The global burden of critical illness falls disproportionately outside high-income countries. Despite younger patient populations with similar or lower disease severity, critical illness outcomes are poor outside high-income countries. A lack of data limits attempts to understand and address the drivers of critical care outcomes outside high-income countries.
We aim to characterize the organization, available resources, and service capacity of public sector critical care units in Malawi and identify barriers to improving care.
We conducted a secondary analysis of the Malawi Emergency and Critical Care Survey, a cross-sectional study performed from January to February 2020 at all four central hospitals and a simple random sample of nine out of 24 public sector district hospitals in Malawi, a predominantly rural, low-income country of 19.6 million in southern Africa. Data from critical care units were used to characterize resources, processes, and barriers to care.
There were four HDUs and four ICUs across the 13 hospitals in the Malawi Emergency and Critical Care Survey sample. The median critical care beds per 1,000,000 catchment was 1.4 (IQR: 0.9 to 6.7). Absent equipment was the most common barrier in HDUs (46% [95% CI: 32% to 60%]). Stockouts was the most common barriers in ICUs (48% [CI: 38% to 58%]). ICUs had a median 3.0 (range: 2 to 8) functional ventilators per unit and reported an ability to perform several quality mechanical ventilation interventions.
Although significant gaps exist, Malawian critical care units report the ability to perform several complex clinical processes. Our results highlight regional inequalities in access to care and support the use of process-oriented questions to assess critical care capacity. Future efforts should focus on basic critical care capacity outside of urban areas and quantify the impact of context-specific variables on critical care mortality.

BACKGROUND: The COVID-19 pandemic and the associated social restrictions may have disrupted the provision of essential services, including family planning (FP) and contraceptive services. This protocol is adapted from a generic study protocol titled \"Health systems analysis and evaluations of the barriers to availability and readiness of sexual and reproductive health services in COVID-19 affected areas,\" conducted by the World Health Organization (WHO) Department of Reproductive Health and Research.
OBJECTIVE: This study aims to assess the availability and use of FP and contraceptive services in primary health facilities during and after the COVID-19 pandemic; assess the risk perceptions of COVID-19 stigma, barriers to access, and quality of services from clients\' and providers\' perspectives in the COVID-19-affected areas; and assess the postpandemic recovery of the facilities in the provision of FP and contraceptive services.
METHODS: In-depth interviews will be conducted with clients-women in the reproductive age group and their male partners who visit the selected health facilities for FP and contraceptive services-and health providers (the most knowledgeable person on FP and contraceptive service provision) at the selected health facilities. Focus group discussions will be conducted with clients at the selected health facilities and in the community. The in-depth interviews and focus group discussions will help to understand clients\' and health service providers\' perspectives of FP and contraceptive service availability and readiness in COVID-19-affected areas. A cross-sectional health facility assessment will be conducted in all the selected health facilities to determine the health facility infrastructure\'s ability and readiness to provide FP and contraceptive services and to capture the trends in FP and contraceptive services available during the COVID-19 pandemic. Scientific approval for this study is obtained from the WHO Research Project Review Panel, and the WHO Ethics Review Committee has given ethical approval in the 3 countries.
RESULTS: Using a standardized research protocol will ensure that the results from this study can be compared across regions and countries. The study was funded in March 2021. It received ethics approval from the WHO Ethics Review Committee in February 2022. We completed data collection in September 2022. We plan to complete the data analysis by March 2023. We plan to publish the study results by Summer 2023.
CONCLUSIONS: The findings from this study will provide a better understanding of the impact of the COVID-19 pandemic on FP and contraceptive services at the facility level, which will help policy makers and health managers develop and strengthen FP policies and services in health facilities to be more responsive to community needs.
UNASSIGNED: DERR1-10.2196/43329.

Most existing facility assessments collect data on a sample of health facilities. Sampling of health facilities may introduce bias into estimates of effective coverage generated by ecologically linking individuals to health providers based on geographic proximity or administrative catchment.
We assessed the bias introduced to effective coverage estimates produced through two ecological linking approaches (administrative unit and Euclidean distance) applied to a sample of health facilities. Our analysis linked MICS household survey data on care-seeking for child illness and childbirth care with data on service quality collected from a census of health facilities in the Savanes region of Cote d\'Ivoire. To assess the bias introduced by sampling, we drew 20 random samples of three different sample sizes from our census of health facilities. We calculated effective coverage of sick child and childbirth care using both ecological linking methods applied to each sampled facility data set. We compared the sampled effective coverage estimates to ecologically linked census-based estimates and estimates based on true source of care. We performed sensitivity analyses with simulated preferential care-seeking from higher-quality providers and randomly generated provider quality scores.
Sampling of health facilities did not significantly bias effective coverage compared to either the ecologically linked estimates derived from a census of facilities or true effective coverage estimates using the original data or simulated random quality sensitivity analysis. However, a few estimates based on sampling in a setting where individuals preferentially sought care from higher-quality providers fell outside of the estimate bounds of true effective coverage. Those cases predominantly occurred using smaller sample sizes and the Euclidean distance linking method. None of the sample-based estimates fell outside the bounds of the ecologically linked census-derived estimates.
Our analyses suggest that current health facility sampling approaches do not significantly bias estimates of effective coverage produced through ecological linking. Choice of ecological linking methods is a greater source of bias from true effective coverage estimates, although facility sampling can exacerbate this bias in certain scenarios. Careful selection of ecological linking methods is essential to minimize the potential effect of both ecological linking and sampling error.

