Protocol-driven primary care and community linkages to improve population health in rural Zambia: the Better Health Outcomes through Mentoring and Assessment (BHOMA) project.

INTRODUCTION: Zambia's under-resourced public health system will not be able to deliver on its health-related Millennium Development Goals without a substantial acceleration in mortality reduction. Reducing mortality will depend not only upon increasing access to health care but also upon improving the quality of care that is delivered. Our project proposes to improve the quality of clinical care and to improve utilization of that care, through a targeted quality improvement (QI) intervention delivered at the facility and community level. DESCRIPTION OF IMPLEMENTATION: The project is being carried out 42 primary health care facilities that serve a largely rural population of more than 450,000 in Zambia's Lusaka Province. We have deployed six QI teams to implement consensus clinical protocols, forms, and systems at each site. The QI teams define new clinical quality expectations and provide tools needed to deliver on those expectations. They also monitor the care that is provided and mentor facility staff to improve care quality. We also engage community health workers to actively refer and follow up patients. EVALUATION DESIGN: Project implementation occurs over a period of four years in a stepped expansion to six randomly selected new facilities every three months. Three annual household surveys will determine population estimates of age-standardized mortality and under-5 mortality in each community before, during, and after implementation. Surveys will also provide measures of childhood vaccine coverage, pregnancy care utilization, and general adult health. Health facility surveys will assess coverage of primary health interventions and measures of health system effectiveness. DISCUSSION: The patient-provider interaction is an important interface where the community and the health system meet. Our project aims to reduce population mortality by substantially improving this interaction. Our success will hinge upon the ability of mentoring and continuous QI to improve clinical service delivery. It will also be critical that once the quality of services improves, increasing proportions of the population will recognize their value and begin to utilize them

Protocol-driven primary care and community linkages to improve population health in rural Zambia: the Better Health Outcomes through Mentoring and Assessment (BHOMA) project

Abstract Introduction Zambia’s under-resourced public health system will not be able to deliver on its health-related Millennium Development Goals without a substantial acceleration in mortality reduction. Reducing mortality will depend not only upon increasing access to health care but also upon improving the quality of care that is delivered. Our project proposes to improve the quality of clinical care and to improve utilization of that care, through a targeted quality improvement (QI) intervention delivered at the facility and community level. Description of implementation The project is being carried out 42 primary health care facilities that serve a largely rural population of more than 450,000 in Zambia’s Lusaka Province. We have deployed six QI teams to implement consensus clinical protocols, forms, and systems at each site. The QI teams define new clinical quality expectations and provide tools needed to deliver on those expectations. They also monitor the care that is provided and mentor facility staff to improve care quality. We also engage community health workers to actively refer and follow up patients. Evaluation design Project implementation occurs over a period of four years in a stepped expansion to six randomly selected new facilities every three months. Three annual household surveys will determine population estimates of age-standardized mortality and under-5 mortality in each community before, during, and after implementation. Surveys will also provide measures of childhood vaccine coverage, pregnancy care utilization, and general adult health. Health facility surveys will assess coverage of primary health interventions and measures of health system effectiveness. Discussion The patient-provider interaction is an important interface where the community and the health system meet. Our project aims to reduce population mortality by substantially improving this interaction. Our success will hinge upon the ability of mentoring and continuous QI to improve clinical service delivery. It will also be critical that once the quality of services improves, increasing proportions of the population will recognize their value and begin to utilize them

Taking ART to Scale: Determinants of the Cost and Cost-Effectiveness of Antiretroviral Therapy in 45 Clinical Sites in Zambia

