Inequality in the Quality of Health Services: Wealth, Content of Care, and Price of Antenatal Consultations in the Democratic Republic of Congo

Abstract

Using unique direct observations of patient-provider interactions linked to patient exit interviews and detailed household surveys, this paper assesses the relationship between patient wealth and the quality and price of antenatal care in the Democratic Republic of Congo. Overall, the analysis finds a significant wealth-quality gradient, with a standard deviation increase in wealth being associated with an increase of 4 percentage points in protocol compliance. This increase in compliance represents 8 percent of the average quality of care received by women in the lowest wealth quintile. Over half of the wealth-quality gradient is driven by lower facility quality in poorer areas. However, the analysis also finds statistically significant within-village and even within-facility wealth-quality relationships. Within villages, wealth-quality gradients are primarily driven by wealthier women seeking care at higher-quality even if more distant facilities. Within the same facilities, poorer women tend to receive worse care, but on average they also pay less for the same quality of care compared with wealthier women. The price gap increases in the local ratio of wealthy to poor households, suggesting that providers do not charge different prices only for redistributive reasons.

Poor quality for the poor? A study of inequalities in service readiness and provider knowledge in Indonesian primary health care facilities

“…In Tanzania, a study conducted in the Arusha region found that the competence of doctors in both private and public sectors was significantly lower in poorer regions [ 18 ]. One study conducted in the Democratic Republic of Congo found that women with lower socio-economic status lived in areas where the quality of care available was low compared to women with higher SES [ 14 ]. Two studies looked at the effect of pay-for-performance (P4P) schemes on inequalities in the performance of providers in Tanzania and Brazil.…”

“…Relatively few studies have looked at this type of inequality, perhaps reflecting the rarity of having data on both quality of care and catchment population SES in the same geographical area. The available studies indicate consistent evidence that areas with low SES tend to be served by providers with lower competence [ 5 , 9 , 11 , 14 , 16 , 18 ] and by facilities with limited equipment and infrastructure [ 25 , 26 ].…”

Inequalities in effective coverage measures: are we asking too much of the data?

“…However, evidence demonstrates that this is not the case; individuals with different characteristics receive different quality of care. 5 14 Estimates of inequalities in effective coverage measures that are derived from linked household and facility data are driven only by the access to care measure.…”

“…Exact-match linking of individuals in population data to the exact health facility they attended will capture systematic differences in care-seeking behaviour between individuals with different characteristics—for example, that wealthier individuals are more likely to bypass their nearest sources of care to seek higher quality care—either outside of their catchment area or at a higher level facility. 5 15 Ecological linking—in which individuals from population data are linked to an average quality score across multiple health facilities—takes us a step further away, since it assumes there are no systematic differences in care-seeking behaviour between individuals with different characteristics. Adjusting for the type of facility that people report receiving care from has been demonstrated to generate valid measures of effective coverage, as likely accounts for some difference in care-seeking behaviour.…”

“… 2–4 Inequalities in the availability and quality of health services exist at all levels: between geographic regions, within geographic regions, and even within individual health facilities and families. 5 To address inequalities effective coverage measures should be disaggregated by key sociodemographic and economic variables 1 —such as wealth, age, ethnicity, gender, education, place of residence. 6 …”