Equity considerations for the implementation of health insurance benefit package in Ethiopia: result of expert Delphi exercise.

BACKGROUND: Efficiency, equity and financial risk protection are key health systems objectives. Equitable distribution of health care is among the priority strategic initiative of the government of Ethiopia. However, data on the distribution of interventions benefits or on disease burden disaggregated by subpopulations to guide health care priority setting is not available in Ethiopia. METHODS: Aligned with policy documents, we identified the following groups to be the worse off in the Ethiopian context: under-five children, women of reproductive age, the poor, and rural residents. We used the Delphi technique by a panel of 28 experts to assign a score for 253 diseases/conditions over a period of two days, in phases. The expert panel represented different institutes and professional mix. Experts assigned a score 1 to 4; where 4 indicates disease/condition predominantly affecting the poor and rural residents and 1 indicates a condition more prevalent among the wealthy and urban residents. Subsequently, the average equity score was computed for each disease/condition. RESULTS: The average scores ranged from 1.11 (for vitiligo) to 3.79 (for obstetric fistula). We standardized the scores to be bounded between 1 and 2; 1 the lowest equity score and 2 the highest equity score. The scores for each disease/condition were then assigned to their corresponding interventions. We used these equity scores to adjust the CEA values for each of the interventions. To adjust the CEA values for equity, we multiplied the health benefits (the denominator of the cost-effectiveness value) of each intervention by the corresponding equity scores, resulting in equity adjusted CEA values. The equity adjusted CEA was then used to rank the interventions using a league table. CONCLUSIONS: The Delphi method can be useful in generating equity scores for prioritizing health interventions where disaggregated data on the distribution of diseases or access to interventions by subpopulation groups are not available.

Contextualization of cost-effectiveness evidence from literature for 382 health interventions for the Ethiopian essential health services package revision.

BACKGROUND: Cost-effectiveness of interventions was a criterion decided to guide priority setting in the latest revision of Ethiopia's essential health services package (EHSP) in 2019. However, conducting an economic evaluation study for a broad set of health interventions simultaneously is challenging in terms of cost, timeliness, input data demanded, and analytic competency. Therefore, this study aimed to synthesize and contextualize cost-effectiveness evidence for the Ethiopian EHSP interventions from the literature. METHODS: The evidence synthesis was conducted in five key steps: search, screen, evaluate, extract, and contextualize. We searched MEDLINE and EMBASE research databases for peer-reviewed published articles to identify average cost-effectiveness ratios (ACERs). Only studies reporting cost per disability-adjusted life year (DALY), quality-adjusted life year (QALY), or life years gained (LYG) were included. All the articles were evaluated using the Drummond checklist for quality, and those with a score of at least 7 out of 10 were included. Information on cost, effectiveness, and ACER was extracted. All the ACERs were converted into 2019 US dollars using appropriate exchange rates and the GDP deflator. RESULTS: In this study, we synthesized ACERs for 382 interventions from seven major program areas, ranging from US$3 per DALY averted (for the provision of hepatitis B vaccination at birth) to US$242,880 per DALY averted (for late-stage liver cancer treatment). Overall, 56% of the interventions have an ACER of less than US$1000 per DALY, and 80% of the interventions have an ACER of less than US$10,000 per DALY. CONCLUSION: We conclude that it is possible to identify relevant economic evaluations using evidence from the literature, even if transferability remains a challenge. The present study identified several cost-effective candidate interventions that could, if scaled up, substantially reduce Ethiopia's disease burden.

Cost-effectiveness of facility-based, stand-alone and mobile-based voluntary counseling and testing for HIV in Addis Ababa, Ethiopia.

