Dataset Information

Hypertension management in rural western Kenya: a needs-based health workforce estimation model.

ABSTRACT: BACKGROUND:Elevated blood pressure is the leading risk for mortality in the world. Task redistribution has been shown to be efficacious for hypertension management in low- and middle-income countries. However, the workforce requirements for such a task redistribution strategy are largely unknown. Therefore, we developed a needs-based workforce estimation model for hypertension management in western Kenya, using need and capacity as inputs. METHODS:Key informant interviews, focus group discussions, a Delphi exercise, and time-motion studies were conducted among administrative leadership, clinicians, patients, community leaders, and experts in hypertension management. These results were triangulated to generate the best estimates for the inputs into the health workforce model. The local hypertension clinical protocol was used to derive a schedule of encounters with different levels of clinician and health facility staff. A Microsoft Excel-based spreadsheet was developed to enter the inputs and generate the full-time equivalent workforce requirement estimates over 3 years. RESULTS:Two different scenarios were modeled: (1) "ramp-up" (increasing growth of patients each year) and (2) "steady state" (constant rate of patient enrollment each month). The ramp-up scenario estimated cumulative enrollment of 7000 patients by year 3, and an average clinical encounter time of 8.9 min, yielding nurse full-time equivalent requirements of 4.8, 13.5, and 30.2 in years 1, 2, and 3, respectively. In contrast, the steady-state scenario assumed a constant monthly enrollment of 100 patients and yielded nurse full-time equivalent requirements of 5.8, 10.5, and 14.3 over the same time period. CONCLUSIONS:A needs-based workforce estimation model yielded health worker full-time equivalent estimates required for hypertension management in western Kenya. The model is able to provide workforce projections that are useful for program planning, human resource allocation, and policy formulation. This approach can serve as a benchmark for chronic disease management programs in low-resource settings worldwide.

SUBMITTER: Vedanthan R

PROVIDER: S-EPMC6636021 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Hypertension management in rural western Kenya: a needs-based health workforce estimation model.

Vedanthan Rajesh R Lee Danielle J DJ Kamano Jemima H JH Herasme Omarys I OI Kiptoo Peninah P Tulienge Deborah D Kimaiyo Sylvester S Balasubramanian Hari H Fuster Valentin V

Human resources for health 20190716 1

<h4>Background</h4>Elevated blood pressure is the leading risk for mortality in the world. Task redistribution has been shown to be efficacious for hypertension management in low- and middle-income countries. However, the workforce requirements for such a task redistribution strategy are largely unknown. Therefore, we developed a needs-based workforce estimation model for hypertension management in western Kenya, using need and capacity as inputs.<h4>Methods</h4>Key informant interviews, focus g ...[more]

PMID: 31311561

Similar Datasets

Project description:BackgroundElevated blood pressure (BP) is the leading global risk factor for mortality. Delay in seeking hypertension care is associated with increased mortality.ObjectivesThis study investigated whether community health workers, equipped with behavioral communication strategies and smartphone technology, can increase linkage of individuals with elevated BP to a hypertension care program in western Kenya and significantly reduce BP.MethodsThe study was a cluster randomized trial with 3 arms: 1) usual care (standard training); 2) "paper-based" (tailored behavioral communication, using paper-based tools); and 3) "smartphone" (tailored behavioral communication, using smartphone technology). The co-primary outcomes were: 1) linkage to care; and 2) change in systolic BP (SBP). A covariate-adjusted mixed-effects model was used, adjusting for differential time to follow-up. Bootstrap and multiple imputation were used to handle missing data.ResultsA total of 1,460 individuals (58% women) were enrolled (491 usual care, 500 paper-based, 469 smartphone). Average baseline SBP was 159.4 mm Hg. Follow-up measures of linkage were available for 1,128 (77%) and BP for 1,106 (76%). Linkage to care was 49% overall, with significantly greater linkage in the usual care and smartphone arms of the trial. Average overall follow-up SBP was 149.9 mm Hg. Participants in the smartphone arm experienced a modestly greater reduction in SBP versus usual care (-13.1 mm Hg vs. -9.7 mm Hg), but this difference was not statistically significant. Mediation analysis revealed that linkage to care contributed to SBP change.ConclusionsA strategy combining tailored behavioral communication and mobile health (mHealth) for community health workers led to improved linkage to care, but not statistically significant improvement in SBP reduction. Further innovations to improve hypertension control are needed. (Optimizing Linkage and Retention to Hypertension Care in Rural Kenya [LARK]; NCT01844596).

