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. 2020 Mar 21;10(1):47–52. doi: 10.5588/pha.19.0068

Effects of real-time electronic data entry on HIV programme data quality in Lusaka, Zambia

K Moomba 1, A Williams 2, T Savory 1,✉, M Lumpa 1, P Chilembo 1, H Tweya 3, A D Harries 4,5, M Herce 1,6
PMCID: PMC7181358  PMID: 32368524

Abstract

Setting:

Human immunodeficiency virus (HIV) clinics in five hospitals and five health centres in Lusaka, Zambia, which transitioned from daily entry of paper-based data records to an electronic medical record (EMR) system by dedicated data staff (Electronic-Last) to direct real-time data entry into the EMR by frontline health workers (Electronic-First).

Objective:

To compare completeness and accuracy of key HIV-related variables before and after transition of data entry from Electronic-Last to Electronic-First.

Design:

Comparative cross-sectional study using existing secondary data.

Results:

Registration data (e.g., date of birth) was 100% complete and pharmacy data (e.g., antiretroviral therapy regimen) was <90% complete under both approaches. Completeness of anthropometric and vital sign data was <75% across all facilities under Electronic-Last, and this worsened after Electronic-First. Completeness of TB screening and World Health Organization clinical staging data was also <75%, but improved with Electronic-First. Data entry errors for registration and clinical consultations decreased under Electronic-First, but errors increased for all anthropometric and vital sign variables. Patterns were similar in hospitals and health centres.

Conclusion:

With the notable exception of clinical consultation data, data completeness and accuracy did not improve after transitioning from Electronic-Last to Electronic-First. For anthropometric and vital sign variables, completeness and accuracy decreased. Quality improvement interventions are needed to improve Electronic-First implementation.

Keywords: anthropometry, EMR, data quality, HIV, SORT IT

Global antiretroviral therapy (ART) scale-up in response to the human immunodeficiency virus (HIV) pandemic has enabled 67% of people living with HIV (PLHIV) in Eastern and Southern Africa to now access treatment.1 In Zambia, approximately 960 000 PLHIV are currently accessing ART out of an estimated 1 200 000 PLHIV nationally.2 The Centre for Infectious Disease Research in Zambia (CIDRZ) is a key partner in Zambia’s national ART scale-up, and supports HIV-related service delivery in two provinces, including Lusaka, the capital city (estimated population: 2.6 million).3

The Zambian public health system consists of three levels of care—first level (health posts, health centres and district hospitals), second level (provincial and general hospitals) and tertiary level (central and teaching hospitals). ART services are offered free of charge at all levels and in all public health facilities. As national ART scale-up has accelerated since 2002,4 and the number of patients on treatment has grown, challenges have emerged with the management of ART programme data. Initially, paper-based registers and treatment cards were used to monitor PLHIV on ART, but as the number of patients and the variety of service delivery outlets grew, it became increasingly difficult to maintain completeness and accuracy of data with these monitoring tools.5 Reliable medical records data are critical for good clinical practice, programme management, and decision making.6

Electronic medical records (EMRs), first developed in the early 1970s,7 facilitate the collection of complete, accurate, and timely data. EMR systems have the potential to improve the quality of patient care, reduce the workload for healthcare workers, strengthen the monitoring and evaluation of health programmes and provide information for decision-making.8–11 However, realizing this potential depends on the completeness and quality of data entered into the EMR, with the possibility that health facilities can misreport performance on key programme indicators.12

The EMR system used throughout Zambia is the SmartCare system, a Windows-based platform developed by the Zambian Ministry of Health (MoH) in collaboration with the Centers for Disease Control and Prevention (CDC). The SmartCare EMR was first introduced in 2004 to capture individual-level HIV patient data and programme indicators, but was later expanded to include other health facility services (e.g., outpatient care, laboratory testing, etc.). The system has become an integral part of the Zambian ART programme, serving as a clinical information management system to promote care continuity, and is vital to national and PEPFAR (President’s Emergency Fund for AIDS Relief) monitoring and evaluation.