BACKGROUND: Globally, there is a consensus to end the HIV/AIDS epidemic by 2030, and one of the strategies to achieve this target is that 90% of people living with HIV should know their HIV status. Even if there is strong evidence of clients' preference for testing in the community, HIV voluntary counseling and testing (VCT) continue to be undertaken predominantly in health facilities. Hence, empirical cost-effectiveness evidence about different HIV counseling and testing models is essential to inform whether such community-based testing are justifiable compared with additional resources required. Therefore, the purpose of this study was to compare the cost-effectiveness of facility-based, stand-alone and mobile-based HIV voluntary counseling and testing methods in Addis Ababa, Ethiopia. METHODS: Annual economic costs of counseling and testing methods were collected from the providers' perspective from July 2016 to June 2017. Ingredients based bottom-up costing approach was applied. The effectiveness of the interventions was measured in terms of the number of HIV seropositive clients identified. Decision tree modeling was built using TreeAge Pro 2018 software, and one-way and probabilistic sensitivity analyses were conducted by varying HIV positivity rate, costs, and probabilities. RESULTS: The cost of test per client for facility-based, stand-alone and mobile-based VCT was $5.06, $6.55 and $3.35, respectively. The unit costs of test per HIV seropositive client for the corresponding models were $158.82, $150.97 and $135.82, respectively. Of the three models, stand-alone-based VCT was extendedly dominated. Mobile-based VCT costs, an additional cost of USD 239 for every HIV positive client identified when compared to facility-based VCT. CONCLUSION: Using a mobile-based VCT approach costs less than both the facility-based and stand-alone approaches, in terms of both unit cost per tested individual and unit cost per HIV seropositive cases identified. The stand-alone VCT approach was not cost-effective compared to facility-based and mobile-based VCT. The incremental cost-effectiveness ratio for mobile-based VCT compared with facility-based VCT was USD 239 per HIV positive case.

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.

Brain clocks capture diversity and disparity in aging and dementia.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer's disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R2=0.37, F2=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer's disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer's disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.

Burden of lower respiratory infections and associated risk factors across regions in Ethiopia: a subnational analysis of the Global Burden of Diseases 2019 study.

OBJECTIVE: This analysis is to present the burden and trends of morbidity and mortality due to lower respiratory infections (LRIs), their contributing risk factors, and the disparity across administrative regions and cities from 1990 to 2019. DESIGN: This analysis used Global Burden of Disease 2019 framework to estimate morbidity and mortality outcomes of LRI and its contributing risk factors. The Global Burden of Disease study uses all available data sources and Cause of Death Ensemble model to estimate deaths from LRI and a meta-regression disease modelling technique to estimate LRI non-fatal outcomes with 95% uncertainty intervals (UI). STUDY SETTING: The study includes nine region states and two chartered cities of Ethiopia. OUTCOME MEASURES: We calculated incidence, death and years of life lost (YLLs) due to LRIs and contributing risk factors using all accessible data sources. We calculated 95% UIs for the point estimates. RESULTS: In 2019, LRIs incidence, death and YLLs among all age groups were 8313.7 (95% UI 7757.6-8918), 59.4 (95% UI 49.8-71.4) and 2404.5 (95% UI 2059.4-2833.3) per 100 000 people, respectively. From 1990, the corresponding decline rates were 39%, 61% and 76%, respectively. Children under the age of 5 years account for 20% of episodes, 42% of mortalities and 70% of the YLL of the total burden of LRIs in 2019. The mortality rate was significantly higher in predominantly pastoralist regions-Benishangul-Gumuz 101.8 (95% UI 84.0-121.7) and Afar 103.7 (95% UI 86.6-122.6). The Somali region showed the least decline in mortality rates. More than three-fourths of under-5 child deaths due to LRIs were attributed to malnutrition. Household air pollution from solid fuel attributed to nearly half of the risk factors for all age mortalities due to LRIs in the country. CONCLUSION: In Ethiopia, LRIs have reduced significantly across the regions over the years (except in elders), however, are still the third-leading cause of mortality, disproportionately affecting children younger than 5 years old and predominantly pastoralist regions. Interventions need to consider leading risk factors, targeted age groups and pastoralist and cross-border communities.

Hospitalization costs for COVID-19 in Ethiopia: Empirical data and analysis from Addis Ababa's largest dedicated treatment center.

