Power Supply and Ultrasound Functionality in Malawi: Findings from the 2019 Harmonized Health Facility Assessment (HHFA)

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Zhang, Pakwanja Twea, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6554481/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Ultrasound sonography (USS) is an essential diagnostic tool with growing relevance in resource-limited health systems. This study examines the association between electricity supply and USS availability and functionality in Malawi’s public and non-public health facilities. Using data from the 2018/2019 Harmonized Health Facility Assessment (HHFA), we conducted a cross-sectional analysis of 596 facilities. Electricity supply was categorized into stable grid, unstable grid, and non-grid. Adjusted prevalence ratios (PR) were estimated using robust Poisson regression. Only 9.9% of facilities had USS available, and of these, 93% had functional equipment. Compared to stable grid facilities, those using non-grid electricity were 85% less likely to have ultrasound available (PR: 0.15, 95% CI: 0.02–0.50) and 86% less likely to have it functional (PR: 0.14, 95% CI: 0.02–0.49). Diagnostic access was also considerably lower in primary-level and government-owned facilities. Spatial mapping revealed that most facilities without USS were primary-level and powered by non-grid or unstable grid sources. While electricity is necessary, it is not sufficient to ensure diagnostic readiness. Findings highlight the need for integrated planning across the energy and health sectors, present an opportunity for stronger government-CHAM collaboration, and support the targeted deployment of handheld ultrasound in energy-constrained settings. Health sciences/Health care/Health policy Health sciences/Health care/Health services Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Ultrasound sonography (USS) is a non-ionizing, non-invasive imaging modality with diverse applications across domains such as general, emergency and maternal medicine ( 1 – 4 ). A portable USS system is defined by its ability to be easily transported and operated in various settings( 5 ). These systems vary in form: some are compact laptop units with dedicated ports for probe connection, while others, termed handheld devices, integrate the probe with an external screen for image display (e.g. smartphone or tablet)( 6 ). Together, these portable versions enable a physician to conduct USS at the patient’s bedside, a practice termed point-of-care ultrasound (POCUS)( 7 ). Fast emerging research continues to demonstrate the role of the handheld USS (HHUS) in both high-income and low-to-middle income (LMIC) health systems( 4 , 8 , 9 ). A 2019 systematic review ( 9 ) incorporated evidence demonstrating that HHUS “may have an impact on clinical management in up to 70% of cases” in LMIC settings and is able to confirm “the initial clinical hypothesis in 66% of patients”. In Malawi, where the fertility rate is high at 3.5 and leading causes of death include HIV/AIDS, tuberculosis, neonatal disorders, and ischaemic heart disease - HHUS presents as a valuable diagnostic tool ( 4 , 10 ). Examples of HHUS’s utility include: ( 1 ) early antenatal screening recommended by the World Health Organization (WHO) to identify high-risk pregnancies; ( 2 ) modern family planning i.e. intrauterine device insertion ; and ( 3 ) for the management of diverse infectious diseases, such as through the FASH (Focused Assessment with Sonography for HIV-associated Tuberculosis) protocol, in which a study showed it was linked to care decisions in 47% of cases and increased “the likelihood of initiating empiric TB treatment from 9–46% among those with probable or confirmed TB.”( 5 , 11 , 12 ). Recognizing the potential of USS, Malawi’s Ministry of Health and its donor partners established the Centre of Excellence in Ultrasound at Kamuzu Central Hospital in 2022 ( 13 ). Through donor partnerships, the centre has supported USS and more specifically, HHUS introduction through projects focused on artificial intelligence and tele-ultrasound to facilitate remote consultation capabilities across all facility levels ( 13 – 15 ). The centre is tasked with “strengthening diagnostic radiology capacity” ( 13 ) and bolstering the ministry’s work towards Sustainable Development Goal (SDG) #3. This support of HHUS innovation is linked to systemic constraints that hinder cart-based USS services in the country – workforce constraints, expensive equipment and maintenance costs ,and restricted potential in rural settings( 1 , 16 ). HHUS’s portability, adaptibility, affordability and proven user-friendliness in clinical and medical education settings, makes it a competitive alternative( 1 , 17 , 18 ). According to Weiner’s( 19 ) Organizational Readiness for Change (OR4C) framework, the successful uptake of health innovations such as the integration of USS into routine care requires a strong foundation of human, financial, informational, and material resources. This study examined electricity (also referred to as power or energy) as a key material resource influencing USS service delivery. Previous studies examining the role of electricity in health facility readiness have primarily focused on devices such as incubators and light microscopes, with limited attention to ultrasound systems( 20 ). In Malawi, approximately 95% of the country’s electricity is generated through climate-sensitive hydropower, which contributes to a frequently overburdened national grid ( 21 , 22 ).Consequently, Malawi ranks among the lowest globally in terms of household electricity access, with only 25.9% of the population connected. Of this, 11.3% is connected through the national grid and 14.6% through off-grid solutions (( 23 ). The generation and distribution of electricity is managed by two state-owned entities: the Electricity Supply Corporation of Malawi (ESCOM) and the Electricity Generation Company (EGENCO). However, poor intra-sectorial coordination is highlighted as a key barrier in the improved access for public institutions( 23 ). In response to these challenges and in preparation for the planned construction of around 900 rural healthcare facilities by 2030, Malawi joined the United Nations Sustainable Energy for All (SE4All) initiative in 2012, committing to achieving universal electricity access by 2030 ( 23 , 24 ). This study aims to understand the relationship between electricity supply and the availability and functionality of USS in Malawi’s healthcare facilities, particularly at the primary care level. Utilizing data from the 2018/2019 Harmonized Health Facility Assessment (HHFA), the study assesses facility-level electricity access and its implications for ultrasound deployment. By analyzing these patterns, this research seeks to inform policy development and guide strategic planning around equipment procurement (traditional versus handheld ultrasound) and electrification priorities in Malawi’s health sector. METHODS Study design and Setting A cross-sectional study design was employed, using survey findings from the 2019 Malawi HHFA facility inventory, which was conducted between November 2018 and March 2019 ( 25 , 26 ). Facilities included in the HHFA survey were identified using the Malawi Facility List (MFL), formally published in January 2019 ( 25 , 26 ). The MFL identifies 1224 facilities and provides a comprehensive overview of the country’s healthcare facilities. Malawi’s healthcare facilities can be categorized by funding source into the following groups: public (n = 799), private non-profit (n = 89), private-for-profit (n = 272), and non-governmental organizations (n = 64) ( 25 , 26 ). In this study, the term “public” includes both government-owned and Christian Health Association of Malawi (CHAM) operated facilities, as CHAM facilities provide free healthcare services in partnership with the government via Service Level Agreements (SLAs) ( 25 , 26 ). For analysis purposes, facilities not classified as government or CHAM ( i.e., private non-profit, private-for-profit, and NGOs) were grouped as “other”or “non-public”. Facilities can also be described according to Malawi’s three-tiered healthcare system, which functions as a referral pyramid with an escalating level of service capacity and specialization. This includes primary care services (health posts, dispensaries, maternity clinics, health centers and community hospitals), district hospitals that serve at the secondary level, and central hospitals at the tertiary level ( 27 , 28 ). Sampling and Selection The Malawi HHFA used a census strategy to sample facilities from the MFL. However, the HHFA only successfully captured data from only 1,106 of the total 1,224 facilities, as 118 facilities were “either not located or no longer existed” ( 25 , 26 ). For this secondary analysis, we drew our sample from the 1,106 facilities successfully surveyed in the HHFA. A subset of 596 facilities was selected based on inclusion and exclusion criteria aligned with the study’s objective. Inclusion criteria were guided by the Malawi Health Sector Strategic Plan II 2017–2022 (HSSP II), which identifies rural community, district, and central hospitals as appropriate sites for USS services( 29 ). In addition, we included health centers and maternity facilities that, based on their scope of services, were considered potentially appropriate for USS. These services included antenatal care, family planning, management of infectious diseases such as complicated malaria, and the provision of inpatient care( 28 – 30 ). Ultrasound is also listed in WHO’s priority medical device guidance as essential at the health center level and above( 31 ). Thus, the final sample for this secondary analysis comprised of 543 primary-level facilities (including 491 health centers, 6 maternity, 40 rural community hospitals and 6 hospitals classed “other”) and 53 secondary and tertiary-level facilities (secondary 47; tertiary 6). Excluded were health posts, dispensaries, clinics and mental health hospitals. Ethical Considerations This study is a secondary analysis of de-identified data from the 2019 Malawi HHFA. Ethical approval for the original data collection was obtained from an institutional review board at the time of the primary study. As this secondary analysis involved no human participants and used anonymized data, additional ethical review was not required. Data Collection and Variables Electricity supply serves as the primary independent variable in this study. This variable was constructed using three HHFA survey questions related to: the facility’s main power source (national grid or off-grid), the reliability of the grid (defined as having no interruptions longer than two hours during operating hours in the past seven days) and the presence of a backup power source ( 32 ). This approach to the construction of the variable was adapted from Suhlrie et al.’s ( 20 ) analysis of Malawi’s 2013/2014 Service Provision Assessment(SPA), using similar same survey questions to classify facilities into six electricity types: uninterrupted grid with backup, interrupted grid with backup, uninterrupted grid without backup, interrupted grid without backup, off-grid electricity, and no electricity. In our study, these six types were consolidated into three categories to facilitate statistical analysis. This decision was informed by the absence of facilities classified as either "uninterrupted grid without backup" or “no electricity" across the two ultrasound outcomes. Our classification was as follows: Stable Grid: Facilities with uninterrupted grid power and a backup power source. Unstable Grid: Facilities are grid-supplied but face challenges such as frequent interruptions or lack a backup power source (interrupted grid with backup, interrupted grid without backup). Non-grid: Facilities relying on off-grid electricity as main power source (solar, battery, fuel-based generator, and hybrid). Other key facility characteristics served as the independent co-variables and included: urbanicity (urban vs. rural), facility level (primary vs. secondary or tertiary), facility ownership (government, CHAM, or other), and region (North, Central, or South). The dependent variables were derived from a single HHFA equipment inventory question, which asked field teams: ‘Please tell me if ultrasound equipment is available and functional today?”