The Incidence of Induced in Kenya

Extended Abstract

Background

Globally, is recognized as a major public health concern and it accounts for 13% of all maternal deaths (1). Every year, 22 million women have an unsafe abortion worldwide and virtually all (98%) of these are occurring in developing countries (1). When faced with an unplanned pregnancy, women will seek abortions regardless of the law. Unsafe abortions are occurring in countries with restrictive laws (2). Kenya lags behind in achieving MDG 5. In fact, according to the recent Kenya Demographic Health Survey (2008-2009), maternal mortality in Kenya is still high at 488 maternal deaths per 100,000 live births which increased compared to 414/100,000 in 2003 (3). Facility based delivery and skilled birth attendance also did not change much between the 2003 and 2008-2009 KDHS survey reflecting minimal or unchanged health outcomes for the mother and infant. In some urban slums in Kenya, the maternal mortality rate is even higher at 706 deaths per 100,000 live births (4). Each year in Kenya, it is estimated that over 3000 women die of complications of unsafe abortion and another half a million more suffer short and long-term morbidities (5). A 2002 study revealed that more than 20,000 women sought medical care for complications from abortion in public health facilities in Kenya annually with an estimated 900 deaths per 100,000 unsafe abortions (6).

The abortion laws in sub-Saharan African countries vary from those countries in which access is highly restrictive to those that allow pregnancy termination on request up to a certain gestation (South Africa). Until recently, the in Kenya was highly restrictive and only permitted abortion to save the life of the woman. With the 2010 promulgation of the new constitution in Kenya, the new abortion law (which is still restrictive and unclear) now states that abortion is permitted if the life or health of the woman is in danger (10). This exception to protect the woman’s health has sparked a great deal of debate in Kenya where some religious bodies believe that induced abortions are now legal under the Kenyan constitution. Yet medical providers are not so sure of whether abortion is legal in Kenya. Unsafe abortions continue to occur due to the restrictive and unclear nature of the new abortion law.

Contraceptive use in Sub-Saharan Africa is still low despite the many efforts made to increase its use. Despite having one of the highest contraceptive prevalence rates among married women in Africa (46%), access to and use of modern contraceptives remains poor for many Kenyan women (only 39% are currently using a modern method) (7). According to the 2008/09 KDHS, more than one in four married women (26%) have an unmet need for family planning. This trend has persisted since 1998 (8). The main reasons cited for having unmet need were because of health concern (15%) and fear of side effects (16%) of using modern contraception (8). As a result, Kenyan women are having larger family sizes (average of 4.6 children) than desired (an average of 3.4 desired children). Low contraceptive use and unmet need for family planning is linked to

1 unwanted or mistimed pregnancies which lead to unsafe abortions especially in countries where the abortion law is highly restrictive (9).

The aim of this study is to estimate the national and regional incidence of induced abortion in Kenya and to provide new and updated information on the extent to which unsafe abortions are occurring. The last national estimate of abortion incidence in Kenya was done over 10 years ago, only in public health facilities, and it did not distinguish between induced and spontaneous abortions (6). This study provides the first complete nationally-representative data on induced abortion in Kenya.

Data and Methods:

Data Sources

This study’s design, protocol and data collection approach to estimate abortion incidence uses an indirect method of estimating abortion incidence and morbidity capture. This combination of the two methodologies, namely the Abortion Incidence Complications Methodology (AICM) for estimating abortion incidence and the Prospective Morbidity Methodology (PMM) for documenting abortion morbidity. The two methodologies were combined to provide the three sources of information used in this study: interviews with post-abortion care (PAC) providers in a nationally-representative sample of 328 public and private health facilities also known as the health facility survey (HFS); prospective data capture of PAC patients presenting over a 30-day period in 326 facilities also known as the prospective data survey (PDS); and surveys with a sample of purposively-selected key informants from different regions of Kenya who were knowledgeable about unsafe abortion and PAC-related issues also known as the health professional survey (HPS). Other data sources used include the Kenya Demographic and Health Surveys from 2003 and 2008 for information on age-specific fertility rates and the 2009 Kenyan Census for the number of women aged 15-49 in the different regions (11). Population numbers were projected for 2012. We used the Kenya Integrated Household Budget Survey, 2006 for the distribution of urban and rural poor- non-poor population.