Project description:BackgroundAssessing the progress in achieving the United Nation's Millennium Development Goals in terms of population health requires consistent and reliable information on cause-specific mortality, which is often rare in resource-constrained countries. Health and demographic surveillance systems (HDSS) have largely used medical personnel to review and assign likely causes of death based on the information gathered from standardized verbal autopsy (VA) forms. However, this approach is expensive and time consuming, and it may lead to biased results based on the knowledge and experience of individual clinicians. We assessed the cause-specific mortality for children under 5 years old (under-5 deaths) in Siaya County, obtained from a computer-based probabilistic model (InterVA-4).DesignSuccessfully completed VA interviews for under-5 deaths conducted between January 2003 and December 2010 in the Kenya Medical Research Institute/US Centers for Disease Control and Prevention HDSS were extracted from the VA database and processed using the InterVA-4 (version 4.02) model for interpretation. Cause-specific mortality fractions were then generated from the causes of death produced by the model.ResultsA total of 84.33% (6,621) childhood deaths had completed VA data during the study period. Children aged 1-4 years constituted 48.53% of all cases, and 42.50% were from infants. A single cause of death was assigned to 89.18% (5,940) of cases, 8.35% (556) of cases were assigned two causes, and 2.10% (140) were assigned 'indeterminate' as cause of death by the InterVA-4 model. Overall, malaria (28.20%) was the leading cause of death, followed by acute respiratory infection including pneumonia (25.10%), in under-5 children over the study period. But in the first 5 years of the study period, acute respiratory infection including pneumonia was the main cause of death, followed by malaria. Similar trends were also reported in infants (29 days-11 months) and children aged 1-4 years.ConclusionsUnder-5 cause-specific mortality obtained using the InterVA-4 model is consistent with existing knowledge on the burden of childhood diseases in rural western Kenya.

Project description:BackgroundRural settings in Sub-Saharan Africa (SSA) consistently report low participation in non-communicable disease (NCD) treatment programs and poor outcomes.ObjectiveThe objective of this study is to assess the impact of the implementation of a patient-centered rural NCD care delivery model called Bridging Income Generation through grouP Integrated Care (BIGPIC).DesignThe study prospectively tracked participation and health outcomes for participants in a screening event and compared linkage frequencies to a historical comparison group.ParticipantsRural Kenyan participants attending a voluntary NCD screening event were included within the BIGPIC model of care.InterventionsThe BIGPIC model utilizes a contextualized care delivery model designed to address the unique barriers faced in rural settings. This model emphasizes the following steps: (1) find patients in the community, (2) link to peer/microfinance groups, (3) integrate education, (4) treat in the community, (5) enhance economic sustainability and (6) generate demand for care through incentives.Main measuresThe primary outcome is the linkage frequency, which measures the percentage of patients who return for care after screening positive for either hypertension and/or diabetes. Secondary measures include retention frequencies defined as the percentage of patients remaining engaged in care throughout the 9-month follow-up period and changes in systolic (SBP) and diastolic blood pressure (DBP) and blood sugar over 12 months.Key resultsOf the 879 individuals who were screened, 14.2 % were confirmed to have hypertension, while only 1.4 % were confirmed to have diabetes. The implementation of a comprehensive microfinance-linked, community-based, group care model resulted in 72.4 % of screen-positive participants returning for subsequent care, of which 70.3 % remained in care through the 12 months of the evaluation period. Patients remaining in care demonstrated a statistically significant mean decline of 21 mmHg in SBP [95 % CI (13.9 to 28.4), P?<?0.01] and 5 mmHg drop in DBP [95 % CI (1.4 to 7.6), P?<?0.01].ConclusionsThe implementation of a contextualized care delivery model built around the unique needs of rural SSA participants led to statistically significant improvements in linkage to care and blood pressure reduction.

Project description:BackgroundHypertension is a major risk factor for cardiovascular disease (CVD), yet treatment and control rates for hypertension are very low in low- and middle-income countries (LMICs). Lack of effective referral networks between different levels of the health system is one factor that threatens the ability to achieve adequate blood pressure control and prevent CVD-related morbidity. Health information technology and peer support are two strategies that have improved care coordination and clinical outcomes for other disease entities in other settings; however, their effectiveness and cost-effectiveness in strengthening referral networks to improve blood pressure control and reduce CVD risk in low-resource settings are unknown.Methods/designWe will use the PRECEDE-PROCEED framework to conduct transdisciplinary implementation research, focused on strengthening referral networks for hypertension in western Kenya. We will conduct a baseline needs and contextual assessment using a mixed-methods approach, in order to inform a participatory, community-based design process to fully develop a contextually and culturally appropriate intervention model that combines health information technology and peer support. Subsequently, we will conduct a two-arm cluster randomized trial comparing 1) usual care for referrals vs 2) referral networks strengthened with our intervention. The primary outcome will be one-year change in systolic blood pressure. The key secondary clinical outcome will be CVD risk reduction, and the key secondary implementation outcomes will include referral process metrics such as referral appropriateness and completion rates. We will conduct a mediation analysis to evaluate the influence of changes in referral network characteristics on intervention outcomes, a moderation analysis to evaluate the influence of baseline referral network characteristics on the effectiveness of the intervention, as well as a process evaluation using the Saunders framework. Finally, we will analyze the incremental cost-effectiveness of the intervention relative to usual care, in terms of costs per unit decrease in systolic blood pressure, per percentage change in CVD risk score, and per disability-adjusted life year saved.DiscussionThis study will provide evidence for the implementation of innovative strategies for strengthening referral networks to improve hypertension control in LMICs. If effective, it has the potential to be a scalable model for health systems strengthening in other low-resource settings worldwide.Trial registrationClinicaltrials.gov, NCT03543787 . Registered on 29 June 2018.

Project description:BackgroundHealth 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.MethodsWe 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).ResultsWe 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.ConclusionsThis 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 registrationTrial Registered on ClinicalTrials.gov NCT03543787 , June 1, 2018.

Dataset Information

Hypertension management in rural western Kenya: a needs-based health workforce estimation model.

Publications

Hypertension management in rural western Kenya: a needs-based health workforce estimation model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets