1. bookVolume 14 (2020): Edition 1 (February 2020)
Détails du magazine
License
Format
Magazine
eISSN
1875-855X
Première parution
01 Jun 2007
Périodicité
6 fois par an
Langues
Anglais
Accès libre

Assessment of maternal health services utilization in Pakistan: the role of socio-demographic characteristics

Publié en ligne: 13 Jul 2020
Volume & Edition: Volume 14 (2020) - Edition 1 (February 2020)
Pages: 3 - 7
Détails du magazine
License
Format
Magazine
eISSN
1875-855X
Première parution
01 Jun 2007
Périodicité
6 fois par an
Langues
Anglais

Maternal health care must be focused to reduce morbidity and mortality of mothers and newborns. Maternal health is an indicator of safe pregnancy, risk-free childbirth, and a step toward a healthy childhood and prosperous life. There is evidence that pregnancy-related complications cause maternal mortality and disabilities [1]. Large variations of maternal mortality ratios represent the consistent gap in health index between developing and developed countries, specifying the dilapidated condition of maternal health care in some developing countries [2]. The Universal Declaration of Human Rights states that special care and assistance should be privileges for pregnant women [3]. The significance of maternal health care was initially acknowledged in 1987 internationally, and a progressive campaign on safe motherhood is continued by several organizations subsequently [4]. More than 50% of all maternal deaths occurred in only six countries, worldwide, including Pakistan where the maternal mortality rate is 276 deaths per 100,000 births reflecting the dire condition of maternal health care [5, 6]. Pakistan is yet a far way to achieve the millennium development goals in maternal health. A reason for this failure is that maternal health care plans have failed to identify the factors that restrict access to pregnancy-related care for women belonging to the social and economic edges of a specific community. Concern has been discussed about the potential determinants of maternal health care utilization across geographical areas. Although research in Pakistan has reported significant socioeconomic factors of maternal health care utilization employing descriptive analyses of covariates, there have been little investigations that could precisely explore the significant causes of this issue using complex statistical methodology [7, 8]. This study examines the predisposing, enabling, and pregnancy-related characteristics of the health behavior model developed by Andersen and Newman using six highly recommended complex count data models for a deeper and precise understanding of maternal health care utilization model.

Methods

Non-parametric tests including Mann–Withney and Kruskal–Wallis tests are used for the bivariate analysis of the number of maternal care visits. Count regression models including Poisson [9, 10], negative binomial (NB) [11, 12, 13], zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), Poisson hurdle (PH), and negative binomial hurdle (NBH) regression model are fitted to identify the significant factors associated with maternal care services utilization. Akaike's information criterion (AIC) is used for assessment and comparison of models.

The data of Pakistan used for this study are obtained from UNICEF's Multiple Indicator Cluster Survey (MICS). This survey was conducted in 2014 with the support of the Bureau of Statistics Punjab, Bureau of Statistics Sindh, Government of the Punjab, and Government of Sindh, Pakistan. MICS is an international program to conduct surveys to collect internationally comparable measures on a wide range of demographic indicators related to women. The secondary dataset of two provinces of Pakistan (Punjab and Sindh) is used for analyses. In this study, women aged 15–49 are included who had pregnancy during the last 2 years before the conduct of the survey. Hence, 16,314 women with complete data of relevant factors are included in the analysis.

Variables

The outcome factor in this study is the number of maternal health care visits utilized by pregnant women. The zero count of maternal visits shows non-use of maternal services. Maternal services utilization is examined regarding pre-disposing, enabling, and pregnancy-related features of the health behavior model developed by Andersen and Newman. Province, woman's age, educational level, husband's age, domestic violence, area, and province and are considered as predisposing factors. Income level, experience of a child's death, access to media, and AIDS awareness are characterized as enabling features. Pregnancy-related factors include desire for pregnancy, parity, and birth order.

Results
Bivariate analysis

Table 1 represents the descriptive statistics of predisposing, enabling, and pregnancy-related features of the sample. Kruskal–Wallis test shows that maternal care utilization is significantly associated with income level. Mann–Whitney test establishes the significant association of maternal care utilization with the province, woman's age, woman's educational attainment, area, domestic violence, access to media, AIDS awareness, an experience of a child's death, desire for pregnancy, parity, and birth order.

Characteristics of the study sample (N = 16,314) and significance of maternal care visits between subgroups

Predisposing characteristicsN (%)Median (P25–P75)P
Province*
Punjab (Pakistan)10,278 (63)3 (2–6)<0.01
Sindh (Pakistan)6,036 (37)2 (1–5)
Age*
≤ 20 years1,773 (10.9)3 (1–5)<0.01
> 20 years14,541 (89.1)3 (1–5)
Education level*
Illterate8,499 (52.1)2 (0–4)<0.01
Literate7,815 (47.9)4 (3–7)
Area*
Urban5,813 (35.6)4 (2–7)<0.01
Rural10,501 (64.4)2 (1–4)
Husband's age*
≤ 30 years6,760 (41.4)3 (1–5)<0.32
> 30 years9,554 (58.6)3 (1–6)
Domestic violence*
Yes8,667 (46.9)2 (1–4)<0.01
No7,647 (53.1)4 (2–7)
Income level
Low7,772 (47.6)2 (0–3)<0.01
Middle3,539 (21.7)3 (2–5)
High5,003 (30.7)5 (3–8)
Access to media*
Yes9,934 (60.9)2 (0–4)<0.01
No6,380 (39.1)4 (2–6)
AIDS awareness*
Yes5,497 (33.7)5 (3–8)<0.01
No10,817 (66.3)2 (1–4)
Experience of a child's death*
Yes3,591 (2.0)2 (1–5)<0.01
No12,723 (78)3 (1–6)
Desire for pregnancy*
Yes14,345 (87.9)3 (1–5)<0.01
No1,969 (12.1)3 (1–5)
Parity*
Primipara3,583 (22)4 (2–7)<0.01
Multipara12,731 (78)3 (1–5)
Birth order*
≤ 412,185 (74.7)3 (2–6)<0.01
> 44,129 (25.3)2 (0–4)

Mann–Whitney test.

Kruskal–Wallis test.

Figure 1 represents that zero counts showing that the non-use of maternal care has the highest proportion than all positive counts. The likelihood ratio test for overdispersion between Poisson and negative binomial distribution shows a χ2 test-statistics = 6113 with a critical value test statistics = 2.7 and P < 0.001 which is strong evidence of overdispersion in the observed data.

Figure 1

Frequency distribution of maternal care visits in Pakistan.

Model comparison

For all count regression models including NB, ZINB, NBH, Poisson, ZIP, and PH, the values of AIC are computed, and the comparison is presented in Figure 2.

Figure 2

The modeling accuracy of count regression models over the data of maternal health care services utilization.

The graphical representation of model assessment criteria demonstrates the difference between performance and accuracy of different count models. Figure 2 shows that models based on NB distribution have higher efficiency with a low value of AIC. This is the indication of overdispersion in the data. Both NBH and ZINB models show nearly similar performance, and hence, any of these models can be used as a final model. Hence, the ZINB model is further fitted as a final model for factor selection for the present data.

Zero-inflated negative binomial regression

Table 2 represents the estimates of odds for the ZI (logistic) regression component and count (NB) regression component of the ZINB regression model. The ZI component refers to the logistic model which predicts the probability of use versus non-use of maternal care services. The ZI regression component shows that the education level of women, the income level of family, access to media, AIDS awareness, desire for pregnancy, parity, and birth order are significant factors of maternal health care services utilization.

Parameter estimates and 95% confidence interval (CI) estimates for ZINB model on maternal care utilization