Health Facility Survey (HFS) and Prospective Data Survey (PDS)

The Ministry of Medical Services department of the Health Management Information System (HMIS) defines six levels of preventive and curative public and private health service provision in Kenya ranging from level I-VI . The facilities selected in this study are those that had the potential to provide post-abortion care services. Therefore, using the most recent Master Facility List from the ministry of medical services in Kenya obtained January 31, 2012, we identified two level VI facilities, 17 level V facilities, 385 level IV facilities, 1027 level III facilities, and 1412 level II facilities. Thus our universe was 2,844 facilities. A stratified random sample was used to select the health facilities to be

2 surveyed. Sampling was stratified by region and level of health facility. We sampled 100% from level V and VI; because these facilities are most likely to manage and treat high numbers of abortion-related complications in Kenya. Level II-IV facilities’ sampling fractions varied as follows; level IV health facilities were sampled at 20% to 25%; level III health facilities at 10% to 21%; while level II facilities were represented at a 5.5% to 10%, depending on the region.

Fieldwork was conducted between April and May 2012. Retrospective estimates of the past month and typical month, or if that was not possible for the respondent to estimate, then a retrospective estimate of the past year and typical year were obtained from the HFS. From the PDS, prospective data capture obtained a count of the number of women who came in during a 30 day period for post-abortion care. Weighting was done to generate nationally representative data, accounting for sampling proportions and non-response. The health facility survey data and the prospective survey data were then averaged to provide both national and regional estimates of post-abortion care provision.

Health professionals’ survey (HPS)

The research team, with the assistance of members of the Advisory Panel, generated a list of key informants who were identified as knowledgeable about the provision of abortion and post-abortion care. Thus, a purposive sample of experts throughout the country was identified. Particular effort was made to include respondents with knowledge on abortion in rural areas as well as representation from all the major geopolitical regions in Kenya to ensure that collectively, the respondents’ answers were able to represent abortion experiences throughout the country. Selected respondents included researchers, OB-GYN specialists, nurses, midwives, lawyers, and clinical officers among others. 114 key informants were interviewed. All were interviewed in- person using a structured questionnaire between April–August 2012. Topics covered in the questionnaire included respondents’ perceptions regarding who women are likely to obtain an abortion from in Kenya, the likelihood of women experiencing complications that required treatment and the likelihood that women who need treatment receive it by the four major sub-groups of women: rural poor, rural non-poor, urban poor and urban non-poor.

Data Analysis

The methodology used in this paper will follow the combination of the AICM and PDS indirect estimate methodologies recently used by Singh et al. in Ethiopia (12) to estimate the annual incidence of induced abortions in Kenya. The following inputs were used:

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1. Estimated number of women treated for post-abortion complications in a typical month or typical year in each selected health facility – HFS 2. Estimated number of women treated for post-abortion complications in the past month or past year in each selected health facility – HFS 3. Number of women prospectively captured in a one month period seeking care for post-abortion complications in each selected health facility – PDS 4. Likelihood estimates of the probability of experiencing a complication from an abortion severe enough to require health care as well as the likelihood of receiving health care for that complication – HPS