The SmartCare system has evolved over time. A paper-based patient file was part of the initial implementation of SmartCare, with data clerks entering all information from the paper-based file into the EMR at the end of the clinic day: this was called Electronic-Last. In 2015, the Zambian MoH introduced a direct entry system for SmartCare, with healthcare workers entering the data in real-time over a local area network: this was called Electronic-First. CIDRZ has been an implementing partner for SmartCare roll-out since the system’s inception. Following this shift from retrospective to real-time data entry, vital questions have arisen about the impact of this new methodology upon the completeness and quality of data entered in the SmartCare EMR.

The aim of the present study was to understand the effects of SmartCare transition to Electronic-First by comparing data completeness and accuracy for key HIV-related variables following change in data entry methodology from Electronic-Last to Electronic-First in selected high-volume facilities in Lusaka, Zambia, over the period of SmartCare transition (2017 and 2019).

METHODS

Study design

We conducted a comparative cross-sectional study using existing secondary data.

Setting and study sites

The study was conducted in Zambia, a landlocked country in southern Africa with a population of approximately 13 million.13 In this population, HIV prevalence is estimated at 12.3% nationally and 16.1% in Lusaka.2 Over 90% of the population is serviced by primary healthcare facilities, with higher-level hospitals such as district, central, and teaching hospitals receiving referrals and providing curative and specialized health services.13

The study included HIV clinics within health facilities from Lusaka Urban District, Zambia, with real-time SmartCare data entry, which had been implemented for at least 6 months between 2017 and 2019, and had between 5000 and 12 000 patients registered to receive ART. Five first-level hospitals and five health centres met the inclusion criteria.

Data collection, variables and analysis

Data entered retrospectively into the SmartCare system was defined as Electronic-Last, while real-time data entry by healthcare workers was defined as Electronic-First. We selected key programme variables for our analysis based on 1) their utility for providing direct clinical care; and 2) their high frequency of entry to ensure a large sample size of observations representative of data in the SmartCare EMR (Table 1). We extracted de-identified patient data for these variables from the last 6 months of data entry for Electronic-Last and the first 6 months of entry for Electronic-First using an SQL script from the main SmartCare EMR server, and assessed these for completeness and accuracy. Completeness was determined by confirming whether data for each of the required variable fields had been checked or entered into the electronic patient file. Data accuracy was evaluated through logic checks and assessment of whether there were outliers from normal accepted ranges. Frequencies and proportions of completeness and errors were calculated for each variable during the periods of interest for Electronic-Last and Electronic-First implementation. Variables for each model and between facilities and service delivery areas were compared by calculating proportions. All analyses were conducted in Microsoft Excel (Microsoft; Redmond, WA, USA) and STATA v15 (Stata Corp, College Station, TX, USA).

TABLE 1.

Variables assessed from available SmartCare EMR data by service delivery area, Lusaka, Zambia, 2017–2019

Registration Anthropometry and vitals Clinical consultation Pharmacy
Patient ID (de-identified) Height Clinical appointment dates ART regimen
ART number Weight TB screening Next appointment date
Date of birth Temperature WHO staging Dispensations
Enrolment date Pulse ART regimen components
Appointment date Respiratory rate
Blood pressure
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EMR = electronic medical record; ART = antiretroviral therapy; TB = tuberculosis; WHO = World Health Organization.

Ethics approval

Ethical approval was granted by the University of Zambia Biomedical Research Ethics Committee (UNZA-228/2019), the National Health Research Authority, the Lusaka District Health Office and the Ethics Advisory Group of the International Union Against Tuberculosis and Lung Disease, Paris, France (EAG 17/19).

RESULTS

Completeness of data

Completeness of data in the transition from Electronic Last to Electronic First is shown aggregated for all 10 health facilities in Table 2.

TABLE 2.