BACKGROUND: The COVID-19 pandemic has caused profound health, economic, and social disruptions globally. We assessed the full costs of hospitalization for COVID-19 disease at Ekka Kotebe COVID-19 treatment center in Addis Ababa, the largest hospital dedicated to COVID-19 patient care in Ethiopia. METHODS AND FINDINGS: We retrospectively collected and analysed clinical and cost data on patients admitted to Ekka Kotebe with laboratory-confirmed COVID-19 infections. Cost data included personnel time and salaries, drugs, medical supplies and equipment, facility utilities, and capital costs. Facility medical records were reviewed to assess the average duration of stay by disease severity (either moderate, severe, or critical). The data collected covered the time-period March-November 2020. We then estimated the cost per treated COVID-19 episode, stratified by disease severity, from the perspective of the provider. Over the study period there were 2,543 COVID-19 cases treated at Ekka Kotebe, of which, 235 were critical, 515 were severe, and 1,841 were moderate. The mean patient duration of stay varied from 9.2 days (95% CI: 7.6-10.9; for moderate cases) to 19.2 days (17.9-20.6; for critical cases). The mean cost per treated episode was USD 1,473 (95% CI: 1,197-1,750), but cost varied by disease severity: the mean cost for moderate, severe, and critical cases were USD 1,266 (998-1,534), USD 1,545 (1,413-1,677), and USD 2,637 (1,788-3,486), respectively. CONCLUSIONS: Clinical management and treatment of COVID-19 patients poses an enormous economic burden to the Ethiopian health system. Such estimates of COVID-19 treatment costs inform financial implications for resource-constrained health systems and reinforce the urgency of implementing effective infection prevention and control policies, including the rapid rollout of COVID-19 vaccines, in low-income countries like Ethiopia.

Cost-analysis of COVID-19 sample collection, diagnosis, and contact tracing in low resource setting: The case of Addis Ababa, Ethiopia.

BACKGROUND: Ethiopia has been responding to the COVID-19 pandemic through a combination of interventions, including non-pharmaceutical interventions, quarantine, testing, isolation, contact tracing, and clinical management. Estimating the resources consumed for COVID-19 prevention and control could inform efficient decision-making for epidemic/pandemic-prone diseases in the future. This study aims to estimate the unit cost of COVID-19 sample collection, laboratory diagnosis, and contact tracing in Addis Ababa, Ethiopia. METHODS: Primary and secondary data were collected to estimate the costs of COVID-19 sample collection, diagnosis, and contact tracing. A healthcare system perspective was used. We used a combination of micro-costing (bottom-up) and top-down approaches to estimate resources consumed and the unit costs of the interventions. We used available cost and outcome data between May and December 2020. The costs were classified into capital and recurrent inputs to estimate unit and total costs. We identified the cost drivers of the interventions. We reported the cost for the following outcome measures: (1) cost per sample collected, (2) cost per laboratory diagnosis, (3) cost per sample collected and laboratory diagnosis, (4) cost per contact traced, and (5) cost per COVID-19 positive test identified. We conducted one-way sensitivity analysis by varying the input parameters. All costs were reported in US dollars (USD). RESULTS: The unit cost per sample collected was USD 1.33. The unit cost of tracing a contact of an index case was USD 0.66. The unit cost of COVID-19 diagnosis, excluding the cost for sample collection was USD 3.91. The unit cost of sample collection per COVID-19 positive individual was USD 11.63. The unit cost for COVID-19 positive test through contact tracing was USD 54.00. The unit cost COVID-19 DNA PCR diagnosis for identifying COVID-19 positive individuals, excluding the sample collection and transport cost, was USD 37.70. The cost per COVID-19 positive case identified was USD 49.33 including both sample collection and laboratory diagnosis costs. Among the cost drivers, personnel cost (salary and food cost) takes the highest share for all interventions, ranging from 51-76% of the total cost. CONCLUSION: The costs of sample collection, diagnosis, and contact tracing for COVID-19 were high given the low per capita health expenditure in Ethiopia and other low-income settings. Since the personnel cost accounts for the highest cost, decision-makers should focus on minimizing this cost when faced with pandemic-prone diseases by strengthening the health system and using digital platforms. The findings of this study can help decision-makers prioritize and allocate resources for effective public health emergency response.

