Analyzing the equity of public primary care provision in Kenya: variation in facility characteristics by local poverty level

Abstract

IntroductionEquitable access to health care is a key health systems goal, and is a particular concern in low-income countries. In Kenya, public facilities are an important resource for the poor, but little is known on the equity of service provision. This paper assesses whether poorer areas have poorer health services by investigating associations between public facility characteristics and the poverty level of the area in which the facility is located.MethodsData on facility characteristics were collected from a nationally representative sample of public health centers and dispensaries across all 8 provinces in Kenya. A two-stage cluster randomized sampling process was used to select facilities. Univariate associations between facility characteristics and socioeconomic status (SES) of the area in which the facility was located were assessed using chi-squared tests, equity ratios and concentration indices. Indirectly standardized concentration indices were used to assess the influence of SES on facility inputs and service availability while controlling for facility type, province, and remoteness.ResultsFor most indicators, we found no indication of variation by SES. The clear exceptions were electricity and laboratory services which showed evidence of pro-rich inequalities, with equity ratios of 3.16 and 3.43, concentration indices of 0.09 (p<0.01) and 0.05 (p=0.01), and indirectly standardized concentration ratios of 0.07 (p<0.01) and 0.05 (p=0.01). There were also some indications of pro-rich inequalities for availability of drugs and qualified staff. The lack of evidence of inequality for other indicators does not imply that availability of inputs and services was invariably high; for example, while availability was close to 90% for water supply and family planning services, under half of facilities offered delivery services or outreach.ConclusionsThe paper shows how local area poverty data can be combined with national health facility surveys, providing a tool for policy makers to assess the equity of input and service availability. There was little evidence of inequalities for most inputs and services, with the clear exceptions of electricity and laboratory services. However, efforts are required to improve the availability of key inputs and services across public facilities in all areas, regardless of SES.

An assessment of equity in the distribution of non-financial health care inputs across public primary health care facilities in Tanzania

“…Facilities serving the poorest patients appeared somewhat more likely to be served by support staffs, including medical attendants and health attendants, than those serving the least poor patients, though this was not supported by the dominance test. We also find a negative but not significant correlation between clinical and nursing staffing levels per 1000 population and distance from the district headquarters, a finding that is similar to previous studies that reported a concentration of health workers in urban areas in countries like Mali, Sudan, Uganda, Botswana, South Africa and Tanzania [55, 64]. Munga and Maestad [39] also found significant inequalities in the distribution of health workers per capita and inequities in the skill mix of health care staff in the districts.…”

“…A study in Kenya examined the distribution of health care inputs at facility level in relation to socio-economic status [55], but such information is not available from other settings. Guidelines for how to allocate inputs across facilities within geographic areas are not always available, with potential for variation across local government authorities in the approach used [60].…”

Implementation of patient charges at primary care facilities in Kenya: implications of low adherence to user fee policy for users and facility revenue

“…To measure the poverty level of the facility’s local area, we used the proportion of the population above the poverty line in the location (second lowest administrative area) in which the sampled facility was located. Methods for the calculation of the poverty level by location are presented elsewhere ( Toda et al 2012 ). Sampled facilities were grouped into weighted socio-economic status (SES) quintiles.…”

Current realities versus theoretical optima: quantifying efficiency and sociospatial equity of travel time to hospitals in low-income and middle-income countries

“…To our knowledge, this is the first multicountry study to quantify the trade-offs between the conflicting goals of hospital care provision efficiency and sociospatial equity, and to identify current areas of substandard performance in sub-Saharan Africa. Using the Malaria Atlas Project’s Friction Surface 2015, we were able to refine the scale of results and generalisability compared with previous studies conducted at the county and sub-district levels 16–18…”

“…Inequity of physical access to surgical care and emergency obstetric care (services usually provided only in hospital settings) in poorer areas/subpopulations has been shown suboptimal compared with wealthier areas/subpopulations in LMICs in a few national and subnational studies 16–18. However, there is still a distinct lack of nationally-representative and generalisable studies in the literature.…”