Prevalence and Determinants of Rural-Urban Utilization of Skilled Delivery Services in Northern Ghana

Background. There are wide differences in the uptake of skilled delivery services between urban and rural women in the northern region of Ghana. This study assessed the rural-urban differences in the prevalence of and factors associated with uptake of skilled delivery in the northern region of Ghana. Methods. The study population comprised postpartum women who had delivered within the last three months prior to the study. The dataset was analyzed using the chi-square test and multivariable logistic regression. Results. The odds of skilled birth attendance (SBA) adjusted for confounding variables in urban areas were higher compared with their rural counterparts (AOR = 1.59; CI: 1. 07-2.37; p=0.02). The determinants of skilled delivery were similar but of different levels and strength in rural and urban areas. The main drivers that explained the relatively high skilled delivery coverage in the urban areas were higher frequency of antenatal care (ANC) attendance, proximity (physical access) to health facility, and greater proportion of women attaining higher educational level of at least secondary school. Distance from health facility less than 4 km was the greatest independent contributor to the variance in skilled delivery in the urban areas, whereas frequency of ANC attendance was the greatest independent contributor in the rural areas. Conclusions. This study identified underlying determinants accounting for rural-urban differences in skilled delivery, and covariate effect was more dominant than coefficient effect. Therefore, urban-rural differences in SBA outcomes were primarily due to differences in the levels of critical determinants rather than the nature of the determinants themselves. Therefore, improving skilled delivery outcomes in this study population and other similar settings will not require different policy frameworks and interventions in dealing with rural-urban disparities in SBA outcomes. However, context-specific tailored approaches and strategies including targeting mechanisms have to be designed differently to reduce the rural-urban differences.

The study was conducted in Nanumba North District which is largely rural and Tamale Metropolis which is essentially urban. “Urban” and “rural” settlements classification was based on population size. Localities with 5,000 or more persons were classified as urban while localities with less than 5,000 persons were classified as rural [13]. The Nanumba North District was created as a separate district in 2004 under LI 1754 of Ghana from the then Nanumba District which was split into two areas: North and South. The district covers an area of 1,986 sq. km and it is located in the eastern part of the Northern Region and lies between latitudes 8.5°N and 9.25°N and longitudes 0.57°E and 0.5°E. The Nanumba North District, which is sparsely populated with an annual growth rate of 2.7%, has population of 141,584 (Population and Housing Census, 2010). This population is served by only six health facilities, one of which is a hospital located in district capital at Bimbilla. The total fertility rate for the district is 3.4. The general fertility rate is 97.5 births per 1,000 women aged 15–49 years. The crude birth rate (CBR) is 22.2 per 1,000 population. The crude death rate for the district is 4.6 per 1,000 [13]. A major health challenge is inadequate health personnel. The district is predominantly agricultural with about 79.4% of the people engaged in the agriculture, forestry, and fishery sector, followed by those in craft and related trade (6.2%). This basically makes the district economy agrarian [13]. Access to potable water, education, health, electricity, and adequate sanitary facilities is limited and nonexistent in some homes and communities [14]. The quality of life of the people in the district is therefore largely constrained by these facilities. The Tamale Metropolis was also established under Legislative Instrument (LI) 1801 of 2004. The population of Tamale Metropolis, according to the 2010 Population and Housing Census, is 233,252 representing 9.4 percent of the region’s population. The metropolis has a total estimated land size of 750 km sq. which is about 13% of the total land area of the Northern Region. Geographically, the metropolis lies between latitudes 9°161 and 9°341 North and longitudes 0°361 and 0°571 West. The metropolis has a teaching hospital and other hospitals that provide healthcare services to the populace. The total fertility rate for the metropolis (2.8) is slightly lower, compared to the regional fertility rate of 3.5. The general fertility rate is 79.9 births per 1000 women aged 15–49 years. The crude birth rate (CBR) is 21.2 per 1000 population. The crude death rate for the metropolis is 5.6 deaths per 1000 [15]. The occupation with the highest population in the metropolis is service and sales workers (33.0%). A comparative cross-sectional study design was used conducted in the Tamale Metropolis and the Nanumba North District which are located in the northern region of Ghana. The study focused on assessing the uptake of skilled delivery services among rural and urban women. A sample size of 720 (360 per study area) was used in order to have 80% power of detecting a significant difference of 15% in the primary outcome measure between the urban and the rural groups at 95% confidence interval, assuming a correction factor of 2 (the “design effect”) for cluster sampling. A provision of 10% of total sample size (66) was also factored into the sample size estimation to take care of incomplete/damaged questionnaires. Postpartum women who had delivered within the last three months prior to the study constituted the study population. A two-stage cluster sampling was used to extract the