The total caseload of the women treated for abortion complications in each of the three estimates are weighted and averaged to account for different levels of health facilities and regions where the data were collected to provide the estimated number of women who obtain treatment in facilities for complications in Kenya in the current year. Since we do not ask respondents to distinguish between complications from induced and spontaneous abortion, the result is then adjusted to remove cases of spontaneous abortion. Responses from the HPS were used to calculate the estimated proportion of women who had an induced abortion that are likely to need and then subsequently receive treatment for complications as the proportion of women who arrive in facilities for care are only a portion of all women obtaining induced abortions. The multiplier, i.e. the number of women having abortions who do not come to health facilities for care, is the inverse of that proportion who seek care. The number of complications that are due to induced abortions multiplied by this multiplier is the estimated number of women who obtained an induced abortion in Kenya in 2012. From this number, the abortion rate (number of abortions per 1000 women of reproductive age (15-49)) was also calculated as well as the abortion ratio (number of abortions per 100 live births).

Results

Table 1 presents the unweighted universe of facilities, facilities selected, facilities that participated in the study and the corresponding response rates. The total number of facilities selected was 348. Of the 348 sampled health facilities, we obtained complete questions from 328 health facilities for the HFS and 326 facilities for the PDS. All public facilities had a response rate of nearly 100%. Level II facilities had a response rate of 96%, 94% for level III, 94% for level IV, 88% for level V and 100% for level VI. Reasons for non-response included inaccessibility for the interviewers due to insecurity, the facility was closed prior to interviewer’s arrival, and access was declined from officials.

Among the key informants interviewed for the HPS (n=114), about 44% had experience working in rural areas for six months or more in the last five years. The majority (39%) of respondents were between 24-34 years of age (Table 2). Nearly half of the respondent (48%) had a medical background (OB-GYN, nurse, clinical officer or medical officer), while the rest had diverse backgrounds in research and law among

4 other topics. More than half of the respondents had more than 10 years of experience in their primary profession.

While over 90% of the facilities had a maternal child health/family planning unit, only 20% had an operating theater and 24% had an evacuation room (Figure 1). Manual (MVA) was the commonest procedure in both public and private facilities with over half of all public facilities using this method for PAC patient treatment (Figure 2). Other common methods include which was more common in private facilities. Significantly, there were a number of health facilities that reported not to use any of the listed procedures in public sector. Majority of those facilities were level II facilities, which usually refer most PAC patients to higher level facilities.

Preliminary results show that 173,512 women are treated at health facilities in Kenya for both induced and spontaneous abortions in 2012 (Table 3). Most women presenting for post-abortion care were treated at higher level facilities (level V). The majority (61%) of patients are treated in public health facilities while the smallest proportions (19%) are treated in private-for-profit facilities. These results suggest that public facilities are bearing a high burden treating these women.

One of the assumptions of the estimation technique is that women with first trimester spontaneous abortions will not seek health care when they miscarry and that all women with second-trimester spontaneous abortions would like to seek health care but not all of them will be successful in obtaining it. Based on the KDHS, we estimated that a higher proportion (65%) of women with spontaneous second trimester miscarriages would obtain health care for possible complications as opposed to proportion of women who deliver in a health facility (42%). Further analysis done to separate out the late (13- 21 weeks) spontaneous miscarriages revealed that 146,427 women were treated for induced abortion complications (Table 4). Based on the fact that they are the largest regions, Nyanza & Western and Rift Valley had the highest number of women treated for induced abortions. Table 5 presents preliminary rates and ratios for abortion in Kenya. The abortion rate for Kenya is estimated to be 55 per 1000 women of reproductive age (15-49). This rate is very high when compared to other rates in Africa. It is on par only with Uganda which in 2003 had a rate of 54 abortions per 1000 women of reproductive age. Reasons for this high rate are still being explored. In addition, the incidence of legal abortion is also being analyzed.

Conclusion:

Unsafe abortion continues to be a major health issue for . According to recently completed incidence studies in Sub-Saharan Africa, Kenya (55 per 1000) has an abortion rate similar to Uganda (12) which is very high when compared to other Sub-

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Saharan African countries like in Ethiopia (23 per 1000), Rwanda (25 per 1000) and Malawi (35 per 1000) (13,14, 15). Like other Sub-Saharan African countries, the incidence rate for Kenya is in part due to the high level of unmet need for family planning. Given that the law in Kenya remains ambiguous, unsafe abortions may continue to occur, endangering women’s health and their lives. The burden on the health care system of treating post-abortion complications is great. Increased access to modern contraceptive services among women in Kenya is paramount to reduce unintended pregnancies and unsafe abortion in order to improve women’s health.