. Response options included: available and functional; available not functional; available don’t know if functional; and not available. For this analysis, these responses were disaggregated into two binary outcomes: availability (yes or no) and functionality (yes or no). Availability was defined as any facility where USS was observed or reported to be present, while functionality referred to ultrasound being observed and confirmed to be working. To visualize the spatial distribution of health facilities lacking USS by electricity supply type, we created a three-panel map using spatial coordinates from the HHFA. Facilities that reported no USS availability were isolated (n = 537), and further stratified by facility level. Facilities missing latitude or longitude data were excluded (n = 2). Shapefiles outlining the country’s national and district boundaries were obtained from the Humanitarian Data Exchange and loaded using the sf package in R studio ( 33 ). Each subset of facilities was converted into a spatial object and mapped using the ggplot2 and ggspatial packages. Facilities were color-coded by facility level, primary (red) and secondary/tertiary (green), to visualize overlaps between energy type and service tier. Individual maps for each electricity category were generated and then combined into a composite panel using ggpubr to complete the figure. Statistical Analysis Two types of analyses were conducted, descriptive and inferential. Descriptive statistics were used to summarize both the facility sample and the USS outcomes across the key independent variables: region, urbanicity, facility level, ownership, and electricity supply. Thereafter, electricity supply was described across these same key variables to provide contextual insight into the energy landscape. To examine changes in electricity supply over time, we compared our findings from the 2018/2019 HHFA with those from Suhlrie et al.’s ( 20 ) analysis of the 2013/2014 2014 Service Provision Assessment (SPA). The HHFA is an extension of the SPA’s core measurements, thus allowing valid comparisons across survey periods( 34 ). To allow for direct comparison, we briefly adopted their categorization of facilities into three levels: community level (clinics, dispensaries, health posts, and maternity units), first level (health centers, community hospitals, and other rural hospitals), and referral level (district and central hospitals). Our analysis excluded dispensaries, clinics, and health posts, making a direct comparison at the community-level facilities infeasible. Missing outcome data were interpreted as an absence of USS equipment rather than as random missingness. To conduct inferential analysis, adjusted Poisson regression (95% CI) with robust standard errors were used to model the associations between electricity type and the two binary outcomes, USS availability and USS functionality. The choice of Poisson regression over logistic regression was informed by literature that recommends prevalence ratios (PRs) over odds ratios in cross-sectional studies, to avoid over-estimation ( 35 – 37 ). Outcomes were tested separately from each other, and each model included the same set of covariates: region, urbanicity, facility level, ownership, and electricity type: $$\:\text{log}it\left(\gamma\:j\right)=\:\beta\:0+\beta\:1Energytypej+\beta\:2Urbanicityj+\:\beta\:3FacilityLevelj\:\:+\beta\:4FacilityOwnershipj+\beta\:5Regionj+\:\epsilon\:j$$ These variables were selected based on their theoretical relevance and support in the literature and analyses were performed using R Studio Version 2024.09.0 + 375 ( 20 ). In this model \(\:\gamma\:j\) represents the binary outcome (1 = USS available or functional; 0 = not available or not functional) for facility \(\:j\) . The covariates include \(\:{{\beta\:}}_{1}\) for energy type, \(\:{{\beta\:}}_{2}\) for urbanicity, \(\:{{\beta\:}}_{3}\) for facility level, \(\:{{\beta\:}}_{4}\) for facility ownership, and \(\:{{\beta\:}}_{5}\) for region. To assess the robustness of our findings to how electricity supply was operationalized, we conducted sensitivity analyses using two alternate categorizations of the energy supply variable. First, a backup-centric classification grouped facilities based on whether they had access to any backup power source (regardless of their primary energy type), creating two categories: Backup Power and No Backup Power. In this test, off-grid facilities were also further disaggregated based on whether they reported a functioning secondary source of electricity. Facilities were categorized as Backup Power if they had uninterrupted or interrupted grid with backup, or off-grid electricity with backup; and as No Backup Power if they had grid without backup, off-grid without backup, or no electricity. This refined grouping tested whether power redundancy alone was associated with ultrasound availability and functionality. Second, we applied a grid-centric classification that grouped facilities into grid-connected and non-grid categories. Grid-connected facilities included all forms of national grid supply, whether uninterrupted or interrupted, and with or without backup. Non-grid facilities relied on off-grid electricity or had no power supply, without further disaggregation by backup status.For all sensitivity models, facility level, ownership, region, and urbanicity were controlled for. Full model outputs are provided in Supplementary Tables S1 and S2. RESULTS Descriptive Statistics Table 1 summarizes the characteristics of the 596 health facilities included in the final analytic sample. Most facilities (91.10%) were primary-level, while only 8.90% were within the secondary and tertiary-level category. Government ownership was predominant, accounting for 70.64% of facilities, followed by CHAM (24.50%) and other owners 4.87%. A majority of facilities (87.08%) were located in rural areas and regionally, the South had the greatest share (43.29%). In terms of energy supply, 24.66% of facilities had access to stable grid electricity, 46.14% were connected to an unstable grid and 29.20% relied on non-grid sources. An analysis of USS outcomes showed that USS is more commonly available in urban settings, CHAM-owned facilities, secondary and tertiary-level facilities, and those with a stable grid power supply. More specifically, 40.36% of urban facilities have USS available compared to 5.39% in rural areas. Access is also limited at the primary level where only 2.76% of facilities have availability compared to 83.02% among secondary and tertiary facilities. CHAM-owned facilities and those with stable grid supply performed best within their respective categories, with ultrasound availability reported at 19.18% and 23.81%, respectively. Functionality was assessed among the 59 facilities with USS available and 93% of them had functional equipment. The patterns of functionality closely follow those seen in availability. Urban facilities show higher functionality at 35.06% compared to 5.39% in rural areas. At the secondary and tertiary level, 75.47 % of facilities achieved functionality, while only 2.76% of primary facilities did. CHAM facilities performed consistently with 19.18% reporting functional equipment, followed after by government facilities (6.18%). Facilities with stable grid supply had the highest proportion at 22.45% compared to its counterparts, each with less than 10% USS functionality. Overall, trends suggest that once a facility has USS available, it is generally functional, especially when supported by stronger infrastructure. Table 1 Availability and Functionality of Ultrasound Equipment in Healthcare Facilities in Malawi 2018/2019 Variable Total Availability Functionality n=596 (100%) n= 59 (9.9% ) n= 55 (9.2%) Region Center 226 (37.92%) 24 (10.62%) 24 (10.62%) North 112 (18.79%) 13 (11.61%) 11 (9.82%) South 258 (43.29%) 22 (8.53%) 20 (7.75%) Urbanicity Rural 519 (87.08%) 28 (5.39%) 28 (5.39%) Urban 77 (12.92%) 31 (40.26%) 27 (35.06%) Facility Level Primary 543 (91.10%) 15 (2.76%) 15 (2.76%) Secondary and Tertiary 53 (8.90%) 44 (83.02%) 40 (75.47%) Facility Ownership CHAM 146 (24.50%) 28 (19.18%) 28 (19.18%) Government 421 (70.64%) 30 (7.13%) 26 (6.18%) Other 29 (4.87%) 1 (3.45%) 1 (3.45%) Power Supply: Energy Type Stable Grid 147 (24.66%) 35 (23.81%) 33 (22.45%) Unstable Grid 275 (46.14%) 22 (8.00%) 20 (7.27%) Non-grid 174 (29.20%) 2 (1.15%) 2 (1.15%) Percentages : Availability/Functionality = row-wise; Total = column-wise. Facility levels: Primary = health centres, maternity, rural/community hospitals; Secondary/Tertiary = district and central hospitals Power supply: Stable Grid = Uninterrupted Grid with Backup; Unstable Grid = Uninterrupted Grid without Back-up, Interrupted Grid with Back-up, Interrupted Grid without Back-up; Non-grid = Off-grid or No electricity Abbreviations: USS, ultrasound; HHFA, Harmonized Health Facility Assessment; CHAM, Christian Health Association of Malawi. Descriptive statistics across 597 facilities. Facility levels follow national classification: Primary level includes health centers, maternity facilities, and rural/community hospitals; Secondary and Tertiary include district and central hospitals. Electricity type groups were derived from six original categories (see Figure 1 caption). Availability = physical presence of USS; Functionality = confirmed working status. Row-wise percentages reflect subgroup-specific prevalence. USS = Ultrasound; HHFA = Harmonized Health Facility Assessment; CHAM = Christian Health Association of Malawi. To further contextualize the disparities observed in Table 1, Figure 1 visualizes how the electricity supply types are distributed across the region, ownership, facility level and urbanicity variables. Unstable grid supply is the most common energy type across all regions, accounting for 50.78% of facilities in the South, 43.81% in the Center, and 40.18% in the North. Stable grid access is highest in the South (29.84%) and lowest in the Center (19.47%), while non-grid use is similar throughout the regions. Urban facilities have better stable grid access at 45.45% compared to 21.58% in rural areas. Rural facilities demonstrate a greater reliance on non-grid sources at 32.37% versus 7.79% in urban areas. At the facility level, 54.72% of secondary and tertiary facilities have stable grid power. Unstable and non-grid sources are more common in primary facilities at 46.78% and 31.49% respectively. Among ownership categories, CHAM facilities primarily rely on an unstable grid (49.32%), followed by stable grid (28.08%) and non-grid supply (22.60%). Government-owned facilities show lower stable grid access (21.62%) and higher reliance on unstable (45.37%) and non-grid electricity (33.02%). Facilities classified under 'Other' ownership report the highest stable grid access at 51.72%. Using Figure 2, we examined changes in energy supply across Malawi’s health system between 2013/2014 and 2018/2019. At the first level, there was a notable shift toward more reliable electricity. The proportion of facilities with uninterrupted grid access and backup increased from 4.67% to 23.09%, representing a nearly fivefold improvement. However, nearly half of first-level facilities remained on unstable grid connections. Interrupted grid access with backup rose from 9.86% to 20.78%, while those without backup declined from 30.34% to 20.07%. These shifts indicate some movement away from the least resilient supply types, but a large proportion of facilities still experience grid interruptions. Reliance on off-grid sources, such as standalone solar systems, declined only marginally, from 33.29% to 29.48%. Facilities without any electricity nearly disappeared, dropping from 7.17% to 1.07%. At the referral level, where service demands and equipment requirements are highest, stable and reliable grid access continued to improve. The share of facilities with uninterrupted grid and backup rose from 46.43% to 60.71%, while reliance on interrupted grid access decreased from 53.57% to 39.29%. No referral-level facilities reported being without electricity or using off-grid sources in either year. These findings reflect meaningful progress, particularly in electrifying and stabilizing power at higher-level facilities. Figure 3 was used to identify implementation gaps and visually represent the intersection of service tier and electricity type in shaping diagnostic access across Malawi’s health system. These stratified maps also offer early insight into which facilities may be more appropriate for handheld versus traditional USS technologies. Of the facilities without USS (n=537), the majority were primary-level (n=528, 98.3%), with few classified as secondary or tertiary-level (n=9, 1.7%). Panel A shows that 112 facilities without USS were operating on stable grid electricity, of which 105 were primary-level and 7 were secondary or tertiary. These cases (concentrated in the central and southern regions) suggest that barriers to USS implementation may extend beyond energy supply, including limitations in workforce capacity, or equipment prioritization. Panel B highlights the 252 facilities without USS on unstable grid supply (n=252), nearly all of which were primary level (n=251). Like Panel A, these facilities show a center-south predominance. Panel C presents 171 facilities without USS powered by non-grid energy sources. These facilities were almost exclusively primary level (n=170) and geographically widespread across all three regions. Factors Associated with Ultrasound Availability and Functionality Table 2 presents the crude and adjusted PRs for the USS outcomes by facility characteristics, particularly power supply type. Primary-level facilities were substantially less likely to have USS available compared to secondary or tertiary-level facilities (crude PR: 0.03; 95% CI: 0.02–0.06; adjusted PR: 0.05; 95% CI: 0.03–0.11; p < 0.001). This association remained strong after adjustment, highlighting persistent structural disparities across facility levels. CHAM-owned facilities had significantly greater availability than government facilities (crude PR: 2.69; 95% CI: 1.60–4.51; adjusted PR: 2.13; 95% CI: 1.12–3.96; p = 0.019), suggesting a relative advantage in equipment distribution or management. Facilities using non-grid electricity had lower USS availability (crude PR: 0.05; 95% CI: 0.01–0.16; adjusted PR: 0.15; 95% CI: 0.02–0.50; p = 0.009). While unstable grid supply was associated with lower availability in unadjusted models (crude PR: 0.34; 95% CI: 0.19–0.57; p < 0.001), the association weakened after adjustment (adjusted PR: 0.58; 95% CI: 0.33–1.02; p = 0.061), indicating some partial confounding by other facility characteristics. Interestingly, rural-urban differences seen in the crude model (crude PR: 0.13; 95% CI: 0.08–0.22; p < 0.001) were not significant in the adjusted model (adjusted PR: 0.81; 95% CI: 0.40–1.69; p = 0.577). Patterns of ultrasound functionality mostly mirrored those seen in availability. Primary-level facilities were markedly less likely to report functional equipment (crude PR: 0.04; 95% CI: 0.02–0.06; adjusted PR: 0.06; 95% CI: 0.03–0.12; p < 0.001), reinforcing concerns about structural barriers at this level of care. CHAM facilities maintained a functional advantage over government