Investing in Hypertension Care in Lagos, Nigeria: Quantifying the Costs to Close the Treatment Gap based on Real-World Data

Investing in Hypertension Care in Lagos, Nigeria: Quantifying the Costs to Close the Treatment Gap based on Real-World Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Investing in Hypertension Care in Lagos, Nigeria: Quantifying the Costs to Close the Treatment Gap based on Real-World Data Charlotte Dieteren, Gloria Gómez-Pérez, Atze Bellaar, Bolanle Baningbe, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5182058/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Low- and middle-income countries (LMICs) house 66% of all hypertension patients, many of whom are undertreated, leading to severe health risks and higher healthcare costs. This study examined the required investments to improve hypertension control in Lagos, Nigeria, using real-world medical records and cost data. We found that both adherence to consultations and medications according to guidelines was significantly associated with reduction of a 5–6 mmHg in systolic blood pressure. These reductions correspond to a 6–14% decrease in cardiovascular complication risk and would require an average annual investment of USD 120 per patient. The medication costs being the main cost driver. Statewide, providing complete care for all hypertension patients would require an annual investment of $ 300 million, or $ 5,000 to $ 13,000 per saved life year. The identified required investments are currently far outside an acceptable range when comparing to the GDP of Lagos State, Nigeria. To make chronic care investments feasible, hypertension management must become more efficient, including reducing high medication costs through bulk purchasing, adopting innovative, group based blended care models, and increasing health insurance coverage. Health sciences/Health care/Health care economics Health sciences/Health care/Health policy Hypertension Non-Communicable Diseases Cost-Effectiveness Health Financing Sub-Saharan Africa Figures Figure 1 Figure 2 Introduction Hypertension is a significant public health challenge worldwide, accounting for an estimated 10.8 million deaths on a yearly basis (GBD 2019 Risk Factors Collaborators, 2020 ). The impact is particularly profound in low- and middle-income countries (LMICs), where the majority (66%) of affected people live (World Health Organization, 2023). This substantial burden of hypertension and related cardiovascular diseases (CVD) represents the leading cause of morbidity and mortality in these regions (Roth et al., 2020 ). Sub-Sahara Africa (SSA) has with 27% the highest prevalence of hypertension worldwide (Geldsetzer et al., 2019 ), partly due to factors such as urbanization, lifestyle changes, and limited access to healthcare services (Minja et al., 2022 ). In Nigeria, hypertension affects over 30% of the population (Adeloye et al., 2021 ). The health burden is further exacerbated by a considerable diagnosis and treatment gap (1 in 5 individuals is diagnosed, of whom 1 in 3 receive adequate treatment), resulting in a high uncontrolled hypertension prevalence and an increased risk for severe health outcomes (Adeloye et al., 2021 ; Odili et al., 2020 ). In SSA, both health system supply and demand side challenges contribute to the frequent non-adherence of diagnosed hypertensive individuals to prescribed care journeys. On the demand side, patients may lack awareness or face financial constraints preventing them from following through with treatment (Naanyu et al., 2016 ; Ng et al., 2021 ). On the supply side, inadequate healthcare infrastructure and inconsistent availability of medications undermine the ability to provide proper care (Gafane-Matemane et al., 2024 ; Otieno, Agyemang, Wainaina, et al., 2023 ). While providing less care than recommended might seem to lead to cost savings in the short term, it inevitably leads to higher long-term healthcare costs due to the increased incidence of severe complications (Kirkland et al., 2018 ). Evidence from high-income settings has consistently shown that underinvestment in preventive care and chronic disease management leads to escalated healthcare expenditures over time (Balabanova et al., 2013 ). This causal relationship is likely applicable to SSA countries as well. Although evidence is growing, significant knowledge gaps, for instance how to best invest in chronic disease management, remain (Kostova et al., 2020 ). Evidence suggests that providing hypertension care can be cost-effective in LMICs, with returns on investment of as much as $ 18 for every $ 1 invested (Global Report on Hypertension, WHO 2023). Effective hypertension management has been shown to improve life expectancy, reduce the risk of cardiovascular complications, and enhance productivity (Carey et al., 2021 ). Various studies have indicated that the cost per life year saved through hypertension interventions in LMICs often falls below the average GDP per capita in these regions, marking it as a cost-effective intervention (Kostova et al., 2020 ). For instance, programs that provide extremely low-cost medications, such as those modeled in studies conducted in India, have demonstrated potential not only for cost-effectiveness but also for overall cost savings (Das et al., 2021 ). These programs highlight the possibility of achieving substantial health benefits at minimal economic cost, if medications and care are accessible and affordable. However, the results of different cost-effectiveness studies are highly variable and often difficult to compare due to differences in study designs, population samples, and healthcare settings (Bryant et al., 2023 ; Chay et al., 2024 ; Davari et al., 2022 ; Kostova et al., 2020 ; Moran et al., 2022 ). Many of these studies are based on small sample sizes from clinical trial environments that do not fully represent real-world care situations. Additionally, the modeled costs of care do not always accurately reflect the actual costs encountered in practice, especially when considering the variability in medication prices and healthcare delivery systems. This discrepancy underscores the need for real-world data to better understand the true costs and potential savings of anti-hypertensive management. In this paper, we investigate the investment implications of incomplete hypertension care journeys in Lagos State, Nigeria, based on medical records and cost data from both public and private healthcare facilities. Using this real-world data, including care utilization records, blood pressure measurements, and actual prices of healthcare services, can provide a more accurate picture of the current required investments to optimize hypertension care according to both national and international guidelines. This study seeks to provide valuable insights into optimizing healthcare resources to improve outcomes for individuals with hypertension and reduce the overall economic burden on the healthcare system in Nigeria. Methods Study design We utilized retrospectively collected data from a longitudinal study assessing the quality of hypertension care in 84 healthcare facilities in Lagos State, Nigeria (Banigbe, 2022 ). We used data of 74 facilities that included hypertension patient medical records of the period January – December 2019. Additionally, between March – May 2023, we collected associated provider costs regarding hypertension care at a representative sample of 26 of these healthcare facilities including public and private healthcare facilities in Lagos State. Study context Nigeria has an estimated population of about 202 million people, accounting for approximately half of the population of West Africa. It is classified as a lower-middle income country with a GDP of 2,162 USD per capita (World Bank, 2024). The country has seen steady GDP growth in the period 2000–2015, which has since flattened and currently suffers from record high inflation rates in 2023 and 2024 ( African Development Bank, 2024). Lagos, the most populous city in Africa, is estimated to have a population of up to 21 million people (Lagos State Government, 2024). The universal health coverage (UHC) index in 2021 was 38% (Global Health Observatory, 2021). To increase UHC, Nigeria has been reforming its health system since the late 20th century and established a National Health Insurance Scheme (NHIS) in 1999 which was decentralized to state levels in 2014, leading to the establishment of Lagos State Health Scheme (LSHS), managed by the Lagos State Health Management Agency (LASHMA). Progress in insurance coverage has been slow, with less than 5% of the population covered by health insurance. To counteract this problem, the NHIS introduced health maintenance organizations (HMOs) to address gaps in quality of healthcare service delivery. HMOs are private organizations that provide a wide range of healthcare services to their enrollees for a pre-determined monthly premium. Although HMOs were supposed to bring the healthcare coverage to a higher level, in 2016 only 0.3% of the Nigerian population was enrolled in HMOs (Onoka et al., 2016 ). Study Population & Sampling Health data The included healthcare facilities were all eligible to participate in LSHS in Lagos State. A two-stage stratified random sampling technique was used to select study facilities. Inclusion criteria mandated that all facilities should be based in Lagos State and should have passed the basic Health Facility Monitoring and Accreditation Agency (HEFAMAA) assessment. Based on the empanelment status in LSHS on September 30, 2020, facilities were classified into either LSHS facilities or non-LSHS facilities. Facilities that were empaneled in LSHS as of September 30, 2020, had also successfully passed the LASHMA validation process. The remaining facilities were qualified for LASHMA assessment but had not yet applied for empanelment as of September 30, 2020. Inclusion criteria for patients within selected facilities were diagnosis of uncomplicated hypertension and at least two clinical visits between January and December of 2019. The exclusion criteria of participants were age < 30 years old, established CVDs at the initial visit in 2019 and diagnosis of hypertension related to pregnancy. Cost data Cost data for essential health interventions that are part of a hypertension care journey (see Table 1 and paragraph on journey cost estimates below) were collected from a sub-sample of 26 facilities selected from the original included 74 healthcare facilities. Facilities were selected to achieve a representative sample of facilities across ownership type, LASHMA empanelment and level of care. Prices were collected for both insured and uninsured patients visiting these facilities. The provided services were costed from the providers’ perspective by a trained fieldworker. Data collection & cleaning Medical Records Data To collect medical records from patients from the included healthcare facilities, medical record abstraction forms (Banigbe, 2022 ) were utilized. This form included information on patient demographics, visit date, physical examinations, prescribed medications, ordered laboratory tests and corresponding laboratory test results for each visit the patient had. Medication use was estimated from the dataset based on both structured recording of use of several antihypertensive medicines: calcium-channel blockers (CCBs, amlodipine), Angiotensin-converting enzyme inhibitors (ACE-inhibitors, lisinopril), angiotensin receptor blockers (ARBs, losartan); and one statin (simvastatin), a medicine used to lower cholesterol. We analyzed free-text fields specifying a list of medication used per patient. Hypertension Care Cost Data Data pertaining to the costs of antihypertensive medication, laboratory tests and lifestyle counselling were collected by independent assessors in the period March – April, 2023. The costs were obtained from four distinct categories of healthcare facilities: non-LSHS empaneled private facilities, non-LSHS empaneled public facilities, LSHS empaneled private facilities and LSHS empaneled public facilities. The different costs consisted of payments paid out of pocket (OOP), payments made by LASHMA insurance, and payments made by private health insurance. All prices were recorded in Nigerian Naira (NGN). Prices were obtained for different medication (amlodipine, lisinopril, losartan and simvastatin) per milligram (mg), laboratory tests (urinalysis, fasting blood sugar and creatinine) and lifestyle counselling. Price information from 26 healthcare facilities was used to compute an average pricelist per facility type and insurance status. Prices in NGN were converted to United States Dollars (USD) using the conversion rate as published by Central Bank of Nigeria on the last business day of April 2023 (Central Bank of Nigeria, 2023). For items where prices were missing, average costs of the most comparable facility type were imputed. Cost of lifestyle counseling was generally stated as ‘free’ in public facilities. For these activities the cost of a consultation was used to simulate cost of lifestyle counseling in these types of facilities. Analyses Population Characteristics The collected data was analyzed using Microsoft Excel 365 version 2305 and STATA version 16.1. Missing data and outliers (> 2 standard deviation (SD) from the mean) were identified and discarded from the dataset. Descriptive statistics were used to present characteristics of the facilities and patients, with categorical variables expressed in percentages and continuous variables expressed in means ± SD. Included variables for descriptive statistics were age, gender, type of facility visited (ownership type private or public, level of care primary or secondary and LASHMA empanelment status), insurance status (defined as mostly insured if 50% or more of visits were labeled as insured), hypercholesteremia (defined as yes if patient was prescribed statins) and severity of baseline blood pressure (BP) levels (defined as normal/prehypertension: systolic blood pressure [SBP] < 140 mmHg, diastolic blood pressure [DBP] = 180 mmHg, DBP > = 110 mmHg) (Guirguis-Blake et al., 2021 ). The crisis category was added to the standard classification of normal/pre-hypertension, grade 1 and grade 2 to highlight the numerous instances of extremely high BP measurements in the dataset. Journey cost estimates & extrapolations Activities to define a complete out-patient journey for hypertension care were defined based on the World Health Organization (WHO) HEARTS Technical Package and the Lagos State standard treatment guidelines, corroborated by an expert voting process (Banigbe, 2022 , Chap. 3). Our definition of a complete journey was a subset of the WHO HEARTS Technical package and summarized in Table 1 . Table 1 Ideal annual out-patient care journey components for a hypertensive patient based on the WHO HEARTS Technical package Journey component Minimum care activities per year Consultations Four consultations of which at least one with medical doctor Lifestyle counseling One lifestyle counseling session Laboratory investigations - Fasting Blood Sugar (at least 1) - Lipid profile (at least 1 - Electrolytes, Urea and Creatinine (at least 1) - Urinalysis (at least 1) Medications Any first line anti-hypertensives (CCBs, ARBs, ACE-inhibitors) and statins as prescribed Notes: CCB = Calcium Channel Blocker; ARB = Angiotensin Receptor Blocker; ACE-inhibitor = Angiotensin Converting Enzyme inhibitor The cost of each journey element within one year of care was calculated based on the average costs of these elements per facility ownership type, level and insurance status of the visit. The investment gap was determined as the actual journey costs minus the ideal journey costs (identified as a journey that includes all recommended activities). The calculated average investment gap per patient and per type of clinic was extrapolated to Lagos State level based on urban prevalence