Network characteristics of a referral system for patients with hypertension in Western Kenya: results from the Strengthening Referral Networks for Management of Hypertension Across the Health System (STRENGTHS) study

Aarti Thakkar, Thomas Valente, Josephine Andesia, Benson Njuguna, Juliet Miheso, Tim Mercer, Richard Mugo, Ann Mwangi, Eunice Mwangi, Sonak D Pastakia, Shravani Pathak, Mc Kinsey M Pillsbury, Jemima Kamano, Violet Naanyu, Makeda Williams, Rajesh Vedanthan, Constantine Akwanalo, Gerald S Bloomfield, Aarti Thakkar, Thomas Valente, Josephine Andesia, Benson Njuguna, Juliet Miheso, Tim Mercer, Richard Mugo, Ann Mwangi, Eunice Mwangi, Sonak D Pastakia, Shravani Pathak, Mc Kinsey M Pillsbury, Jemima Kamano, Violet Naanyu, Makeda Williams, Rajesh Vedanthan, Constantine Akwanalo, Gerald S Bloomfield

Abstract

Background: Health system approaches to improve hypertension control require an effective referral network. A national referral strategy exists in Kenya; however, a number of barriers to referral completion persist. This paper is a baseline assessment of a hypertension referral network for a cluster-randomized trial to improve hypertension control and reduce cardiovascular disease risk.

Methods: We used sociometric network analysis to understand the relationships between providers within a network of nine geographic clusters in western Kenya, including primary, secondary, and tertiary care facilities. We conducted a survey which asked providers to nominate individuals and facilities to which they refer patients with controlled and uncontrolled hypertension. Degree centrality measures were used to identify providers in prominent positions, while mixed-effect regression models were used to determine provider characteristics related to the likelihood of receiving referrals. We calculated core-periphery correlation scores (CP) for each cluster (ideal CP score = 1.0).

Results: We surveyed 152 providers (physicians, nurses, medical officers, and clinical officers), range 10-36 per cluster. Median number of hypertensive patients seen per month was 40 (range 1-600). While 97% of providers reported referring patients up to a more specialized health facility, only 55% reported referring down to lower level facilities. Individuals were more likely to receive a referral if they had higher level of training, worked at a higher level facility, were male, or had more job experience. CP scores for provider networks range from 0.335 to 0.693, while the CP scores for the facility networks range from 0.707 to 0.949.

Conclusions: This analysis highlights several points of weakness in this referral network including cluster variability, poor provider linkages, and the lack of down referrals. Facility networks were stronger than provider networks. These shortcomings represent opportunities to focus interventions to improve referral networks for hypertension.

Trial registration: Trial Registered on ClinicalTrials.gov NCT03543787 , June 1, 2018.

Keywords: Hypertension; Network analysis; Referral patterns.

Conflict of interest statement

The authors have no competing interests to declare.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Screening and enrollment diagram
Fig. 2
Fig. 2
Facility (A) and Provider (B) Level Networks. Nodes are colored by geographic cluster. The size of each node represents in-degree nominations: size increase proportionally with nominations. Thicker edges (Arrows) demonstrate a greater number of connections between specific nodes. Panel A shows the facility referral network model and Panel B shows the provider referral network model