Entry completeness for key HIV-related variables during 6 months of Electronic-Last and 6 months of Electronic-First in 10 facilities between 2017 and 2019 in Lusaka urban district, Zambia

Category and type of HIV-related variables Assessment of the variables All facilities Hospitals Health centres
Electronic-Last % Electronic-First % % difference Electronic-Last % Electronic-First % % difference Electronic-Last % Electronic-First % % difference
Registration (IHAP), n
 10597 13355 6783 9003 4927 6128
 Patient ID Number % completeness 100 100 0 100 100 0 100 100 0
 ART number 100 100 0 100 100 0 100 100 0
 Date of birth 100 100 0 100 100 0 100 100 0
 Enrolment date 100 100 0 100 100 0 100 100 0
 Appointment date 100 100 0 100 100 0 100 100 0
Anthropometry and vitals, n 116228 175998 74767 99319 41521 76679
 Height % completeness 53 41 −12 55 46 −9 48 35 −18
 Weight 69 65 −4 64 66 +2 77 64 −13
 Temperature 51 35 −16 49 31 −17 45 41 −14
 Pulse 38 37 −1 40 40 0 32 33 +1
 Respiratory rate 28 17 −11 28 15 −13 27 19 −8
 Blood pressure 45 42 −3 44 47 +3 48 37 −11
Clinical consultation, n 63106 86452 39653 52711 23453 33741
 Clinical appointment dates % completeness 100 100 100 100 100 100 0
 TB screening 42 57 +15 49 58 +9 30 58 +28
 WHO staging 73 80 +7 72 80 +8 74 80 +6
Pharmacy 139820 184581 57254 97646 77403 86935
 ART regimen % completeness 95 93 −2 94 92 −2 95 95 0
 Next appointment date 99 99 0 99 99 0 99 99 0
Dispensations, n 174636 338964 80378 186344 94258 152620
 Quantity, frequency and dosage 99 99 0 99 99 0 99 99 0
 ART regimen components 95 97 +2 95 96 +1 94 98 +4
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HIV = human immunodeficiency virus; IHAP = initial history and physical examination; ART = antiretroviral therapy; TB = tuberculosis; WHO = World Health Organization.

At the point of registration, data (patient identifier, ART number, date of birth, date of enrolment and scheduled appointment date) completeness was 100% in Electronic Last and Electronic First at all health facilities.

For anthropometric and vital signs, overall completeness ranged from 28% to 69% with Electronic Last and from 17% to 65% with Electronic First, with decreases in completeness for every variable as Electronic Last transitioned to Electronic First. At the hospital level, there was a similar pattern in findings, except that completeness for weight increased during transition from Electronic Last to Electronic First. At the health centre level, there was a similar pattern in findings, with decreases in completeness of variables from Electronic Last to Electronic First.

With regard to data on clinical consultation (clinical appointment dates, TB screening and WHO clinical staging), overall completeness ranged from 42% to 100% with Electronic Last and 57% to 100% with Electronic First. Clinical appointment dates were always at 100%, but completeness for TB screening and WHO staging increased from Electronic Last to Electronic First. This pattern was similar in hospitals and health centres.

For pharmacy visits and dispensations, overall completeness ranged from 95% to 99% with Electronic Last and from 93% to 99% with Electronic First. Generally, the changes from Electronic Last to Electronic First were small and varied from being more to less complete, and this pattern was found in both hospitals and the health centres.

Errors of data entry

Errors of data entry in the transition from Electronic Last to Electronic First are shown for all 10 health facilities in Table 3. At the point of registration (ART number and scheduled appointment date), errors ranged from 2% to 39% with Electronic-Last and from 1% to 21% with Electronic-First, with errors decreasing from Electronic-Last to Electronic-First. A similar pattern was observed for hospitals and health centres. For anthropometric and vital signs, errors ranged from 33% to 76% with Electronic-Last and from 36% to 87% with Electronic-First, with errors all increasing from Electronic-Last to Electronic-First. A similar pattern was observed for hospitals and health centres.

TABLE 3.