study population from two districts (one rural and one urban). The study was conducted in 30 clusters each from the Tamale Metropolis and the Nanumba North District. The clusters were selected using probability proportional to size (PPS). All the households in each cluster were serially numbered; the total number of households in a cluster was divided by the sample size to give the sampling interval. The first household was randomly selected by picking any number within the sample interval. Subsequent selections were made by adding the sampling interval to the selected number. This was done until the sample size was obtained. Structured pretested questionnaires were used to collect quantitative data which included the sociodemographic characteristics of the respondents, maternal history of antenatal care (ANC) utilization, knowledge of complications in labour, uptake of skilled delivery services, perceived quality of care, household decision-making, and household wealth index. The main outcome measure (dependent variable) was utilization of skilled attendance at birth. The independent variables included geographic access (distance to nearest health facility), maternal autonomy in taking decisions that affect mother’s health, utilization of antenatal care services and quality of antenatal care services, and sociodemographic characteristics including age of mother, parity, marital status, religion, educational background of mothers, and household wealth index [16]. A brief description of main independent and dependent variables is as follows. Skilled attendance rate, the main dependent variable, was measured by asking respondents where delivery of youngest child took place and who assisted with the delivery. A score of 1 was given for delivery by SDBAs (that is, doctor, nurse, or midwife, auxiliary nurse or midwife) while zero (0) was assigned for delivery assisted by persons other than an SBA. Household wealth index which is a proxy measure of socioeconomic status was used to categorize study participating households. Principal components analysis (PCA) was used to quantify this as per respondent ownership of specified durable goods (television, radio, car, mobile telephone, etc.) and housing characteristics (access to electricity, source of drinking water, type of toilet facilities, type of flooring material, and type of cooking fuel) [17]. To ensure proper utilization of health services, it is critical that potential users of these services have a perceived need for them [18]. It is expected that an increase in the need may lead to an increase in the use of health services and vice versa [18]. Perceived need (self-reported need) of women is based on their belief, previous experience, and the need for skilled health services at birth [18, 19]. Need for skilled health services at birth would be influenced by certain constraints or barriers that the woman perceived. In this study, perceived need for supervised delivery was quantified indirectly by a composite index constructed based on responses to 11 identified key perceived barriers to supervised skilled delivery. A score of 1 was assigned to each barrier if the woman mentioned it as the main reason for not delivering at a health facility. The barriers were no difficulty in previous deliveries, no health facility available, long distance from health facility, bad attitude of health workers, cost of delivery (bed preparedness), lack of privacy during delivery, presence of male staff members during delivery, hospital staffs not allowing women to deliver in their preferred way (squatting), cultural/religious beliefs in conflict with hospital delivery, fear of caesarean delivery, and transportation difficulties. The total score for each woman was then categorized into low (<median score) or high (≥median score). The assumption is that women with a lower composite barrier score will have a higher need for skilled birth attendance. Women were asked whether specific services including taking of weight and height, measurement of blood pressure, and taking of blood or urine samples were carried out for them. A composite index comprising ten of these essential services received during ANC was created by assigning a score of 1 for having received a particular service and zero for not receiving the service. The index served as a proxy measure of antenatal care quality. The total score for each woman was then categorized as low (<median score) or high (≥median score). The assumption is that women who received high content ANC services will have higher perceived quality of maternal healthcare than those who received lower quality ANC, which is likely to have a positive effect on their use of SBAs. Though a number of dimensions are used in quantifying women's autonomy in the literature [20, 21], three were assessed in this study: decision autonomy, movement autonomy, and maternal financial independence. Each dimension had a number of items that were scored and added to arrive at each respondent's total score. Women's decision autonomy was estimated from 9 questions on who makes decisions at home; movement autonomy was based on 6 questions on whether women need permission to visit places outside home. Decision autonomy was estimated from nine questions on decision-making (e.g., children's healthcare, education, buying/selling property, and what to cook) [22]. The responses were scored as follows: 2 points for decisions made by the woman; 1 point for decisions made jointly by both the woman and her husband; and 0 for all of decisions taken by others. The independent variables for maternal decision-making power include final say on (1) own healthcare, (2) making large household purchases, (3) making household purchases for daily needs, (4) visits to family or relatives, and (5) foods to be cooked each day. The responses were scored as follows: 