Economic evaluation of Health Extension Program packages in Ethiopia.

BACKGROUND: Ethiopia launched the Health Extension Program (HEP) in 2004, aimed at ensuring equitable community-level healthcare services through Health Extension Workers. Despite the program's being a flagship initiative, there is limited evidence on whether investment in the program represents good value for money. This study assessed the cost and cost-effectiveness of HEP interventions to inform policy decisions for resource allocation and priority setting in Ethiopia. METHODS: Twenty-one health care interventions were selected under the hygiene and sanitation, family health services, and disease prevention and control sub-domains. The ingredient bottom-up and top-down costing method was employed. Cost and cost-effectiveness were assessed from the provider perspective. Health outcomes were measured using life years gained (LYG). Incremental cost per LYG in relation to the gross domestic product (GDP) per capita of Ethiopia (US$852.80) was used to ascertain the cost-effectiveness. All costs were collected in Ethiopian birr and converted to United States dollars (US$) using the average exchange rate for 2018 (US$1 = 27.67 birr). Both costs and health outcomes were discounted by 3%. RESULT: The average unit cost of providing selected hygiene and sanitation, family health, and disease prevention and control services with the HEP was US$0.70, US$4.90, and US$7.40, respectively. The major cost driver was drugs and supplies, accounting for 53% and 68%, respectively, of the total cost. The average annual cost of delivering all the selected interventions was US$9,897. All interventions fall within 1 times GDP per capita per LYG, indicating that they are very cost-effective (ranges: US$22-$295 per LYG). Overall, the HEP is cost-effective by investing US$77.40 for every LYG. CONCLUSION: The unit cost estimates of HEP interventions are crucial for priority-setting, resource mobilization, and program planning. This study found that the program is very cost-effective in delivering community health services.

Cost-effectiveness of treating multidrug-resistant tuberculosis in treatment initiative centers and treatment follow-up centers in Ethiopia.

BACKGROUND: In Ethiopia, MDR-TB has become a significant public health threat; therefore, the Ministry of Health introduced two treatment approaches for MDR-TB cases: treatment initiative center (TIC) and treatment follow-up center (TFC). TIC is where patients usually are diagnosed and start the treatment. At TFC, we follow MDR-TB patients until they completed the treatment. However, there is no evidence about the cost-effectiveness of the approaches. Therefore, this study aimed to analyze the cost-effectiveness of MDR-TB treatment in TIC and TFC. METHODS: In this study, we employed a full economic evaluation from a providers' perspective. We followed a hypothetical cohort of individuals from the age of 15 for a lifetime using a Markov model with five mutually exclusive health states. We used both primary and secondary data sources for the study. Ingredient-based costing approach was used. The costs include healthcare provider costs (recurrent and capital cost) and patient-side costs (direct and indirect). We use a human capital approach to estimate the indirect cost. The cost estimates were reported in the 2017 United States Dollar (US$), and effectiveness was measured using disability-adjusted life-years (DALYs) averted. Both costs and health benefits were discounted using a 3% discount rate. Both average and incremental cost-effectiveness ratios (ICER) were reported calculated. One-way and probabilistic sensitivity analyses were reported to determine the robustness of the estimates. RESULTS: The cost per HIV negative patient successfully treated for MDR-TB was $8,416 at TIC and $6,657 at TFC. The average cost-effectiveness ratio per DALY averted at TFC was $671 and $1,417 per DALY averted at TIC. The incremental cost-effectiveness ratio (ICER) of MDR-TB treatment at TIC was $1,641 per DALYs averted. CONCLUSION: This study indicates that the treatment of MDR-TB at both TIC and TFC are cost-effective interventions compared with the willingness to pay threshold of three-times the GDP per capita in Ethiopia.