facilities (adjusted PR: 2.31; 95% CI: 1.20–4.38; p = 0.011), while non-grid power supply was again significantly associated with poor functionality (adjusted PR: 0.14; 95% CI: 0.02–0.49; p = 0.009). Facilities using unstable grid connections were also less likely to report functional USS compared to those on a stable grid (adjusted PR: 0.54; 95% CI: 0.29–0.95; p = 0.037). Urban-rural differences in functionality, although significant in unadjusted models (crude PR: 0.15; 95% CI: 0.09–0.26; p < 0.001), were not statistically significant after adjustment (adjusted PR: 0.91; 95% CI: 0.44–1.91; p = 0.795). Sensitivity Analyses Results from sensitivity analyses confirmed the direction and strength of the main associations. In the backup-centric model (Table S1), facilities without backup power were 94% less likely to have ultrasound available and functional (PR = 0.06, 95% CI: 0.003–0.294; p = 0.006). The grid-centric model (Table S2) showed that facilities classified as non-grid were significantly less likely to have ultrasound available (PR = 0.19, 95% CI: 0.031–0.636; p = 0.024) and functional (PR = 0.20, 95% CI: 0.032–0.653; p = 0.026) compared to those connected to the grid. Associations for facility level and ownership remained robust across all model specifications. Table 2. Crude and Adjusted Prevalence Ratios for Ultrasound Availability and Functionality by Facility Characteristics and Electricity Type Table 2 Crude and Adjusted Prevalence Ratios of USS Outcomes and Power supply Energy Types in Malawi, from the 2018/2019 HHFA Variable Ultrasound Availability Model PR crude (95% CI) p-value PR adjusted (95% CI) p-value Region Center ref South 0.8 (0.45, 1.43) 0.457 1.03 (0.56, 1.9) 0.915 North 1.09(0.54, 2.11) 0.796 1.31 (0.63, 2.62) 0.454 Urbanicity Urban ref Rural 0.13(0.08, 0.22) <0.001 0.81 (0.4, 1.69) 0.577 Facility Level Secondary and Tertiary ref Primary 0.03(0.02, 0.06) <0.001 0.05(0.03, 0.11) <0.001 Ownership Government ref Other 0.48(0.03, 2.25) 0.475 0.18(0.01, 0.87) 0.093 CHAM 2.69(1.6, 4.51) <0.001 2.13(1.12, 3.96) 0.019 Power Supply: Energy Type Stable Grid ref Unstable Grid 0.34(0.19, 0.57) <0.001 0.58(0.33, 1.02) 0.061 Non-grid 0.05(0.01, 0.16) <0.001 0.15(0.02, 0.5) 0.009 Variable Ultrasound Functionality Model PR crude (95% CI) p-value PR adjusted (95% CI) p-value Region Center ref South 0.73 (0.4, 1.32) 0.299 0.95(0.5, 1.77) 0.868 North 0.92(0.44, 1.84) 0.83 1.1(0.5, 2.26) 0.807 Urbanicity Urban ref Rural 0.15(0.09, 0.26) <0.001 0.91(0.44, 1.91) 0.795 Facility Level Secondary and Tertiary ref Primary 0.04(0.02, 0.06) <0.001 0.06(0.03, 0.12) <0.001 Ownership Government ref Other 0.56(0.03, 2.62) 0.567 0.21(0.01, 1.03) 0.129 CHAM 3.11(1.82, 5.32) <0.001 2.31(1.2, 4.38) 0.011 Power Supply: Energy Type Stable Grid ref Unstable Grid 0.32(0.18, 0.56 <0.001 0.54(0.29, 0.95) 0.037 Non-grid 0.05(0.01, 0.17) <0.001 0.14(0.02, 0.49) 0.009 * PR = Prevalence Ratio, CI = confidence Interval Results from multivariable Poisson regression models using robust standard errors to assess associations between facility characteristics and two outcomes: ultrasound (USS) availability and USS functionality. Independent variables included region, urbanicity (urban or rural), facility level, ownership type, and electricity supply. The table presents crude and adjusted prevalence ratios (PR), 95% confidence intervals (CI), and p-values. HHFA = Harmonized Health Facility Assessment; CHAM = Christian Health Association of Malawi. DISCUSSION Key Findings This study sought to evaluate the association between power supply and USS availability and functionality across 596 health facilities in Malawi. While 93% of facilities with USS equipment achieved functionality, only 9.9% of all facilities had USS available. Together, the findings suggest that once a facility acquires ultrasound equipment, it is generally well maintained and functional. Thus contrasting with assumptions that equipment availability and functionality are equally unreliable in LMIC settings. Statistical analysis revealed considerable structural disparity across three variables: facility level, ownership, and energy type. Primary level facilities, accounting for over 90% of Malawi’s health system, were less likely to have USS available or functional, even after adjusting for other facility characteristics. Additionally, ownership and electricity supply also contributed to diagnostic inequities. CHAM-owned facilities outperformed their government counterparts, and facilities relying on unstable or non-grid electricity sources were marginally disadvantaged. Structural Disparities in Ultrasound Access Ultrasound access in Malawi is heavily concentrated at higher levels of care. Adjusted models revealed that primary-level facilities were 95% less likely to have ultrasound available and 94% less likely to report functionality compared to secondary and tertiary facilities. These disparities align with broader trends in LMICs, “where only 19% of patients access appropriate diagnostics at the primary level”(38). In Malawi, gaps in USS access at the facility level reflect long-standing health policy design choices. For example, the country’s health strategic plan does not mandate ultrasound provision at health centres or maternity units (29). This omission persists despite these facilities being responsible for delivering a Health Benefits Package (HBP), which includes services such as antenatal screening, modern family planning, management of complicated infectious (e.g. malaria) and non-communicable diseases, domains where ultrasound plays a well-established role(28,39). Historically, service inclusion within the HBP is dependant on pre-determined cost-effectiveness thresholds. Fast-evolving, more affordable technologies like HHUS, represent new opportunities to expand healthcare access for Malawi’s underserved population. For instance, a study conducted in rural Nepal estimated the cost of handheld ultrasound at “$65 per life saved” (39), placing it within Malawi’s documented cost-effectiveness benchmark (40,41). This is especially pertinent as country policy reports note that inclusion within the HBP menu does not guarantee implementation, largely due to persistent health financing gaps(39). Ownership-related disparities were also evident. CHAM-managed facilities outperformed government facilities in both ultrasound availability and functionality. This may be linked to CHAM’s comparatively stronger governance structures and diversified funding sources, such as SLAs, donor funding and user fees (42–44). This diversity in arrangement allow CHAMs to maintain basic service continuity, even where public sector investment is limited (45). While partnerships between government and CHAM can help extend ultrasound access across the country, they also present potential risks. Without clearly defined accountability mechanisms and effective coordination, there is a risk that over-reliance on CHAM may inadvertently delay the procurement of ultrasound innovations for government facilities. Over time, this could undermine the development of sustainable diagnostic capacity within the public sector and place additional strain on an already fragile partnership between the two entities (45). Strengthening partnerships through transparency, aligned incentives and shared responsibility will be critical to achieving sustainable basic diagnostic access at the primary-level(45). Electricity as an Enabling Factor Our evidence shows that electricity supply shaped patterns of ultrasound availability and functionality but did not act alone. Stable grid access with backup was an important enabler across facilities. However, Figure 2 shows that, despite some progress in grid performance between 2013/14 and 2018/19 (particularly among referral-level facilities), nearly half of the first-level facilities continued to operate under unstable or non-grid conditions. These patterns highlight broader system gaps. While Malawi’s national energy policy targets an increase in renewable energy share to 96.1% by 2030, our findings at the health facility level suggest that improvements during the 2013-2019 period were concentrated on stabilizing existing grid systems (23). Regardless, planned transitions toward renewables demand for even further investment in innovative energy solutions, as popular options like standalone solar systems often lack the capacity to manage medical equipment demands (23). Our findings emphasize that electricity itself, is shaped by enabling factors. Literature identifies “laws, policies, regulations, markets and institutions to support equitable access”(46) as critical to the effective provision of power supply, many of the same contextual factors that govern ultrasound deployment. Thus suggests while electricity is necessary, it remains insufficient in isolation. Figure 3 reinforces this point by showing that several facilities with stable grid power still lacked ultrasound services. Nonetheless, stable electricity provides a critical foundation for community health systems (46). The WHO identifies electricity as playing a catalytic role in strengthening system readiness (46). Facilities with consistent energy access are better positioned to attract and retain health workers, support innovation (e.g. tele-health technologies) and expand access to public health information. These functions collectively deliver the broader contextual factors needed to support basic diagnostic imaging, particularly in rural and underserved settings.(46). Within this context, the exclusion of energy-health collaboration in Malawi’s 2020 National Radiology Policy (47) signals a critical gap. Aligning diagnostic expansion with energy-sector development (particularly through strengthened cross-ministerial collaboration) represents an important opportunity to advance diagnostic equity. Better integration between energy and health planning will be essential to ensure that electricity investments, including renewable innovations, meaningfully improve diagnostic readiness at all levels of care. Strategic Implications for Handheld Ultrasound (HHUS) Deployment Figure 3 maps the distribution of facilities without ultrasound, categorized by electricity type and facility level. While we do not recommend that all such facilities automatically receive equipment, the map provides a valuable geographic profile to inform strategic expansion. It highlights where diagnostic gaps are most concentrated and can help guide decisions about which areas may benefit from ultrasound investment and what device type (cart-based or handheld) is most appropriate, depending on electricity access and facility level. Facilities with stable grid supply but no ultrasound equipment (Panel A) were mostly primary-level. In select cases, these may be suitable candidates for conventional cart-based systems, provided consistent and sufficient power is available. These systems typically require 800 to 1000 watts and a steady alternating current supply (46). In contrast, facilities operating on unstable (Panel B) or non-grid (Panel C) power sources face more substantial infrastructure limitations. In these settings, HHUS offers a more adaptable solution. WHO guidance emphasizes the value of energy-informed procurement strategies for medical equipment, which can help achieve energy-efficient building design standards. Handheld devices are portable, battery-powered, and require only 6 to 28 watts (46). This makes HHUS well suited to the limited daily energy available at most primary health centres, which average just 8.2 and 1.9 kilowatt-hours per day (48). Beyond energy compatibility, both ultrasound types will require supporting systems. These include equipment maintenance routines, and evidence-based clinical guidelines(4,17). Ongoing implementation initiatives (e.g., Centre of Excellence in Ultrasound) will be key to supporting this transition. When matched to the local energy environment and supported by adequate system infrastructure, HHUS offers a promising pathway to expand diagnostic access in underserved areas. Strengths and Limitations This study offers several strengths. It utilizes nationally representative data from the country’s 2018/2019 HHFA, allowing for broad generalizability across Malawi’s public and non-public health sectors. The inclusion of stratified spatial maps (Figure 3) enhances contextual interpretation by linking facility characteristics to regional electricity profiles. Adjusted regression models were applied to control for confounding across key facility variables, improving the internal validity of our findings. Sensitivity analyses were conducted using two alternative electricity groupings with consistent direction and strength of associations observed across both approaches, reinforcing the stability of our results. Additionally, direct linkages were made between facility-level energy profiles and national policy priorities, strengthening the study’s relevance for diagnostic planning and energy investment strategies. While grounded in Malawi’s system, the approach and findings are likely relevant to other sub-Saharan African settings undergoing similar transitions in infrastructure and primary care expansion. However, there are important limitations to acknowledge. The cross-sectional design limits causal inferences between electricity type and ultrasound outcomes. Electricity supply was recorded categorically and did not differentiate specific sources (e.g., solar, generator, or battery), restricting interpretation especially regarding renewable energy progress. USS availability and functionality were assessed based on a single day’s observation, which may not capture longer-term patterns. Finally, the exclusion of lower-tier facilities, such as health posts, may have led to an underestimation of diagnostic gaps at the community level. CONCLUSION