of hypertension (33.5%) and population of Lagos State (Lagos State Government, 2023; Adeloye et al., 2021 ). Relationship between completeness of journeys and change in blood pressure levels To estimate the relationship between completeness of journeys and change in blood pressure after one year of care, a completeness score of the care journey was used. This score was based on four components of the care journey being consultations, lifestyle counseling, laboratory investigations and medications. For each component a score between 0 and 1 was calculated by dividing the actual costs of care by the costs of an ideal journey for each patient, with a maximum of 1, generating a completeness score based on the performed activities weighted by the costs of these activities. The relationship between each journey component completeness score and trends in blood pressure over time was estimated by fitting a linear random effects model of systolic blood pressure according to the following Eq. 1: $$\:{SBP}_{it}=\alpha\:+{\beta\:}_{\:}{X}_{it}+{u}_{i}+{ϵ}_{it}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ where, α represents the intercept, X it represents the explanatory variables, ս i are the individual random effects and ε it the model residuals. We were interested in coefficient β, which measures the effect size of each explanatory variable. Included explanatory variables in Eq. 1 were completeness score for each journey component, demographic factors, facility characteristics, active health insurance covering costs of care and co-morbidities and baseline systolic blood pressure values. The linear random effects model was chosen as it allowed to incorporate multiple measurements per individual in the dataset, even though the timing and frequency of these measurements varied per subject. Cost-estimates and prevalence of complications The estimates from the random effects model and the calculated costs of journeys were used to model the investment needed to reach lower blood pressures if all patients were to access complete care journeys. The yearly costs of complications were based on estimates reported in studies describing the costs of care for CVDs in Nigeria and Western Sub-Saharan Africa (Aminde et al., 2021 ; Iseko1 et al., 2018 ; Nyassinde et al., 2021 ; Rosendaal et al., 2016 ). Complications considered in the calculations were Myocardial Infarctions (MI) and stroke, as these are the most commonly reported CVDs in this population (Li et al., 2023 ). For each patient the expected reduction in risk of complications was modeled with 3 different methods to ensure robustness: Globorisk office score, Globorisk laboratory score ( Risk Charts | Globorisk; Ueda et al., 2017 ) and with the age-specific hazard ratio's (HR) associated with each 5 mmHg reduction in SBP as estimated in a meta-analysis by Rahimi et al ( 2021 ) (Rahimi model). In the Globorisk score approaches, the expected risk was first calculated using the SBP derived from a regression analysis of the measured SBPs at each visit in the dataset. Then, the expected reduced SBP with a complete patient journey was calculated based on the statistically significant regression coefficients from the random effects model for each journey element and the individual's level of completeness for that journey element. For the Globorisk office score, body-mass index (BMI) and smoking status was estimated or imputed, as BMI was available for only 13 patients (1%) in the dataset and smoking status was not recorded. For 621 patients (49.7%) weight was recorded and BMI was estimated based on the average height for males and females in Nigeria (Adebayo et al., 2014 ). If weight was not recorded, BMI was imputed based on the average BMI of the gender and age-category of the patient (Akarolo-Anthony et al., 2014 ). Smoking was imputed by randomly generating a 0 (no smoking) or 1 (smoking) with a chance of 10.4% to get a 1 generated, based on the smoking prevalence in Nigeria (Adeloye et al., 2019 ). For the Globorisk laboratory score the total cholesterol value was available in the data for 32 patients (2.6%). For all other patients a value of 3 mmol/L was used. In the Rahimi model the age-corrected hazard ratio and expected reduction in SBP due to complete journeys were used to calculate the risk reduction using the following equation: $$\:Risk\:reduction=\:Hazard\:Ratio^\left(\frac{SBP\:reduction}{5}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ With these risk-reduction estimates, the costs of averted complications through improved hypertension management were calculated. Subsequently, the expected cost investment per saved life year was calculated for each of the risk modeling approaches using Eq. 3: $$\:\:Expected\:cost\:investment\:per\:saved\:life\:year=$$ $$\:\:\frac{investment\:gap\:for\:complete\:journeys-cost\:of\:prevented\:complications}{saved\:life\:years}\left(3\right)$$ Ethical and data-privacy considerations Ethical approval for the collection and analysis of this data was obtained through Lagos State University College of Medicine (LASUCOM) institutional review board (Approval Number: LREC/06/10/1342) and Boston University Medical Campus Institutional Review Board (IRB number: H-39941). The healthcare facility survey instrument for this study contained a consent statement outlining the methods of data analysis and ensuring the confidentiality of the health facilities during reporting. This statement was verbally communicated to the facility managers and data collection commenced only after the facility manager gave consent. For retrospective medical chart data collection, explicit informed consent could not be obtained from patients due to logistical difficulties in tracing them back from the year 2019 onwards. Prior to analysis, data were fully anonymized for privacy reasons. Regarding the data collection of hypertension medical costs of healthcare facilities, a formal letter was sent to each facility and consent obtained from the facilities prior to the collection of price data. Data was available to the researchers in anonymized format only through a password-protected database. Data was analyzed in Stata 16.1 on a 2-factor protected computer network. Results Facility characteristics Table 2 provides the characteristics of the 74 included health facilities included in this study, of which 68% were private facilities and 32% public facilities. Among all visits, 39% were by insured patients, who more frequently utilized public facilities. Table 2 Facility characteristics Number of facilities Number of patients % insured visits Total a 74 1249 39% Ownership Level of care LASHMA Private Primary LASHMA 2 (3%) 11 (1%) 38% Private Secondary LASHMA 21 (28%) 329 (26%) 36% Private Primary Non-LASHMA 4 (5%) 33 (3%) 38% Private Secondary Non-LASHMA 24 (32%) 346 (28%) 36% Public Primary LASHMA 11 (15%) 261 (21%) 45% Public Secondary LASHMA 8 (11%) 122 (10%) 45% Public Primary Non-LASHMA 9 (12%) 174 (14%) 38% Public Secondary Non-LASHMA 2 (3%) 2 (0%) 83% a Total number of facilities and patients is less than sum of all rows, as certain facilities provide both primary and secondary care services and are thus reported in multiple rows. Patient characteristics Through the medical records of the 74 included health facilities, data was collected for 1,249 patients with hypertension. Table 3 provides an overview of the demographic and comorbidity characteristics stratified by uncontrolled and controlled values at baseline. The majority (82%) had uncontrolled BP-values at the first recorded visit in the dataset and more than a quarter had crisis-level BP-values (SBP > 180 mmHg or DBP > 110 mmHg) recorded during the first included visit (28%). Eighty percent of patients were aged below 65 years and 40% were under the age of 50 years. Patients with controlled baseline BP levels were more likely to visit primary care (44% versus 36%, p < 0.05) and public facilities (51% versus 43%, p < 0.05) and were mostly insured (47% versus 25%, p < 0.05). Table 3 Baseline patient characteristics Total Uncontrolled at baseline Controlled at baseline Number of patients (%) Number of patients (%) Average SBP at baseline (SD) Average DBP at baseline (SD) Number of patients (%) Average SBP at baseline (SD) Average DBP at baseline (SD) Total 1,249 (100%) 1,018 (82% a ) 163 (23) 99 (16) 196 (16% a ) 123 (10) 77 (7) Gender Female 700 (56%) 562 (55%) 162 (22) 98 (14) 117 (60%) 124 (9) 77 (7) Male 549 (44%) 456 (45%) 166 (25) 101 (17) 79 (40%) 123 (12) 76 (8) Age 18–34 65 (5%) 54 (5%) 160 (24) 99 (13) 7 (4%) 116 (12) 75 (8) 35–49 468 (37%) 372 (37%) 164 (23) 99 (15) 81 (41%) 124 (10) 78 (7) 50–64 461 (37%) 383 (38%) 164 (22) 99 (16) 69 (35%) 123 (9) 75 (7) 65+ 255 (20%) 209 (21%) 164 (26) 99 (17) 39 (20%) 124 (11) 76 (8) LASHMA empaneled Yes 707 (57%) 581 (57%) 163 (22) 99 (15) 106 (54%) 123 (10) 76 (8) No 542 (43%) 437 (43%) 164 (25) 100 (16) 90 (46%) 124 (10) 77 (7) Ownership* Private 698 (56%) 585 (57%) 164 (25) 100 (16) 97 (49%) 124 (10) 77 (7) Public 551 (44%) 433 (43%) 163 (22) 99 (15) 99 (51%) 123 (10) 76 (8) Level of care* Primary 472 (38%) 369 (36%) 163 (22) 99 (14) 86 (44%) 124 (11) 77 (8) Secondary 777 (62%) 649 (64%) 164 (24) 100 (16) 110 (56%) 123 (10) 77 (7) Insurance level* b Mostly uninsured 884 (71%) 765 (75%) 166 (24) 100 (16) 104 (53%) 123 (11) 76 (8) Mostly insured 365 (29%) 253 (25%) 156 (21) 99 (13) 92 (47%) 124 (9) 77 (7) Statins prescribed Yes 209 (17%) 170 (17%) 170 (26) 103 (17) 31 (16%) 125 (8) 78 (6) No 1040 (83%) 848 (83%) 162 (23) 99 (15) 165 (84%) 123 (10) 76 (7) Baseline BP severity* c Missing 35 (3%) Crisis 355 (28%) 355 (35%) 185 (24) 113 (16) NA NA NA Grade 2 339 (27%) 339 (33%) 159 (11) 96 (8) NA NA NA Grade 1 324 (26%) 324 (32%) 145 (9) 88 (7) NA NA NA Normal 196 (16%) NA NA NA 196 (100%) 123 (10) 77 (7) a Percentage indicated is percentage of all patients. Controlled and uncontrolled does not add up to 100% as there are 35 patients with missing BP baseline data. b Insurance status was defined as mostly insured if 50% or more of visits were labeled as insured. c Severity of baseline BP levels was defined as normal/pre-hypertension: SBP < 140 mmHg, DBP = 180 mmHg, DBP > = 110 mmHg). * p < 0.05 for chi-square test comparing distribution among controlled and uncontrolled group Journey costs, treatment gaps and extrapolation Table 4 provides an overview of estimated costs of the ideal hypertension care journey according to guidelines (reflected in Table 1 ), based on the collected price of care activities per type of clinic. The average cost of a complete journey is estimated at USD 148 per patient per year. In all facility types, costs of medications are the largest driver accounting for 70% of total journey costs. Costs per type of clinic vary from USD 72 per patient per year in public primary care facilities to more than 3-fold costs in private primary care facilities. Private sector costs are on average three times higher than in public sector facilities. No obvious costs differences were observed between clinics empaneled to LASHMA and those that were not. Table 4 Expected journey costs per patient per year if all recommended care activities would have been performed Descriptives Average costs of complete journey per patient per year in 2023 USD a N facilities N patients Total journey Consultations (% full journey) Counseling Medications Labworks Total 74 1,249 148 6 (4%) 9 (6%) 104 (70%) 30 (20%) Ownership Level of care Private Primary 6 42 230 9 (4%) 20 (8%) 168 (73%) 34 (15%) Private Secondary 45 656 207 10 (5%) 15 (7%) 146 (70%) 36 (18%) Public Primary 20 430 72 1 (2%) 1 (1%) 49 (68%) 22 (30%) Public Secondary 10 121 71 1 (2%) 1 (1%) 48 (67%) 22 (30%) a Average costs are in USD based on Central Bank of Nigeria exchange rate of last business day of April 2023 b Total number of facilities and patients is less than sum of all rows, as certain facilities provide both primary and secondary care services and are thus reported in multiple rows In Table 5 , we show the estimated annual investment per insured and uninsured patient, calculated by subtracting the actual journey costs from the costs of a complete journey. On average 81% of complete journey costs are not provided leading to an investment gap of USD 120 per patient per year. Uninsured patients have an average investment gap of 85% of the journey costs versus 68% in insured patients. The smallest relative investment gap is seen in insured patients in private, secondary care level clinics. Table 5 Average estimated annual investment in USD per patient stratified per facility and insurance characteristics N patients Full journey costs in USD Estimated investment in USD % of full journey cost Consultations (% total gap) Counseling Medications Lab costs Insurance Ownership Level of care Total uninsured 884 167 -142 85% -1 (1%) -8 (6%) -105 (74%) -28 (20%) Uninsured Private Primary 28 264 -214 81% 0 (0%) -16 (7%) -168 (78%) -31 (14%) Uninsured Private Secondary 482 227 -192 85% -2 (1%) -13 (7%) -145 (76%) -32 (17%) Uninsured Public Primary 292 84 -72 86% 0 (0%) -1 (1%) -49 (68%) -22 (31%) Uninsured Public Secondary 82 82 -72 88% 0 (0%) -1 (1%) -48 (67%) -23 (32%) Total insured 365 101 -69 68% 3 (-4%) -11 (15%) -44 (64%) -17 (25%) Insured Private Primary 14 163 -108 66% 6 (-6%) -27 (25%) -66 (62%) -21 (19%) Insured Private Secondary 174 152 -99 65% 5 (-5%) -19 (20%) -58 (58%) -27 (27%) Insured Public Primary 138 47 -37 79% 0 (-1%) -1 (2%) -29 (78%) -8 (21%) Insured Public Secondary 39 47 -33 70% 1 (-2%) -1 (2%) -25 (77%) -7 (23%) Total all patients and clinics 1,249 148 -120 81% 0 (0%) -9 (7%) -87 (72%) -25 (21%) Total private 698 208 -168 81% 0 (0%) -15 (9%) -123 (73%) -30 (18%) Total public 551 72 -60 83% 0 (0%) -1 (1%) -42 (69%) -18 (29%) Notes: Average costs are in USD based on Central Bank of Nigeria exchange rate of last business day of April 2023; Insurance status was defined as insured if 50% or more of visits were labeled as insured Extrapolation of investments to Lagos State population level Based on a hypertension prevalence rate of 33% in the adult urban and semi-urban population and a current treatment rate of 25% in Nigeria (Adeloye et al., 2021 ), there is an estimated required investment of 104 million USD per year for treatment of patients with hypertension in Lagos State. If the entire Nigerian population with hypertension were to be treated the required investment increases to 489 million USD. This includes the investments for those currently treated and the full journey costs for those not treated. For this extrapolation we assumed the data sample is representative of types and frequency of facilities visited by Lagos State population. Blood pressures over time A clinically and statistically significant decrease in blood pressure was observed over time. The average SBP dropped from 158 mmHg (SD = 26) to 145 mmHg (SD = 23) (p < 0.001), and DBP from 96 mmHg (SD = 17) to 89 mmHg (SD 16) (p < 0.001). The proportion of patients with crisis or grade 2 hypertension also decreased (Fig. 1 ) , with the largest SBP reductions seen in those initially at crisis levels (Fig. 2 ). In contrast, patients with normal blood pressure or pre-hypertension at baseline experienced slight increases, reflecting disease progression without full care. Predictors of improved blood pressure over time We estimated the relation of demographic variables, clinic and health system variables, journey completeness and disease severity and comorbidity to SBP with a linear random effect model (Table 7 ). No significant effects of either demographic or clinic/health system variables were found. However, there was a significant positive correlation between completeness of consultation and medication elements and degree of SBP reduction, with adherence to guidelines for these aspects each contributing to a 5–6 mmHg reduction in blood pressure. Other factors significantly correlated to higher SBP over time were hypertension severity