Haiti » ssessing the quality of primary care in Haiti

Anna D Gage a, Hannah H Leslie a, Asaf Bitton b, J Gregory Jerome c, Roody Thermidor d, Jean Paul Joseph c & Margaret E Kruk a

a. Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, United States of America (USA).
b. Department of Health Care Policy, Harvard Medical School, Boston, USA.
c. Zanmi LaSante, Cange, Haiti.
d. Ministry of Health, Port-au-Prince, Haiti.

Correspondence to Anna D Gage (email: agage@hsph.harvard.edu).

(Submitted: 03 June 2016 – Revised version received: 16 September 2016 – Accepted: 26 September 2016 – Published online: 08 February 2017.)

Bulletin of the World Health Organization 2017;95:182-190. doi: http://dx.doi.org/10.2471/BLT.16.179846

ntroduction

Thirty years after the Declaration of Alma-Ata, the 2008 World Health Report declared that primary health care was a global priority “now more than ever”.1 Primary care forms the cornerstone of a functional health system. High-quality primary care systems can improve health outcomes, increase equity in health care and optimize efficient use of resources.2–4 In low- and middle-income countries, however, primary care is often poor, with a general lack of provider effort, high rates of misdiagnosis and incorrect treatment, and long wait times.5–8

Research on the quality of primary care includes investigations of provider behaviour and knowledge,5 programme evaluations9 and small-scale case studies.10 Broader assessments of primary care systems, particularly in the wake of conflict11 or natural disaster,12 have included the development of balanced scorecards. These scorecards have focused on infrastructure inputs and community perspectives and given relatively little attention to the processes of care. One limitation of the research in this field is the lack of a comprehensive definition of primary care quality that is applicable across contexts and countries.

In an effort to guide quality measurement and improvement in the field of primary care, the Primary Health Care Performance Initiative reviewed over 40 different conceptual frameworks of primary care and consolidated them into a single framework.13 This framework, which is still evolving, unifies previous work into five key areas: system, inputs, service delivery, outputs and outcomes. An important contribution of this framework is the delineation of the service delivery area, a critical but understudied element of primary care quality, into five interconnected domains. These are population health management, e.g. community engagement; facility management and organization; access to care that is timely and affordable; the availability of effective services; and high-quality primary health care. The final domain follows from the others and encompasses Starfield’s formulation of primary care’s roles and functions: coordination, comprehensiveness, continuity and first-contact access.14

The development of new metrics based on this framework is a critical next step in assessing the quality of the delivery of primary health care. Metrics that align with updated theoretical frameworks and shed light on the quality of care provided to patients are needed to understand primary care performance more fully. Such metrics can help health ministries identify shortfalls in the provision of quality primary care and prioritize appropriate action.

Given its poor population health outcomes and its recent attempts to build a strong primary care system, Haiti presents a compelling case study of primary care quality. Life expectancy at birth is 65 years, and mortality among children younger than five years is more than double that in the neighbouring Dominican Republic.15 There is only one doctor or nurse per 3000 population and public sector health spending is among the lowest in the world. An earthquake in January 2010 placed further strain on the health system and caused tremendous loss of life and immense physical damage, destroying 50 health facilities.16,17 Despite natural disasters, poverty and underinvestment in health, Haiti has achieved some notable health gains in recent decades, including a steady decline in mortality among children younger than five years.15

In 2008, Haiti’s primary care system was classified as selective, with targeted application of high-impact interventions in facilities that, in general, struggled with the provision of routine care.18 In 2007, the Haitian Ministry of Health’s National Quality Committee launched HIVQual, a system for data collection, based on electronic medical records, designed to measure and improve the quality of services for people living with the human immunodeficiency virus (HIV).19 In 2012, this system was expanded to cover some non-HIV services’ care and to reach a larger number of facilities.19 As global health policy pivots towards universal health coverage and to tackling the broad array of health challenges outlined in the sustainable development goals,20,21 it is an opportune moment to test a methodology for assessing coverage of comprehensive, high-quality primary care.

Below, we describe the development of a theoretically grounded metric of primary care quality, based on existing survey and geospatial data, and the metric’s application in measuring the quality of Haiti’s primary care system. We drew on a census of Haiti’s health facilities to evaluate the performance of the country’s primary care system in 2013, describe geographical access to quality care and assess the disparities in such access. To highlight the challenges and opportunities of measurement in this understudied area, we focused on the service delivery component of the Primary Health Care Performance Initiative’s framework.