This study examined the role of electricity supply in shaping the availability and functionality of ultrasound services across Malawi’s health facilities. Findings showed that stable grid access improves ultrasound readiness, yet it remains insufficient without broader system support. Disparities by facility level and ownership point to structural design gaps that extend beyond energy access alone. As Malawi expands its diagnostic capacity, handheld ultrasound offers a cost-effective and energy-adaptable solution for future integration into Health Benefits Packages (HBPs), particularly in primary level and energy-constrained settings. Mapping facility characteristics alongside electricity profiles, as demonstrated here, can guide energy-informed procurement and strategic ultrasound planning. Broader intra-sectoral coordination within ministries is needed to facilitate intersectoral collaboration between the energy and health sectors. This foundation can support strengthened entity partnership between CHAM and government actors, improve policy alignment, and contribute to more integrated energy-infrastructure design and renewable investment. Declarations CONTRIBUTIONS LN, JW, CRZ and AX conducted the literature review. LN drafted the initial manuscript, and AX, JW, CRZ, BJW, and YS participated in revising the manuscript. PT and JCB provided and curated the data. LN analyzed the data and interpreted the results, with YS verifying the results. LN compiled the tables and developed the figures. YS led the study design, methodology development, and result interpretation. All authors made substantial contributions, critically revised the manuscript, and approved the final version of this paper. All authors read and approved the final manuscript. ACKNOWLEDGMENTS Data collection for the primary study was supported by the Trond Mohn Foundation and the Norwegian Agency for Development Cooperation (NORAD) [project number 874789542]. The funders had no role in the design, analysis, interpretation, or writing of this secondary analysis. The authors thank the Malawi Ministry of Health for their support and collaboration. Special thanks to Liling Shen for replicating the results and to Emily Chu and Orvalho Augusto for their valuable commentary. COMPETING INTERESTS All authors declare no financial or non-financial competing interests. DATA AVAILABILITY The data that support the findings of this study are available from the Malawi Ministry of Health, but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are available from the authors upon reasonable request and with permission of the Malawi Ministry of Health. CODE AVAILABILITY The underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author. ETHICS APPROVAL STATEMENT The primary study involved human subjects and approval by an Institutional Review Board (IRB). Our study, a secondary analysis, does not involve human subjects and therefore did require IRB approval. References Kim ET, Singh K, Moran A, Armbruster D, Kozuki N. Obstetric ultrasound use in low and middle income countries: a narrative review. Reprod Health. 2018 Jul 20;15:129. Carovac A, Smajlovic F, Junuzovic D. Application of Ultrasound in Medicine. Acta Inform Med. 2011 Sep;19(3):168–71. Ginsburg AS, Liddy Z, Khazaneh PT, May S, Pervaiz F. A survey of barriers and facilitators to ultrasound use in low- and middle-income countries. Sci Rep. 2023 Feb 27;13(1):3322. Abrokwa SK, Ruby LC, Heuvelings CC, Bélard S. 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Supplementary Files SupplementaryInformationPowerSupplyandUltrasound.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Jul, 2025 Reviews received at journal 25 Jun, 2025 Reviewers agreed at journal 27 May, 2025 Reviews received at journal 25 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers invited by journal 19 May, 2025 Editor assigned by journal 29 Apr, 2025 Submission checks completed at journal 29 Apr, 2025 First submitted to journal 29 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6554481","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449892051,"identity":"c34fa86f-7aa0-4475-ab9b-9df38a3852a9","order_by":0,"name":"Luyanda Ngongoma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYBACPghlIwemeIjRwgah0oxJ1nI4sYF4LdLtFx8X/GJO33AjgfHB2zZitMicKTae2ceWC9TCbDiXKC0SOWnSvD08uRtuJ7BJ85KgRSLd4HYC+28itaQfk+b5YZAA1MLGTKwtzMa8DQmGM+8/bJacc44ILfwS6Q8f8/z5L8935vDBD2/KiNACjAsDBkawexgbiFIPBOwPGBj+EKt4FIyCUTAKRiQAAOnTMxtyXok1AAAAAElFTkSuQmCC","orcid":"","institution":"University of Washington","correspondingAuthor":true,"prefix":"","firstName":"Luyanda","middleName":"","lastName":"Ngongoma","suffix":""},{"id":449892052,"identity":"ccdc0f6e-c431-47c0-9d65-a0cf1e11519e","order_by":1,"name":"Aryn Xing","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Aryn","middleName":"","lastName":"Xing","suffix":""},{"id":449892057,"identity":"379026bc-bc3d-429b-98f7-b639a3e67f01","order_by":2,"name":"Jingning Wang","email":"","orcid":"","institution":"International School","correspondingAuthor":false,"prefix":"","firstName":"Jingning","middleName":"","lastName":"Wang","suffix":""},{"id":449892059,"identity":"26fd7cc9-1b3a-4a5f-9fd4-709f05cdbf07","order_by":3,"name":"Claire R. 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The figure highlights how unstable grid supply remains dominant nationally, while stable grid access is more common in urban, secondary/tertiary, and CHAM-owned (Christian Health Association of Malawi) facilities. HHFA = Harmonized Health Facility Assessment; CHAM = Christian Health Association of Malawi.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6554481/v1/819a85ccca47d63141460ba7.png"},{"id":81798337,"identity":"7b20ce13-87ca-42ba-8165-056cced904b9","added_by":"auto","created_at":"2025-05-02 04:53:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87788,"visible":true,"origin":"","legend":"\u003cp\u003eChange in Electricity Supply Type by Facility Level Between the 2013/2014 SPA and 2018/2019 HHFA\u003c/p\u003e\n\u003cp\u003eThis figure compares \u0026nbsp;\u0026nbsp;electricity supply types across first- and referral-level facilities between \u0026nbsp;\u0026nbsp;two national surveys: the 2013/2014 Service Provision Assessment (SPA) and \u0026nbsp;\u0026nbsp;the 2018/2019 HHFA. Facility levels were harmonized using Suhlrie et al.’s \u0026nbsp;\u0026nbsp;framework: first level (health centers, rural/community hospitals), and \u0026nbsp;\u0026nbsp;referral level (district and central hospitals). The figure highlights \u0026nbsp;\u0026nbsp;improvements in uninterrupted grid access, especially at referral level, with \u0026nbsp;\u0026nbsp;minimal progress in off-grid electrification. SPA = Service Provision \u0026nbsp;\u0026nbsp;Assessment; HHFA = Harmonized Health Facility Assessment.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6554481/v1/11c0d2ba4b38bfdc5c0e5607.png"},{"id":81798340,"identity":"a232c492-9c0b-4fd2-a99f-ccdf07c03437","added_by":"auto","created_at":"2025-05-02 04:53:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":342145,"visible":true,"origin":"","legend":"\u003cp\u003eGeospatial Distribution of Facilities in Malawi Without Ultrasound by Electricity Supply Type and Facility Level, 2018/2019\u003c/p\u003e\n\u003cp\u003eThis three-panel map displays 537 health facilities without ultrasound equipment in Malawi, based on data from the 2018/2019 HHFA. Facilities are stratified by electricity supply type: (A) stable grid, (B) unstable grid, and (C) non-grid. Primary-level facilities are marked in red, and secondary/tertiary facilities are marked in green. Geospatial coordinates were visualized using geographic information system (GIS) tools in R (packages: sf, ggplot2, and ggspatial). The maps show that most facilities without USS are primary level and rely on unstable or non-grid electricity. HHFA = Harmonized Health Facility Assessment; USS = Ultrasound Sonography; GIS = Geographic Information System.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6554481/v1/f272ea5e939482dba58b99fa.png"},{"id":81798703,"identity":"25d194de-4e60-4e6e-b6d7-68a921b94113","added_by":"auto","created_at":"2025-05-02 05:01:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1334637,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6554481/v1/78973271-aefd-44b8-ae9f-b9d8ac9677fd.pdf"},{"id":81798339,"identity":"55a33555-45fe-4a4d-8065-670eac984f84","added_by":"auto","created_at":"2025-05-02 04:53:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27957,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationPowerSupplyandUltrasound.docx","url":"https://assets-eu.researchsquare.com/files/rs-6554481/v1/858ddd9901b167cbcead6e60.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Power Supply and Ultrasound Functionality in Malawi: Findings from the 2019 Harmonized Health Facility Assessment (HHFA)","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eUltrasound sonography (USS) is a non-ionizing, non-invasive imaging modality with diverse applications across domains such as general, emergency and maternal medicine (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). A portable USS system is defined by its ability to be easily transported and operated in various settings(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). These systems vary in form: some are compact laptop units with dedicated ports for probe connection, while others, termed handheld devices, integrate the probe with an external screen for image display (e.g. smartphone or tablet)(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Together, these portable versions enable a physician to conduct USS at the patient\u0026rsquo;s bedside, a practice termed point-of-care ultrasound (POCUS)(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Fast emerging research continues to demonstrate the role of the handheld USS (HHUS) in both high-income and low-to-middle income (LMIC) health systems(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). A 2019 systematic review (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) incorporated evidence demonstrating that HHUS \u0026ldquo;may have an impact on clinical management in up to 70% of cases\u0026rdquo; in LMIC settings and is able to confirm \u0026ldquo;the initial clinical hypothesis in 66% of patients\u0026rdquo;. In Malawi, where the fertility rate is high at 3.5 and leading causes of death include HIV/AIDS, tuberculosis, neonatal disorders, and ischaemic heart disease - HHUS presents as a valuable diagnostic tool (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Examples of HHUS\u0026rsquo;s utility include: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) early antenatal screening recommended by the World Health Organization (WHO) to identify high-risk pregnancies; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) modern family planning i.e. intrauterine device insertion ; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) for the management of diverse infectious diseases, such as through the FASH (Focused Assessment with Sonography for HIV-associated Tuberculosis) protocol, in which a study showed it was linked to care decisions in 47% of cases and increased \u0026ldquo;the likelihood of initiating empiric TB treatment from 9\u0026ndash;46% among those with probable or confirmed TB.\u0026rdquo;(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecognizing the potential of USS, Malawi\u0026rsquo;s Ministry of Health and its donor partners established the Centre of Excellence in Ultrasound at Kamuzu Central Hospital in 2022 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Through donor partnerships, the centre has supported USS and more specifically, HHUS introduction through projects focused on artificial intelligence and tele-ultrasound to facilitate remote consultation capabilities across all facility levels (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The centre is tasked with \u0026ldquo;strengthening diagnostic radiology capacity\u0026rdquo; (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and bolstering the ministry\u0026rsquo;s work towards Sustainable Development Goal (SDG) #3. This support of HHUS innovation is linked to systemic constraints that hinder cart-based USS services in the country \u0026ndash; workforce constraints, expensive equipment and maintenance costs ,and restricted potential in rural settings(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). HHUS\u0026rsquo;s portability, adaptibility, affordability and proven user-friendliness in clinical and medical education settings, makes it a competitive alternative(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to Weiner\u0026rsquo;s(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) Organizational Readiness for Change (OR4C) framework, the successful uptake of health innovations such as the integration of USS into routine care requires a strong foundation of human, financial, informational, and material resources. This study examined electricity (also referred to as power or energy) as a key material resource influencing USS service delivery. Previous studies examining the role of electricity in health facility readiness have primarily focused on devices such as incubators and light microscopes, with limited attention to ultrasound systems(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In Malawi, approximately 95% of the country\u0026rsquo;s electricity is generated through climate-sensitive hydropower, which contributes to a frequently overburdened national grid (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).Consequently, Malawi ranks among the lowest globally in terms of household electricity access, with only 25.9% of the population connected. Of this, 11.3% is connected through the national grid and 14.6% through off-grid solutions ((\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The generation and distribution of electricity is managed by two state-owned entities: the Electricity Supply Corporation of Malawi (ESCOM) and the Electricity Generation Company (EGENCO). However, poor intra-sectorial coordination is highlighted as a key barrier in the improved access for public institutions(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In response to these challenges and in preparation for the planned construction of around 900 rural healthcare facilities by 2030, Malawi joined the United Nations Sustainable Energy for All (SE4All) initiative in 2012, committing to achieving universal electricity access by 2030 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to understand the relationship between electricity supply and the availability and functionality of USS in Malawi\u0026rsquo;s healthcare facilities, particularly at the primary care level. Utilizing data from the 2018/2019 Harmonized Health Facility Assessment (HHFA), the study assesses facility-level electricity access and its implications for ultrasound deployment. By analyzing these patterns, this research seeks to inform policy development and guide strategic planning around equipment procurement (traditional versus handheld ultrasound) and electrification priorities in Malawi\u0026rsquo;s health sector.