measured as value of the first SBP measurement and comorbidity measured as use of statins (borderline significant, p = 0.078). Overall R 2 of the model was 0.30 and between R 2 0.53 indicating a good model fit. Table 6 Linear random effects model exploring effects of hypertension care journey completeness on development of systolic blood pressure over time Coef. 95% Confidence Interval p-value Demographics Age -0.028 -0.078 0.023 0.285 Gender (female) ref male -0.058 -1.354 1.239 0.930 Facility characteristics & insurance Facility type (private) ref public -0.161 -2.21 1.887 0.877 Lashma (yes) ref not-LSHS 0.188 -1.11 1.485 0.777 Level (primary) ref secondary -1.045 -2.951 0.86 0.282 Health insurance (no) ref yes -0.678 -2.066 0.71 0.338 Completeness Hypertension journey elements Counseling -0.097 -5.14 4.946 0.970 Consultations -5.906*** -8.908 -2.904 0.000 Laboratory works -1.143 -3.277 0.99 0.294 Medications -5.055*** -7.786 -2.323 0.000 Comorbidity and hypertension severity Number of anti-HT medications -0.047 -0.929 0.836 0.917 Statin use (no) ref yes 1.494* -0.166 3.154 0.078 Baseline SBP .45*** 0.424 0.477 0.000 Constant 85.76*** 79.851 91.664 0.000 Mean dependent var 145.242 SD dependent var 20.722 Model fit Overall r-squared 0.304 Number of obs 4,196 Chi-square 1409.35 Prob > chi2 0 R-squared within 0 R-squared between 0.529 * p-value < 0.1; *** p-value < 0.0005 Estimated risk reduction of complications and avoided loss of life years by investing in complete hypertension journeys An overview of estimated risk reductions and associated avoided loss of life years and costs is provided in Table 7 . The Globorisk score in both the office and laboratory version yielded comparable results, with estimated average risk reductions of 5.93% and 5.63% respectively. The final estimates were derived by averaging these two reductions, being 5.78%. When using the age-based hazard ratios per 5 mmHg reduction in SBP from the modeling of Rahimi et al. ( 2021 ), the average reduction in risk of developing cardiovascular complications was 13.8%. Table 7 Estimated risk reduction for developing cardiovascular disease and associated averted events, life years saved and costs Globorisk Rahimi Estimated 10-year CVD-risk reduction 5.78% 13.78% Averted cardiovascular events 2,891 6,894 Life years saved 22,880 54,557 Cost per averted event $ 101,078 $ 40,536 Cost per saved life year $ 12,772 $ 5,122 CVD = cardiovascular disease We estimated the average investment gap to complete both the medication and consultation elements of patient journeys at 87 USD per patient per year (Table 5 ). We calculated the potential life years saved through risk reduction due to complete patient care journeys, based on the 2019 Burden of Disease study which estimated the years of life lost due to cardiovascular complications in Nigeria in 2040 (Foreman et al., 2018 ).. We assumed 15% of the total burden could be allocated to Lagos State, based on the proportion of individuals and the increased risk in urban populations. Using the average of the Globorisk model reductions we estimated 22,880 life years to be saved yearly in Lagos State with complete journeys, versus 54,557 life years saved according to the Rahimi model. We calculated the costs of avoided complications based on the average initial risk from the laboratory and office Globorisk score as applied to our dataset (14%) and the expected risk reductions. With a population of 21,000,000 in Lagos State, of whom 50% are adults and a hypertension prevalence of 33% we estimated 2,891 (Globorisk) and 6,894 (Rahimi) less cardiovascular events yearly with complete journeys. Based on various publications of costs of stroke and ischemic heart disease in Nigeria and surrounding countries (Aminde et al., 2021 ; Iseko1 et al., 2018 ; Nyassinde et al., 2021 ; Rosendaal et al., 2016 ), we estimated the average yearly costs of treatment of complications at ~ 1,000 USD, with all cost estimates translated to 2023 USD, based on an average yearly inflation rate of 2.45% from 2008–2023 (World Bank, 2024). We estimated the median survival rate with complications at 3 years (Akinyemi et al., 2021 ). This leads to an expected 9,3 million USD (Globorisk) or 22,2 million USD (Rahimi) in savings on costs of complications when investing in complete consultation and medication journeys. The required investment in complete journeys was estimated at 301 million USD based on the before mentioned population of Lagos, urban prevalence of hypertension and estimated 87 USD per patient per year investment gap in consultation and medication elements of the care journeys (Table 5 ). Based on these assumptions the estimated yearly costs per saved life year with complete medication and consultation journeys ranged from 5,122 USD (Rahimi model) to 12,772 USD (Globorisk model). Discussion This study explored the required investments to close the treatment gap based on real-world medical record and cost data in Lagos State, Nigeria. We identified an average investment gap of 120 USD per patient per year, with the medication costs being the main cost driver. Importantly, receiving of prescribed required antihypertensive medications and consultations were significantly correlated with lower SBP at endline. If investments to complete care journeys were made for all hypertension patients in Lagos State, we expect a yearly investment of ~ 300 million USD to be necessary, which translates to an investment per saved life year of ~ 5,000 to 13,000 USD. We used real-world medical record data and costs data of almost 1,250 hypertensive patients from 74 clinics in Lagos State Nigeria. The average cost of a patient journey according to guidelines was found to be 148 USD per patient per year, with medication costs constituting 70% of those costs. There was significant cost variation between clinics, with estimated total journey costs being two to three times higher in the private sector compared to the public sector. When comparing actual care activities to guideline recommendations, 81% of the costs of a complete journey were not provided. Uninsured patients face an even higher cost gap of 85% versus 68% for insured patients. Despite these gaps, a significant reduction of 13 mmHg in average SBP was observed over time, particularly in the crisis category of hypertension patients. This is an impactful reduction of blood pressure, correlating to over 20% reduction in risk of complications (Rahimi et al., 2021 ). After adjusting for demographic and clinic/health system variables, receiving both consultations according to protocol and complete yearly set of medications as prescribed were significantly correlated with an extra 5–6 mmHg reduction in blood pressure for each variable. These two variables are somewhat correlated, as a low number of consultations means medications cannot be picked up as prescribed. These reductions in SBP are estimated to yield an additional 6–14% reduction in risk of cardiovascular complications. Context and considerations The estimated yearly costs per saved life year are somewhat comparable to a previous study estimating cost-effectiveness of a hypertension screening and treatment model in Kwara State, a more rural area in Nigeria, where they estimated up to 7,815 USD per disability adjusted life year (DALY) averted in 2012 USD, which translates to ~ 10,000 in 2023 USD (Rosendaal et al., 2016 ). These results are not one-on-one comparable as they are based on DALY instead of YLL but do give an indication of a comparable range. One other study modelled likelihoods of cost-effectiveness of different anti-hypertensive drugs in Nigeria, depending on willingness to pay thresholds ranging from 1,300 to 16,000 USD per DALY, showing thiazide diuretics to be most cost-effective (Ekwunife et al., 2013 ). This modelling yields a very different outcome than costs per averted LYY or DALY and is therefore difficult to compare. To our knowledge, no studies have estimated the cost-effectiveness of hypertension interventions in Nigeria, despite the critical need for such data. This information is invaluable for policy makers to make informed decisions on resource allocation. There are, however, several other studies from SSA, which show highly variable results (Ngalesoni et al., 2016 ; Robberstad et al., 2007 ) ranging from ~ 200 USD for hypertension treatment in Ethiopia (Tolla et al., 2016 ) to ~ 8,000 USD for calcium channel blocker based treatment in Ghana (Gad et al., 2020 ) and ~ 17,000 USD for treatment of general population with hypertension in South Africa (Gaziano et al., 2005 ). The found investments of ~ 5,000–13,000 USD per saved life year can be put in perspective by comparing to GDP per capita. In 2023, the GDP per capita was 2,162 USD for Nigeria (World Bank, 2023) and approximately 4,000 USD for Lagos State (World Bank, 2023). Our findings show that the necessary investments may be difficult to implement in the short to medium term. Generally, acceptable costs per saved life year are considered to range from 60–140% of GDP per capita (Yanovskiy et al., 2022 ). Even if the investment required were closer to this acceptable range, the large number of patients needing treatment may make it even less likely to be a feasible investment. However, several remarks can be made about both the required investment and the potential benefits of such an investment. Regarding the benefits, investing in chronic care could lead to cost savings not only by preventing complications but also by increasing productivity (WHO, 2018), a factor not accounted for in this study. Additionally, other hypertension complications such as kidney failure, which has high associated costs and is also partially associated with hypertension (Ruilope & Bakris, 2011 ), was not included in the analysis and will further increase the estimated saved costs on complications. Interventions are generally more cost-effective for groups with high absolute risk of complications (Gad et al., 2020 ; Gaziano et al., 2005 ; Ortegón et al., 2012 ). Targeting high-risk groups with more extensive hypertension treatment interventions can be a strategy to increase the benefits of investment. Regarding the required increased investments there is considerable room for improvement to achieve more efficient spending of available resources. There are substantial differences in the costs of care between various clinics, including within the private sector (Tables 4 and 5 ). While some of this variation is justified by quality differences, we expect these differences are too high to be fully explained by variation in quality alone but this is speculative. Improvements in the health procurement system could contribute significantly to reducing costs and enhancing the affordability of chronic care. A more efficient procurement system can lead to more realistic and consistent healthcare costs. Inconsistent pricing, particularly for medications which take up 70% of the costs of care, is a major challenge in Nigeria. Centralized procurement and bulk purchasing of quality assured generics can lower costs by securing better prices from suppliers. By standardizing procurement processes and negotiating as a collective, health facilities and insurers can reduce the unit costs of medications and medical supplies. Investment in local production capacity of quality generics could also highly increase access to low-cost medications in Nigeria and surrounding countries (Hanson et al., 2022 ). Another aspect to consider is the integration of technology in the procurement process. Digital platforms for procurement can streamline operations, improve accountability, and reduce the time and cost associated with manual processes. Such systems can also provide real-time data on inventory levels, helping to avoid shortages and overstock situations. Additionally, transparent procurement processes can minimize corruption and ensure that funds are used effectively (WHO, 2019). In addition, group-based care and purchasing and implementing digital approaches such as telemedicine and digital coaching can also offer significant advantages for managing chronic conditions like hypertension (WHO & ITU, 2024; Sanya et al., 2023 ). Group care involves treating patients with similar conditions together, providing them with education, support, and shared medical appointments and group-based support sessions. This approach has several benefits. Patients in group settings can share experiences and support each other, which can improve adherence to treatment regimens and lifestyle changes. Group consultations allow healthcare providers to see more patients in less time, reducing the burden on individual consultations and making better use of healthcare resources. Group purchasing of medications and supplies can lead to significant cost reductions. By buying in bulk, patient groups can secure lower prices, reducing the overall cost burden on individuals and the healthcare system. Patients receiving care in groups often have better health outcomes due to increased engagement, peer support, and more consistent follow-ups (Otieno, Agyemang, Wao, et al., 2023 ). All these considerations will be far more effective when coinciding with an increase in insurance coverage with affordable packages that include these group-based and technology driven approaches to care. This provides patients with reliable and affordable access to care and insurers with sufficient purchasing power to manage costs. There are several ongoing mobile health and telemedicine initiatives and the National Hypertension Control Initiative in Nigeria that can be built on to more widely implement effective hypertension management strategies (Ogungbe et al., 2024 ; Stokes et al., 2022 ). Strengths & limitations This study presents unique, highly detailed healthcare utilization data from Nigeria across a representative sample of diverse types of clinics. To our knowledge, there is only limited data available from this region that provides insight into health care utilization, care journeys and outcomes at an individual patient level. The opportunity to combine health outcome data with cost data allows for a unique analysis of the cost-effectiveness of hypertension care based on real-world data. Also, pricing information across different tiers of clinics shows the effect size of cost differences across the entire health system. There are several limitations to this study. Pricing fluctuates highly, especially for medications, which comprise approximately 70% of total costs of hypertension care. This study provides a point-estimate based on a price assessment at one moment in time, which may not capture ongoing price volatility. Real inflation versus economic inflation of the Naira compared to USD makes translation of prices in Naira to USD unreliable, though it was done consistently across the collected data and based on the inflation rate at the time of collection of price data. We do want to note that we linked pricing information collected in 2023 to health care utilization collected in 2019. As price fluctuations probably also affect health care utilization, there might be a discrepancy there. However, we still believe the fact that our analysis is based on real-world utilization data is of high added value to existing studies that mainly use modeling approaches. Key patient data, such as BMI and other comorbidities, were widely missing from the dataset, preventing a more comprehensive analysis. Our dataset also lacked information on patients' health insurance types, limiting our ability to investigate the investment gap specifically among those with social health insurance. Future studies could benefit from including this analysis. Furthermore, healthcare investments in a limited resource setting should be considered holistically, not just focusing on one risk factor and its complications (Baltussen et al., 2023 ). Our study only incorporated data on hypertension, a specific