CoefficientsZero-inflated regressionCount regression
Intercept0.12 (0.07–0.18)3.85 (3.59–4.13)
Province (Punjab)RefRef
Sindh0.95 (0.83–1.09)1.02 (0.99–1.05)
Age (≤ 20 years)RefRef
> 20 years1.13 (0.87–1.48)1.13 (1.08–1.18)*
Education level (Illiterate)RefRef
Literate0.43 (0.35–0.54) *1.15 (1.12–1.19) *
Area (Urban)RefRef
Rural0.89 (0.75–1.06)0.94 (0.92–0.97) *
Domestic Violence (No)RefRef
Yes1.06 (0.92–1.22)0.91 (0.89–0.93) *
Income level (Low)RefRef
Middle0.53 (0.43–0.65) *1.25 (1.20–1.29) *
High0.31 (0.23–0.42) *1.56 (1.50–1.62) *
Access to media (No)RefRef
Yes0.52 (0.45–0.60) *1.07 (1.04–1.10) *
AIDS Awareness (Yes)RefRef
No1.75 (1.36–2.24) *0.86 (0.83–0.88) *
Experience of a child's death (Yes)RefRef
No1.13 (0.97–1.32)0.90 (0.87–0.93) *
Desire for pregnancy (Yes)RefRef
No1.24 (1.03–1.49) *1.03 (0.99–1.07)
Parity (Primipara)RefRef
Multipara1.59 (1.28–1.99) *0.85 (0.83–0.88) *
Birth order (≤4)RefRef
>41.41 (1.21–1.64) *0.90 (0.87–0.93) *

P < 0.05.

Furthermore, the results of the ZI component show that unwanted pregnancy increases the odds of not utilizing maternal care services by 24%. It is found that middle and high-income levels decrease the odds of non-use of maternal care services by 47% and 69%, respectively.

The count regression component refers to the NB model employed for positive counts. The count component shows that age, education level of women, area, domestic violence, the income level of family, access to media, AIDS awareness, an experience of a child's death, parity, and birth order are significant factors of maternal health care services utilization. Further analysis shows that the expected number of maternal care visits for women aged ≤ 20 years is 13% higher than the average visits of women >20 years. Similarly, 15% higher average maternal care visits are observed for literate women.

Discussion

This article considers several count data models to examine factors associated with maternal health care visits using a dataset with overdispersion and inflated with zeros from UNICEF's MICS 2014 in Pakistan. Six count regression models are compared in terms of AIC. The model comparison identifies that ZINB and NBH models are better fitted for modeling the observed data with excess zeros and overdispersion but ZINB was the best choice due to easy interpretation and understandability.

Poisson and NB models are insufficient in the presence of excess zero counts. A past study has reported the performance of count models for health data concluding the ZINB model to be best fitted for overdispersed and zero-inflated response variables [11]. However, in the presence of overdispersion and excess of zeros, the NBH model is a better choice compared to other zero adjusted models [14].

Several studies have been conducted to examine the association of maternal care utilization with different factors. The present study shows that consistent with the general findings of earlier studies, the likelihood of maternal care utilization is influenced by the levels of maternal education and maternal age [15, 16]. Rural area resident women show a reduced average number of visits for maternal care. Previous studies showed that women of rural areas had fewer maternal visits compared to women of urban areas. Women with advanced aged husbands show increased expected positive counts of antenatal care visits for the present analysis. A previous study showed that older husbands increased antenatal care utilization for their wives for rural and urban areas [17]. The current results show that domestic violence decreases the average of attending maternal care visits. Past research reported that abused women were less likely to have adequate prenatal care visits [18]. The expected number of antenatal care visits increases with increasing income levels. Past evidence supported that increase in income level of family raised the probability of antenatal care utilization [15, 19]. Affirming to past studies access of women toward media significantly increased the chances of greater use of antenatal care visits [20, 21]. Consistent with previous research studies desirability of pregnancy is found significantly associated with antenatal care utilization [15, 17]. Birth order ranking is found associated with the use and non-use as well as frequency of maternal care visits for the observed data. Previous studies explained the possible reason for this association including the cognition and confidence gained from previous birth experiences [22]. Multiparae women consumed less average of antenatal care visits for the observed data. Past research has been proven for Asia and Sub-Saharan Africa [15, 23]. Women who had not experienced the death of a child had reduced expected maternal care visits. Consistent with this result, previous terminated pregnancy or child death is associated with an increased number of maternal care visits [24].

Conclusion

This study attempts to compare models to handle count data with overdispersion and excess of zeros to efficiently determine the factors associated with maternal health care utilization. ZINB regression model is found best fitted for the observed zero-inflated count data. It presents a key advancement for the analysis of health care utilization data. It is recommended, based on study findings, that interventions to improve maternal care services utilization should address individuals and systems to reduce social and economic marginalization.

