Saharan Africa: The QUALMAT experience

International Journal of Medical Informatics ◽

10.1016/j.ijmedinf.2015.05.002 ◽

2015 ◽

Vol 84 (9) ◽

pp. 647-657 ◽

Cited By ~ 15

Author(s):

Felix Sukums ◽

Nathan Mensah ◽

Rose Mpembeni ◽

Siriel Massawe ◽

Els Duysburgh ◽

...

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Clinical Decision ◽

Sub Saharan Africa ◽

Intrapartum Care ◽

Healthcare Facilities ◽

Sub Saharan ◽

Electronic Clinical Decision Support

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Travel-time, bikes, and HIV elimination in Malawi: a geospatial modeling analysis

10.1101/2020.09.01.20186072 ◽

2020 ◽

Author(s):

Laurence Palk ◽

Justin T Okano ◽

Luckson Dullie ◽

Sally Blower

Keyword(s):

Travel Time ◽

Rural Areas ◽

Hiv Treatment ◽

Sub Saharan Africa ◽

People Living With Hiv ◽

Travel Times ◽

Geospatial Modeling ◽

Healthcare Facilities ◽

Fast Tracking ◽

Sub Saharan

Background: UNAIDS has prioritized Malawi and 21 other countries in sub-Saharan Africa (SSA) for "fast-tracking" the end of their HIV epidemics. To achieve elimination requires treating 90% of people living with HIV (PLHIV); coverage is already fairly high (70-75%). However, many individuals in SSA have to walk to access healthcare. We use data-based geospatial modeling to determine whether the need to travel long distances to access treatment and limited transportation in rural areas are barriers to HIV elimination in Malawi. Additionally, we evaluate the effect on treatment coverage of increasing the availability of bicycles in rural areas. Methods: We build a geospatial model that we use to estimate, for every PLHIV, their travel-time to access HIV treatment if driving, bicycling, or walking. We estimate the travel-times needed to achieve 70% or 90% coverage. Our model includes a spatial map of healthcare facilities (HCFs), the geographic coordinates of residencies for all PLHIV, and an "impedance" map. We quantify impedance using data on road/river networks, land cover, and topography. Findings: To cross an area of one km2 in Malawi takes from ~60 seconds (driving on main roads) to ~60 minutes (walking in mountainous areas); ~80% of PLHIV live in rural areas. At ~70% coverage, HCFs can be reached within: ~45 minutes if driving, ~65 minutes if bicycling, and ~85 minutes if walking. Increasing coverage above ~70% will become progressively more difficult. To achieve 90% coverage, the travel-time for many PLHIV (who have yet to initiate treatment) will be almost twice as long as those currently on treatment. Increasing bicycle availability in rural areas reduces round-trip travel-times by almost one hour (in comparison with walking), and could substantially increase coverage levels. Interpretation: Geographic inaccessibility to treatment coupled with limited transportation in rural areas are substantial barriers to reaching 90% coverage in Malawi. Increased bicycle availability could help eliminate HIV. Funding: National Institute of Allergy and Infectious Diseases

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