Entry errors for selected key HIV-related variables based on specific logic rule sets during 6 months of Electronic-Last and 6 months of Electronic-First in 10 facilities between 2017 and 2019 in Lusaka urban district, Zambia

Variable assessed Assessment of variables All facilities Hospitals Health centres
Electronic-Last % Electronic-First % % difference Electronic-Last % Electronic-First % % difference Electronic-Last % Electronic-First % % difference
Registration, n 10597 113355 6783 9003 4927 6128
 ART number % error 2% 1% −1 2 1 −1 1 1 0
 Appointment date 39% 21% −18 36 17 −19 47 36 −11
Anthropometry and vitals, n 1116 228 175998 74767 99319 41521 76679
 Height % error 48% 60% +12 53 55 +2 52 65 +13
 Weight 33% 36% +3 24 35 +11 23 37 +14
 Temperature 49% 65% +16 45 70 +25 55 59 +14
 Pulse 64% 65% +1 68 62 −8 66 68 +2
 Respiratory rate 76% 87% +11 77 89 +12 73 86 +13
 Blood pressure 57% 61% +4 55 56 +1 52 66 +14
Clinical consultation 63106 86452 35959 40421 23453 33741
 Clinical appointment dates % error 25% 11% −14 21 8 −13 31 14 −17
Open in a new tab

HIV = human immunodeficiency virus; ART = antiretroviral therapy.

With clinical consultation dates, the proportion of errors decreased from 25% to 11% overall when moving from Electronic Last to Electronic First, with a similar pattern being observed in hospitals and health centres.

DISCUSSION

We assessed the completeness and accuracy of data for key HIV programme variables needed for monitoring the health of PLHIV when starting and sustaining ART in Lusaka, Zambia. We observed important differences in data completeness and accuracy for these variables when transitioning from Electronic-Last to Electronic-First data capture at all health facilities. This information may be useful to clinicians, health facilities, implementing partners and the MoH for improving patient management, the ART programme, and planning the scale-up of Electronic-First across all health facilities in Zambia.

There was no difference in data completeness at registration service points at all health facilities when compared before and after the transition. Completeness of data at these registration service points has been of consistently high standards during both Electronic-First and Electronic-Last implementation. However, there was a slight improvement in registration data accuracy with the change to Electronic-First, particularly for appointment dates.

There was an increase in the completeness and accuracy of clinical consultation data observed at all health facilities. This is similar to what was found in Haiti during a quality assessment of their EMR, and in Ethiopia following transition from a paper-based registry to an EMR.14,15 However, the completeness of data for TB screening was still poor, at 58%. This could be an indicator that TB screening is not being routinely performed or that it is taking place but not being recorded. Routine TB screening in PLHIV is recommended by the WHO and is important for early detection and treatment of the disease.16,17 PLHIV are at greater risk of TB, with TB being the leading cause of death amongst those with HIV.18 Zambia is a high TB burden country, with 59% of patients with new or relapse TB having HIV infection.19 It is therefore vital that TB screening is performed and recorded at every clinic interaction.

Overall completeness of ART regimen data at pharmacies slightly decreased following the transition; this was in line with reports from in Ethiopia.15 This incompleteness, while involving small margins, can nonetheless lead to critical misreporting, which might impact programme implementation, leading to potential stock outs of commodities. The impact of incomplete data at facility level cascades upward to the national level, and affects the procurement, planning and budgeting of drugs for health services.20 Data from SmartCare EMR is used by the MoH and other implementing partners to report to donors, and this can also affect overall planning for health services across Zambia.