Figure 1

Frequency distribution of maternal care visits in Pakistan.
Frequency distribution of maternal care visits in Pakistan.

Figure 2

The modeling accuracy of count regression models over the data of maternal health care services utilization.
The modeling accuracy of count regression models over the data of maternal health care services utilization.

Characteristics of the study sample (N = 16,314) and significance of maternal care visits between subgroups

Predisposing characteristicsN (%)Median (P25–P75)P
Province*
Punjab (Pakistan)10,278 (63)3 (2–6)<0.01
Sindh (Pakistan)6,036 (37)2 (1–5)
Age*
≤ 20 years1,773 (10.9)3 (1–5)<0.01
> 20 years14,541 (89.1)3 (1–5)
Education level*
Illterate8,499 (52.1)2 (0–4)<0.01
Literate7,815 (47.9)4 (3–7)
Area*
Urban5,813 (35.6)4 (2–7)<0.01
Rural10,501 (64.4)2 (1–4)
Husband's age*
≤ 30 years6,760 (41.4)3 (1–5)<0.32
> 30 years9,554 (58.6)3 (1–6)
Domestic violence*
Yes8,667 (46.9)2 (1–4)<0.01
No7,647 (53.1)4 (2–7)
Income level
Low7,772 (47.6)2 (0–3)<0.01
Middle3,539 (21.7)3 (2–5)
High5,003 (30.7)5 (3–8)
Access to media*
Yes9,934 (60.9)2 (0–4)<0.01
No6,380 (39.1)4 (2–6)
AIDS awareness*
Yes5,497 (33.7)5 (3–8)<0.01
No10,817 (66.3)2 (1–4)
Experience of a child's death*
Yes3,591 (2.0)2 (1–5)<0.01
No12,723 (78)3 (1–6)
Desire for pregnancy*
Yes14,345 (87.9)3 (1–5)<0.01
No1,969 (12.1)3 (1–5)
Parity*
Primipara3,583 (22)4 (2–7)<0.01
Multipara12,731 (78)3 (1–5)
Birth order*
≤ 412,185 (74.7)3 (2–6)<0.01
> 44,129 (25.3)2 (0–4)

Parameter estimates and 95% confidence interval (CI) estimates for ZINB model on maternal care utilization

CoefficientsZero-inflated regressionCount regression
Intercept0.12 (0.07–0.18)3.85 (3.59–4.13)
Province (Punjab)RefRef
Sindh0.95 (0.83–1.09)1.02 (0.99–1.05)
Age (≤ 20 years)RefRef
> 20 years1.13 (0.87–1.48)1.13 (1.08–1.18)*
Education level (Illiterate)RefRef
Literate0.43 (0.35–0.54) *1.15 (1.12–1.19) *
Area (Urban)RefRef
Rural0.89 (0.75–1.06)0.94 (0.92–0.97) *
Domestic Violence (No)RefRef
Yes1.06 (0.92–1.22)0.91 (0.89–0.93) *
Income level (Low)RefRef
Middle0.53 (0.43–0.65) *1.25 (1.20–1.29) *
High0.31 (0.23–0.42) *1.56 (1.50–1.62) *
Access to media (No)RefRef
Yes0.52 (0.45–0.60) *1.07 (1.04–1.10) *
AIDS Awareness (Yes)RefRef
No1.75 (1.36–2.24) *0.86 (0.83–0.88) *
Experience of a child's death (Yes)RefRef
No1.13 (0.97–1.32)0.90 (0.87–0.93) *
Desire for pregnancy (Yes)RefRef
No1.24 (1.03–1.49) *1.03 (0.99–1.07)
Parity (Primipara)RefRef
Multipara1.59 (1.28–1.99) *0.85 (0.83–0.88) *
Birth order (≤4)RefRef
>41.41 (1.21–1.64) *0.90 (0.87–0.93) *

World Health Organization. Household-to hospital continuum of maternal and newborn care. [online] 2005 [cited 2016 Dec 5]. Available from: https://www.who.int/pmnch/media/publications/aonsectionII.pdfWorld Health OrganizationHousehold-to hospital continuum of maternal and newborn care[online]2005[cited 2016 Dec 5]. Available from: https://www.who.int/pmnch/media/publications/aonsectionII.pdfSearch in Google Scholar

