×

Tweedie model for predicting factors associated with distance traveled to access inpatient services in Kenya. (English) Zbl 07549619

Summary: Aim. This study aims to examine which factors influence the distance traveled by patients for inpatient care in Kenya. Methods. We used data from the fourth round of the Kenya Household Health Expenditure and Utilization survey. Our dependent variable was the self-reported distance traveled by patients to access inpatient care at public health facilities. As the clustered data were correlated, we used the generalized estimating equations approach with an exchangeable correlation under a Tweedie distribution. To select the best-fit covariates for predicting distance, we adopted a variable selection technique using the \(QI C_u\) and \(R^2\) criteria, wherein the lowest (highest) value for the former (latter) is preferred. Results. Using data for 451 participants from 47 counties, we found that three-fifths were admitted between 1 and 5 days, two-thirds resided in rural areas, and 90% were satisfied with the facilities’ service. Wealth quintiles were evenly distributed across respondents. Most admissions (81%) comprised \(<15\), \(>65\), and 25–54 years. Many households were of medium size (4–6 members) and had low education level (48%), and nine-tenths had no access to insurance. While two-thirds reported employment-based income, the same number reported not having cash to pay for inpatient services; 6 out of 10 paid over 3000 KES. Thus, differences in employment, ability to pay, and household size influence the distance traveled to access government healthcare facilities in Kenya. Interpretation. Low-income individuals more likely have large households and live in rural areas and, thus, are forced to travel farther to access inpatient care. Unlike the unemployed, the employed may have better socioeconomic status and possibly live near inpatient healthcare facilities, thereby explaining their short distances to access these services. Policymakers must support equal access to inpatient services, prioritize rural areas, open job opportunities, and encourage smaller families.

MSC:

62Jxx Linear inference, regression
62Pxx Applications of statistics
62-XX Statistics

References:

[1] MoH, Kenya Household Health Expenditure and Utilization Survey (2014), Nairobi, Kenya: Ministry of Health, Nairobi, Kenya
[2] MoH, Kenya Household Health Expenditure and Utilization Survey (2018), Nairobi, Kenya: Ministry of Health, Nairobi, Kenya
[3] Knbs, Economic Survey (2020), Nairobi, Kenya: Kenya National Bureau of Statistics, Nairobi, Kenya
[4] Nganga, S., Exploring the Fiscal Space of the Health Sector in Kenya (2021), Nairobi, Kenya: KPMG East Africa, Nairobi, Kenya
[5] Who, Abuja Declaration: Ten Years on (2010), Geneva, Switzerland: Worla Health Organization, Geneva, Switzerland
[6] Turin, D. R., Health care utilization in the Kenyan health system: challenges and opportunities, Inquiries Journal/Student Pulse, 2 (2010)
[7] Macharia, P. M.; Mumo, E.; Okiro, E. A., Modelling geographical accessibility to urban centres in Kenya in 2019, PLoS One, 16, 5 (2021) · doi:10.1371/journal.pone.0251624
[8] Power, K., Kenya Leads East Africa Peers in Access to Electricity (2018), Nairobi, Kenya: Kenya Power and Lightning Company (KPLC), Nairobi, Kenya
[9] Knbs, 2019 Kenya Population and Housing Census; Distribution of Population by Socio-Economic Characteristics (2019), Nairobi, Kenya: Kenya National Bureau of Statistics, Nairobi, Kenya
[10] Mariita, A., Kenya’s Health Structure and the Six Levels of Hospitals -RoGGKenya (2019), Nairobi, Kenya: Transparency International Kenya, Nairobi, Kenya