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and Setting\u003c/h2\u003e \u003cp\u003eA cross-sectional study design was employed, using survey findings from the 2019 Malawi HHFA facility inventory, which was conducted between November 2018 and March 2019 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Facilities included in the HHFA survey were identified using the Malawi Facility List (MFL), formally published in January 2019 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The MFL identifies 1224 facilities and provides a comprehensive overview of the country\u0026rsquo;s healthcare facilities. Malawi\u0026rsquo;s healthcare facilities can be categorized by funding source into the following groups: public (n\u0026thinsp;=\u0026thinsp;799), private non-profit (n\u0026thinsp;=\u0026thinsp;89), private-for-profit (n\u0026thinsp;=\u0026thinsp;272), and non-governmental organizations (n\u0026thinsp;=\u0026thinsp;64) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In this study, the term \u0026ldquo;public\u0026rdquo; includes both government-owned and Christian Health Association of Malawi (CHAM) operated facilities, as CHAM facilities provide free healthcare services in partnership with the government via Service Level Agreements (SLAs) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). For analysis purposes, facilities not classified as government or CHAM ( i.e., private non-profit, private-for-profit, and NGOs) were grouped as \u0026ldquo;other\u0026rdquo;or \u0026ldquo;non-public\u0026rdquo;. Facilities can also be described according to Malawi\u0026rsquo;s three-tiered healthcare system, which functions as a referral pyramid with an escalating level of service capacity and specialization. This includes primary care services (health posts, dispensaries, maternity clinics, health centers and community hospitals), district hospitals that serve at the secondary level, and central hospitals at the tertiary level (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling and Selection\u003c/h3\u003e\n\u003cp\u003eThe Malawi HHFA used a census strategy to sample facilities from the MFL. However, the HHFA only successfully captured data from only 1,106 of the total 1,224 facilities, as 118 facilities were \u0026ldquo;either not located or no longer existed\u0026rdquo; (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). For this secondary analysis, we drew our sample from the 1,106 facilities successfully surveyed in the HHFA. A subset of 596 facilities was selected based on inclusion and exclusion criteria aligned with the study\u0026rsquo;s objective. Inclusion criteria were guided by the Malawi Health Sector Strategic Plan II 2017\u0026ndash;2022 (HSSP II), which identifies rural community, district, and central hospitals as appropriate sites for USS services(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In addition, we included health centers and maternity facilities that, based on their scope of services, were considered potentially appropriate for USS. These services included antenatal care, family planning, management of infectious diseases such as complicated malaria, and the provision of inpatient care(\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Ultrasound is also listed in WHO\u0026rsquo;s priority medical device guidance as essential at the health center level and above(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Thus, the final sample for this secondary analysis comprised of 543 primary-level facilities (including 491 health centers, 6 maternity, 40 rural community hospitals and 6 hospitals classed \u0026ldquo;other\u0026rdquo;) and 53 secondary and tertiary-level facilities (secondary 47; tertiary 6). Excluded were health posts, dispensaries, clinics and mental health hospitals.\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThis study is a secondary analysis of de-identified data from the 2019 Malawi HHFA. Ethical approval for the original data collection was obtained from an institutional review board at the time of the primary study. As this secondary analysis involved no human participants and used anonymized data, additional ethical review was not required.\u003c/p\u003e\n\u003ch3\u003eData Collection and Variables\u003c/h3\u003e\n\u003cp\u003eElectricity supply serves as the primary independent variable in this study. This variable was constructed using three HHFA survey questions related to: the facility\u0026rsquo;s main power source (national grid or off-grid), the reliability of the grid (defined as having no interruptions longer than two hours during operating hours in the past seven days) and the presence of a backup power source (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This approach to the construction of the variable was adapted from Suhlrie et al.\u0026rsquo;s (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) analysis of Malawi\u0026rsquo;s 2013/2014 Service Provision Assessment(SPA), using similar same survey questions to classify facilities into six electricity types: uninterrupted grid with backup, interrupted grid with backup, uninterrupted grid without backup, interrupted grid without backup, off-grid electricity, and no electricity. In our study, these six types were consolidated into three categories to facilitate statistical analysis. This decision was informed by the absence of facilities classified as either \"uninterrupted grid without backup\" or \u0026ldquo;no electricity\" across the two ultrasound outcomes. Our classification was as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStable Grid: Facilities with uninterrupted grid power and a backup power source.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnstable Grid: Facilities are grid-supplied but face challenges such as frequent interruptions or lack a backup power source (interrupted grid with backup, interrupted grid without backup).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNon-grid: Facilities relying on off-grid electricity as main power source (solar, battery, fuel-based generator, and hybrid).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eOther key facility characteristics served as the independent co-variables and included: urbanicity (urban vs. rural), facility level (primary vs. secondary or tertiary), facility ownership (government, CHAM, or other), and region (North, Central, or South). The dependent variables were derived from a single HHFA equipment inventory question, which asked field teams: \u0026lsquo;Please tell me if ultrasound equipment is available and functional today?\u0026rdquo;. Response options included: available and functional; available not functional; available don\u0026rsquo;t know if functional; and not available. For this analysis, these responses were disaggregated into two binary outcomes: availability (yes or no) and functionality (yes or no). Availability was defined as any facility where USS was observed or reported to be present, while functionality referred to ultrasound being observed and confirmed to be working.\u003c/p\u003e \u003cp\u003eTo visualize the spatial distribution of health facilities lacking USS by electricity supply type, we created a three-panel map using spatial coordinates from the HHFA. Facilities that reported no USS availability were isolated (n\u0026thinsp;=\u0026thinsp;537), and further stratified by facility level. Facilities missing latitude or longitude data were excluded (n\u0026thinsp;=\u0026thinsp;2). Shapefiles outlining the country\u0026rsquo;s national and district boundaries were obtained from the Humanitarian Data Exchange and loaded using the sf package in R studio (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Each subset of facilities was converted into a spatial object and mapped using the ggplot2 and ggspatial packages. Facilities were color-coded by facility level, primary (red) and secondary/tertiary (green), to visualize overlaps between energy type and service tier. Individual maps for each electricity category were generated and then combined into a composite panel using ggpubr to complete the figure.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eTwo types of analyses were conducted, descriptive and inferential. Descriptive statistics were used to summarize both the facility sample and the USS outcomes across the key independent variables: region, urbanicity, facility level, ownership, and electricity supply. Thereafter, electricity supply was described across these same key variables to provide contextual insight into the energy landscape. To examine changes in electricity supply over time, we compared our findings from the 2018/2019 HHFA with those from Suhlrie et al.\u0026rsquo;s (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) analysis of the 2013/2014 2014 Service Provision Assessment (SPA). The HHFA is an extension of the SPA\u0026rsquo;s core measurements, thus allowing valid comparisons across survey periods(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). To allow for direct comparison, we briefly adopted their categorization of facilities into three levels: community level (clinics, dispensaries, health posts, and maternity units), first level (health centers, community hospitals, and other rural hospitals), and referral level (district and central hospitals). Our analysis excluded dispensaries, clinics, and health posts, making a direct comparison at the community-level facilities infeasible. Missing outcome data were interpreted as an absence of USS equipment rather than as random missingness. To conduct inferential analysis, adjusted Poisson regression (95% CI) with robust standard errors were used to model the associations between electricity type and the two binary outcomes, USS availability and USS functionality. The choice of Poisson regression over logistic regression was informed by literature that recommends prevalence ratios (PRs) over odds ratios in cross-sectional studies, to avoid over-estimation (\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Outcomes were tested separately from each other, and each model included the same set of covariates: region, urbanicity, facility level, ownership, and electricity type:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{log}it\\left(\\gamma\\:j\\right)=\\:\\beta\\:0+\\beta\\:1Energytypej+\\beta\\:2Urbanicityj+\\:\\beta\\:3FacilityLevelj\\:\\:+\\beta\\:4FacilityOwnershipj+\\beta\\:5Regionj+\\:\\epsilon\\:j$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThese variables were selected based on their theoretical relevance and support in the literature and analyses were performed using R Studio Version 2024.09.0\u0026thinsp;+\u0026thinsp;375 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In this model \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\gamma\\:j\\)\u003c/span\u003e\u003c/span\u003e represents the binary outcome (1\u0026thinsp;=\u0026thinsp;USS available or functional; 0\u0026thinsp;=\u0026thinsp;not available or not functional) for facility\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e. The covariates include \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{1}\\)\u003c/span\u003e\u003c/span\u003e for energy type, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{2}\\)\u003c/span\u003e\u003c/span\u003e for urbanicity, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{3}\\)\u003c/span\u003e\u003c/span\u003e for facility level, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{4}\\)\u003c/span\u003e\u003c/span\u003e for facility ownership, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{5}\\)\u003c/span\u003e\u003c/span\u003efor region.\u003c/p\u003e \u003cp\u003eTo assess the robustness of our findings to how electricity supply was operationalized, we conducted sensitivity analyses using two alternate categorizations of the energy supply variable. First, a backup-centric classification grouped facilities based on whether they had access to any backup power source (regardless of their primary energy type), creating two categories: Backup Power and No Backup Power. In this test, off-grid facilities were also further disaggregated based on whether they reported a functioning secondary source of electricity. Facilities were categorized as Backup Power if they had uninterrupted or interrupted grid with backup, or off-grid electricity with backup; and as No Backup Power if they had grid without backup, off-grid without backup, or no electricity. This refined grouping tested whether power redundancy alone was associated with ultrasound availability and functionality. Second, we applied a grid-centric classification that grouped facilities into grid-connected and non-grid categories. Grid-connected facilities included all forms of national grid supply, whether uninterrupted or interrupted, and with or without backup. Non-grid facilities relied on off-grid electricity or had no power supply, without further disaggregation by backup status.For all sensitivity models, facility level, ownership, region, and urbanicity were controlled for. Full model outputs are provided in Supplementary Tables S1 and S2.