Rosenfield A, Maine D, Freedman L. Meeting MDG-5: an impossible dream? Lancet. 2006; 368:1133–5.RosenfieldAMaineDFreedmanLMeeting MDG-5: an impossible dream?Lancet20063681133510.1016/S0140-6736(06)69386-0Search in Google Scholar

Assembly UN General. Universal declaration of human rights. New York: UN General Assembly; 1948.Assembly UN GeneralUniversal declaration of human rightsNew YorkUN General Assembly1948Search in Google Scholar

Starrs AM. Safe motherhood initiative: 20 years and counting. Lancet. 2006; 368:1130–2.StarrsAMSafe motherhood initiative: 20 years and countingLancet20063681130210.1016/S0140-6736(06)69385-9Search in Google Scholar

Hogan MC, Foreman KJ, Naghavi M, Ahn SY, Wang M, Makela SM, et al. Maternal mortality for 181 countries, 1980–2008: a systematic analysis of progress towards Millennium Development Goal 5. Lancet. 2010; 375:1609–23.HoganMCForemanKJNaghaviMAhnSYWangMMakelaSMMaternal mortality for 181 countries, 1980–2008: a systematic analysis of progress towards Millennium Development Goal 5Lancet201037516092310.1097/01.aoa.0000397097.96320.28Search in Google Scholar

Mahmood A, Sultan M. National Institute of Population Studies (NIPS) (Pakistan), and Macro International Inc. Pakistan Demographic and Health Survey. 2006; 7:123–45.MahmoodASultanMNational Institute of Population Studies (NIPS) (Pakistan), and Macro International IncPakistan Demographic and Health Survey2006712345Search in Google Scholar

National Institute of Population Studies. Pakistan Demographic and Health Survey 2012–13 Islamabad. [online]. 2013 [cited 2016 Dec 5]. Available from: https://dhsprogram.com/pubs/pdf/FR290/FR290.pdfNational Institute of Population StudiesPakistan Demographic and Health Survey 2012–13 Islamabad[online].2013[cited 2016 Dec 5]. Available from: https://dhsprogram.com/pubs/pdf/FR290/FR290.pdfSearch in Google Scholar

Agha S. Determinants of facility delivery in rural Jhang Pakistan [PhD thesis]. New Orleans: Department of International Health, Tulane University; 2010.AghaSDeterminants of facility delivery in rural Jhang Pakistan[PhD thesis].New OrleansDepartment of International Health, Tulane University201010.1186/1475-9276-10-31Search in Google Scholar

Mullahy J. Specification and testing of some modified count data models. J Econ. 1986; 33:341–65.MullahyJSpecification and testing of some modified count data modelsJ Econ1986333416510.1016/0304-4076(86)90002-3Search in Google Scholar

Consul PC, Famoye F. Generalized Poisson regression model. Comm Stat Theory Methods. 1992; 21:89–109.ConsulPCFamoyeFGeneralized Poisson regression modelComm Stat Theory Methods1992218910910.1080/03610929208830766Search in Google Scholar

Hu MC, Pavlicova M, Nunes EV. Zero-inflated and hurdle models of count data with extra zeros: examples from an HIV-risk reduction intervention trial. Am J Drug Alcohol Abuse. 2011; 37:367–75.HuMCPavlicovaMNunesEVZero-inflated and hurdle models of count data with extra zeros: examples from an HIV-risk reduction intervention trialAm J Drug Alcohol Abuse2011373677510.3109/00952990.2011.597280323813921854279Search in Google Scholar

Joe H, Zhu R. Generalized Poisson distribution: the property of mixture of Poisson and comparison with negative binomial distribution. Biom J. 2005; 47:219–29.JoeHZhuRGeneralized Poisson distribution: the property of mixture of Poisson and comparison with negative binomial distributionBiom J2005472192910.1002/bimj.20041010216389919Search in Google Scholar

Flynn M, Francis LA. More flexible glms zero-inflated models and hybrid models. Casualty Actuarial Soc. 2009; 2009:148–224.FlynnMFrancisLAMore flexible glms zero-inflated models and hybrid modelsCasualty Actuarial Soc20092009148224Search in Google Scholar