<div><h3>Background</h3><p>We estimated the unit costs and cost-effectiveness of a government ART program in 45 sites in Zambia supported by the Centre for Infectious Disease Research Zambia (CIDRZ).</p> <h3>Methods</h3><p>We estimated per person-year costs at the facility level, and support costs incurred above the facility level and used multiple regression to estimate variation in these costs. To estimate ART effectiveness, we compared mortality in this Zambian population to that of a cohort of rural Ugandan HIV patients receiving co-trimoxazole (CTX) prophylaxis. We used micro-costing techniques to estimate incremental unit costs, and calculated cost-effectiveness ratios with a computer model which projected results to 10 years.</p> <h3>Results</h3><p>The program cost $69.7 million for 125,436 person-years of ART, or$556 per ART-year. Compared to CTX prophylaxis alone, the program averted 33.3 deaths or 244.5 disability adjusted life-years (DALYs) per 100 person-years of ART. In the base-case analysis, the net cost per DALY averted was $833 compared to CTX alone. More than two-thirds of the variation in average incremental total and on-site cost per patient-year of treatment is explained by eight determinants, including the complexity of the patient-case load, the degree of adherence among the patients, and institutional characteristics including, experience, scale, scope, setting and sector.</p> <h3>Conclusions and Significance</h3><p>The 45 sites exhibited substantial variation in unit costs and cost-effectiveness and are in the mid-range of cost-effectiveness when compared to other ART programs studied in southern Africa. Early treatment initiation, large scale, and hospital setting, are associated with statistically significantly lower costs, while others (rural location, private sector) are associated with shifting cost from on- to off-site. This study shows that ART programs can be significantly less costly or more cost-effective when they exploit economies of scale and scope, and initiate patients at higher CD4 counts.</p> </div

Distribution of cost sub-components between total and on-site components.

<p>Distribution of cost sub-components between total and on-site components.</p

Estimated costs of ART service delivery per patient year by ART facility characteristic.

<p>Figure shows total and on-site costs. Author’s estimates constructed from the semilog specification in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051993#pone-0051993-t005" target="_blank">Table 5</a>. The “reference” facility is an urban public sector clinic with less than 91% adherence, less than 2 years’ experience, fewer than 300 patients, and late starting patients. Estimated effects of facility characteristics accumulate from left to right.</p

Bivariate analysis of association between average total cost and its components and eight explanatory variables.

<p>Column definitions:</p><p>(1) Variable definitions.</p><p>(2) Specifies whether variable is continuous or discrete, and if discrete, the criterion used to define the dummy variable.</p><p>(3) Mean value of the explanatory variable over all 45 observations. For discrete versions of the variables, the mean is the proportion of observations for which D  = 1.</p><p>(4) Standard deviation of the explanatory variables.</p><p>(5) Correlation between a continuous explanatory variable and average total cost.</p><p>(6) Mean value of average total cost for values of the dummy variable equal to zero.</p><p>(7) Mean value of average total cost for values of the dummy variable equal to one.</p><p>(8) p-value of the test of the null hypothesis of no relationship between the explanatory variable and average total cost.</p><p>(9) – (12): Same definitions as (5) – (8), respectively, except for average on-site cost instead of average total cost.</p><p>(13) – (16): Same definitions as (5) – (8), respectively, except for average off-site cost instead of average total cost.</p

Breakdown of incremental ART program costs by major expenditure categories.

<p>Breakdown of incremental ART program costs by major expenditure categories.</p

One-way sensitivity analysis.

1<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051993#pone-0051993-t001" target="_blank">Table 1</a> for switch rate per year per successive year of treatment.</p><p>Cost per DALY averted when six key model input variables are varied to 50% and 150% of base case values.</p

CIDRZ Zambia ART program cost-effectiveness in 45 sites.

<p>Cost effectiveness is presented as costs and mortality reductions following initial 16 weeks of therapy; and for the entire period between ART services initiation at each site and July 1, 2008. Means are the sum of all DALYs or deaths averted across all sites, divided by the sum of the costs at all sites. Standard deviations are calculated for the mean values of all sites.</p

Estimated regressions of average total cost per DALY averted of antiretroviral therapy in 45 facilities in Zambia.

<p>Note: **p<0.05,</p>*<p>p<0.1,+p<0.2. p-values are given in parentheses to the right of the coefficients in columns (1), (2) and (4).</p>c<p>Values of p for “Early Start” are the significance level at which the joint hypothesis can be rejected that neither baseline CD4 nor proportion of patients initiating at WHO stage IV influences the dependent variable.</p