Anthropometric measurement and vital sign data did not improve in completeness, and in fact became more inaccurate following transition in data entry methodology from Electronic-Last to Electronic-First. Similar findings of missing and incorrect anthropometric and vital sign data in an EMR were reported in Mozambique.21 Although anthropometric and vital sign data may not influence programme management, it is still critical that these data are accurate for monitoring a patient’s clinical status. Changes in a patient’s clinical indicators may be an early sign of ART failure or the development of new opportunistic infections or malignancy. Monitoring a patient’s blood pressure is important for the early detection of hypertension. PLHIV on ART are at a higher risk for cardiovascular disease (CVD) because of a number of traditional and non-traditional risk factors.22–24 As access to ART increases and virologic suppression is achieved, HIV-infected patients will live longer,25 and experience a longer life expectancy with increasing risk of CVD such as coronary heart disease.23 The estimated prevalence of hypertension in Zambia is 25.9%.26,27 It is therefore vital that blood pressure be monitored accurately and routinely as part of a comprehensive hypertension management in PLHIV.

Growth monitoring (height and weight) is an essential part of providing care for children living with HIV. Poor growth could indicate ART treatment failure, or the presence of TB, opportunistic infections, malnutrition, or malignancy. Weight is also critical for drug-dose calculations in children and for monitoring progress of adults on ART. In Mozambique, height and weight data were the most commonly missed and inaccurate variables.21

Anthropometric and vital sign data may be missing or inaccurate due to lack or limited availability of equipment, such as a sphygmomanometer or weight scale. Inadequate lay health worker training and supervision on taking anthropometric and vital sign measurements, and entering these data into SmartCare, may also have contributed to poor data quality due to the limited understanding of normal anthropometric ranges among this cadre. Task shifting to lay health workers has become a normal part of care across the health care sector and is a well-established practice in HIV clinics in Lusaka.28,29 As discussed above, the collection of anthropometric and vital sign data is important for clinical decision-making, and lay health workers who have been tasked with this responsibility need to be adequately trained and mentored on the importance of accurate and well-recorded data.22

An important strength of this study was the analysis of a large data set representative of key programme variables used in the SmartCare EMR for “real world” management of PLHIV in Zambia. This strength notwithstanding, our study had several limitations. First, at the time of analysis, only 10 health facilities were eligible to participate in the Lusaka Urban District. Restricting our analysis to Lusaka hindered our ability to detect important differences in data accuracy and completeness in other districts, limiting the generalisability of our findings. Second, because of our pragmatic, before-after descriptive approach, we could not account for factors in our analysis that may have influenced data accuracy and completeness during the transition from Electronic-Last to Electronic-First, such as health worker training on data management.

Based on our study findings, we make the following recommendations for improved SmartCare EMR implementation under the Electronic-First strategy: 1) improve comprehensive training and mentorship of lay health workers in task-shifted anthropometric and vital sign data capture and entry; 2) remind clinicians and nurses about the importance of conducting and documenting routine TB screening; 3) incorporate automated data validation checks into the SmartCare EMR to only accept inputs within logical numerical data ranges; 4) regularly supervise and mentor staff on complete and accurate data entry; and 5) conduct routine data audits to assess the completeness and accuracy of SmartCare EMR data. Finally, additional implementation research should be performed to identify human-centred strategies to improve the user-friendliness of the SmartCare EMR and the data management systems built around it to enhance overall HIV programme data quality.

CONCLUSION

With the notable exception of clinical consultation data, there was no discernable improvement in the completeness or accuracy of data following the transition of data entry method from Electronic-Last to Electronic-First at the health facilities studied. Quality improvement interventions should be undertaken to ensure that the data entered into the SmartCare EMR is complete and accurate across all health facility levels and departments. These findings may be useful for other health actors looking to implement similar EMR systems in Zambia and other low and middle-income country settings.

Acknowledgments

This research was conducted through the Structured Operational Research and Training Initiative (SORT IT), a global partnership led by the Special Programme for Research and Training in Tropical Diseases at the World Health Organization (WHO/TDR). The model is based on a course developed jointly by the International Union Against Tuberculosis and Lung Disease (The Union) and Medécins Sans Frontières (MSF/Doctors Without Borders). The specific SORT IT programme which resulted in this publication was jointly organised, implemented and mentored by the Centre for Operational Research, The Union, Paris, France; MSF-Luxembourg (MSF LuxOR); MSF-Belgium (MSF-OCB); the University of Bergen, Bergen, Norway and the London School of Hygiene & Tropical Medicine, London, UK. Funding was from the United Kingdom’s Department for International Development (London, UK) and La Fondation Veuve Emile Metz-Tesch (Luxembourg). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Footnotes

Conflicts of interest: none declared.

References

  • 1.UNAIDS Global AIDS Update 2019: Fact Sheet. Geneva, Switzerland: UNAIDS; 2020. https://www.unaids.org/en/resources/fact-sheet Accessed February 2020. [Google Scholar]
  • 2.Ministry of Health, Zambia Zambia population-based HIV Impact Assessment (ZAMPHIA) 2016. Lusaka, Zambia: Ministry of Health; 2019. http://phia.icap.columbia.edu. [Google Scholar]
  • 3.Majeed A, Car J, Sheikh A. Accuracy and completeness of electronic patient. Family Pract. 2008;25(4):213–214. doi: 10.1093/fampra/cmn047. [DOI] [PubMed] [Google Scholar]
  • 4.Kombe G, Smith O. The costs of anti-retroviral treatment in Zambia. The Partners for Health Reformplus Project, Abt Associates Inc; 2003. https://www.who.int/hiv/amds/countries/zmb_CostsARVTreatment.pdf Accessed February 2020. [Google Scholar]
  • 5.Lowrance D, Filler S, Makombe S et al. Assessment of a national monitoring and evaluation system for rapid expansion of antiretroviral treatment in Malawi: Expanding ARV in Malawi. Trop Med Int Health. 2007;12(3):377–381. doi: 10.1111/j.1365-3156.2006.01800.x. [DOI] [PubMed] [Google Scholar]
  • 6.Stausberg J, Koch D, Ingenerf J, Betzler M. Comparing paper-based with electronic patient records: lessons learned during a study on diagnosis and procedure codes. J Am Med Inform Assoc. 2003;10(5):470–477. doi: 10.1197/jamia.M1290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Weed L L. Medical records that guide and teach. New Engl J Med. 1968;278(11):593–600. doi: 10.1056/NEJM196803142781105. [DOI] [PubMed] [Google Scholar]
  • 8.Chaudhry B, Wang J, Wu S et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2005;144(10):742–752. doi: 10.7326/0003-4819-144-10-200605160-00125. [DOI] [PubMed] [Google Scholar]
  • 9.Oluoch T, Kwaro D, Ssempijja V et al. Better adherence to pre-antiretroviral therapy guidelines after implementing an electronic medical record system in rural Kenyan HIV clinics: a multicenter pre–post study. Int J Infect Dis. 2015;33:109–113. doi: 10.1016/j.ijid.2014.06.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Shekelle P, Keeler E, Morton S C. Costs and benefits of health information technology. Rockville, MD, USA: US Agency for Healthcare Research and Quality; 2006. 06-E006 ed. Evidence Reports/Technology Assessments No. 132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Buntin M B, Burke M F, Hoaglin M C, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Affairs. 2011;30(3):464–471. doi: 10.1377/hlthaff.2011.0178. [DOI] [PubMed] [Google Scholar]
  • 12.Kalogriopoulos N A, Baran J, Nimunkar A J, Webster J G. Electronic medical record systems for developing countries. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Minneapolis, MN, USA: IEEE; 2009. pp. 1730–1733. http://ieeexplore.ieee.org/document/5333561/ Accessed February 2020. [DOI] [PubMed] [Google Scholar]
  • 13.Central Statistical Office 2010 Census of population national analytical report. Lusaka, Zambia: CSO; 2012. [Google Scholar]
  • 14.Puttkammer N, Baseman J G, Devine E B et al. An assessment of data quality in a multi-site electronic medical record system in Haiti. Int J Med Inform. 2016;86:104–116. doi: 10.1016/j.ijmedinf.2015.11.003. [DOI] [PubMed] [Google Scholar]
  • 15.Abiy R, Gashu K, Asemaw T et al. A comparison of electronic medical record data to paper records in antiretroviral therapy clinic in Ethiopia: what is affecting the quality of the data? Online J Public Health Inform. 2018;10(2) doi: 10.5210/ojphi.v10i2.8309. e212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.World Health Organization Systematic screening for active tuberculosis: principles and recommendations. Geneva, Switzerland: WHO; 2013. WHO/HTM/TB/2013.04. [PubMed] [Google Scholar]
  • 17.World Health Organization Global Tuberculosis Programme. Latent tuberculosis infection: updated and consolidated guidelines for programmatic management. Geneva, Switzerland: WHO; 2018. WHO/CDS/TB/2018.4. [PubMed] [Google Scholar]
  • 18.World Health Organization HIV-associated tuberculosis. Geneva, Switzerland: WHO; 2019. https://www.who.int/tb/areas-of-work/tb-hiv/tbhiv_factsheet.pdf?ua=1 Accessed February 2020. [Google Scholar]
  • 19.World Health Organization Global tuberculosis report, 2019. Geneva, Switzerland: WHO; 2019. WHO/CDS/TB/2019.15. [Google Scholar]
  • 20.Ministry of Health, Zambia Health sector supply chain and implementation plan 2015–2017. Lusaka, Zambia: MoH; 2015. [Google Scholar]
  • 21.Lambdin B H, Micek M A, Koepsell T D et al. An assessment of the accuracy and availability of data in electronic patient tracking systems for patients receiving HIV treatment in central Mozambique. BMC Health Serv Res. 2012;12(1):30. doi: 10.1186/1472-6963-12-30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Patel P, Speight C, Maida A et al. Integrating HIV and hypertension management in low-resource settings: Lessons from Malawi. PLoS Med. 2018;15(3) doi: 10.1371/journal.pmed.1002523. e1002523. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Mensah G, Roth G, Sampson U et al. Mortality from cardiovascular diseases in sub-Saharan Africa, 1990–2013: a systematic analysis of data from the Global Burden of Disease Study 2013: cardiovascular topic. Cardiovasc J Africa. 2015;26(2):S6–S10. doi: 10.5830/CVJA-2015-036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Islam F, Wu J, Jansson J, Wilson D. Relative risk of cardiovascular disease among people living with HIV: a systematic review and meta-analysis: cardiovascular disease and HIV. HIV Med. 2012;13(8):453–468. doi: 10.1111/j.1468-1293.2012.00996.x. [DOI] [PubMed] [Google Scholar]
  • 25.Lessells R J, Mutevedzi P C, Iwuji C C, Newell M-L. Reduction in early mortality on antiretroviral therapy for adults in rural South Africa since change in CD4+ cell count eligibility criteria. JAIDS. 2014;65(1):e17–24. doi: 10.1097/QAI.0b013e31829ceb14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.World Health Organization Raised blood pressure (SBP ⩾ 140 OR DBP ⩾ 90), age-standardized (%): estimates by country [Internet]. Global Health Observatory data repository. Geneva, Switzerland: WHO; 2017. http://apps.who.int/gho/data/node.main.A875STANDARD?lang=en Accessed February 2020. [Google Scholar]
  • 27.Goma F M, Mwewa B, Tembo G K et al. May Measurement Month 2017: blood pressure screening results from Zambia—sub-Saharan Africa. Eur Heart J Suppl. 2019;21(Supplement_D):D130–132. doi: 10.1093/eurheartj/suz077. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Morris M B, Chapula B T, Chi B H et al. Use of task-shifting to rapidly scale-up HIV treatment services: experiences from Lusaka, Zambia. BMC Health Serv Res. 2009;9(1):5. doi: 10.1186/1472-6963-9-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Philips M, Zachariah R, Venis S. Task shifting for antiretroviral treatment delivery in sub-Saharan Africa: not a panacea. Lancet. 2008;371(9613):682–684. doi: 10.1016/S0140-6736(08)60307-4. [DOI] [PubMed] [Google Scholar]

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