Gurmu S, Trivedi PK. Excess zeros in count models for recreational trips. J Bus Econ Stat. 1996; 14:469–77.GurmuSTrivediPKExcess zeros in count models for recreational tripsJ Bus Econ Stat1996144697710.1080/07350015.1996.10524676Search in Google Scholar

Guliani H, Sepehri A, Serieux J. Determinants of prenatal care use: evidence from 32 low-income countries across Asia, Sub-Saharan Africa and Latin America. Health Pol Plan. 2013;czt045.GulianiHSepehriASerieuxJDeterminants of prenatal care use: evidence from 32 low-income countries across Asia, Sub-Saharan Africa and Latin AmericaHealth Pol Plan2013czt045.10.1093/heapol/czt04523894068Search in Google Scholar

Sepehri A, Sarma S, Simpson W, Moshiri S. How important are individual, household and commune characteristics in explaining utilization of maternal health services in Vietnam? Soc Sci Med. 2008; 67:1009–17.SepehriASarmaSSimpsonWMoshiriSHow important are individual, household and commune characteristics in explaining utilization of maternal health services in Vietnam?Soc Sci Med20086710091710.1016/j.socscimed.2008.06.00518635302Search in Google Scholar

Alexandre PK, Saint-Jean G, Crandall L, Fevrin E. Prenatal care utilization in rural areas and urban areas of Haiti. Pan Am J Pub Health. 2005; 18:84–92.AlexandrePKSaint-JeanGCrandallLFevrinEPrenatal care utilization in rural areas and urban areas of HaitiPan Am J Pub Health200518849210.1590/S1020-49892005000700002Search in Google Scholar

Cha S, Masho SW. Intimate partner violence and utilization of prenatal care in the United States. J Interpers Violence. 2014; 29:911–27.ChaSMashoSWIntimate partner violence and utilization of prenatal care in the United StatesJ Interpers Violence2014299112710.1177/088626051350571124203982Search in Google Scholar

Gage AJ. Barriers to the utilization of maternal health care in rural Mali. Soc Sci Med. 2007; 65:1666–82.GageAJBarriers to the utilization of maternal health care in rural MaliSoc Sci Med20076516668210.1016/j.socscimed.2007.06.00117643685Search in Google Scholar

Bbaale E. Factors influencing timing and frequency of antenatal care in Uganda. Aus Med J. 2011; 4:431.BbaaleEFactors influencing timing and frequency of antenatal care in UgandaAus Med J2011443110.4066/AMJ.2011.729356288323393530Search in Google Scholar

Tarekegn SM, Lieberman LS, Giedraitis V. Determinants of maternal health service utilization in Ethiopia: analysis of the 2011 Ethiopian demographic and health survey. BMC Pregnancy Childbirth. 2014; 14:1.TarekegnSMLiebermanLSGiedraitisVDeterminants of maternal health service utilization in Ethiopia: analysis of the 2011 Ethiopian demographic and health surveyBMC Pregnancy Childbirth201414110.1186/1471-2393-14-161402297824886529Search in Google Scholar

Jejeebhoy SJ, Sathar ZA. Women's autonomy in India and Pakistan: the influence of religion and region. Popul Dev Rev. 2001; 27:687–712.JejeebhoySJSatharZAWomen's autonomy in India and Pakistan: the influence of religion and regionPopul Dev Rev20012768771210.1111/j.1728-4457.2001.00687.xSearch in Google Scholar

Short SE, Zhang F. Use of maternal health services in rural China. Popul Stud. 2004; 58:3–19.ShortSEZhangFUse of maternal health services in rural ChinaPopul Stud20045831910.1080/003247203200017544615204259Search in Google Scholar

Zaky HHM, Armanious D M, Hussein MA. Impact of the changes in women's characteristics over time on antenatal health care utilization in Egypt (2000–2008). Open J Obstet Gynecol. 2015; 5:542.ZakyHHMArmaniousDMHusseinMAImpact of the changes in women's characteristics over time on antenatal health care utilization in Egypt (2000–2008)Open J Obstet Gynecol2015554210.4236/ojog.2015.510078Search in Google Scholar

Articles recommandés par Trend MD

Planifiez votre conférence à distance avec Sciendo