[11] Knbs, 2009 Kenya Population and Housing Census; Analytical Report on Population Projections (2012), Nairobi, Kenya: Kenya National Bureau of Statistics, Nairobi, Kenya
[12] MoH, Kenya Harmonized Health Facility Assessment (2019), Nairobi, Kenya: Ministry of Health, Nairobi, Kenya
[13] MoH, National Health Sector Strategic Plan of Kenya (2005), Nairobi, Kenya: Ministry of Health, Nairobi, Kenya
[14] Kelly, C.; Hulme, C.; Farragher, T.; Clarke, G., Are differences in travel time or distance to healthcare for adults in global north countries associated with an impact on health outcomes? a systematic review, BMJ Open, 6 (2016) · doi:10.1136/bmjopen-2016-013059
[15] Karra, M.; Fink, G.; Canning, D., Facility distance and child mortality: a multi-country study of health facility access, service utilization, and child health outcomes, International Journal of Epidemiology, 46, 3, 817-826 (2017) · doi:10.1093/ije/dyw062
[16] Noor, A. M.; Zurovac, D.; Hay, S. I.; Ochola, S. A.; Snow, R. W., Defining equity in physical access to clinical services using geographical information systems as part of malaria planning and monitoring in Kenya, Tropical Medicine and International Health, 8, 10, 917-926 (2003) · doi:10.1046/j.1365-3156.2003.01112.x
[17] Escamilla, V.; Calhoun, L.; Winston, J.; Speizer, I. S., The role of distance and quality on facility selection for maternal and child health services in urban Kenya, Journal of Urban Health, 95, 1, 1-12 (2018) · doi:10.1007/s11524-017-0212-8
[18] Knbs, Kenya Demographic and Health Survey 2014 (2015), Rockville, MD, USA: Kenya National Bureau of Statistics, Rockville, MD, USA
[19] Moindi, R. O.; Ngari, M. M.; Nyambati, V. C. S.; Mbakaya, C., Why mothers still deliver at home: understanding factors associated with home deliveries and cultural practices in rural coastal Kenya, a cross-section study, BMC Public Health, 16, 1, 114 (2016) · doi:10.1186/s12889-016-2780-z
[20] Mwaliko, E.; Downing, R.; O’Meara, W.; Chelagat, D.; Obala, A.; Downing, T.; Simiyu, C.; Odhiambo, D.; Ayuo, P.; Menya, D.; Khwa-Otsyula, B., “Not too far to walk”: the influence of distance on place of delivery in a western Kenya health demographic surveillance system, BMC Health Services Research, 14, 1, 212 (2014) · doi:10.1186/1472-6963-14-212
[21] Kukla, M.; McKay, N.; Rheingans, R.; Harman, J.; Schumacher, J.; Kotloff, K. L.; Levine, M. M.; Breiman, R.; Farag, T.; Walker, D.; Nasrin, D.; Omore, R.; O’Reilly, C.; Mintz, E., The effect of costs on Kenyan households’ demand for medical care: why time and distance matter, Health Policy and Planning, 32, 10, 1397-1406 (2017) · doi:10.1093/heapol/czx120
[22] Mochida, K.; Nonaka, D.; Wamulume, J.; Kobayashi, J., Supply-side barriers to the use of public healthcare facilities for childhood illness care in rural zambia: a cross-sectional study linking data from a healthcare facility census to a household survey, International Journal of Environmental Research and Public Health, 18, 10, 5409 (2021) · doi:10.3390/ijerph18105409
[23] Kahabuka, C.; Kvåle, G.; Hinderaker, S. G., Care-seeking and management of common childhood illnesses in Tanzania—results from the 2010 demographic and health survey, PLoS One, 8, 3 (2013) · doi:10.1371/journal.pone.0058789
[24] Feikin, D. R.; Nguyen, L. M.; Adazu, K.; Ombok, M.; Audi, A.; Slutsker, L.; Lindblade, K. A., The impact of distance of residence from a peripheral health facility on pediatric health utilisation in rural western Kenya, Tropical Medicine & International Health, 14, 1, 54-61 (2009) · doi:10.1111/j.1365-3156.2008.02193.x
[25] Jørgensen, B., The Theory of Dispersion Models (1997), Boca Raton, FL, USA: Chapman and Hall/CRC, Boca Raton, FL, USA · Zbl 0928.62052
[26] Manikandan, S., Data transformation, Journal of Pharmacology and Pharmacotherapeutics, 1, 2, 126-127 (2010) · doi:10.4103/0976-500x.72373
[27] Su, S.; Dzupire, N. C.; Ngare, P.; Odongo, L., A poisson-gamma model for zero inflated rainfall data, Journal of Probability and Statistics, 2018 (2018) · Zbl 1431.62517 · doi:10.1155/2018/1012647
[28] Swallow, B.; Buckland, S. T.; King, R.; Toms, M. P., Bayesian hierarchical modelling of continuous non-negative longitudinal data with a spike at zero: an application to a study of birds visiting gardens in winter, Biometrical Journal, 58, 2, 357-371 (2016) · Zbl 1381.62283 · doi:10.1002/bimj.201400081
[29] Ding, B. Y.; Gao, W.; Dai, S.; Abhadiomhen, S. E.; He, W.; Yin, X., Low rank correlation representation and clustering, Scientific Programming, 2021 (2021) · doi:10.1155/2021/6639582
[30] Mwenda, N.; Kosgei, M.; Kerich, G.; Nduati, R., Predictors of household spending on out-patient expenses in Kenya (2020), https://www.preprints.org/manuscript/202012.0374/v1
[31] Swan, T., Generalized Estimating Equations when the Response Variable Has a Tweedie Distribution: An Application for Multi-Site Rainfall Modelling (2006), Toowoomba, Australia: The University of Southern Queensland, Toowoomba, Australia
[32] Mwenda, N.; Nduati, R.; Kosgei, M.; Kerich, G., Skewed logit model for analyzing correlated infant morbidity data, PLoS One, 16, 2 (2021) · doi:10.1371/journal.pone.0246269
[33] Bureau, U. C., International programs—census and survey processing system overview—people and households (2013), https://www.r-project.org/
[34] Fielding, S.; Fayers, P. M.; McDonald, A.; McPherson, G.; Campbell, M. K.; The RECORD Study Group, Simple imputation methods were inadequate for missing not at random (MNAR) quality of life data, Health and Quality of Life Outcomes, 6, 1, 57 (2008) · doi:10.1186/1477-7525-6-57
[35] Gabrysch, S.; Cousens, S.; Cox, J.; Campbell, O. M. R., The influence of distance and level of care on delivery place in rural Zambia: a study of linked national data in a geographic information system, PLoS Medicine, 8, 1 (2011) · doi:10.1371/journal.pmed.1000394
[36] Kadobera, D.; Sartorius, B.; Masanja, H.; Mathew, A.; Waiswa, P., The effect of distance to formal health facility on childhood mortality in rural Tanzania, 2005-2007, Global Health Action, 5, 1 (2012) · doi:10.3402/gha.v5i0.19099
[37] Schoeps, A.; Gabrysch, S.; Niamba, L.; Sié, A.; Becher, H., The effect of distance to health-care facilities on childhood mortality in rural burkina faso, American Journal of Epidemiology, 173, 5, 492-498 (2011) · doi:10.1093/aje/kwq386
[38] Stock, R., Distance and the utilization of health facilities in rural Nigeria, Social Science & Medicine, 17, 9, 563-570 (1983), doi:10.1016/0277-9536(83)90298-8 · doi:10.1016/0277-9536(83)90298-8
[39] Awoyemi, T. T.; Obayelu, O. A.; Opaluwa, H. I., Effect of distance on utilization of health care services in rural kogi state, Nigeria, Journal of Human Ecology, 35, 1, 1-9 (2011) · doi:10.1080/09709274.2011.11906385
[40] Biswas, R. K.; Kabir, E., Influence of distance between residence and health facilities on non-communicable diseases: an assessment over hypertension and diabetes in Bangladesh, PLoS One, 12 (2017) · doi:10.1371/journal.pone.0177027
[41] Nic Carthaigh, N.; De Gryse, B.; Esmati, A. S.; Nizar, B.; Van Overloop, C.; Fricke, R.; Bseiso, J.; Baker, C.; Decroo, T.; Philips, M., Patients struggle to access effective health care due to ongoing violence, distance, costs and health service performance in Afghanistan, International Health, 7, 3, 169-175 (2014) · doi:10.1093/inthealth/ihu086