References

    1. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2012;380(9859):2224–2260.
    1. World Health Organization. Noncommunicable diseases and mental health. Global status report on noncommunicable diseases 2010. Geneva: World Health Organization; 2010.
    1. Lawes CM, Hoorn S. Vander, Rodgers a. global burden of blood-pressure-related disease, 2001. Lancet. 2008;371(9623):1513–1518.
    1. Chow CK, Teo KK, Rangarajan S, Islam S, Gupta R, Avezum A, et al. Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries. JAMA. 2013;310(9):959–968.
    1. Irazola VE, Bloomfield GS, Carrillo-Larco RM, Dorairaj P, Gaziano T, Levitt NS, et al. Hypertension prevalence, awareness, treatment, and control in selected communities of nine low-and middle income countries: results from the NHLBI/UHG network of centers of excellence for chronic diseases HHS public access. Glob Heart. 2016;11(1):47–59.
    1. Geldsetzer P, Manne-Goehler J, Marcus ME, Ebert C, Zhumadilov Z, Wesseh CS, et al. The state of hypertension care in 44 low-income and middle-income countries: a cross-sectional study of nationally representative individual-level data from 1·1 million adults. Lancet. 2019;394(10199):652–662.
    1. Mohamed SF, Mutua MK, Wamai R, Wekesah F, Haregu T, Juma P, et al. Prevalence, awareness, treatment and control of hypertension and their determinants: results from a national survey in Kenya. BMC Public Health. 2018;18(Suppl 3) Available from: . Cited 2021 Nov 30.
    1. Beaglehole R, Epping-Jordan JA, Patel V, Chopra M, Ebrahim S, Kidd M, et al. Improving the prevention and management of chronic disease in low-income and middle-income countries: a priority for primary health care. Lancet. 2008;372:940–949.
    1. Allotey P, Reidpath DD, Yasin S, Chan CK, De-Graft Aikins A. Rethinking health-care systems: a focus on chronicity. Lancet. 2011;377:450–451.
    1. Samb B, Desai N, Nishtar S, Mendis S, Bekedam H, Wright A, et al. Prevention and management of chronic disease: a litmus test for health-systems strengthening in low-income and middle-income countries. Lancet. 2010;376:1785–1797.
    1. Dans A, Ng N, Varghese C, Tai ES, Firestone R, Bonita R. The rise of chronic non-communicable diseases in southeast Asia: time for action. Lancet. 2011;377:680–689.
    1. Sweeney B. The referral system. BMJ. 1994;309:1180.
    1. Levitt NS, Puoane T, Denman CA, Abrahams-Gessel S, Surka S, Mendoza C, et al. Referral outcomes of individuals identified at high risk of cardiovascular disease by community health workers in Bangladesh, Guatemala, Mexico, and South Africa. Glob Health Action. 2015;8(1):26318.
    1. Grace SL, Chessex C, Arthur H, Chan S, Cyr C, Dafoe W, et al. Systematizing inpatient referral to cardiac rehabilitation 2010: Canadian association of cardiac rehabilitation and Canadian cardiovascular society joint position paper. Can J Cardiol. 2011;27(2):192–199.
    1. Grace SL, Leung YW, Reid R, Oh P, Wu G, Alter DA. The role of systematic inpatient cardiac rehabilitation referral in increasing equitable access and utilization. J Cardiopulm Rehabil Prev. 2012;32(1):41–47.
    1. Ministry of Health. Kenya health sector referral implementation guidelines 2014. Nairobi: Government of Kenya; 2014.
    1. Pierre Ilboudo T, Chou Y-J, Huang N. Compliance with referral for curative care in rural Burkina Faso. Health Policy Plan. 2012;27(3):256–264.
    1. Nanyonjo A, Bagorogoza B, Kasteng F, Ayebale G, Makumbi F, Tomson G, et al. Estimating the cost of referral and willingness to pay for referral to higher-level health facilities: a case series study from an integrated community case management programme in Uganda. BMC Health Serv Res. 2015;15(1):1–10.
    1. Chacour Bahous M, Shadmi E. Health information exchange and information gaps in referrals to a pediatric emergency department. Int J Med Inform. 2016;87:68–74.
    1. Kowalewski M, Jahn A, Kimatta SS. Why do at-risk mothers fail to reach referral level? Barriers beyond distance and cost. Afr J Reprod Health. 2000;4(1):100–109.
    1. Orimadegun AE, Akinbami FO, Akinsola AK, Okereke JO. Contents of referral letters to the children emergency unit of a teaching hospital, southwest of Nigeria. Pediatr Emerg Care. 2008;24(3):153–156.
    1. Pembe AB, Carlstedt A, Urassa DP, Lindmark G, Nyström L, Darj E. Effectiveness of maternal referral system in a rural setting: a case study from Rufiji district, Tanzania. 2010.
    1. Pembe AB, Urassa DP, Darj E, Carlstedt A, Olsson P. Qualitative study on maternal referrals in rural Tanzania: decision making and acceptance of referral advice. Afr J Reprod Health. 2008;12(2):120–131.
    1. . The field guide to human-centered design. 1. San Francisco: IDEO; 2015.