but widespread health issue, and investment versus prevention of complications was calculated in this limited context. We also included only provider costs, while other investments – and return on investments – go beyond the provider perspective and have further consequences on the societal and patient level. Additionally, this analysis assumes that by paying for care as needed through a healthcare payment system, healthcare providers will be able to invest in quality improvements necessary to provide this care without additional investments. Lastly, we concluded the analyses with an extrapolation to Lagos State, but generalizations should be made cautiously, as certain population segments may not be fully represented in our sample. Conclusion This study identifies important gaps in hypertension care costs as actually delivered versus what should be delivered according to national and international guidelines. It quantifies the investment needed in optimizing hypertension care journeys to yield significant improvements in blood pressure levels, associated with a reduction in complications and their associated costs. These investments required per saved life year are currently far outside an acceptable range when comparing to the GDP of Lagos State, Nigeria. To increase feasibility of investing in chronic care in the shorter term, more efficiency and effectiveness of hypertension management is needed, as well as the creation of innovative and affordable insurance models. By addressing high medication costs, the considerable cost-variation between clinics and by leveraging technology and group-based purchasing and care models, substantial savings and health benefits can be achieved. These strategies, alongside a robust procurement system, can help make hypertension care more affordable and effective, ultimately improving health outcomes and reducing the economic burden of chronic disease management. Declarations Conflicting interests The authors have no competing interests to declare. Author contributions TrW, GG, and CD initiated and conceptualized the study. BB and ID led the development and execution of data collection related to the medical records data, while GG and CD designed and supervised the collection of the cost data. JvA was responsible for the core analysis approach of this study. AB contributed to the data analysis. AB, MI, TrW, GG, CD and JvA were involved in interpretating the findings. CD and JvA drafted the initial manuscript. EZ provided crucial context-specific information for the paper. All authors reviewed and approved the final manuscript for submission. 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Growth of health maintenance organisations in Nigeria and the potential for a role in promoting universal coverage efforts. Social Science & Medicine (1982) , 162 , 11–20. https://doi.org/10.1016/j.socscimed.2016.06.018 Organization, W. H., & Union, I. T. (2024). Going digital for noncommunicable diseases: The case for action . World Health Organization. https://iris.who.int/handle/10665/378478 Ortegón, M., Lim, S., Chisholm, D., & Mendis, S. (2012). Cost effectiveness of strategies to combat cardiovascular disease, diabetes, and tobacco use in sub-Saharan Africa and South East Asia: Mathematical modelling study. The BMJ , 344 , e607. https://doi.org/10.1136/bmj.e607 Otieno, P., Agyemang, C., Wainaina, C., Igonya, E. K., Ouedraogo, R., Wambiya, E. O. A., Osindo, J., & Asiki, G. (2023). Perceived health system facilitators and barriers to integrated management of hypertension and type 2 diabetes in Kenya: A qualitative study. BMJ Open , 13 (8), e074274. https://doi.org/10.1136/bmjopen-2023-074274 Otieno, P., Agyemang, C., Wao, H., Wambiya, E., Ng’oda, M., Mwanga, D., Oguta, J., Kibe, P., & Asiki, G. (2023). Effectiveness of integrated chronic care models for cardiometabolic multimorbidity in sub-Saharan Africa: A systematic review and meta-analysis. BMJ Open , 13 (6), e073652. https://doi.org/10.1136/bmjopen-2023-073652 Rahimi, K., Bidel, Z., Nazarzadeh, M., Copland, E., Canoy, D., Ramakrishnan, R., Pinho-Gomes, A.-C., Woodward, M., Adler, A., Agodoa, L., Algra, A., Asselbergs, F. W., Beckett, N. S., Berge, E., Black, H., Brouwers, F. P. J., Brown, M., Bulpitt, C. J., Byington, R. P., … Davis, B. R. (2021). Pharmacological blood pressure lowering for primary and secondary prevention of cardiovascular disease across different levels of blood pressure: An individual participant-level data meta-analysis. The Lancet , 397 (10285), 1625–1636. https://doi.org/10.1016/S0140-6736(21)00590-0 Recommendations on digital interventions for health system strengthening . (n.d.). Retrieved July 26, 2024, from https://www.who.int/publications/i/item/9789241550505 Risk Charts | Globorisk . (n.d.). Retrieved July 26, 2024, from https://www.globorisk.org/risk-charts Robberstad, B., Hemed, Y., & Norheim, O. F. (2007). Cost-effectiveness of medical interventions to prevent cardiovascular disease in a sub-Saharan African country – the case of Tanzania. Cost Effectiveness and Resource Allocation , 5 , 3. https://doi.org/10.1186/1478-7547-5-3 Rosendaal, N. T. A., Hendriks, M. E., Verhagen, M. D., Bolarinwa, O. A., Sanya, E. O., Kolo, P. M., Adenusi, P., Agbede, K., Eck, D. van, Tan, S. S., Akande, T. M., Redekop, W., Schultsz, C., & Gomez, G. B. (2016). Costs and Cost-Effectiveness of Hypertension Screening and Treatment in Adults with Hypertension in Rural Nigeria in the Context of a Health Insurance Program. PLOS ONE , 11 (6), e0157925. https://doi.org/10.1371/journal.pone.0157925 Roth, G. A., Mensah, G. A., Johnson, C. O., Addolorato, G., Ammirati, E., Baddour, L. M., Barengo, N. C., Beaton, A. Z., Benjamin, E. J., Benziger, C. P., Bonny, A., Brauer, M., Brodmann, M., Cahill, T. J., Carapetis, J., Catapano, A. L., Chugh, S. S., Cooper, L. T., Coresh, J., … Fuster, V. (2020). Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019. Journal of the American College of Cardiology , 76 (25), 2982–3021. https://doi.org/10.1016/j.jacc.2020.11.010 Ruilope, L. M., & Bakris, G. L. (2011). Renal function and target organ damage in hypertension. European Heart Journal , 32 (13), 1599–1604. https://doi.org/10.1093/eurheartj/ehr003 Sanya, R. E., Johnston, E. S., Kibe, P., Werfalli, M., Mahone, S., Levitt, N. S., Klipstein-Grobusch, K., & Asiki, G. (2023). Effectiveness of self-financing patient-led support groups in the management of hypertension and diabetes in low- and middle-income countries: Systematic review. Tropical Medicine & International Health: TM & IH , 28 (2), 80–89. https://doi.org/10.1111/tmi.13842 Stokes, K., Oronti, B., Cappuccio, F. P., & Pecchia, L. (2022). Use of technology to prevent, detect, manage and control hypertension in sub-Saharan Africa: A systematic review. BMJ Open , 12 (4), e058840. https://doi.org/10.1136/bmjopen-2021-058840 Tolla, M. T., Norheim, O. F., Memirie, S. T., Abdisa, S. G., Ababulgu, A., Jerene, D., Bertram, M., Strand, K., Verguet, S., & Johansson, K. A. (2016). Prevention and treatment of cardiovascular disease in Ethiopia: A cost-effectiveness analysis. Cost Effectiveness and Resource Allocation : C/E , 14 , 10. https://doi.org/10.1186/s12962-016-0059-y Ueda, P., Woodward, M., Lu, Y., Hajifathalian, K., Al-Wotayan, R., Aguilar-Salinas, C. A., Ahmadvand, A., Azizi, F., Bentham, J., Cifkova, R., Di Cesare, M., Eriksen, L., Farzadfar, F., Ferguson, T. S., Ikeda, N., Khalili, D., Khang, Y.-H., Lanska, V., León-Muñoz, L., … Danaei, G. (2017). Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: A pooled analysis of prospective cohorts and health surveys. The Lancet. Diabetes & Endocrinology , 5 (3), 196–213. https://doi.org/10.1016/S2213-8587(17)30015-3 World Bank Open Data . (n.d.). World Bank Open Data. Retrieved June 11, 2024, from https://data.worldbank.org World Health Organization. (n.d.). Non communicable diseases Key Facts . World Health Organization. Retrieved August 23, 2018, from http://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases Yanovskiy, M., Levy, O. N., Shaki, Y. Y., Zigdon, A., & Socol, Y. (2022). Cost-Effectiveness Threshold for Healthcare: Justification and Quantification. Inquiry: A Journal of Medical Care Organization, Provision and Financing , 59 , 00469580221081438. https://doi.org/10.1177/00469580221081438 Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5182058","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":364975169,"identity":"08d4b78e-4b6a-4384-b948-81464721ec5a","order_by":0,"name":"Charlotte Dieteren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYHACgwMQmvkAMwlaEkA0WwLxWhggWngMiNPC38C88cDHHzb5/NI93x4X1NyL5m8//PDhjz8Mcub9C7BqkTjAVnBwRkKa5cw5Z7cbzzhWnDvjTJqxMW8bg7HMjQc4XMVjcJgn4bCBwY3cbdI8bAm5DTd42KQZGxgSZ0gcwK3lT8J/A/sbOc+kef4l5M6/wcP+E+gw/FoYEg4YGEjksEnztiXkbgDawgBEiTP4G7D75TDQLz1pyQYSN9LMjXn7EnI3Av0C1CthLCGBI8Tamzd/+GFjZ8A/I/nZY55vCbnzjh9++PHHHxs5CX7sDmNAigs2FOuBKAG7FiTAhsbHZcsoGAWjYBSMNAAA+qddIQu0bRMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7809-1657","institution":"PharmAccess Foundation","correspondingAuthor":true,"prefix":"","firstName":"Charlotte","middleName":"","lastName":"Dieteren","suffix":""},{"id":364975170,"identity":"9beebbc3-f972-4958-91c7-5a43dbcd1e77","order_by":1,"name":"Gloria Gómez-Pérez","email":"","orcid":"https://orcid.org/0000-0001-8555-8596","institution":"PharmAccess Foundation","correspondingAuthor":false,"prefix":"","firstName":"Gloria","middleName":"","lastName":"Gómez-Pérez","suffix":""},{"id":364975171,"identity":"dea3f285-9f35-4d7d-911f-0d81fd454ec6","order_by":2,"name":"Atze Bellaar","email":"","orcid":"","institution":"PharmAccess Foundation","correspondingAuthor":false,"prefix":"","firstName":"Atze","middleName":"","lastName":"Bellaar","suffix":""},{"id":364975172,"identity":"495b8443-63fd-40a6-9575-c1b1ea69c772","order_by":3,"name":"Bolanle Baningbe","email":"","orcid":"","institution":"Resolve to Save Lives","correspondingAuthor":false,"prefix":"","firstName":"Bolanle","middleName":"","lastName":"Baningbe","suffix":""},{"id":364975173,"identity":"39acc334-27ac-42a9-89d4-ccd12030f8c3","order_by":4,"name":"Martilord Ifeanyichi","email":"","orcid":"","institution":"PharmAccess Foundation","correspondingAuthor":false,"prefix":"","firstName":"Martilord","middleName":"","lastName":"Ifeanyichi","suffix":""},{"id":364975174,"identity":"242a652e-127a-48f2-89c8-1901d3e0c984","order_by":5,"name":"Tobias de Wit","email":"","orcid":"","institution":"Amsterdam Institute of Global Health and Development","correspondingAuthor":false,"prefix":"","firstName":"Tobias","middleName":"","lastName":"de Wit","suffix":""},{"id":364975175,"identity":"1e8a9078-d727-4414-8906-557f348c5b21","order_by":6,"name":"Ibironke Dada","email":"","orcid":"","institution":"PharmAccess Foundation Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Ibironke","middleName":"","lastName":"Dada","suffix":""},{"id":364975176,"identity":"046ad476-f55f-48c8-97f6-15f94c993fdb","order_by":7,"name":"Emmanuella Zamba","email":"","orcid":"","institution":"Lagos State Health Management Agency","correspondingAuthor":false,"prefix":"","firstName":"Emmanuella","middleName":"","lastName":"Zamba","suffix":""},{"id":364975177,"identity":"a3c9960e-14d2-4723-b662-f94bbf5ee2a8","order_by":8,"name":"Judith van Andel","email":"","orcid":"https://orcid.org/0000-0002-3550-6939","institution":"Amsterdam Institute of Global Health and Development","correspondingAuthor":false,"prefix":"","firstName":"Judith","middleName":"van","lastName":"Andel","suffix":""}],"badges":[],"createdAt":"2024-09-30 15:36:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5182058/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5182058/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70043955,"identity":"1606e658-19e1-40bd-8a83-006f191daa21","added_by":"auto","created_at":"2024-11-27 18:45:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131654,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of classification of the first and last blood pressure measurement (after approximately 1 year). Y axis shows in bold letters the number of observations per BP category. Classification of BP levels was defined as normal/pre-hypertension: SBP\u0026lt;140 mmHg, DBP \u0026lt; 90 mmHg; grade 1: SBP 140-159 mmHg, DBP 90-99 mmHg; grade 2: SBP 160-179 mmHg, DBP 100-109 mmHg; crisis: SBP\u0026gt;=180 mmHg, DBP\u0026gt;=110 mmHg)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5182058/v1/d2e3737c024423a2555e1f71.png"},{"id":70043956,"identity":"556c0327-a2a5-4625-8842-c3bc3a0a34de","added_by":"auto","created_at":"2024-11-27 18:45:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197618,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression plots of development of SBP over time for each individual patient (blue lines) and average per severity of baseline BP values (red lines). Y axis shows BP in mmHG; X axis shows the time in days since the first visit. Classification of BP levels was defined as normal/pre-hypertension: SBP\u0026lt;140 mmHg, DBP \u0026lt; 90 mmHg; grade 1: SBP 140-159 mmHg, DBP 90-99 mmHg; grade 2: SBP 160-179 mmHg, DBP 100-109 mmHg; crisis: SBP\u0026gt;=180 mmHg, DBP\u0026gt;=110 mmHg)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5182058/v1/80e2a868a3fc6bc2b7f29fd4.png"},{"id":70044307,"identity":"d8dcb9b5-b0fb-400f-8bd9-1d41e4ffe0c2","added_by":"auto","created_at":"2024-11-27 18:53:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1867579,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5182058/v1/51d9f8e0-9dce-460d-b1f0-1574aa6bcb2c.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Investing in Hypertension Care in Lagos, Nigeria: Quantifying the Costs to Close the Treatment Gap based on Real-World Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension is a significant public health challenge worldwide, accounting for an estimated 10.8\u0026nbsp;million deaths on a yearly basis (GBD 2019 Risk Factors Collaborators, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The impact is particularly profound in low- and middle-income countries (LMICs), where the majority (66%) of affected people live (World Health Organization, 2023). This substantial burden of hypertension and related cardiovascular diseases (CVD) represents the leading cause of morbidity and mortality in these regions (Roth et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Sub-Sahara Africa (SSA) has with 27% the highest prevalence of hypertension worldwide (Geldsetzer et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), partly due to factors such as urbanization, lifestyle changes, and limited access to healthcare services (Minja et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Nigeria, hypertension affects over 30% of the population (Adeloye et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The health burden is further exacerbated by a considerable diagnosis and treatment gap (1 in 5 individuals is diagnosed, of whom 1 in 3 receive adequate treatment), resulting in a high uncontrolled hypertension prevalence and an increased risk for severe health outcomes (Adeloye et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Odili et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn SSA, both health system supply and demand side challenges contribute to the frequent non-adherence of diagnosed hypertensive individuals to prescribed care journeys. On the demand side, patients may lack awareness or face financial constraints preventing them from following through with treatment (Naanyu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ng et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the supply side, inadequate healthcare infrastructure and inconsistent availability of medications undermine the ability to provide proper care (Gafane-Matemane et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Otieno, Agyemang, Wainaina, et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While providing less care than recommended might seem to lead to cost savings in the short term, it inevitably leads to higher long-term healthcare costs due to the increased incidence of severe complications (Kirkland et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Evidence from high-income settings has consistently shown that underinvestment in preventive care and chronic disease management leads to escalated healthcare expenditures over time (Balabanova et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This causal relationship is likely applicable to SSA countries as well. Although evidence is growing, significant knowledge gaps, for instance how to best invest in chronic disease management, remain (Kostova et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Evidence suggests that providing hypertension care can be cost-effective in LMICs, with returns on investment of as much as \u003cspan\u003e$\u003c/span\u003e18 for every \u003cspan\u003e$\u003c/span\u003e1 invested (Global Report on Hypertension, WHO 2023). Effective hypertension management has been shown to improve life expectancy, reduce the risk of cardiovascular complications, and enhance productivity (Carey et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Various studies have indicated that the cost per life year saved through hypertension interventions in LMICs often falls below the average GDP per capita in these regions, marking it as a cost-effective intervention (Kostova et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For instance, programs that provide extremely low-cost medications, such as those modeled in studies conducted in India, have demonstrated potential not only for cost-effectiveness but also for overall cost savings (Das et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These programs highlight the possibility of achieving substantial health benefits at minimal economic cost, if medications and care are accessible and affordable.