[42] Filmer, D.; Pritchett, L. H., Estimating wealth effects without expenditure data-or tears: an application to educational enrollments in states of India, Demography, 38, 1, 115-132 (2001) · doi:10.2307/3088292
[43] Rippin, H. L.; Hutchinson, J.; Greenwood, D. C.; Jewell, J.; Breda, J. J.; Martin, A.; Rippin, D. M.; Schindler, K.; Rust, P.; Fagt, S.; Matthiessen, J.; Nurk, E.; Nelis, K.; Kukk, M.; Tapanainen, H.; Valsta, L.; Heuer, T.; Sarkadi-Nagy, E.; Bakacs, M.; Tazhibayev, S.; Sharmanov, T.; Spiroski, I.; Beukers, M.; van Rossum, C.; Ocke, M.; Lindroos, A. K.; Warensjö Lemming, E.; Cade, J. E., Inequalities in education and national income are associated with poorer diet: pooled analysis of individual participant data across 12 European countries, PLoS One, 15, 5 (2020) · doi:10.1371/journal.pone.0232447
[44] Oecd, Employment Rate by Age Group (indicator) (2020), Paris, France: Organisation for Economic Co-operation and Development, Paris, France
[45] Liang, K.-Y.; Zeger, S. L., Longitudinal data analysis using generalized linear models, Biometrika, 73, 1, 13-22 (1986) · Zbl 0595.62110 · doi:10.1093/biomet/73.1.13
[46] Hardin, J. W.; Hilbe, J. M., Generalized Estimating Equations (2012), Boca Raton, FL, USA: CRC Press, Boca Raton, FL, USA, 2nd edition
[47] Dunn, P. K.; Smyth, G. K., Evaluation of Tweedie exponential dispersion model densities by Fourier inversion, Statistics and Computing, 18, 1, 73-86 (2008) · doi:10.1007/s11222-007-9039-6
[48] Giner, G.; Smyth, G. K., Statmod: probability calculations for the inverse Gaussian distribution, The R Journal, 8, 1, 339-351 (2016) · doi:10.32614/rj-2016-024
[49] McCullagh, P.; Nelder, J. A., Generalized Linear Models (1989), Boca Raton, FL, USA: CRC Press, Boca Raton, FL, USA, 2nd edition · Zbl 0556.62041 · doi:10.1016/0377-2217(84)90282-0
[50] Pan, W., Akaike’s information criterion in generalized estimating equations, Biometrics, 57, 1, 120-125 (2001) · Zbl 1210.62099 · doi:10.1111/j.0006-341x.2001.00120.x
[51] Bureau, U. S. C., R: a language and environment for statistical computing (2017), https://www.R-project.org/
[52] Dunn, P. K.; Smyth, G. K., Series evaluation of tweedie exponential dispersion model densities, Statistics and Computing, 15, 4, 267-280 (2005) · doi:10.1007/s11222-005-4070-y
[53] Mahfouz, A. A. R.; Hamid, A. M., An epidemiologic study of primary health care service utilization of summer visitors to Abha, Asir, Saudi Arabia, Journal of Community Health, 18, 2, 121-125 (1993) · doi:10.1007/bf01324420
[54] Mwaliko, E.; Van Hal, G.; Bastiaens, H.; Van Dongen, S.; Gichangi, P.; Otsyula, B.; Naanyu, V.; Temmerman, M., Early detection of cervical cancer in western Kenya: determinants of healthcare providers performing a gynaecological examination for abnormal vaginal discharge or bleeding, BMC Family Practice, 22, 1, 52 (2021) · doi:10.1186/s12875-021-01395-y
[55] Rosen-Zvi, M.; Shpigelman, L.; Kalton, A.; Weissbrod, O.; Akindeinde, S.; Benefeldt, S.; Bentley, A.; Everett, T.; Jajinskiji, J.; Kweyu, E. M.; Neti, C.; Saab, J.; Stewart, O.; Ward, M.; Xie, G., Estimating the impact of prevention action: a simulation model of cervical cancer progression, Studies in Health Technology and Informatics, 205, 288-292 (2014)
[56] Makau-Barasa, L. K.; Greene, S. B.; Othieno-Abinya, N. A.; Wheeler, S.; Skinner, A.; Bennett, A. V., Improving access to cancer testing and treatment in Kenya, Journal of Global Oncology, 4 (2018) · doi:10.1200/jgo.2017.010124