References :

1. World Health Organization (WHO). 2011. Unsafe Abortion: Global and Regional Estimates of the Incidence of Unsafe Abortion and Associated Mortality in 2008. Sixth edition. Geneva.

2. Shah, I & Ahman, E. 2012. Unsafe abortion differentials in 2008 by age and developing country region: high burden among young women. Reproductive Health Matters, 20 (39) 169-173 and Sedgh G, Henshaw S, Singh S, Åhman E, Shah I. Induced abortion: estimated rates and trends worldwide from 1995 to 2008. Lancet, 2012, 379(9816):625-632.

3. United Nations Development Goals for Kenya :( 2011). The State of MDG’s in Kenya. http://www.ke.undp.org/index.php/the-s Accessed September 15 2012 .

4. Ziraba A et al., 2009. Maternal mortality in the informal settlements of Nairobi city; what do we know? Reproductive Health.6 (6): xxx-xxx.

5. Shah, I & Ahman, E. 2010. Unsafe abortion in 2008: Global and regional levels and trends. Reproductive Health matters, 18(10) 90-101.

6. Gebreselassie, H et.al. 2005. The Magnitude of abortion complications in Kenya. BJOG. Sep;112(9):1229-35.

7. United Nations Development goals for Kenya: (2011). About UNDP Kenya – Goal 5: Improve Maternal health. http://www.ke.undp.org/index.php/mdgs/goal-5-improve- maternal-health Accessed September 16, 2012.

8. Kenya National Bureau of Statistics (KNBS) and ICF Macro. 2008-09. Kenya Demographic and Health Survey, Nairobi, Kenya: KNBS; and Calverton, MD USA: ICF Macro, 2010.

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9. Gorrette N, Nabukera S, Salihu HM. The abortion paradox in Uganda: fertility regulator or cause of maternal mortality. Journal of Obstetrics and Gynecology, 2005, 25(8):776-780.

10. National Council for Law Reporting (NCLR). 2010. The , 2010 revised edition, Nairobi, Kenya.

11. Kenya National Bureau of Statistics (KNBS). 2009. Kenya Population and Housing Census, Nairobi, Kenya: KNBS; August, 2010.

12. Singh et al. 2008. The estimated Incidence of Induced Abortion in Ethiopia, 2008. International Perspectives on Sexual and Reproductive Health, 36(1):16-25.

13. Singh et al. 2005. The Incidence of Induced . International Family Planning Perspectives, 31(4):183-191.

14. Basinga et al. Abortion and Postabortion Care in Rwanda. 2012. Abortion and Incidence and Postabortion Care in Rwanda, 43(1):11-20.

15. Levandowski et al. 2011. The Estimated Incidence of Abortion in Malawi. Poster presentation at the Population Association of America meetings. March 31-April 2. Washington, DC.

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Table 1: Characteristics of sample, by level of facility and ownership, Health Facility and Prospective data Survey, 2012

Type of No. of Selected Response No. facility Facilities sample rate (%) interviewed Private Private Private Private Private Private for not for for not for Private not for Private for not for Level Total Public profit profit Total Public profit profit Total Public for profit profit Total Public profit profit Total 2844 1911 502 431 348 212 63 72 94 100 87 85 328 212 55 61 Level II 1412 999 243 170 98 68 19 11 96 100 89 82 94 68 17 9 Level III 1027 657 173 197 132 69 22 41 94 99 100 83 124 68 22 34 Level V 385 240 82 63 100 63 18 19 94 100 72 95 94 63 13 18 Level V 17 12 4 1 16 11 4 1 88 100 75 0 14 11 3 0 Level VI* 2 2 0 0 2 2 0 0 100 100 0 0 2 2 0 0 Source: Ministry of Medical Services department of the Health Management Information System (HMIS), Health Facility Survey and Prospective Data Survey, Kenya 2012