\u003c/p\u003e \u003cp\u003eHowever, the results of different cost-effectiveness studies are highly variable and often difficult to compare due to differences in study designs, population samples, and healthcare settings (Bryant et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chay et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Davari et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kostova et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Moran et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Many of these studies are based on small sample sizes from clinical trial environments that do not fully represent real-world care situations. Additionally, the modeled costs of care do not always accurately reflect the actual costs encountered in practice, especially when considering the variability in medication prices and healthcare delivery systems. This discrepancy underscores the need for real-world data to better understand the true costs and potential savings of anti-hypertensive management.\u003c/p\u003e \u003cp\u003eIn this paper, we investigate the investment implications of incomplete hypertension care journeys in Lagos State, Nigeria, based on medical records and cost data from both public and private healthcare facilities. Using this real-world data, including care utilization records, blood pressure measurements, and actual prices of healthcare services, can provide a more accurate picture of the current required investments to optimize hypertension care according to both national and international guidelines. This study seeks to provide valuable insights into optimizing healthcare resources to improve outcomes for individuals with hypertension and reduce the overall economic burden on the healthcare system in Nigeria.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003eStudy design\u003c/h2\u003e\n\u003cp\u003eWe utilized retrospectively collected data from a longitudinal study assessing the quality of hypertension care in 84 healthcare facilities in Lagos State, Nigeria (Banigbe, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We used data of 74 facilities that included hypertension patient medical records of the period January \u0026ndash; December 2019. Additionally, between March \u0026ndash; May 2023, we collected associated provider costs regarding hypertension care at a representative sample of 26 of these healthcare facilities including public and private healthcare facilities in Lagos State.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy context\u003c/h2\u003e \u003cp\u003eNigeria has an estimated population of about 202\u0026nbsp;million people, accounting for approximately half of the population of West Africa. It is classified as a lower-middle income country with a GDP of 2,162 USD per capita (World Bank, 2024). The country has seen steady GDP growth in the period 2000\u0026ndash;2015, which has since flattened and currently suffers from record high inflation rates in 2023 and 2024 \u003cem\u003e(\u003c/em\u003eAfrican Development Bank, 2024). Lagos, the most populous city in Africa, is estimated to have a population of up to 21\u0026nbsp;million people (Lagos State Government, 2024). The universal health coverage (UHC) index in 2021 was 38% (Global Health Observatory, 2021). To increase UHC, Nigeria has been reforming its health system since the late 20th century and established a National Health Insurance Scheme (NHIS) in 1999 which was decentralized to state levels in 2014, leading to the establishment of Lagos State Health Scheme (LSHS), managed by the Lagos State Health Management Agency (LASHMA). Progress in insurance coverage has been slow, with less than 5% of the population covered by health insurance. To counteract this problem, the NHIS introduced health maintenance organizations (HMOs) to address gaps in quality of healthcare service delivery. HMOs are private organizations that provide a wide range of healthcare services to their enrollees for a pre-determined monthly premium. Although HMOs were supposed to bring the healthcare coverage to a higher level, in 2016 only 0.3% of the Nigerian population was enrolled in HMOs (Onoka et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population \u0026 Sampling\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eHealth data\u003c/h2\u003e \u003cp\u003eThe included healthcare facilities were all eligible to participate in LSHS in Lagos State. A two-stage stratified random sampling technique was used to select study facilities. Inclusion criteria mandated that all facilities should be based in Lagos State and should have passed the basic Health Facility Monitoring and Accreditation Agency (HEFAMAA) assessment. Based on the empanelment status in LSHS on September 30, 2020, facilities were classified into either LSHS facilities or non-LSHS facilities. Facilities that were empaneled in LSHS as of September 30, 2020, had also successfully passed the LASHMA validation process. The remaining facilities were qualified for LASHMA assessment but had not yet applied for empanelment as of September 30, 2020. Inclusion criteria for patients within selected facilities were diagnosis of uncomplicated hypertension and at least two clinical visits between January and December of 2019. The exclusion criteria of participants were age\u0026thinsp;\u0026lt;\u0026thinsp;30 years old, established CVDs at the initial visit in 2019 and diagnosis of hypertension related to pregnancy.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCost data\u003c/h3\u003e\n\u003cp\u003eCost data for essential health interventions that are part of a hypertension care journey (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and paragraph on journey cost estimates below) were collected from a sub-sample of 26 facilities selected from the original included 74 healthcare facilities. Facilities were selected to achieve a representative sample of facilities across ownership type, LASHMA empanelment and level of care. Prices were collected for both insured and uninsured patients visiting these facilities. The provided services were costed from the providers\u0026rsquo; perspective by a trained fieldworker.\u003c/p\u003e\n\u003ch3\u003eData collection \u0026 cleaning\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMedical Records Data\u003c/h2\u003e \u003cp\u003eTo collect medical records from patients from the included healthcare facilities, medical record abstraction forms (Banigbe, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were utilized. This form included information on patient demographics, visit date, physical examinations, prescribed medications, ordered laboratory tests and corresponding laboratory test results for each visit the patient had. Medication use was estimated from the dataset based on both structured recording of use of several antihypertensive medicines: calcium-channel blockers (CCBs, amlodipine), Angiotensin-converting enzyme inhibitors (ACE-inhibitors, lisinopril), angiotensin receptor blockers (ARBs, losartan); and one statin (simvastatin), a medicine used to lower cholesterol. We analyzed free-text fields specifying a list of medication used per patient.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHypertension Care Cost Data\u003c/h3\u003e\n\u003cp\u003eData pertaining to the costs of antihypertensive medication, laboratory tests and lifestyle counselling were collected by independent assessors in the period March \u0026ndash; April, 2023. The costs were obtained from four distinct categories of healthcare facilities: non-LSHS empaneled private facilities, non-LSHS empaneled public facilities, LSHS empaneled private facilities and LSHS empaneled public facilities. The different costs consisted of payments paid out of pocket (OOP), payments made by LASHMA insurance, and payments made by private health insurance. All prices were recorded in Nigerian Naira (NGN). Prices were obtained for different medication (amlodipine, lisinopril, losartan and simvastatin) per milligram (mg), laboratory tests (urinalysis, fasting blood sugar and creatinine) and lifestyle counselling. Price information from 26 healthcare facilities was used to compute an average pricelist per facility type and insurance status. Prices in NGN were converted to United States Dollars (USD) using the conversion rate as published by Central Bank of Nigeria on the last business day of April 2023 (Central Bank of Nigeria, 2023). For items where prices were missing, average costs of the most comparable facility type were imputed. Cost of lifestyle counseling was generally stated as \u0026lsquo;free\u0026rsquo; in public facilities. For these activities the cost of a consultation was used to simulate cost of lifestyle counseling in these types of facilities.\u003c/p\u003e\n\u003ch3\u003eAnalyses\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Characteristics\u003c/h2\u003e \u003cp\u003eThe collected data was analyzed using Microsoft Excel 365 version 2305 and STATA version 16.1. Missing data and outliers (\u0026gt;\u0026thinsp;2 standard deviation (SD) from the mean) were identified and discarded from the dataset. Descriptive statistics were used to present characteristics of the facilities and patients, with categorical variables expressed in percentages and continuous variables expressed in means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Included variables for descriptive statistics were age, gender, type of facility visited (ownership type private or public, level of care primary or secondary and LASHMA empanelment status), insurance status (defined as mostly insured if 50% or more of visits were labeled as insured), hypercholesteremia (defined as yes if patient was prescribed statins) and severity of baseline blood pressure (BP) levels (defined as normal/prehypertension: systolic blood pressure [SBP]\u0026thinsp;\u0026lt;\u0026thinsp;140 mmHg, diastolic blood pressure [DBP]\u0026thinsp;\u0026lt;\u0026thinsp;90 mmHg; hypertension grade 1: SBP 140\u0026ndash;159 mmHg, DBP 90\u0026ndash;99 mmHg; hypertension grade 2: SBP 160\u0026ndash;179 mmHg, DBP 100\u0026ndash;109 mmHg; hypertensive crisis: SBP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;180 mmHg, DBP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;110 mmHg) (Guirguis-Blake et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The crisis category was added to the standard classification of normal/pre-hypertension, grade 1 and grade 2 to highlight the numerous instances of extremely high BP measurements in the dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eJourney cost estimates \u0026amp; extrapolations\u003c/h2\u003e \u003cp\u003eActivities to define a complete out-patient journey for hypertension care were defined based on the World Health Organization (WHO) HEARTS Technical Package and the Lagos State standard treatment guidelines, corroborated by an expert voting process (Banigbe, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Chap.\u0026nbsp;3). Our definition of a complete journey was a subset of the WHO HEARTS Technical package and summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIdeal annual out-patient care journey components for a hypertensive patient based on the WHO HEARTS Technical package\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJourney component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum care activities per year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsultations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFour consultations of which at least one with medical doctor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLifestyle counseling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne lifestyle counseling session\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory investigations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- Fasting Blood Sugar (at least 1)\u003c/p\u003e \u003cp\u003e- Lipid profile (at least 1\u003c/p\u003e \u003cp\u003e- Electrolytes, Urea and Creatinine (at least 1)\u003c/p\u003e \u003cp\u003e- Urinalysis (at least 1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAny first line anti-hypertensives (CCBs, ARBs, ACE-inhibitors) and statins as prescribed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNotes: CCB\u0026thinsp;=\u0026thinsp;Calcium Channel Blocker; ARB\u0026thinsp;=\u0026thinsp;Angiotensin Receptor Blocker; ACE-inhibitor\u0026thinsp;=\u0026thinsp;Angiotensin Converting Enzyme inhibitor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe cost of each journey element within one year of care was calculated based on the average costs of these elements per facility ownership type, level and insurance status of the visit. The investment gap was determined as the actual journey costs minus the ideal journey costs (identified as a journey that includes all recommended activities). The calculated average investment gap per patient and per type of clinic was extrapolated to Lagos State level based on urban prevalence of hypertension (33.5%) and population of Lagos State (Lagos State Government, 2023; Adeloye et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between completeness of journeys and change in blood pressure levels\u003c/h2\u003e \u003cp\u003eTo estimate the relationship between completeness of journeys and change in blood pressure after one year of care, a completeness score of the care journey was used. This score was based on four components of the care journey being consultations, lifestyle counseling, laboratory investigations and medications. For each component a score between 0 and 1 was calculated by dividing the actual costs of care by the costs of an ideal journey for each patient, with a maximum of 1, generating a completeness score based on the performed activities weighted by the costs of these activities. The relationship between each journey component completeness score and trends in blood pressure over time was estimated by fitting a linear random effects model of systolic blood pressure according to the following Eq.