2 points for decisions made by the woman; 1 point for decisions made jointly by both the woman and her husband; and 0 for all of decisions taken by others. Movement autonomy was based on 6 questions regarding whether women need permission to visit places outside home (e.g., market, health centre, and relatives' home) [22]. The responses were scored as 1 (no permission required) and 0 (yes, permission always required). Maternal financial independence was assessed based on four questions that relate to sources of income and savings. An overall composite index of women's autonomy (CIWA) was calculated by combining the three dimensions. As was done by Singh et al. [23], two categories (that is, low and high) of the individual components and the CIWA were created based on the average value of the index. The women receiving less than the average score were put in the low autonomy category, and those of at least the average score were categorized as high autonomy. Data cleaning and analysis were carried out using statistical weighted analysis in SPSS Complex Samples module for Windows 21.0 (SPSS Inc., Chicago). This was done in order to make statistically valid population inferences and computed standard errors from sample data. The analytical approach used in this study is a modified version of that of Smith et al. [24] which was used to assess rural-urban disparity in child malnutrition. The analysis sought to establish whether the levels of determinants such as mother's educational level (covariate effect) and their strength of association (coefficient effect) with uptake of skilled delivery services differed between rural and urban populations. The basic assumption in this analysis is that if a determinant, for example, education, is found to be higher in urban than rural areas, but it has a very weak association with the outcome variable (e.g., SBA) in urban relative to rural areas, then it cannot definitively be concluded that education is one of the responsible determinants [24]. In the first step of the analysis, we tested for structural differences in the determinants of skilled birth attendance (SBA) and their strength of association across urban and rural areas. The initial analyses therefore involved estimating the levels of both proximal and distal determinants of SBA in both rural and urban samples. The factors tested are known in the literature to influence uptake of skilled delivery services, and they included socioeconomic status (SES) of mother, antenatal care (ANC) attendance, proximity to health facility, perceived barriers to institutional delivery, and women's decision-making autonomy. For example, what is the proportion of the respondents who had attained higher educational level? Logistic regression analysis was carried out to estimate the strength of association (coefficient effect) of SBA determinants among urban as well as rural women. Two separate models for urban and rural samples were fitted. The first model (Rural Model) was fitted to identify the determinants of utilization of skilled delivery for rural women. The second model (Urban Model) identified the key determinants of SBA among women living in urban areas. The same sets of independent variables were used for both the rural and urban areas in order to enable a comparison between both areas. To estimate the strength of association of each determinant with the outcome variable, odds ratio (OR) and 95% confidence interval (CI) were computed. In the second step of the analysis, we compared the levels of determinants across urban and rural areas, taking into account any structural differences found in the determinants. In comparing the levels of the proximal and distal determinants across rural and urban areas, we tested for significance in the differences in levels across the areas. If the measure of the determinant is continuous, the t-test was used for differences in means. If the determinant is categorical, the chi-squared analysis was performed to test for differences in proportions. The final stage of the analysis involved a comparison of differences in the levels and strength of association of selected determinants of skilled delivery attendance in rural and urban areas. At this stage, a decision was made as to whether the determinant was having a covariate effect, a coefficient effect, or both. To determine whether a factor had “covariate” or “coefficient” effect, we compared both differences in levels as well as strength of association. If the urban-rural difference of a determinant's level was found to be statistically significant and this determinant was significantly associated with SBA, then that determinant is considered to have both covariate and coefficient effects in the urban setting. On the other hand, if there is significant difference in the level of the determinant but lack of association with the outcome variable, then the determinant will only exhibit covariate effect. If a determinant is associated more significantly with the urban areas than with rural areas but there is no discernible difference in the level of the determinant, then it will be classified as having only coefficient effect. In the final stage of the analysis, the kind of effect that dominated in the urban areas was assessed by noting how many effects of each kind were shown in the urban area. The study protocol was approved by the Scientific Review and Ethics Committee of the School of Allied Health Sciences, University for Development Studies, Ghana. Informed consent was also obtained after providing the required information and explanation. In situations where the respondent could not write or read, verbal informed consent was obtained. In addition, verbal informed consent was sought from all the study participants before the commencement of any interview.

• saaka, mahama
• akuamoah-boateng, jones