Methods
Study sample

We used data from the Service Provision Assessment, which is a census survey of health facilities conducted in Haiti in 2013 by the Demographic and Health Survey Program. The census included a facility assessment, a questionnaire for health-care providers, observations of sick child, antenatal care and family planning visits, and exit interviews with observed clients. We limited our analysis to the data collected on outpatient primary care facilities, i.e. dispensaries and health centres with or without beds.22

We also used WorldPop maps to obtain estimates of the 2015 population density of Haiti, at a resolution of 100 m2.23
Measuring primary care quality

We developed metrics of service delivery quality following the Primary Health Care Performance Initiative’s framework. Several modifications were required to adapt the framework for health facility assessment (Fig. 1). We excluded the domain “population health management”, because of a lack of relevant facility-related data. For clarity, we also altered the labels for two of the domains, using “effective service delivery” for the availability of effective services and “primary care functions” for high-quality primary health care.14
Fig. 1. Conceptual framework of quality in primary health care
Fig. 1. <b>Conceptual framework of quality in primary health care</b>
Source: Adapted from the Primary Health Care Performance Initiative’s framework,13 for use in Haiti.
Figure 1 - full screen

We reviewed the data available in the survey and selected 28 indicators that most appropriately matched each of the quality subdomains included in our analysis. For this selection, we were guided by the Primary Health Care Performance Initiative’s method note.13 Each indicator is a proportion or an index that ranges from 0 to 1. For example, the indicator “sick child did not first visit traditional healer” measures first-contact access to a facility as the proportion of sick children who came to the facility for care without first visiting a traditional healer. All selected indicator definitions are available from the corresponding author. Within the survey data, we were unable to find relevant indicators for two of the subdomains that we wished to investigate: geographical access and the organization of team-based care. As people need to be able to access health facilities to benefit from quality care, we used the WorldPop maps to determine geographical access to facilities.

For each primary care facility, we calculated a score for each of four service delivery domains: (i) accessible care; (ii) effective service delivery; (iii) management and organization; and (iv) primary care functions. Each of these scores, which could range from 0 to 1, was the mean of all the indicators under the domain. As we considered the four domains to be equally important elements of quality primary care, we took the mean of the four scores calculated for each facility as the overall measurement of the quality of the facility’s service delivery for primary care.

Although the census covered all but two of the health facilities in Haiti in 2013, two of the survey tools, i.e. clinical observations and patient interviews, were applied only in a selected subset of facilities. For each indicator included in our analysis, we used multiple imputation to generate five versions of a completed data set for all quality indicators. We based the imputation on observed covariates, e.g. management type and urban, and the non-missing indicators.

Finally, we assessed the distribution of indicators across facilities and sought valid groupings of better and worse quality. Given the lack of universally defined minimum quality thresholds and the rudimentary nature of many of the indicators included in our analysis, we divided the facility scores into three categories of quality. Scores of less than 0.50, 0.50–0.74 and at least 0.75 were considered indicative of poor, fair and good quality, respectively.
Covariates

We defined each 100 m2 block of population as an urban or rural population using the census’ urban or rural classification of the facility nearest to the centre of the block. As a sensitivity check, we also defined an urban population as one in which there were at least five people per 100 m2 block.
Analysis

We calculated descriptive statistics of the primary care facilities with non-response weights. We summarized mean values and uncertainty intervals for each indicator, domain and overall quality score for service delivery. As the data we analysed provided a census of the primary care facilities in Haiti in 2013, the uncertainty intervals that we calculated indicate the measurement error attributable to missing data.24 Using inverse distance-weighted interpolation, we mapped, across Haiti, the quality of the primary care available to a nearby population. In the resultant map, the colour of each 100 m2 block indicates whether the quality of the nearest primary care facility was poor, fair or good. We used the global Moran’s I statistic, which tests for the presence of spatial autocorrelation,25 to investigate whether facilities of good or poor quality, in terms of each of the four domains of interest, were clustered geographically. Moran’s I can range from −1 to 1. In our analyses, positive I values would indicate that primary care facilities of similar quality were clustered together. We defined proximity using an inverse-distance weight matrix.26 In keeping with prior research on physical access to care in Haiti,27 we calculated the percentages of the entire Haitian population, rural population and urban population living within 5 km of any facility and within the same distance of a facility with a good overall score. Finally, we mapped the areas that lay within 5 km of any facility and a facility with a good overall care score.

Multiple imputation was conducted in R 3.2 (R Core Team, Vienna, Austria). All other analyses were conducted in Stata version 14.0 (StataCorp, LP, College Station, United States of America). We used QGIS version 2.1228 to map the data.
Ethical approval

The Harvard University Human Research Protection Program categorized this secondary analysis of data as exempt from human subjects review.
Results

The survey obtained detailed data from 905 (99.8%) of the 907 health facilities in Haiti in 2013, 786 of which were primary care facilities and included in the analysis (Table 1). Most primary care facilities were classified as rural, although there was a high concentration of primary care facilities in and around Port-au-Prince. Fig. 2 summarizes the performance of the primary care facilities across the four domains of primary care service delivery. At the average facility, 86% and 94% of clients, respectively, stated that they did not find wait times or the costs of care to be a problem, even though about half of all primary care services required payment and over half of the primary care facilities had mean wait times in excess of one hour. Large gaps in quality were evident in the metrics for the availability of effective services. The indicators for provider motivation and safety were found to be especially low. Basic elements of clinical care were not universally followed. For example, at the average facility only 57% of the providers asked about maternal age at a first visit for antenatal care. Low quality scores for primary care functions were partially attributable to poor provider communication. Under management and organization, only 2% (18) of the primary care facilities had a system for gathering feedback from their clients and nearly three-quarters (577) did not have routine quality assurance processes. For their overall quality of service delivery, the primary care facilities in Haiti achieved a mean score of 0.59.

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