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003ch2\u003eDescriptive Statistics\u003c/h2\u003e\n\u003cp\u003eTable 1 summarizes the characteristics of the 596 health facilities included in the final analytic sample. Most facilities (91.10%) were primary-level, while only 8.90% were within the secondary and tertiary-level category. Government ownership was predominant, accounting for 70.64% of facilities, followed by CHAM (24.50%) and other owners 4.87%. A majority of facilities (87.08%) were located in rural areas and regionally, the South had the greatest share (43.29%). In terms of energy supply, 24.66% of facilities had access to stable grid electricity, 46.14% were connected to an unstable grid and 29.20% relied on non-grid sources. An analysis of USS outcomes showed that USS is more commonly available in urban settings, CHAM-owned facilities, secondary and tertiary-level facilities, and those with a stable grid power supply. More specifically, 40.36% of urban facilities have USS available compared to 5.39% in rural areas. Access is also limited at the primary level where only 2.76% of facilities have availability compared to 83.02% among secondary and tertiary facilities. CHAM-owned facilities and those with stable grid supply performed best within their respective categories, with ultrasound availability reported at 19.18% and 23.81%, respectively. Functionality was assessed among the 59 facilities with USS available and 93% of them had functional equipment. The patterns of functionality closely follow those seen in availability. Urban facilities show higher functionality at 35.06% compared to 5.39% in rural areas. At the secondary and tertiary level, 75.47 % of facilities achieved functionality, \u0026nbsp;while only 2.76% of primary facilities did. CHAM facilities performed consistently with 19.18% reporting functional equipment, followed after by government facilities (6.18%). Facilities with stable grid supply had the highest proportion at 22.45% compared to its counterparts, each with less than 10% USS functionality. Overall, \u0026nbsp;trends suggest that once a facility has USS available, it is generally functional, especially when supported by stronger infrastructure.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"653\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 653px;\"\u003e\n \u003cp\u003eTable 1 Availability and Functionality of Ultrasound Equipment in Healthcare Facilities in Malawi\u0026nbsp;2018/2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctionality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en=596 (100%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en= 59 (9.9% )\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en= 55 (9.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eCenter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e226 (37.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e24 (10.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e24 (10.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e112 (18.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e13 (11.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e11 (9.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e258 (43.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e22 (8.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e20 (7.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrbanicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e519 (87.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e28 (5.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e28 (5.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e77 (12.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e31 (40.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e27 (35.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacility Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e543 (91.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e15 (2.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e15 (2.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eSecondary and Tertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e53 (8.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e44 (83.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e40 (75.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacility Ownership\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eCHAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e146 (24.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e28 (19.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e28 (19.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eGovernment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e421 (70.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e30 (7.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e26 (6.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e29 (4.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e1 (3.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e1 (3.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 307px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePower Supply: Energy Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Stable Grid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e147 (24.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e35 (23.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e33 (22.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Unstable Grid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e275 (46.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e22 (8.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e20 (7.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;Non-grid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e174 (29.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e2 (1.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 173px;\"\u003e\n \u003cp\u003e2 (1.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePercentages\u003c/strong\u003e: Availability/Functionality = row-wise; Total = column-wise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFacility levels:\u003c/strong\u003e Primary = health centres, maternity, rural/community hospitals; Secondary/Tertiary = district and central hospitals\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePower supply:\u003c/strong\u003e Stable Grid = Uninterrupted Grid with Backup; Unstable Grid = Uninterrupted Grid without Back-up, Interrupted Grid with Back-up, Interrupted Grid without Back-up; Non-grid = Off-grid or No electricity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e USS, ultrasound; HHFA, Harmonized Health Facility Assessment; CHAM, Christian Health Association of Malawi.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics across 597 facilities. Facility levels follow national classification: Primary level includes health centers, maternity facilities, and rural/community hospitals; Secondary and Tertiary include district and central hospitals. Electricity type groups were derived from six original categories (see Figure 1 caption). Availability = physical presence of USS; Functionality = confirmed working status. Row-wise percentages reflect subgroup-specific prevalence.\u003cbr\u003e\u003cem\u003eUSS = Ultrasound; HHFA = Harmonized Health Facility Assessment; CHAM = Christian Health Association of Malawi.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo further contextualize the disparities observed in Table 1, Figure 1 visualizes how the electricity supply types are distributed across the region, ownership, facility level and urbanicity variables. Unstable grid supply is the most common energy type across all regions, accounting for 50.78% of facilities in the South, 43.81% in the Center, and 40.18% in the North. Stable grid access is highest in the South (29.84%) and lowest in the Center (19.47%), while non-grid use is similar throughout the regions. Urban facilities have better stable grid access at 45.45% compared to 21.58% in rural areas. Rural facilities demonstrate a greater reliance on non-grid sources at 32.37% versus 7.79% in urban areas. At the facility level, 54.72% of secondary and tertiary facilities have stable grid power. Unstable and non-grid sources are more common in primary facilities at 46.78% and 31.49% respectively. Among ownership categories, CHAM facilities primarily rely on an unstable grid (49.32%), followed by stable grid (28.08%) and non-grid supply (22.60%). Government-owned facilities show lower stable grid access (21.62%) and higher reliance on unstable (45.37%) and non-grid electricity (33.02%). Facilities classified under \u0026apos;Other\u0026apos; ownership \u0026nbsp;report the highest stable grid access at 51.72%.\u003c/p\u003e\n\u003cp\u003eUsing Figure 2, we examined changes in energy supply across Malawi\u0026rsquo;s health system between 2013/2014 and 2018/2019. At the first level, there was a notable shift toward more reliable electricity. The proportion of facilities with uninterrupted grid access and backup increased from 4.67% to 23.09%, representing a nearly fivefold improvement. However, nearly half of first-level facilities remained on unstable grid connections. Interrupted grid access with backup rose from 9.86% to 20.78%, while those without backup declined from 30.34% to 20.07%. These shifts indicate some movement away from the least resilient supply types, but a large proportion of facilities still experience grid interruptions. Reliance on off-grid sources, such as standalone solar systems, declined only marginally, from 33.29% to 29.48%. Facilities without any electricity nearly disappeared, dropping from 7.17% to 1.07%. At the referral level, where service demands and equipment requirements are highest, stable and reliable grid access continued to improve. The share of facilities with uninterrupted grid and backup rose from 46.43% to 60.71%, while reliance on interrupted grid access decreased from 53.57% to 39.29%. No referral-level facilities reported being without electricity or using off-grid sources in either year. These findings reflect meaningful progress, particularly in electrifying and stabilizing power at higher-level facilities.\u003c/p\u003e\n\u003cp\u003eFigure 3 was used to identify implementation gaps and visually represent the intersection of service tier and electricity type in shaping diagnostic access across Malawi\u0026rsquo;s health system. These stratified maps also offer early insight into which facilities may be more appropriate for handheld versus traditional USS technologies. Of the facilities without USS (n=537), the majority were primary-level (n=528, 98.3%), with few classified as secondary or tertiary-level (n=9, 1.7%). Panel A shows that 112 facilities without USS were operating on stable grid electricity, of which 105 were primary-level and 7 were secondary or tertiary. These cases (concentrated in the central and southern regions) suggest that barriers to USS implementation may extend beyond energy supply, including limitations in workforce capacity, or equipment prioritization. Panel B highlights the 252 facilities without USS on unstable grid supply (n=252), nearly all of which were primary level (n=251). Like Panel A, these facilities show a center-south predominance. Panel C presents 171 facilities without USS powered by non-grid energy sources. These facilities were almost exclusively primary level (n=170) and geographically widespread across all three regions.\u003c/p\u003e\n\u003ch2\u003eFactors Associated with Ultrasound Availability and Functionality\u003c/h2\u003e\n\u003cp\u003eTable 2 presents the crude and adjusted PRs for the USS outcomes by facility characteristics, particularly power supply type. Primary-level facilities were substantially less likely to have USS available compared to secondary or tertiary-level facilities (crude PR: 0.03; 95% CI: 0.02\u0026ndash;0.06; adjusted PR: 0.05; 95% CI: 0.03\u0026ndash;0.11; p \u0026lt; 0.001). This association remained strong after adjustment, highlighting persistent structural disparities across facility levels. CHAM-owned facilities had significantly greater availability than government facilities (crude PR: 2.69; 95% CI: 1.60\u0026ndash;4.51; adjusted PR: 2.13; 95% CI: 1.12\u0026ndash;3.96; p = 0.019), suggesting a relative advantage in equipment distribution or management. Facilities using non-grid electricity had lower USS availability (crude PR: 0.05; 95% CI: 0.01\u0026ndash;0.16; adjusted PR: 0.15; 95% CI: 0.02\u0026ndash;0.50; p = 0.009). While unstable grid supply was associated with lower availability in unadjusted models (crude PR: 0.34; 95% CI: 0.19\u0026ndash;0.57; p \u0026lt; 0.001), the association weakened after adjustment (adjusted PR: 0.58; 95% CI: 0.33\u0026ndash;1.02; p = 0.061), indicating some partial confounding by other facility characteristics. Interestingly, rural-urban differences seen in the crude model (crude PR: 0.13; 95% CI: 0.08\u0026ndash;0.22; p \u0026lt; 0.001) were not significant in the adjusted model (adjusted PR: 0.81; 95% CI: 0.40\u0026ndash;1.69; p = 0.577).\u003c/p\u003e\n\u003cp\u003ePatterns of ultrasound functionality mostly mirrored those seen in availability. Primary-level facilities were markedly less likely to report functional equipment (crude PR: 0.04; 95% CI: 0.02\u0026ndash;0.06; adjusted PR: 0.06; 95% CI: 0.03\u0026ndash;0.12; p \u0026lt; 0.001), reinforcing concerns about structural barriers at this level of care. CHAM facilities maintained a functional advantage over government facilities (adjusted PR: 2.31; 95% CI: 1.20\u0026ndash;4.38; p = 0.011), while non-grid power supply was again significantly associated with poor functionality (adjusted PR: 0.14; 95% CI: 0.02\u0026ndash;0.49; p = 0.009). Facilities using unstable grid connections were also less likely to report functional USS compared to those on a stable grid (adjusted PR: 0.54; 95% CI: 0.29\u0026ndash;0.95; p = 0.037). Urban-rural differences in functionality, although significant in unadjusted models (crude PR: 0.15; 95% CI: 0.09\u0026ndash;0.26; p \u0026lt; 0.001), were not statistically significant after adjustment (adjusted PR: 0.91; 95% CI: 0.44\u0026ndash;1.91; p = 0.795).\u003c/p\u003e\n\u003ch2\u003eSensitivity Analyses\u003c/h2\u003e\n\u003cp\u003eResults from sensitivity analyses confirmed the direction and strength of the main associations. In the backup-centric model (Table S1), facilities without backup power were 94% less likely to have ultrasound available and functional (PR = 0.06, 95% CI: 0.003\u0026ndash;0.294; p = 0.006). The grid-centric model (Table S2) showed that facilities classified as non-grid were significantly less likely to have ultrasound available (PR = 0.19, 95% CI: 0.031\u0026ndash;0.636; p = 0.024) and functional (PR = 0.20, 95% CI: 0.032\u0026ndash;0.653; p = 0.026) compared to those connected to the grid. Associations for facility level and ownership remained robust across all model specifications.\u0026nbsp;\u003cbr\u003e\u0026nbsp;Table 2. Crude and Adjusted Prevalence Ratios for Ultrasound Availability and Functionality by Facility Characteristics and Electricity Type\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003eTable 2 Crude and Adjusted Prevalence Ratios of USS Outcomes and Power supply Energy Types in Malawi, from the 2018/2019 HHFA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eUltrasound Availability Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e\u003csub\u003ecrude\u003c/sub\u003e\u003cstrong\u003e\u0026nbsp;(95% CI)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e\u003csub\u003eadjusted\u0026nbsp;\u003c/sub\u003e\u003cstrong\u003e(95% CI)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCenter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eref\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.8 (0.45, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.03 (0.56, 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.09(0.54, 2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.31 (0.63, 2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUrbanicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eref\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.13(0.08, 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.81 (0.4, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFacility Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSecondary and Tertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eref\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.03(0.02, 0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.05(0.03, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOwnership\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGovernment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eref\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.48(0.03, 2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.18(0.01, 0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.69(1.6, 4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.13(1.12, 3.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePower Supply: Energy Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStable Grid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eref\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUnstable Grid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.34(0.19, 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.58(0.33, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-grid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.05(0.01, 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.15(0.02, 0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eUltrasound Functionality Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e\u003csub\u003ecrude\u003c/sub\u003e\u003cstrong\u003e\u0026nbsp;(95% CI)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e\u003csub\u003eadjusted\u0026nbsp;\u003c/sub\u003e\u003cstrong\u003e(95% CI)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCenter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eref\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.73 (0.4, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.95(0.5, 1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.92(0.44, 1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.1(0.5, 2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUrbanicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eref\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.15(0.09, 0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.91(0.44, 1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFacility Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSecondary and Tertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eref\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.04(0.02, 0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.06(0.03, 0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOwnership\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGovernment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eref\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.56(0.03, 2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.21(0.01, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.11(1.82, 5.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.31(1.2, 4.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePower Supply: Energy Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStable Grid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eref\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUnstable Grid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.32(0.18, 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.54(0.29, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-grid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.05(0.01, 0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.14(0.02, 0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e* PR = Prevalence Ratio, CI = confidence Interval\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults from multivariable Poisson regression models using robust standard errors to assess associations between facility characteristics and two outcomes: ultrasound (USS) availability and USS functionality. Independent variables included region, urbanicity (urban or rural), facility level, ownership type, and electricity supply. The table presents crude and adjusted prevalence ratios (PR), 95% confidence intervals (CI), and p-values. HHFA = Harmonized Health Facility Assessment; CHAM = Christian Health Association of Malawi.\u003c/p\u003e"},{"header":"DISCUSSION ","content":"\u003ch2\u003eKey Findings\u003c/h2\u003e\n\u003cp\u003eThis study sought to evaluate the association between power supply and USS availability and functionality across 596 health facilities in Malawi. While 93% of facilities with USS equipment achieved functionality, only 9.9% of all facilities had USS available. Together, the findings suggest that once a facility acquires ultrasound equipment, it is generally well maintained and functional. Thus contrasting with assumptions that equipment availability and functionality are equally unreliable in LMIC settings. Statistical analysis revealed considerable structural disparity across three variables: facility level, ownership, and energy type. Primary level facilities, accounting for over \u0026nbsp;90% of Malawi’s health system, were less likely to have USS available or functional, even after adjusting for other facility characteristics. Additionally, ownership and electricity supply also contributed to diagnostic inequities. CHAM-owned facilities outperformed their government counterparts, and facilities relying on unstable or non-grid electricity sources were marginally disadvantaged.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStructural Disparities in Ultrasound Access\u003c/h2\u003e\n\u003cp\u003eUltrasound access in Malawi is heavily concentrated at higher levels of care. Adjusted models revealed that primary-level facilities were 95% less likely to have ultrasound available and 94% less likely to report functionality compared to secondary and tertiary facilities. These disparities align with broader trends in LMICs, “where only 19% of patients access appropriate diagnostics at the primary level”(38).\u0026nbsp;In Malawi, gaps \u0026nbsp;in USS access at the facility level reflect long-standing health policy design choices. For example, the country’s health strategic plan does not mandate ultrasound provision at health centres or maternity units (29). This omission persists despite these facilities being responsible for delivering a Health Benefits Package (HBP), which includes services such as antenatal screening, modern family planning, management of complicated infectious (e.g. malaria) and non-communicable diseases, domains where ultrasound plays a well-established role(28,39). Historically, service inclusion within the HBP is dependant on pre-determined cost-effectiveness thresholds. Fast-evolving, more affordable technologies like HHUS, represent new opportunities to expand healthcare access for Malawi’s underserved population.\u0026nbsp;For instance, a study conducted in rural Nepal estimated the cost of handheld ultrasound at “$65 per life saved”\u0026nbsp;\u0026nbsp;(39), placing it within Malawi’s documented cost-effectiveness benchmark\u0026nbsp;(40,41). This is especially pertinent as country policy reports note that inclusion within the HBP menu does not guarantee implementation, largely due to persistent health financing gaps(39).\u003c/p\u003e\n\u003cp\u003eOwnership-related disparities were also evident. CHAM-managed facilities outperformed government facilities in both ultrasound availability and functionality. This may be linked to \u0026nbsp;CHAM’s comparatively stronger governance structures and diversified funding sources, such as SLAs, donor funding and user fees (42–44). This diversity in arrangement allow CHAMs to maintain basic service continuity, even where public sector investment is limited (45). While partnerships between government and CHAM can help extend ultrasound access across the country, they also present potential risks. Without clearly defined accountability mechanisms and effective coordination, there is a risk that over-reliance on CHAM may inadvertently delay the procurement of ultrasound innovations for government facilities. Over time, this could undermine the development of sustainable diagnostic capacity within the public sector and place additional strain on an already fragile partnership between the two entities (45). Strengthening partnerships through transparency, aligned incentives and shared responsibility will be critical to achieving sustainable basic diagnostic access at the primary-level(45).\u003c/p\u003e\n\u003ch2\u003eElectricity as an Enabling Factor\u003c/h2\u003e\n\u003cp\u003eOur evidence shows that electricity supply shaped patterns of ultrasound availability and functionality but did not act alone. Stable grid access with backup was an important enabler across facilities. However, Figure 2 shows that, despite some progress in grid performance between 2013/14 and 2018/19 (particularly among referral-level facilities), \u0026nbsp;nearly half of the first-level facilities continued to operate under unstable or non-grid conditions. These patterns highlight broader system gaps. While Malawi’s national energy policy targets an increase in renewable energy share to 96.1% by 2030, our findings at the health facility level suggest that improvements during the 2013-2019 period were concentrated on stabilizing existing grid systems (23). Regardless, planned transitions toward renewables demand for even further investment in innovative energy solutions, as popular options like standalone solar systems often lack the capacity to manage \u0026nbsp;medical equipment demands (23). Our findings emphasize that electricity itself, is shaped by enabling factors. Literature identifies “laws, policies, regulations, markets and institutions to support equitable access”(46) as critical to the effective provision of power supply, many of the same contextual factors that govern ultrasound deployment. Thus suggests while electricity is necessary, it remains insufficient in isolation. Figure 3 reinforces this point by showing that several facilities with stable grid power still lacked ultrasound services. Nonetheless, stable electricity provides a critical foundation for community health systems (46). The WHO identifies electricity as playing a catalytic role in strengthening system readiness (46). Facilities with consistent energy access are better positioned to attract and retain health workers, support innovation (e.g. tele-health technologies) and expand access to public health information.\u0026nbsp;These functions collectively deliver the broader contextual factors needed to support basic diagnostic imaging, particularly in rural and underserved settings.