[57] Chuma, J.; Maina, T., Catastrophic health care spending and impoverishment in Kenya, BMC Health Services Research, 12, 1, 413 (2012) · doi:10.1186/1472-6963-12-413
[58] Allin, S.; Masseria, C.; Mossialos, E., Measuring socioeconomic differences in use of health care services by wealth versus by income, American Journal of Public Health, 99, 1849-1855 (2009) · doi:10.2105/AJPH.2008.141499
[59] Kuddus, M. A.; Tynan, E.; McBryde, E., Urbanization: a problem for the rich and the poor?, Public Health Reviews, 41, 1, 1 (2020) · doi:10.1186/s40985-019-0116-0
[60] Gething, P. W.; Johnson, F. A.; Frempong-Ainguah, F.; Nyarko, P.; Baschieri, A.; Aboagye, P.; Falkingham, J.; Matthews, Z.; Atkinson, P. M., Geographical access to care at birth in Ghana: a barrier to safe motherhood, BMC Public Health, 12, 1, 991 (2012) · doi:10.1186/1471-2458-12-991
[61] Lohela, T. J.; Campbell, O. M. R.; Gabrysch, S., Distance to care, facility delivery and early neonatal mortality in Malawi and Zambia, PLoS One, 7, 12 (2012) · doi:10.1371/journal.pone.0052110
[62] Knbs, Kenya Population and Housing Census (2019), Nairobi, Kenya: Kenya National Bureau of Statistics, Nairobi, Kenya
[63] Okwi, P. O.; Ndeng’e, G.; Kristjanson, P.; Arunga, M.; Notenbaert, A.; Omolo, A.; Henninger, N.; Benson, T.; Kariuki, P.; Owuor, J., Spatial determinants of poverty in rural Kenya, Proceedings of the National Academy of Sciences, 104, 43, 16769-16774 (2007) · doi:10.1073/pnas.0611107104
[64] Awiti, J. O., Poverty and health care demand in Kenya, BMC Health Services Research, 14, 1, 560 (2014) · doi:10.1186/s12913-014-0560-y
[65] Meyer, D. F.; Nishimwe-Niyimbanira, R., The impact of household size on poverty: an analysis of various low-income townships in the northern free state region, South Africa, African Population Studies, 30, 2 (2016) · doi:10.11564/30-2-811
[66] Ozawa, S.; Grewal, S.; Bridges, J. F. P., Household size and the decision to purchase health insurance in cambodia: results of a discrete-choice experiment with scale adjustment, Applied Health Economics and Health Policy, 14, 2, 195-204 (2016) · doi:10.1007/s40258-016-0222-9
[67] Thaddeus, S.; Maine, D., Too far to walk: maternal mortality in context, Social Science & Medicine, 38, 1091-1110 (1994)
[68] Miseda, M. H.; Were, S. O.; Murianki, C. A.; Mutuku, M. P.; Mutwiwa, S. N., The implication of the shortage of health workforce specialist on universal health coverage in Kenya, Human Resources for Health, 15, 1, 80 (2017) · doi:10.1186/s12960-017-0253-9
[69] Kuria, G., Cuba to send more than 100 doctors to kenya as part of medical exchange program (2021), https://africa.cgtn.com/2021/06/09/cuba-to-send-more-than-100-doctors-to-kenya-as-part-of-medical-exchange-program/
[70] Farmer, J.; Clark, A.; Munoz, S.-A., Is a global rural and remote health research agenda desirable or is context supreme?, Australian Journal of Rural Health, 18, 3, 96-101 (2010) · doi:10.1111/j.1440-1584.2010.01140.x
[71] Strasser, R., Rural health around the world: challenges and solutions, Family Practice, 20, 4, 457-463 (2003) · doi:10.1093/fampra/cmg422
[72] Masaba, B. B.; Moturi, J. K.; Taiswa, J.; Mmusi-Phetoe, R. M., Devolution of healthcare system in Kenya: progress and challenges, Public Health, 189, 135-140 (2020) · doi:10.1016/j.puhe.2020.10.001
[73] Moses, M. W.; Korir, J.; Zeng, W.; Musiega, A.; Oyasi, J.; Lu, R.; Chuma, J.; Di Giorgio, L., Performance assessment of the county healthcare systems in Kenya: a mixed-methods analysis, BMJ Global Health, 6, 6 (2021) · doi:10.1136/bmjgh-2020-004707
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.