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Antimicrobial resistance (AMR) is a major global health issue that exacerbates the burden of infectious diseases and healthcare costs. However, the scarcity of national-level AMR data in African countries hampers our understanding of its scale and contributing factors in the region. To gain insights into AMR prevalence in Africa, we collected and analyzed retrospective AMR data from 14 countries. We estimated bacterial AMR prevalence, defined as the proportion of resistant human isolates tested from antimicrobial susceptibility (AST) data collected retrospectively for 2016-2019 from 205 laboratories across 14 African countries. We generated 95% confidence intervals (CIs) for aggregated AMR estimates to account for data quality disparities across countries; the median data quality score was 73.1%, ranging from 56.4% to 80.8%. We assessed 819,584 culture records covering 9,266 pathogen-drug combinations, of which 187,832 (22.9%) were positive cultures with AST results. The most frequently cultured specimens were urine (32.0%) and purulent samples (28.1%), and the most frequently isolated pathogens were Escherichia coli (22.2%) and Staphylococcus aureus (15.0%). Aggregated AMR estimates did not change significantly across the years studied (p > 0.337); however, there were significant variations in AMR prevalence estimates in culture-positive samples across countries, regions, patient departments (inpatient/outpatient), and specimen sources (p < 0.05). Male sex (adjusted odds ratio [aOR] 1.15; 95% CI [1.09,1.21]; p < 0.0001), ages above 65 (aOR 1.28; 95% CI [1.16-1.41]; p < 0.0001), and inpatient department (aOR 1.24; 95% CI [1.13-1.35]; p < 0.0001) were associated with higher AMR prevalence among culture-positive samples. The lack of routine testing, as reflected in the low data volume from most contributing laboratories, and the absence of patient clinical information, represent significant limitations of this study. Analysis of the largest retrospective AMR dataset in Africa indicates high variability in AMR prevalence across countries, coupled with differences in AMR testing capacities, data quality, and AMR estimates. Gaps in AST practices and inadequate digital infrastructures for data collection and reporting represent barriers to estimating the true AMR burden in the region. These barriers warrant large-scale investments to expand healthcare access and strengthen bacteriology laboratory capacities.

A national study, 'Costing of healthcare services in India' (CHSI) aimed at generating reliable healthcare cost estimates for health technology assessment and price-setting is being undertaken in India. CHSI sampled 52 public and 40 private hospitals in 13 states and used a mixed micro-costing approach. This paper aims to outline the process, challenges and critical lessons of cost data collection to feed methodological and quality improvement of data collection. An exploratory survey with 3 components-an online semi-structured questionnaire, group discussion and review of monitoring data, was conducted amongst CHSI data collection teams. There were qualitative and quantitative components. Difficulty in obtaining individual data was rated on a Likert scale. Mean time taken to complete cost data collection in one department/speciality was 7.86(±0.51) months, majority of which was spent on data entry and data issues resolution. Data collection was most difficult for determination of equipment usage (mean difficulty score 6.59±0.52), consumables prices (6.09±0.58), equipment price(6.05±0.72), and furniture price(5.64±0.68). Human resources, drugs & consumables contributed to 78% of total cost and 31% of data collection time. However, furniture, overheads and equipment consumed 51% of time contributing only 9% of total cost. Seeking multiple permissions, absence of electronic records, multiple sources of data were key challenges causing delays. Micro-costing is time and resource intensive. Addressing key issues prior to data collection would ease the process of data collection, improve quality of estimates and aid priority setting. Electronic health records and availability of national cost data base would facilitate conducting costing studies.

Background & objectives: The Government of India has initiated a population based screening (PBS) for noncommunicable diseases (NCDs). A health technology assessment agency in India commissioned a study to assess the cost-effectiveness of screening diabetes and hypertension. The present study was undertaken to estimate the cost of PBS for Type II diabetes and hypertension. Second, out-of-pocket expenditure (OOPE) for outpatient care and health-related quality of life (HRQoL) among diabetes and hypertension patients were estimated. Methods: Economic cost of PBS of diabetes and hypertension was assessed using micro-costing methodology from a health system perspective in two States. A total of 165 outpatients with diabetes, 300 with hypertension and 497 with both were recruited to collect data on OOPE and HRQoL. Results: On coverage of 50 per cent, the PBS of diabetes and hypertension incurred a cost of 45.2 per person screened. The mean OOPE on outpatient consultation for a patient with diabetes, hypertension and both diabetes and hypertension was 4381 (95% confidence interval [CI]: 3786-4976), 1427 (95% CI: 1278-1576) and 3932 (95% CI: 3614-4250), respectively. Catastrophic health expenditure was incurred by 20, 1.3 and 14.8 per cent of patients with diabetes, hypertension and both diabetes and hypertension, respectively. The mean HRQoL score of patients with diabetes, hypertension and both was 0.76 (95% CI: 0.72-0.8), 0.89 (95% CI: 0.87-0.91) and 0.68 (95% CI: 0.66-0.7), respectively. Interpretations & conclusions: The findings of our study are useful for assessing cost-effectiveness of screening strategies for diabetes and hypertension.

Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private providers. To generate base cost evidence for evidence-based policymaking the Costing of Health Services in India (CHSI) study was commissioned in 2018 for the price setting of health benefit packages. This paper reports the findings of a process evaluation of the cost data collection in the private hospitals. The process evaluation of health system costing in private hospitals was an exploratory survey with mixed methods (quantitative and qualitative). We used three approaches-an online survey using a semi-structured questionnaire, in-depth interviews, and a review of monitoring data. The process of data collection was assessed in terms of time taken for different aspects, resources used, level and nature of difficulty encountered, challenges and solutions. The mean time taken for data collection in a private hospital was 9.31 (± 1.0) person months including time for obtaining permissions, actual data collection and entry, and addressing queries for data completeness and quality. The longest time was taken to collect data on human resources (30%), while it took the least time for collecting information on building and space (5%). On a scale of 1 (lowest) to 10 (highest) difficulty levels, the data on human resources was the most difficult to collect. This included data on salaries (8), time allocation (5.5) and leaves (5). Cost data from private hospitals is crucial for mixed health systems. Developing formal mechanisms of cost accounting data and data sharing as pre-requisites for empanelment under a national insurance scheme can significantly ease the process of cost data collection.

Background: The Government of India introduced a new cadre of Community Health Officers (CHOs) in the primary health-care system through the Ayushman Bharat Health and Wellness Centres (HWCs) program. Objectives: The study aimed to assess the activities performed and time spent by the existing and new primary health-care team members at the HWC level. Methods: A time and motion study was undertaken in four HWCs in Punjab over a period of 3 months, to assess the time spent by auxiliary nurse midwives (ANMs) and CHOs on different services and activities. Data were collected through direct continuous observation of four CHOs and four ANMs during working hours for a period of 6 consecutive days of a week, along with structured time allocation interviews of all participants. Results: The CHOs spent 5.7 (5.6-5.9) hours per day on duty of which 57% was productively involved in service delivery. The average time spent by ANMs was 4.9 (4.5-5.3) hours per day, with nearly 62% productive time. While the CHOs spent nearly 40% of their time on services for non-communicable diseases (NCDs), the ANMs spent 51% of their time on maternal, infant, child, and adolescent health services. Conclusion: The introduction of HWCs and CHOs has nudged the health system toward comprehensive primary health care by placing a renewed emphasis on NCDs. The study provides useful evidence for staff, program implementers, and policymakers, to aid informed decision-making for human resource management.

BackgroundAntimicrobial resistance (AMR) is a major global health issue that exacerbates the burden of infectious diseases and healthcare costs. However, the scarcity of national-level AMR data in African countries hampers our understanding of its scale and contributing factors in the region. To gain insights into AMR prevalence in Africa, we collected and analyzed retrospective AMR data from 14 countries.Methods and findingsWe estimated bacterial AMR prevalence, defined as the proportion of resistant human isolates tested from antimicrobial susceptibility (AST) data collected retrospectively for 2016-2019 from 205 laboratories across 14 African countries. We generated 95% confidence intervals (CIs) for aggregated AMR estimates to account for data quality disparities across countries; the median data quality score was 73.1%, ranging from 56.4% to 80.8%. We assessed 819,584 culture records covering 9,266 pathogen-drug combinations, of which 187,832 (22.9%) were positive cultures with AST results. The most frequently cultured specimens were urine (32.0%) and purulent samples (28.1%), and the most frequently isolated pathogens were Escherichia coli (22.2%) and Staphylococcus aureus (15.0%). Aggregated AMR estimates did not change significantly across the years studied (p > 0.337); however, there were significant variations in AMR prevalence estimates in culture-positive samples across countries, regions, patient departments (inpatient/outpatient), and specimen sources (p < 0.05). Male sex (adjusted odds ratio [aOR] 1.15; 95% CI [1.09,1.21]; p < 0.0001), ages above 65 (aOR 1.28; 95% CI [1.16-1.41]; p < 0.0001), and inpatient department (aOR 1.24; 95% CI [1.13-1.35]; p < 0.0001) were associated with higher AMR prevalence among culture-positive samples. The lack of routine testing, as reflected in the low data volume from most contributing laboratories, and the absence of patient clinical information, represent significant limitations of this study.ConclusionAnalysis of the largest retrospective AMR dataset in Africa indicates high variability in AMR prevalence across countries, coupled with differences in AMR testing capacities, data quality, and AMR estimates. Gaps in AST practices and inadequate digital infrastructures for data collection and reporting represent barriers to estimating the true AMR burden in the region. These barriers warrant large-scale investments to expand healthcare access and strengthen bacteriology laboratory capacities.