(46). Within this context, the exclusion of energy-health collaboration in Malawi’s 2020 National Radiology Policy\u0026nbsp;(47)\u0026nbsp;signals a critical gap. Aligning diagnostic expansion with energy-sector development (particularly through strengthened cross-ministerial collaboration) represents an important opportunity to advance diagnostic equity. Better integration between energy and health planning will be essential to ensure that electricity investments, including renewable innovations, meaningfully improve diagnostic readiness at all levels of care.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eStrategic Implications for Handheld Ultrasound (HHUS) Deployment\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eFigure 3 maps the distribution of facilities without ultrasound, categorized by electricity type and facility level. While we do not recommend that all such facilities automatically receive equipment, the map provides a valuable geographic profile to inform strategic expansion. It highlights where diagnostic gaps are most concentrated and can help guide decisions about which areas may benefit from ultrasound investment and what device type (cart-based or handheld) is most appropriate, depending on electricity access and facility level. Facilities with stable grid supply but no ultrasound equipment (Panel A) were mostly primary-level. In select cases, these may be suitable candidates for conventional cart-based systems, provided consistent and sufficient power is available. These systems typically require 800 to 1000 watts and a steady alternating current supply (46). In contrast, facilities operating on unstable (Panel B) or non-grid (Panel C) power sources face more substantial infrastructure limitations. In these settings, HHUS offers a more adaptable solution. WHO guidance emphasizes the value of energy-informed procurement strategies for medical equipment, which can help achieve energy-efficient building design standards. Handheld devices are portable, battery-powered, and require only 6 to 28 watts (46). This makes HHUS well suited to the limited daily energy available at most primary health centres, which average just 8.2 and 1.9 kilowatt-hours per day (48). Beyond energy compatibility, both ultrasound types will require supporting systems. These include equipment maintenance routines, and evidence-based clinical guidelines(4,17). Ongoing implementation initiatives (e.g., Centre of Excellence in Ultrasound) will be key to supporting this transition. When matched to the local energy environment and supported by adequate system infrastructure, HHUS offers a promising pathway to expand diagnostic access in underserved areas.\u003c/p\u003e\n\u003ch2\u003eStrengths and Limitations\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study offers several strengths. It utilizes nationally representative data from the country’s 2018/2019 HHFA, allowing for broad generalizability across Malawi’s public and non-public health sectors. The inclusion of stratified spatial maps (Figure 3) enhances contextual interpretation by linking facility characteristics to regional electricity profiles. Adjusted regression models were applied to control for confounding across key facility variables, improving the internal validity of our findings. Sensitivity analyses were conducted using two alternative electricity groupings with consistent direction and strength of associations observed across both approaches, reinforcing the stability of our results. Additionally, direct linkages were made between facility-level energy profiles and national policy priorities, strengthening the study’s relevance for diagnostic planning and energy investment strategies. While grounded in Malawi’s system, the approach and findings are likely relevant to other sub-Saharan African settings undergoing similar transitions in infrastructure and primary care expansion. However, there are important limitations to acknowledge. The cross-sectional design limits causal inferences between electricity type and ultrasound outcomes. Electricity supply was recorded categorically and did not differentiate specific sources (e.g., solar, generator, or battery), restricting interpretation especially regarding renewable energy progress. USS availability and functionality were assessed based on a single day’s observation, which may not capture longer-term patterns. Finally, the exclusion of lower-tier facilities, such as health posts, may have led to an underestimation of diagnostic gaps at the community level.\u003c/p\u003e"},{"header":"CONCLUSION ","content":"\u003cp\u003eThis study examined the role of electricity supply in shaping the availability and functionality of ultrasound services across Malawi\u0026rsquo;s health facilities. Findings showed that stable grid access improves ultrasound readiness, yet it remains insufficient without broader system support. Disparities by facility level and ownership point to structural design gaps that extend beyond energy access alone. As Malawi expands its diagnostic capacity, handheld ultrasound offers a cost-effective \u0026nbsp;and energy-adaptable solution for future integration into Health Benefits Packages (HBPs), particularly in primary level and energy-constrained settings. Mapping facility characteristics alongside electricity profiles, as demonstrated here, can guide energy-informed procurement and strategic ultrasound planning. Broader intra-sectoral coordination within ministries is needed to facilitate intersectoral collaboration between the energy and health sectors. This foundation can support strengthened entity partnership between CHAM and government actors, improve policy alignment, and contribute to more integrated energy-infrastructure design and \u0026nbsp;renewable investment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCONTRIBUTIONS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLN, JW, CRZ and AX conducted the literature review. LN drafted the initial manuscript, and AX, JW, CRZ, BJW, and YS participated in revising the manuscript. PT and JCB provided and curated the data. LN analyzed the data and interpreted the results, with YS verifying the results. LN compiled the tables and developed the figures. YS led the study design, methodology development, and result interpretation. All authors made substantial contributions, critically revised the manuscript, and approved the final version of this paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eACKNOWLEDGMENTS\u003c/p\u003e\n\u003cp\u003eData collection for the primary study was supported by the Trond Mohn Foundation and the Norwegian Agency for Development Cooperation (NORAD) [project number 874789542]. The funders had no role in the design, analysis, interpretation, or writing of this secondary analysis. The authors thank the Malawi Ministry of Health for their support and collaboration. Special thanks to Liling Shen for replicating the results and to Emily Chu and Orvalho Augusto for their valuable commentary.\u003c/p\u003e\n\u003cp\u003eCOMPETING INTERESTS\u003c/p\u003e\n\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e\n\u003cp\u003eDATA AVAILABILITY\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the Malawi Ministry of Health, but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are available from the authors upon reasonable request and with permission of the Malawi Ministry of Health.\u003c/p\u003e\n\u003cp\u003eCODE AVAILABILITY\u003c/p\u003e\n\u003cp\u003eThe underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003eETHICS APPROVAL STATEMENT\u003c/p\u003e\n\u003cp\u003eThe primary study involved human subjects and approval by an Institutional Review Board (IRB). Our study, a secondary analysis, does not involve human subjects and therefore did require IRB approval. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKim ET, Singh K, Moran A, Armbruster D, Kozuki N. Obstetric ultrasound use in low and middle income countries: a narrative review. Reprod Health. 2018 Jul 20;15:129.\u003c/li\u003e\n \u003cli\u003eCarovac A, Smajlovic F, Junuzovic D. Application of Ultrasound in Medicine. Acta Inform Med. 2011 Sep;19(3):168\u0026ndash;71.\u003c/li\u003e\n \u003cli\u003eGinsburg AS, Liddy Z, Khazaneh PT, May S, Pervaiz F. A survey of barriers and facilitators to ultrasound use in low- and middle-income countries. Sci Rep. 2023 Feb 27;13(1):3322.\u003c/li\u003e\n \u003cli\u003eAbrokwa SK, Ruby LC, Heuvelings CC, B\u0026eacute;lard S. Task shifting for point of care ultrasound in primary healthcare in low- and middle-income countries-a systematic review. eClinicalMedicine. 2022 Mar;45:101333.\u003c/li\u003e\n \u003cli\u003eBeam M, Abdull Wahab SF, Ramos M. Point-of-Care Ultrasound in Resource-Limited Settings. Medical Clinics of North America. 2025 Jan;109(1):313\u0026ndash;24.\u003c/li\u003e\n \u003cli\u003eUschnig C, Recker F, Blaivas M, Dong Y, Dietrich CF. Tele-ultrasound in the Era of COVID-19: A Practical Guide. Ultrasound in Medicine \u0026amp; Biology. 2022 Jun;48(6):965\u0026ndash;74.\u003c/li\u003e\n \u003cli\u003eArnold MJ, Jonas CE, Carter RE. 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Health Policy Open. 2025 Mar 12;8:100139.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization, World Bank. Access to modern energy services for health facilities in resource-constrained settings: a review of status, significance, challenges and measurement [Internet]. Reprinted in 2015 with changes. Geneva: World Health Organization; 2014 [cited 2025 Apr 25]. 94 p. Available from: https://iris.who.int/handle/10665/156847\u003c/li\u003e\n \u003cli\u003eMalawi Ministry of Health and Population. MALAWI NATIONAL RADIOLOGY POLICY 2020 [Internet]. 2020 [cited 2024 Feb 19]. Available from: https://nkhokwe.kuhes.ac.mw/server/api/core/bitstreams/d07dd2ec-404e-4147-9689-aa848bd30350/content\u003c/li\u003e\n \u003cli\u003eUnited Nations, Ministry of Health and Population,, Malawi Energy Regulatory Authority (MERA). Energy needs assessment of Malawi\u0026rsquo;s health sector Empowering health services.pdf [Internet]. 2018 [cited 2024 May 9]. Available from: https://www.unicef.org/malawi/media/5526/file/Energy%20needs%\u003cbr\u003e20assessment%20of%20Malawi\u0026apos;s%20health%20sector:%20Empowering%20health%20services.pdf\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Health Systems](https://www.nature.com/npjhealthsyst/)","snPcode":"44401","submissionUrl":"https://submission.springernature.com/new-submission/44401/3","title":"npj Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6554481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6554481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUltrasound sonography (USS) is an essential diagnostic tool with growing relevance in resource-limited health systems. This study examines the association between electricity supply and USS availability and functionality in Malawi\u0026rsquo;s public and non-public health facilities. Using data from the 2018/2019 Harmonized Health Facility Assessment (HHFA), we conducted a cross-sectional analysis of 596 facilities. Electricity supply was categorized into stable grid, unstable grid, and non-grid. Adjusted prevalence ratios (PR) were estimated using robust Poisson regression. Only 9.9% of facilities had USS available, and of these, 93% had functional equipment. Compared to stable grid facilities, those using non-grid electricity were 85% less likely to have ultrasound available (PR: 0.15, 95% CI: 0.02\u0026ndash;0.50) and 86% less likely to have it functional (PR: 0.14, 95% CI: 0.02\u0026ndash;0.49). Diagnostic access was also considerably lower in primary-level and government-owned facilities. Spatial mapping revealed that most facilities without USS were primary-level and powered by non-grid or unstable grid sources. While electricity is necessary, it is not sufficient to ensure diagnostic readiness. Findings highlight the need for integrated planning across the energy and health sectors, present an opportunity for stronger government-CHAM collaboration, and support the targeted deployment of handheld ultrasound in energy-constrained settings.\u003c/p\u003e","manuscriptTitle":"Power Supply and Ultrasound Functionality in Malawi: Findings from the 2019 Harmonized Health Facility Assessment (HHFA)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-02 04:53:12","doi":"10.21203/rs.3.rs-6554481/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-09T00:20:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-26T02:48:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147271858249033932296053237868376805828","date":"2025-05-27T15:37:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-25T20:35:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149040104657745659361675703841642337533","date":"2025-05-21T20:06:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163683403220882466977192339564696236438","date":"2025-05-19T17:27:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-19T17:10:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-29T21:28:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-29T15:51:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Health Systems","date":"2025-04-29T08:41:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Health Systems](https://www.nature.com/npjhealthsyst/)","snPcode":"44401","submissionUrl":"https://submission.springernature.com/new-submission/44401/3","title":"npj Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"43d3bc32-9c4c-4a82-9889-ef54cc816382","owner":[],"postedDate":"May 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":47875390,"name":"Health sciences/Health care/Health policy"},{"id":47875391,"name":"Health sciences/Health care/Health services"}],"tags":[],"updatedAt":"2025-11-21T17:08:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-02 04:53:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6554481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6554481","identity":"rs-6554481","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}