    1. Mercer T, Njuguna B, Bloomfield GS, Dick J, Finkelstein E, Kamano J, et al. Strengthening referral networks for management of hypertension across the health system (STRENGTHS) in western Kenya: a study protocol of a cluster randomized trial. Trials. 2019;20(1):554.
    1. Valente TW. Social networks and health: models, methods, and applications. New York: Oxford University Press; 2010. pp. 1–222.
    1. Borgatti SP, Everett MG, Johnson JC. Analyzing social networks. Thousand Oaks: SAGE Publications; 2013.
    1. Blok DJ, de Vlas SJ, van Empelen P, van Lenthe FJ. The role of smoking in social networks on smoking cessation and relapse among adults: a longitudinal study. Prev Med (Baltim) 2017;99:105–110.
    1. Christakis NA, Fowler JH. The spread of obesity in a large social network over 32 years. N Engl J Med. 2007;357(4):370–379.
    1. Valente TW, Gallaher P, Mouttapa M. Using social networks to understand and prevent substance use: a transdisciplinary perspective. Subst Use Misuse. 2004;39(10-12):1685–1712.
    1. Einterz RM, Kimaiyo S, Mengech HNK, Khwa-Otsyula BO, Esamai F, Quigley F, et al. Responding to the HIV pandemic: the power of an academic medical partnership. Acad Med. 2007;82(8):812–818.
    1. Mercer T, Gardner A, Andama B, Chesoli C, Christoffersen-Deb A, Dick J, et al. Leveraging the power of partnerships: spreading the vision for a population health care delivery model in western Kenya. Glob Health. 2018;14:44.
    1. Mamlin J, Kimaiyo S, Nyandiko W, Tierney W. Academic institutions linking access to treatment and prevention: case study. Geneva: World Health Organ; 2004.
    1. Vedanthan R, Kamano JH, Bloomfield GS, Manji I, Pastakia S, Kimaiyo S. Engaging the entire care cascade in Western Kenya: a model to achieve the cardiovascular disease secondary prevention roadmap goals. Glob Heart. 2015;10:313–317.
    1. Binanay CA, Akwanalo CO, Aruasa W, Barasa FA, Corey GR, Crowe S, et al. Building sustainable capacity for cardiovascular care at a public hospital in Western Kenya. J Am Coll Cardiol. 2015;66:2550–2560.
    1. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–381.
    1. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95:103208.
    1. Barman-Adhikari A, Begun S, Rice E, Yoshioka-Maxwell A, Perez-Portillo A. Sociometric network structure and its association with methamphetamine use norms among homeless youth. Soc Sci Res. 2016;58:292–308.
    1. Freeman LC. Centrality in social networks conceptual clarification. Soc Networks. 1978;1(3):215–239.
    1. Sillabutra J, Kitidamrongsuk P, Viwatwongkasem C, Ujeh C, Sae-tang S, Donjdee K. Bootstrapping with R to Make Generalized Inference for Regression Model. Procedia Computer Science. 2016;86:228-31.
    1. Borgatti SP, Everett MG. Models of core/periphery structures. Soc Networks. 2000;21(4):375–395.
    1. Puck Rombach M, Porter MA, Fowler JH, Mucha PJ. Core-periphery structure in networks. SIAM J Appl Math. 2014;74(1):167–190.
    1. Csermely P, London A, Wu LY, Uzzi B. Structure and dynamics of core/periphery networks. J Complex Networks. 2013;1(2):93-123.
    1. Holtrop JS, Ruland S, Diaz S, Morrato EH, Jones E. Using social network analysis to examine the effect of care management structure on chronic disease management communication within primary care. J Gen Intern Med. 2018;33(5):612–620.
    1. Schneider CM, Moreira AA, Andrade JS, Havlin S, Herrmann HJ. Mitigation of malicious attacks on networks. Proc Natl Acad Sci U S A. 2011;108(10):3838–3841.
    1. Bastian M, Heymann S, Jacomy M. International AAAI Conference on Weblogs and Social Media. 2009. Gephi: an open source software for exploring and manipulating networks.
    1. Lehmann U, Dieleman M, Martineau T. Staffing remote rural areas in middle- and low-income countries: a literature review of attraction and retention. BMC Health Serv Res. 2008;8(1):19.
    1. Agampodi TC, Agampodi SB, Glozier N, Siribaddana S. Measurement of social capital in relation to health in low and middle income countries (LMIC): a systematic review. Soc Sci Med. 2015;128:95–104.
    1. Musalia J. Social capital and health in Kenya: a multilevel analysis. Soc Sci Med. 2016;167:11–19.
    1. Thomas JC, Reynolds H, Bevc C, Tsegaye A. Integration opportunities for HIV and family planning services in Addis Ababa, Ethiopia: an organizational network analysis. BMC Health Serv Res. 2014;14(1):1–8.
    1. Huisman M. Imputation of missing network data: some simple procedures. Encycl Soc Netw Anal Min. 2014:707–15 Available from: . Cited 2021 Dec 1.
    1. Costenbader E, Valente TW. The stability of centrality measures when networks are sampled. Soc Netw. 2003;25(4):283–307.

Source: PubMed

Sponsorer og samarbeidspartnere

Medisinsk tilstand

Legemiddelintervensjoner

3
Abonnere