Pakistan is facing a challenging situation in terms of high newborn mortality rate. Securing pregnancy and delivery care may not bring a substantial reduction in neonatal mortality, unless coupled with the provision of quality inpatient care for small and sick newborns and young infants (NYIs). We undertook this study to assess the availability and quality of newborn care services provided and the readiness of inpatient care for NYIs in Pakistan.
We conducted a cross-sectional study across Pakistan from February to June 2019, using a purposive sample of 61% (23) of the 38 sick newborn care units at public sector health care facilities providing inpatient care for small and sick NYIs. We interviewed facility managers and health care providers by using structured questionnaires. We observed facility infrastructure and relevant metrics related to the quality of inpatient care such as types of infant care units and essential equipment, drugs, staffing cadre and facility management practices, quality assurance activities, essential services for small and sick NYI care, discharge planning, and support, quality of NYIs care record, and health information system.
Of the 23 facilities assessed, 83% had newborn intensive care units (NICUs), 74% reported Special Care Units (SCUs), and only 44% had Kangaroo Mother Care (KMC) Units. All facilities had at least one paediatrician, 13% had neonatologists and neonatal surgeons each. Around 61 and 13% of the facilities had staff trained in neonatal resuscitation and parental counseling, respectively. About 35% of the facilities monitored nosocomial infection rates, with management and interdisciplinary team meetings reported from 17 and 30% of the facilities respectively preceding the survey. Basic interventions for NYIs were available in 43% of the facilities, only 35% of facilities had system in place to monitor nosocomial infections for NYI care. Most (73%) of reviewed records of NYIs at 1-2 days had information on the birth weight, temperature recording (52%), while only a quarter (25%) of the observed records documented danger signs. Mechanism to support discharge care by having linkages with community workers was present in 13% of the facilities, while only 35% of the facilities have strategies to promote adherence after discharge. Majority (78%) of facilities reported monitoring any newborn/ neonatal care indicators, while none of the sub-units within facilities had consolidated information on stillbirths and neonatal deaths.
The study has demonstrated important gaps in the quality of small and sick NYI inpatient care in the country. To avert neonatal mortality in the country, provincial and district governments have to take actions in improving the quality of inpatient care.

OBJECTIVE: The study aimed to assess the functionality of labour rooms by evaluating the labour room infrastructure with reference to the standard guidelines, the status of the availability of human resources, the availability of essential equipment and consumables in the labour room and by documenting the knowledge of the healthcare provider in terms of labour room practices. The study also explored the facility parameters associated with its delivery load taking the facility as a unit of analysis.
METHODS: A cross-sectional analytical study.
METHODS: India has realised the importance of improving the quality of care in public health facilities, and steps are being taken to make healthcare more responsive to women\'s needs. With an increase in the proportion of institutional deliveries in India, the outcome of the delivery process can be improved by optimising the health facility components.
METHODS: The study was conducted in 52 health facilities and healthcare providers involved in the delivery process in the selected facilities.
RESULTS: The infrastructure of the facilities was found to be the best for medical college followed by district hospitals, Community Health Centres (CHCs), Primary Health Centres (PHCs) and subcentres. Similar findings were observed in terms of the availability of equipment and consumables. Lack of healthcare providers was observed as only 20% of the posts for health personnel were fulfilled in CHCs followed by PHCs, subcentres and district hospitals where 43, 50 and 79% of the available vacancies were fulfilled. The level of knowledge of healthcare providers in terms of partograph, active management of the third stage of labour and post-partum haemorrhage ranged as per their designation. The specialists were the most knowledgeable while the Auxiliary Nurse Midwife (AMNs) were the least. All the components of structural capacity, i.e. infrastructure (r 2 = 0.377, P value < 0.001), equipment and consumable (r 2 = 0.606, P value < 0.001) and knowledge of healthcare providers (r 2 = 0.456, P value < 0.001) along with the overall facility score were positively correlated with the average delivery load of the health facility. The results from multivariate linear regression depict significant relation between the delivery load and availability of equipment and consumables (t = 4.015, P < 0.01) and with the knowledge of healthcare providers (t = 2.129, P = 0.039).
CONCLUSIONS: The higher facilities were better equipped to provide delivery and newborn care. A higher delivery load was found at high-level facilities which can be attributed to better infrastructure, adequate supply of equipment and consumables and availability of trained human resources.

Geographic proximity is often used to link household and health provider data to estimate effective coverage of health interventions. Existing household surveys often provide displaced data on the central point within household clusters rather than household location. This may introduce error into analyses based on the distance between households and providers.
We assessed the effect of imprecise household location on quality-adjusted effective coverage of child curative services estimated by linking sick children to providers based on geographic proximity. We used data on care-seeking for child illness and health provider quality in Southern Province, Zambia. The dataset included the location of respondent households, a census of providers, and data on the exact outlets utilized by sick children included in the study. We displaced the central point of each household cluster point five times. We calculated quality-adjusted coverage by assigning each sick child to a provider\'s care based on three measures of geographic proximity (Euclidean distance, travel time, and geographic radius) from the household location, cluster point, and displaced cluster locations. We compared the estimates of quality-adjusted coverage to each other and estimates using each sick child\'s true source of care. We performed sensitivity analyses with simulated preferential care-seeking from higher-quality providers and randomly generated provider quality scores.
Fewer children were linked to their true source of care using cluster locations than household locations. Effective coverage estimates produced using undisplaced or displaced cluster points did not vary significantly from estimates produced using household location data or each sick child\'s true source of care. However, the sensitivity analyses simulating greater variability in provider quality showed bias in effective coverage estimates produced with the geographic radius and travel time method using imprecise location data in some scenarios.
Use of undisplaced or displaced cluster location reduced the proportion of children that linked to their true source of care. In settings with minimal variability in quality within provider categories, the impact on effective coverage estimates is limited. However, use of imprecise household location and choice of geographic linking method can bias estimates in areas with high variability in provider quality or preferential care-seeking.