\u0026nbsp;1:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{SBP}_{it}=\\alpha\\:+{\\beta\\:}_{\\:}{X}_{it}+{u}_{i}+{ϵ}_{it}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, α represents the intercept, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e represents the explanatory variables, \u003cem\u003eս\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e are the individual random effects and \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e the model residuals. We were interested in coefficient β, which measures the effect size of each explanatory variable.\u003c/p\u003e \u003cp\u003eIncluded explanatory variables in Eq.\u0026nbsp;1 were completeness score for each journey component, demographic factors, facility characteristics, active health insurance covering costs of care and co-morbidities and baseline systolic blood pressure values. The linear random effects model was chosen as it allowed to incorporate multiple measurements per individual in the dataset, even though the timing and frequency of these measurements varied per subject.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCost-estimates and prevalence of complications\u003c/h2\u003e \u003cp\u003eThe estimates from the random effects model and the calculated costs of journeys were used to model the investment needed to reach lower blood pressures if all patients were to access complete care journeys. The yearly costs of complications were based on estimates reported in studies describing the costs of care for CVDs in Nigeria and Western Sub-Saharan Africa (Aminde et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Iseko1 et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nyassinde et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rosendaal et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Complications considered in the calculations were Myocardial Infarctions (MI) and stroke, as these are the most commonly reported CVDs in this population (Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For each patient the expected reduction in risk of complications was modeled with 3 different methods to ensure robustness: Globorisk office score, Globorisk laboratory score \u003cem\u003e(\u003c/em\u003eRisk Charts | Globorisk; Ueda et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and with the age-specific hazard ratio's (HR) associated with each 5 mmHg reduction in SBP as estimated in a meta-analysis by Rahimi et al (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Rahimi model). In the Globorisk score approaches, the expected risk was first calculated using the SBP derived from a regression analysis of the measured SBPs at each visit in the dataset. Then, the expected reduced SBP with a complete patient journey was calculated based on the statistically significant regression coefficients from the random effects model for each journey element and the individual's level of completeness for that journey element. For the Globorisk office score, body-mass index (BMI) and smoking status was estimated or imputed, as BMI was available for only 13 patients (1%) in the dataset and smoking status was not recorded. For 621 patients (49.7%) weight was recorded and BMI was estimated based on the average height for males and females in Nigeria (Adebayo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). If weight was not recorded, BMI was imputed based on the average BMI of the gender and age-category of the patient (Akarolo-Anthony et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Smoking was imputed by randomly generating a 0 (no smoking) or 1 (smoking) with a chance of 10.4% to get a 1 generated, based on the smoking prevalence in Nigeria (Adeloye et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For the Globorisk laboratory score the total cholesterol value was available in the data for 32 patients (2.6%). For all other patients a value of 3 mmol/L was used. In the Rahimi model the age-corrected hazard ratio and expected reduction in SBP due to complete journeys were used to calculate the risk reduction using the following equation:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Risk\\:reduction=\\:Hazard\\:Ratio^\\left(\\frac{SBP\\:reduction}{5}\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWith these risk-reduction estimates, the costs of averted complications through improved hypertension management were calculated. Subsequently, the expected cost investment per saved life year was calculated for each of the risk modeling approaches using Eq.\u0026nbsp;3:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\:Expected\\:cost\\:investment\\:per\\:saved\\:life\\:year=$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\:\\frac{investment\\:gap\\:for\\:complete\\:journeys-cost\\:of\\:prevented\\:complications}{saved\\:life\\:years}\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEthical and data-privacy considerations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003efor the collection and analysis of this data was obtained through Lagos State University College of Medicine (LASUCOM) institutional review board (Approval Number: LREC/06/10/1342) and Boston University Medical Campus Institutional Review Board (IRB number: H-39941). The healthcare facility survey instrument for this study contained a consent statement outlining the methods of data analysis and ensuring the confidentiality of the health facilities during reporting. This statement was verbally communicated to the facility managers and data collection commenced only after the facility manager gave consent. For retrospective medical chart data collection, explicit informed consent could not be obtained from patients due to logistical difficulties in tracing them back from the year 2019 onwards. Prior to analysis, data were fully anonymized for privacy reasons. Regarding the data collection of hypertension medical costs of healthcare facilities, a formal letter was sent to each facility and consent obtained from the facilities prior to the collection of price data. Data was available to the researchers in anonymized format only through a password-protected database. Data was analyzed in Stata 16.1 on a 2-factor protected computer network.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFacility characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides the characteristics of the 74 included health facilities included in this study, of which 68% were private facilities and 32% public facilities. Among all visits, 39% were by insured patients, who more frequently utilized public facilities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFacility characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNumber of facilities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e% insured visits\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003e74\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e1249\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e39%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOwnership\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLevel of care\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLASHMA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLASHMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLASHMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-LASHMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-LASHMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLASHMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLASHMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-LASHMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-LASHMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Total number of facilities and patients is less than sum of all rows, as certain facilities provide both primary and secondary care services and are thus reported in multiple rows.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eThrough the medical records of the 74 included health facilities, data was collected for 1,249 patients with hypertension. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides an overview of the demographic and comorbidity characteristics stratified by uncontrolled and controlled values at baseline. The majority (82%) had uncontrolled BP-values at the first recorded visit in the dataset and more than a quarter had crisis-level BP-values (SBP\u0026thinsp;\u0026gt;\u0026thinsp;180 mmHg or DBP\u0026thinsp;\u0026gt;\u0026thinsp;110 mmHg) recorded during the first included visit (28%). Eighty percent of patients were aged below 65 years and 40% were under the age of 50 years. Patients with controlled baseline BP levels were more likely to visit primary care (44% versus 36%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and public facilities (51% versus 43%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and were mostly insured (47% versus 25%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline patient characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e \u003cp\u003eUncontrolled at baseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c15\" namest=\"c10\"\u003e \u003cp\u003eControlled at baseline\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of patients (%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNumber of patients (%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eAverage SBP at baseline (SD)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eAverage DBP at baseline (SD)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cem\u003eNumber of patients (%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u003cem\u003eAverage SBP at baseline (SD)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e\u003cem\u003eAverage DBP at baseline (SD)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1,249 (100%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1,018 (82%\u003c/em\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e163 (23)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003e99 (16)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cem\u003e196 (16%\u003c/em\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u003cem\u003e123 (10)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e\u003cem\u003e77 (7)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLASHMA empaneled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwnership*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of care*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance level*\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMostly uninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMostly insured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins prescribed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline BP severity*\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMissing\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrisis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e \u003cem\u003ePercentage indicated is percentage of all patients. Controlled and uncontrolled does not add up to 100% as there are 35 patients with missing BP baseline data.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eInsurance status was defined as mostly insured if 50% or more of visits were labeled as insured.\u003c/em\u003e \u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eSeverity of baseline BP levels was defined as normal/pre-hypertension: SBP\u0026thinsp;\u0026lt;\u0026thinsp;140 mmHg, DBP\u0026thinsp;\u0026lt;\u0026thinsp;90 mmHg; grade 1: SBP 140\u0026ndash;159 mmHg, DBP 90\u0026ndash;99 mmHg; grade 2: SBP 160\u0026ndash;179 mmHg, DBP 100\u0026ndash;109 mmHg; crisis: SBP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;180 mmHg, DBP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;110 mmHg).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for chi-square test comparing distribution among controlled and uncontrolled group\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eJourney costs, treatment gaps and extrapolation\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides an overview of estimated costs of the ideal hypertension care journey according to guidelines (reflected in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), based on the collected price of care activities per type of clinic. The average cost of a complete journey is estimated at USD 148 per patient per year. In all facility types, costs of medications are the largest driver accounting for 70% of total journey costs. Costs per type of clinic vary from USD 72 per patient per year in public primary care facilities to more than 3-fold costs in private primary care facilities. Private sector costs are on average three times higher than in public sector facilities. No obvious costs differences were observed between clinics empaneled to LASHMA and those that were not.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExpected journey costs per patient per year if all recommended care activities would have been performed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDescriptives\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAverage costs of complete journey per patient per year in 2023 USD\u003c/em\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eN facilities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eN patients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTotal journey\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eConsultations \u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(% full journey)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eCounseling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eMedications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eLabworks\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e104 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30 (20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOwnership\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLevel of care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e168 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e146 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36 (18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22 (30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22 (30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eAverage costs are in USD based on Central Bank of Nigeria exchange rate of last business day of April 2023\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eTotal number of facilities and patients is less than sum of all rows, as certain facilities provide both primary and secondary care services and are thus reported in multiple rows\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, we show the estimated annual investment per insured and uninsured patient, calculated by subtracting the actual journey costs from the costs of a complete journey. On average 81% of complete journey costs are not provided leading to an investment gap of USD 120 per patient per year. Uninsured patients have an average investment gap of 85% of the journey costs versus 68% in insured patients. The smallest relative investment gap is seen in insured patients in private, secondary care level clinics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage estimated annual investment in USD per patient stratified per facility and insurance characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull journey costs in USD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEstimated investment in USD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e% of full journey cost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eConsultations (% total gap)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCounseling\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMedications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLab costs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOwnership\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel of care\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal uninsured\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-8 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-105 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-28 (20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-16 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-168 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-31 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-13 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-145 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-32 (17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-49 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-22 (31%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-48 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-23 (32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal insured\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (-4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-11 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-44 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-17 (25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 (-6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-27 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-66 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-21 (19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (-5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-19 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-58 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-27 (27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (-1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-29 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-8 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (-2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-25 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-7 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal all patients and clinics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-9 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-87 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-25 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal private\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-15 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-123 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-30 (18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal public\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-42 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-18 (29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNotes: Average costs are in USD based on Central Bank of Nigeria exchange rate of last business day of April 2023; Insurance status was defined as insured if 50% or more of visits were labeled as insured\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eExtrapolation of investments to Lagos State population level\u003c/h2\u003e \u003cp\u003eBased on a hypertension prevalence rate of 33% in the adult urban and semi-urban population and a current treatment rate of 25% in Nigeria (Adeloye et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), there is an estimated required investment of 104\u0026nbsp;million USD per year for treatment of patients with hypertension in Lagos State. If the entire Nigerian population with hypertension were to be treated the required investment increases to 489\u0026nbsp;million USD. This includes the investments for those currently treated and the full journey costs for those not treated. For this extrapolation we assumed the data sample is representative of types and frequency of facilities visited by Lagos State population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eBlood pressures over time\u003c/h2\u003e \u003cp\u003eA clinically and statistically significant decrease in blood pressure was observed over time. The average SBP dropped from 158 mmHg (SD\u0026thinsp;=\u0026thinsp;26) to 145 mmHg (SD\u0026thinsp;=\u0026thinsp;23) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and DBP from 96 mmHg (SD\u0026thinsp;=\u0026thinsp;17) to 89 mmHg (SD 16) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of patients with crisis or grade 2 hypertension also decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, with the largest SBP reductions seen in those initially at crisis levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, patients with normal blood pressure or pre-hypertension at baseline experienced slight increases, reflecting disease progression without full care.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of improved blood pressure over time\u003c/h2\u003e \u003cp\u003eWe estimated the relation of demographic variables, clinic and health system variables, journey completeness and disease severity and comorbidity to SBP with a linear random effect model (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). No significant effects of either demographic or clinic/health system variables were found. However, there was a significant positive correlation between completeness of consultation and medication elements and degree of SBP reduction, with adherence to guidelines for these aspects each contributing to a 5\u0026ndash;6 mmHg reduction in blood pressure. Other factors significantly correlated to higher SBP over time were hypertension severity measured as value of the first SBP measurement and comorbidity measured as use of statins (borderline significant, p\u0026thinsp;=\u0026thinsp;0.078). Overall R\u003csup\u003e2\u003c/sup\u003e of the model was 0.30 and between R\u003csup\u003e2\u003c/sup\u003e 0.53 indicating a good model fit.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear random effects model exploring effects of hypertension care journey completeness on development of systolic blood pressure over time\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFacility characteristics \u0026amp; insurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacility type (private)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLashma (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enot-LSHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel (primary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance (no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCompleteness Hypertension journey elements\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounseling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsultations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.906***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory works\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.055***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity and hypertension severity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of anti-HT medications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin use (no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.494*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline SBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.45***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.76***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSD dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall r-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNumber of obs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1409.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared within\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eR-squared between\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1; *** p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0005\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEstimated risk reduction of complications and avoided loss of life years by investing in complete hypertension journeys\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAn overview of estimated risk reductions and associated avoided loss of life years and costs is provided in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The Globorisk score in both the office and laboratory version yielded comparable results, with estimated average risk reductions of 5.93% and 5.63% respectively. The final estimates were derived by averaging these two reductions, being 5.78%. When using the age-based hazard ratios per 5 mmHg reduction in SBP from the modeling of Rahimi et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the average reduction in risk of developing cardiovascular complications was 13.8%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated risk reduction for developing cardiovascular disease and associated averted events, life years saved and costs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGloborisk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRahimi\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated 10-year CVD-risk reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverted cardiovascular events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife years saved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22,880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54,557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost per averted event\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e 101,078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e 40,536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost per saved life year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e 12,772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e 5,122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eCVD\u0026thinsp;=\u0026thinsp;cardiovascular disease\u003c/h2\u003e \u003cp\u003eWe estimated the average investment gap to complete both the medication and consultation elements of patient journeys at 87 USD per patient per year (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). We calculated the potential life years saved through risk reduction due to complete patient care journeys, based on the 2019 Burden of Disease study which estimated the years of life lost due to cardiovascular complications in Nigeria in 2040 (Foreman et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).. We assumed 15% of the total burden could be allocated to Lagos State, based on the proportion of individuals and the increased risk in urban populations. Using the average of the Globorisk model reductions we estimated 22,880 life years to be saved yearly in Lagos State with complete journeys, versus 54,557 life years saved according to the Rahimi model. We calculated the costs of avoided complications based on the average initial risk from the laboratory and office Globorisk score as applied to our dataset (14%) and the expected risk reductions. With a population of 21,000,000 in Lagos State, of whom 50% are adults and a hypertension prevalence of 33% we estimated 2,891 (Globorisk) and 6,894 (Rahimi) less cardiovascular events yearly with complete journeys. Based on various publications of costs of stroke and ischemic heart disease in Nigeria and surrounding countries (Aminde et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Iseko1 et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nyassinde et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rosendaal et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), we estimated the average yearly costs of treatment of complications at ~\u0026thinsp;1,000 USD, with all cost estimates translated to 2023 USD, based on an average yearly inflation rate of 2.45% from 2008\u0026ndash;2023 (World Bank, 2024). We estimated the median survival rate with complications at 3 years (Akinyemi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This leads to an expected 9,3\u0026nbsp;million USD (Globorisk) or 22,2\u0026nbsp;million USD (Rahimi) in savings on costs of complications when investing in complete consultation and medication journeys. The required investment in complete journeys was estimated at 301\u0026nbsp;million USD based on the before mentioned population of Lagos, urban prevalence of hypertension and estimated 87 USD per patient per year investment gap in consultation and medication elements of the care journeys (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Based on these assumptions the estimated yearly costs per saved life year with complete medication and consultation journeys ranged from 5,122 USD (Rahimi model) to 12,772 USD (Globorisk model).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored the required investments to close the treatment gap based on real-world medical record and cost data in Lagos State, Nigeria. We identified an average investment gap of 120 USD per patient per year, with the medication costs being the main cost driver. Importantly, receiving of prescribed required antihypertensive medications and consultations were significantly correlated with lower SBP at endline. If investments to complete care journeys were made for all hypertension patients in Lagos State, we expect a yearly investment of ~\u0026thinsp;300\u0026nbsp;million USD to be necessary, which translates to an investment per saved life year of ~\u0026thinsp;5,000 to 13,000 USD.\u003c/p\u003e \u003cp\u003eWe used real-world medical record data and costs data of almost 1,250 hypertensive patients from 74 clinics in Lagos State Nigeria. The average cost of a patient journey according to guidelines was found to be 148 USD per patient per year, with medication costs constituting 70% of those costs. There was significant cost variation between clinics, with estimated total journey costs being two to three times higher in the private sector compared to the public sector. When comparing actual care activities to guideline recommendations, 81% of the costs of a complete journey were not provided. Uninsured patients face an even higher cost gap of 85% versus 68% for insured patients. Despite these gaps, a significant reduction of 13 mmHg in average SBP was observed over time, particularly in the crisis category of hypertension patients. This is an impactful reduction of blood pressure, correlating to over 20% reduction in risk of complications (Rahimi et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). After adjusting for demographic and clinic/health system variables, receiving both consultations according to protocol and complete yearly set of medications as prescribed were significantly correlated with an extra 5\u0026ndash;6 mmHg reduction in blood pressure for each variable. These two variables are somewhat correlated, as a low number of consultations means medications cannot be picked up as prescribed. These reductions in SBP are estimated to yield an additional 6\u0026ndash;14% reduction in risk of cardiovascular complications.