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Table 2: Characteristics of health professional survey respondents

Characteristic % (N=114)

Age 24-34 39 43 35-44 34 37 45-64 27 29

Sex Male 27 31 Female 73 83

Primary Profession Medical Background 49 56 Researcher 5 6 Lawyer/Advocacy 6 6 Counselor 4 5 Other 36 41

Duration of Experience Less than 4 years 21 24 5 to 10 years 33 38 more than 10 years 46 52

Rural work experience > 6 months Yes 44 50 No 56 64

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Figure 1: Characteristics of facilities: Infrastructure

100.0 100 91.8 89.7 90 80 68.0 69.2 70 63.4 60 50 43.0 40 24.3 30 20.3 20 10.2 12.3 10 3.3 0

Figure 2: Procedures used to treat PAC patients by ownership of facilities

Public Private 70 61.1 60 51.3 50

40 30.0 30 26.1 24.6 22.1 21.8 20 14.0 10.4 8.6 10 4.6 4.2 5.1 1.3 3.2 2.1 0 D & E D & C MVA EVA Medical Digital Other None Evacuation

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Table 3: Estimated total number of women treated annually for post-abortion complications by level of facility and ownership (Weighted)

Type of facility and ownership AVG-PAC Typical YR Past YR Annual PDS

Total 173512 203094 152595 157717

Level Level II 50772 59577 43998 48030 Level III 56822 65657 50358 54188 Level IV 51784 60357 45029 45325 Level V 11382 14445 10163 8026 Level VI 2752 3060 3048 2148

Ownership Public 105922 120309 91852 100054 Level II 28422 31179 25086 28290 Level III 21913 24641 17275 23825 Level IV 42041 47818 36917 38063 Level V 10793 13612 9527 7728 Level VI 2752 3060 3048 2148

Private for profit 33015 40771 24682 32014 Level II 8180 11732 4057 8749 Level III 20426 23207 17777 20033 Level IV 3820 4999 2212 2934 Level V 589 833 637 298

Private not for profit 34575 42014 36061 25650 Level II 14170 16665 14855 10991 Level III 14482 17809 15306 10331 Level IV 5923 7540 5900 4328 Source: Health Facility Survey and Prospective Data Survey, Kenya, 2012

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Table 4: Calculating the number of women treated for complications of induced abortions in a health facility in 2003 , by region Women with Women Women miscarriages treated for treated for Live births treated in a induced abortion among women Women with health abortion Region complications 15-49 miscarriages facility complications Total 173,512 1,221,957 41,669 27,085 146,427 Central & Nairobi 36,164 204,764 6,982 4,539 31,626 Coast & N. Eastern 24,356 160,143 5,461 3,550 20,806 Eastern 13,142 160,688 5,479 3,562 9,581 Nyanza & Western 48,103 354,717 12,096 7,862 40,240 Rift valley 51,747 341,645 11,650 7,573 44,174

Table 5: Abortion rate and ratio for each region and for Kenya in 2003, by level of multiplier Region WoRA* Abortion Rate Abortion Ratio Low Medium High Low Medium High Total 10,056,069 40 55 70 33 45 57 Central & Nairobi 2,290,469 34 48 62 38 54 69 Coast & N. Eastern 667,775 93 124 155 39 52 65 Eastern 1,447,621 20 26 33 18 24 30 Nyanza & Western 2,440,743 49 66 82 34 45 56 Rift valley 2,516,935 52 70 87 39 51 64 *WoRA - Women of reproductive age: Projections of Women of reproductive age for 2012 from the 2009 Census 2009

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