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eContext and considerations\u003c/h2\u003e \u003cp\u003eThe estimated yearly costs per saved life year are somewhat comparable to a previous study estimating cost-effectiveness of a hypertension screening and treatment model in Kwara State, a more rural area in Nigeria, where they estimated up to 7,815 USD per disability adjusted life year (DALY) averted in 2012 USD, which translates to ~\u0026thinsp;10,000 in 2023 USD (Rosendaal et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These results are not one-on-one comparable as they are based on DALY instead of YLL but do give an indication of a comparable range. One other study modelled likelihoods of cost-effectiveness of different anti-hypertensive drugs in Nigeria, depending on willingness to pay thresholds ranging from 1,300 to 16,000 USD per DALY, showing thiazide diuretics to be most cost-effective (Ekwunife et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This modelling yields a very different outcome than costs per averted LYY or DALY and is therefore difficult to compare. To our knowledge, no studies have estimated the cost-effectiveness of hypertension interventions in Nigeria, despite the critical need for such data. This information is invaluable for policy makers to make informed decisions on resource allocation. There are, however, several other studies from SSA, which show highly variable results (Ngalesoni et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Robberstad et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) ranging from ~\u0026thinsp;200 USD for hypertension treatment in Ethiopia (Tolla et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to ~\u0026thinsp;8,000 USD for calcium channel blocker based treatment in Ghana (Gad et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and ~\u0026thinsp;17,000 USD for treatment of general population with hypertension in South Africa (Gaziano et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe found investments of ~\u0026thinsp;5,000\u0026ndash;13,000 USD per saved life year can be put in perspective by comparing to GDP per capita. In 2023, the GDP per capita was 2,162 USD for Nigeria (World Bank, 2023) and approximately 4,000 USD for Lagos State (World Bank, 2023). Our findings show that the necessary investments may be difficult to implement in the short to medium term. Generally, acceptable costs per saved life year are considered to range from 60\u0026ndash;140% of GDP per capita (Yanovskiy et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Even if the investment required were closer to this acceptable range, the large number of patients needing treatment may make it even less likely to be a feasible investment.\u003c/p\u003e \u003cp\u003eHowever, several remarks can be made about both the required investment and the potential benefits of such an investment. Regarding the benefits, investing in chronic care could lead to cost savings not only by preventing complications but also by increasing productivity (WHO, 2018), a factor not accounted for in this study. Additionally, other hypertension complications such as kidney failure, which has high associated costs and is also partially associated with hypertension (Ruilope \u0026amp; Bakris, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), was not included in the analysis and will further increase the estimated saved costs on complications. Interventions are generally more cost-effective for groups with high absolute risk of complications (Gad et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gaziano et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Orteg\u0026oacute;n et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Targeting high-risk groups with more extensive hypertension treatment interventions can be a strategy to increase the benefits of investment.\u003c/p\u003e \u003cp\u003eRegarding the required increased investments there is considerable room for improvement to achieve more efficient spending of available resources. There are substantial differences in the costs of care between various clinics, including within the private sector (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). While some of this variation is justified by quality differences, we expect these differences are too high to be fully explained by variation in quality alone but this is speculative. Improvements in the health procurement system could contribute significantly to reducing costs and enhancing the affordability of chronic care. A more efficient procurement system can lead to more realistic and consistent healthcare costs. Inconsistent pricing, particularly for medications which take up 70% of the costs of care, is a major challenge in Nigeria. Centralized procurement and bulk purchasing of quality assured generics can lower costs by securing better prices from suppliers. By standardizing procurement processes and negotiating as a collective, health facilities and insurers can reduce the unit costs of medications and medical supplies. Investment in local production capacity of quality generics could also highly increase access to low-cost medications in Nigeria and surrounding countries (Hanson et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother aspect to consider is the integration of technology in the procurement process. Digital platforms for procurement can streamline operations, improve accountability, and reduce the time and cost associated with manual processes. Such systems can also provide real-time data on inventory levels, helping to avoid shortages and overstock situations. Additionally, transparent procurement processes can minimize corruption and ensure that funds are used effectively (WHO, 2019).\u003c/p\u003e \u003cp\u003eIn addition, group-based care and purchasing and implementing digital approaches such as telemedicine and digital coaching can also offer significant advantages for managing chronic conditions like hypertension (WHO \u0026amp; ITU, 2024; Sanya et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Group care involves treating patients with similar conditions together, providing them with education, support, and shared medical appointments and group-based support sessions. This approach has several benefits. Patients in group settings can share experiences and support each other, which can improve adherence to treatment regimens and lifestyle changes. Group consultations allow healthcare providers to see more patients in less time, reducing the burden on individual consultations and making better use of healthcare resources. Group purchasing of medications and supplies can lead to significant cost reductions. By buying in bulk, patient groups can secure lower prices, reducing the overall cost burden on individuals and the healthcare system. Patients receiving care in groups often have better health outcomes due to increased engagement, peer support, and more consistent follow-ups (Otieno, Agyemang, Wao, et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). All these considerations will be far more effective when coinciding with an increase in insurance coverage with affordable packages that include these group-based and technology driven approaches to care. This provides patients with reliable and affordable access to care and insurers with sufficient purchasing power to manage costs. There are several ongoing mobile health and telemedicine initiatives and the National Hypertension Control Initiative in Nigeria that can be built on to more widely implement effective hypertension management strategies (Ogungbe et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Stokes et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eStrengths \u0026amp; limitations\u003c/h2\u003e \u003cp\u003eThis study presents unique, highly detailed healthcare utilization data from Nigeria across a representative sample of diverse types of clinics. To our knowledge, there is only limited data available from this region that provides insight into health care utilization, care journeys and outcomes at an individual patient level. The opportunity to combine health outcome data with cost data allows for a unique analysis of the cost-effectiveness of hypertension care based on real-world data. Also, pricing information across different tiers of clinics shows the effect size of cost differences across the entire health system.\u003c/p\u003e \u003cp\u003eThere are several limitations to this study. Pricing fluctuates highly, especially for medications, which comprise approximately 70% of total costs of hypertension care. This study provides a point-estimate based on a price assessment at one moment in time, which may not capture ongoing price volatility. Real inflation versus economic inflation of the Naira compared to USD makes translation of prices in Naira to USD unreliable, though it was done consistently across the collected data and based on the inflation rate at the time of collection of price data. We do want to note that we linked pricing information collected in 2023 to health care utilization collected in 2019. As price fluctuations probably also affect health care utilization, there might be a discrepancy there. However, we still believe the fact that our analysis is based on real-world utilization data is of high added value to existing studies that mainly use modeling approaches.\u003c/p\u003e \u003cp\u003eKey patient data, such as BMI and other comorbidities, were widely missing from the dataset, preventing a more comprehensive analysis. Our dataset also lacked information on patients' health insurance types, limiting our ability to investigate the investment gap specifically among those with social health insurance. Future studies could benefit from including this analysis. Furthermore, healthcare investments in a limited resource setting should be considered holistically, not just focusing on one risk factor and its complications (Baltussen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our study only incorporated data on hypertension, a specific but widespread health issue, and investment versus prevention of complications was calculated in this limited context. We also included only provider costs, while other investments \u0026ndash; and return on investments \u0026ndash; go beyond the provider perspective and have further consequences on the societal and patient level. Additionally, this analysis assumes that by paying for care as needed through a healthcare payment system, healthcare providers will be able to invest in quality improvements necessary to provide this care without additional investments. Lastly, we concluded the analyses with an extrapolation to Lagos State, but generalizations should be made cautiously, as certain population segments may not be fully represented in our sample.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies important gaps in hypertension care costs as actually delivered versus what should be delivered according to national and international guidelines. It quantifies the investment needed in optimizing hypertension care journeys to yield significant improvements in blood pressure levels, associated with a reduction in complications and their associated costs. These investments required per saved life year are currently far outside an acceptable range when comparing to the GDP of Lagos State, Nigeria. To increase feasibility of investing in chronic care in the shorter term, more efficiency and effectiveness of hypertension management is needed, as well as the creation of innovative and affordable insurance models. By addressing high medication costs, the considerable cost-variation between clinics and by leveraging technology and group-based purchasing and care models, substantial savings and health benefits can be achieved. These strategies, alongside a robust procurement system, can help make hypertension care more affordable and effective, ultimately improving health outcomes and reducing the economic burden of chronic disease management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicting interests\u003c/h2\u003e \u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eTrW, GG, and CD initiated and conceptualized the study. BB and ID led the development and execution of data collection related to the medical records data, while GG and CD designed and supervised the collection of the cost data. JvA was responsible for the core analysis approach of this study. AB contributed to the data analysis. AB, MI, TrW, GG, CD and JvA were involved in interpretating the findings. CD and JvA drafted the initial manuscript. EZ provided crucial context-specific information for the paper. All authors reviewed and approved the final manuscript for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe gratefully acknowledge the funding of PharmAccess by the Netherlands Ministry of Foreign Affairs. We also thank all participating healthcare providers and field enumerators in Lagos.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe data is available at reasonable requests by the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbout Lagos. (n.d.). \u003cem\u003eLands Bureau\u003c/em\u003e - \u003cem\u003eLagos State Government\u003c/em\u003e. Retrieved July 26, 2024, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://landsbureau.lagosstate.gov.ng/about-lagos/\u003c/span\u003e\u003cspan address=\"https://landsbureau.lagosstate.gov.ng/about-lagos/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdebayo, R. A., Balogun, M. O., Adedoyin, R. A., Obashoro-John, O. A., Bisiriyu, L. A., \u0026amp; Abiodun, O. O. (2014). 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Retrieved June 11, 2024, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org\u003c/span\u003e\u003cspan address=\"https://data.worldbank.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. (n.d.). \u003cem\u003eNon communicable diseases Key Facts\u003c/em\u003e. World Health Organization. Retrieved August 23, 2018, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases\u003c/span\u003e\u003cspan address=\"http://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYanovskiy, M., Levy, O. N., Shaki, Y. Y., Zigdon, A., \u0026amp; Socol, Y. (2022). Cost-Effectiveness Threshold for Healthcare: Justification and Quantification. \u003cem\u003eInquiry: A Journal of Medical Care Organization, Provision and Financing\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e, 00469580221081438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/00469580221081438\u003c/span\u003e\u003cspan address=\"10.1177/00469580221081438\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hypertension, Non-Communicable Diseases, Cost-Effectiveness, Health Financing, Sub-Saharan Africa","lastPublishedDoi":"10.21203/rs.3.rs-5182058/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5182058/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLow- and middle-income countries (LMICs) house 66% of all hypertension patients, many of whom are undertreated, leading to severe health risks and higher healthcare costs. This study examined the required investments to improve hypertension control in Lagos, Nigeria, using real-world medical records and cost data.\u003c/p\u003e \u003cp\u003eWe found that both adherence to consultations and medications according to guidelines was significantly associated with reduction of a 5\u0026ndash;6 mmHg in systolic blood pressure. These reductions correspond to a 6\u0026ndash;14% decrease in cardiovascular complication risk and would require an average annual investment of USD 120 per patient. The medication costs being the main cost driver. Statewide, providing complete care for all hypertension patients would require an annual investment of \u003cspan\u003e$\u003c/span\u003e300\u0026nbsp;million, or \u003cspan\u003e$\u003c/span\u003e5,000 to \u003cspan\u003e$\u003c/span\u003e13,000 per saved life year.\u003c/p\u003e \u003cp\u003eThe identified required investments are currently far outside an acceptable range when comparing to the GDP of Lagos State, Nigeria. To make chronic care investments feasible, hypertension management must become more efficient, including reducing high medication costs through bulk purchasing, adopting innovative, group based blended care models, and increasing health insurance coverage.\u003c/p\u003e","manuscriptTitle":"Investing in Hypertension Care in Lagos, Nigeria: Quantifying the Costs to Close the Treatment Gap based on Real-World Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-27 18:44:56","doi":"10.21203/rs.3.rs-5182058/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1884a3d-7726-474f-85b2-db567bce4284","owner":[],"postedDate":"November 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":38835761,"name":"Health sciences/Health care/Health care economics"},{"id":38835762,"name":"Health sciences/Health care/Health policy"}],"tags":[],"updatedAt":"2025-10-06T10:11:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-27 18:44:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5182058","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5182058","identity":"rs-5182058","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}